The key word here is ‘event’. An event is a record of a single instance of behaviour taking place on a website or app. It’s tied to a user’s identity and useful information (attributes) can be stored inside it.

This practice is sometimes referred to as trigger marketing, event tracking, event driven marketing or event streaming. The sheer power and potential of this new type of marketing is driving massive advances in the way digital marketers, growth hackers and product marketers work.

There are two components: managing identity and recording behaviour.

Identity

When a user visits a website or mobile app they are automatically assigned an anonymous identity. The anonymous identity is used to store the record of their behaviour until, and if, the user becomes ‘identified’. Identified users are known users who already have a record in the user database. At this point, the anonymous identity and the known identity are joined together so that all past behaviour recorded against the anonymous user is now stored directly against the known user.

Without a user’s identity we would not be able to assign the stored behaviour to a particular person. This would limit our ability to communicate with the user (we can’t send them an email or push notification if we don’t know who they are). Instead, we would receive general data about the way the whole service is being used by all users (think Google Analytics). Identity based marketing also allows us to track users across different devices more easily.

Basic Recipe of an Identify Call

analytics.identify([userId], [traits], [options], [callback]);

Behaviour

Behaviour is information that describes how the user interacts with the website or app. It is recorded as an ‘event’. An event is a single instance of measured behaviour; a single event in the journey of the customer. The event could describe a users explicit action such as ‘Clicked on Playlist’ or an implicit action such as ‘Moused Over Playlist’.

Basic Recipe of an Event

analytics.track(event, [properties], [options], [callback]);

Attributes can be parsed in to the event allowing for unique, and highly relevant knowledge to be stored inside the event itself. For example, the playlist name, the playlist ID, the duration, and the description could all be stored in the event ‘Clicked on Playlist’.

analytics.track("Clicked on Playlist", {
playlistName: "Australian Top 50",
durationMins: 132,
playlistID: 2388383820022992,
playlistDescription: "The latest and greatest hits topping the charts down under."
});

By measuring behaviour in this way and storing key information about the behaviour inside the event, we can send the user a much more contextual message or prompt when trying to engage them.

Origins

Event stream processing has long been used by software engineers to build event-driven systems. An event would be use to record a significant change in the state of the object. By measuring behaviour in such incremental detail we are able to more clearly identify exactly where the user stops in a given flow or process. Algorithmic trading desks are an example of such systems. In this example each change in the price of a stock is measured as a single change in state, down to the cent. By monitoring all movement in the stock price, an algorithm can treat each event as a new state, and better predict the next movement based on the current and past states. Such granular measurement allows the algorithm to react quickly and accurately. Contrast this to simply knowing the starting price and the current price of a stock at any given time of the day (the way humans typically monitor stocks).

Event-based marketing is literally the marketers equivalent of this practice. By recording much more information about a user’s behaviour—each change in the user’s state—we know exactly where a user is in their journey and can construct a highly relevant and personalised message to deliver to them.

The word ‘event’ might ring some bells for us old-school marketers. Google Analytics has long offered the functionality of recording and storing custom events about how users interact with the product. This was great, and certainly offered us some insights into those more granular and unique behaviours, but there was always one problem… identity. Who are these people?

Example

Let’s use a typical abandoned cart campaign as a way to compare event-based marketing to traditional behavioural campaigns.

Traditional Behavioural Campaigns

Typically when implementing an abandoned cart campaigns we track two stages:
1) when the user starts the checkout process and
2) when the user successfully checks out.

More advanced implementations might track the step in the checkout process the user was at. This allows them to send the user back to the exact step they were up to. In total, most abandoned cart campaigns are measuring two or three stages. More often than not, this is done by querying the clickstream data (specifically the URL path) for users who have viewed the ‘checkout page’, but not viewed the ‘payment confirmation’ page. Because of the vagueness around this method of tracking, we typically wait 2-3 hours before sending the abandoned cart campaign.

Event-based Campaigns

When implementing an event-based abandoned cart campaign, we can record many more events:
1) User adds item to cart
2) User starts checkout process
3) User confirms shipping details
4) User confirms billing details
5) Payment successful
6) Payment unsuccessful
7) User successfully checks out

Each time a user triggers the next event in the sequence their state changes. This helps clearly indicate the exact point in the flow that they are up to. The moment events stop being received (the user abandons the checkout process) we need only wait a short period of time before sending the abandoned cart email (20 minutes for example). If the user stops at step 4, where they have confirmed their billing details but no result has been received for payment, we need simply send them an email encouraging them to finish paying.

On top of this, we are able to store information about the progression through the cart inside the event itself. This can be parsed through from the email to the target (website or app) by attaching the information in parameters on the link.

How to Implement Event-based Marketing

Event-based marketing significantly improves the capability, speed and quality of marketing. The technology is rapidly advancing and new tools are emerging designed specifically around these capabilities. This comprehensive guide to event-based marketing is designed to help digital marketers utilise javascript “events” to trigger marketing, product, analytics, advertising and communication campaigns based on user behaviour.

To be clear, this is not a guide about promoting physical events. This is a guide about using javascript based events to automatically trigger marketing.

In the context of digital marketing, “events” record a single instance of a behaviour occurring on your website or app. They are called events because:

1. they record a change in state, and
2. Javascript technology uses events to trigger functions.

I’ve worked with event-based marketing since 2013 and in this article, I’m going to teach you everything I know about this field of marketing.

Does event-based marketing work?

Yes, event-based marketing works and it’s probably the single biggest opportunity for growth in almost every business today.

I’ve been using event-based marketing for around six years now. I’ve implemented it successfully in my own business, and have helped many other companies implement it. In every single case, those businesses saw an immediate company-wide increase in every metric that matters. From revenue to email deliverability. I’ve literally seen 400% to 600% increases in conversion metrics overnight. Event-based marketing absolutely works.

The performance of every single marketing channel and activity is enhanced when event-based marketing is added. Even interstitial popups convert well when enhanced with event-based marketing.

Yet, despite all of this… very few people know how to do it.

This makes sense though because it’s virtually impossible to learn. There is literally zero content online about it. Not a single guide or course… a few articles here and there, but that’s about it. So, being the content-savvy marketer I like to think I am, I started writing about it (and teaching it in real life). This was a great decision because it’s helped me refine all of the content I’m about to share with you. The diagrams, the explanations, the metaphors, the strategies and most importantly the application of the concepts are all better because of the real-life testing that has taken place in the classroom. Let’s begin…

Technical background to event-based marketing

It wasn’t until the early 90s that marketers first started tracking people visiting their websites. Unlike the way most marketing professionals approached their job at the time (think Don Draper), this new breed of marketer was fascinated by measuring whether their marketing activities actually worked. These data-driven-pioneers were the first ‘digital marketers’ because of one thing: data.

Access to data was the turning point because it closed the feedback loop of marketing. Immediately every single marketing function became measurable, which in turn creates an opportunity to optimise our marketing. Data also speeds the entire marketing process up; if you’ve ever tried to optimise a website with no traffic you’ll know what I’m talking about.

From this moment on, data gave us the ability to measure our performance as marketers and compared to the incumbent alternative of little to no measurement, it probably felt like it was all happening in real time. Indeed, this was the birth of digital marketing, and the data source we came to rely on was server logs.

Server logs

Initially, the ability to actually measure the traffic to your website and the pages people were interested in relied on the most basic analytics technology we have: server logs. Server logs still work to this day. They show us the documents being requested by people who visit our website. When PHP, XML and HTML were the default web languages this worked well because every change in the state of a page (and the information contained upon it) relied on a call being made to a server. The typical user path on a website would look something like this:

The server log would tell us which documents were being requested on the server, and in which order. Beyond this, marketers couldn’t do much more… but neither could websites, so the data still felt pretty powerful. However, far more meaningful metrics would become available if we moved the tracking scripts to the client side. Enter ‘click-stream analytics’.

Click-stream analytics

Although they were not the first with this technology, the release of Google Analytics in 2005 democratised online analytics by introducing a free analytics package that would enable anyone with a website to begin collecting in-depth analytics about site usage.

The technical innovation was based on the repositioning of the data collection away from a server-side implementation to a client-side implementation. Rather than tracking document requests on the server, we would monitor requests from the front end of the website itself using a piece of javascript. In other words, we tracked the “click” side of a document request, rather than the response side of a document request. Clicks are an important evolution because they also represent a shift towards intention based metrics.

This repositioning of tracking allowed us to obtain an ever increasing number of new data points. Things like bounce rate, time on page, session durations along with new browser information such as screen size suddenly all became possible. The click-stream still recorded the movement of a user between documents, but it leveraged the user’s browser to achieve it (rather than the server).

This was a major breakthrough but it was appropriate given the changing purposes of websites. Rather than websites being brochures online, websites were becoming shops… and applications (such as forums and trading sites like eBay). This change in the way we used the Internet also spawned the development of a new series of technologies designed to facilitate a more lightweight transfer of data between a user (client) and the server. Not only would this speed browsing experiences up, but it would also take an enormous computational burden off the servers themselves (simply by reducing the size of the requests).

The things we cared about, such as a reply to our message or the change of an item’s price on eBay represented a very small portion of the overall HTML document. So instead of needing to reload the entire document, we were increasingly only reloading the portions of the document that had changed (often just a single line or value). AJAX and Javascript languages offered a way for websites to achieve this, by transferring small packets of data within the page itself.

Google Analytics quickly added support for these in-page changes of state using a new report called “events”, but the fact that user data is not allowed inside Google Analytics largely renders this function useless. Events in GA can be used for goal tracking and simple reporting, but not much more. This was not a problem for click-stream analytics at first, until Javascript took over most of the web in the form of Single Page Applications (SPAs). Yep, I’m talking about Angular and React, along with back-end frameworks like node.js.

Suddenly a single document became an entire website.

Event-streaming analytics

As websites quickly evolved into web applications a document based tracking method was no longer adequate. Users could spend 10 minutes on a single page clicking, liking, saving, posting and otherwise interacting with the page despite the document path not changing. Think of Facebook.com’s home page news feed for example, as a user scrolls through their uniquely personalised news feed liking and commenting on posts, they are creating an enormous amount of data, but the document path is not changing. In this scenario, a click-stream based analytics tool like Google Analytics would simply report time on page of say 10 minutes with a 0% bounce rate.

The fact that all of these sites run on Javascript technology means that all of these interactions are already being recorded using javascript events. Amongst other things, these js events are used by the application to change what the user sees (the DOM) and how they interact with content whilst avoiding reloading the entire site itself. These events trigger client side or server side functions and can transfer data… again, all without requiring the page to be reloaded.

For example, imagine a user finding a product on an e-commerce website, adding the item to their cart, and commencing the checkout process. The event-stream would look something like this. Notice how it’s sitting on top of the document (server logs) and click-stream.

You can see from the diagram above that the click-stream is really not that useful at all. So, now we arrive at the present day.

Event-based marketing today

Today, javascript events are running most of the front-end and back-end of websites and applications, but marketers… and product people… and even engineers sometimes just aren’t using them. Or are they?

In 2017 Facebook announced the transition away from the 8 default ‘conversion pixels’ to a new ‘smart pixel’. You’ve probably guessed it, but the old conversion pixel was a server log based analytics method, much like the 1px by 1px image contained within emails to record email opens. The old Facebook pixel would trigger a request to a document on Facebooks server, located at a unique path where it could then be mapped as a conversion. The conversion count was simply the number of times the document had been requested on the server. The ‘smart pixel’, again you’ve probably already guessed it, is a new javascript based event tracking script. Javascript events would fire, sending a record of an event having occurred back to the Facebook conversion server. The number of conversions would be equal to the number of times the event had fired. This matters because marketers are interested in who triggered the event, not just the fact that an event was triggered.

So, that leads us to the present day. Any marketer using Facebook advertising is already using ‘events’ to measure conversion (they just don’t know it) and the worlds number one analytics platform doesn’t support personalised event tracking. Can you see the opportunity?

What is an “event” in the context of event-based marketing?

Definition

An event is a record of a single instance of behaviour taking place on a website or app. It’s pinned to a single identity (known or unknown) and we can store useful information inside it.

Think of an event like a box that you can store useful information inside of. It gets automatically created when a user performs an action or a task (a behaviour) you are interested in observing and measuring. This box can then be stored, and sent anywhere you desire. If you want your email marketing platform to know about the event, send the event there. If you want to serve the user an advert in Facebook based on this behaviour, send the event there. If you want to create a report of the number of times this event was fired, you might send a copy into Amplitude to help generate the report. If you want to send a push notification a week after the user triggers the event, send it to Vero.

Anatomy of an event

There are three parts to every event:

1. An identity (even if it’s anonymous)
2. A unique event name
3. Attributes stored inside the event.

Again, an event is a single instance of behaviour with information stored inside it. People trigger events, which means we can attach an identity to the event. As marketers, this means we know what happened, when it happened and who did it.

Why do we use the phrase ‘event stream’?

As users move around our website or app, clicking and interacting with things, they create a stream of data. This stream describes the user’s actions in chronological order. The stream really matters because it helps us perform what I can only describe as ‘reactive marketing’. We can react to users actions in real time by creating conditional rules. For example: if a user triggers, “add an item to cart” and does not trigger “started checkout” you may wish to commence display advertising containing the products the user just added to their cart. The fact that events happen in a particular order means we can more easily build conditional sequences around our user’s behaviour.

Stateful and stateless data

Lastly, it’s beneficial to know about the differences between stateful and stateless data. This is a very technical concept, but it fundamentally underpins event-based marketing.

Stateful data allow us to create, store and read the memory of the data generated in the lead up to a stateless change in our database (like filling in a form).

Stateless data holds no memory of the past, it merely contains the most recent version of a record (such as the information submitted by the form).

Most marketing platforms were built for stateless data streams. This means that marketers can only react to stateless changes in the database, such as a user creating an account, adding an item to their cart or making a purchase. However, to build a truly customised event-based marketing program, we must be able to trigger marketing automation on the smallest changes, and these can be easily measured using stateful data points.

Right person, right message, right time

The post What is Event-based Marketing? appeared first on Will Egan.

Let’s explore data collection methods for the effective measurement of how our products and campaigns perform.

This is a huge topic, and very few people truly understand it.

All online analytics packages rely on two types of data collection:

1. People
2. Behaviour

To track people online, we use cookies, sessions or other stored identity data points. To track behaviour, we use two primary methods of data collection: click-stream data, and event-stream data. The latter (event-streaming) is poorly understood yet offers the greatest value.

In an age where the word ‘personalisation’ is thrown around like glitter at Sydney’s Mardi Gras, event-based marketing is the backbone of this technology. Very few marketers understand the event stream, let alone realise that it probably already running on their very own website or app. For example, Facebook’s marketing platform relies on the event-stream.

Tracking User Identities

In order for data to be of value to the marketing and product development processes, we first need to be able to identify the unique people (both known users and anonymous users) on our app or website.

There are two common methods to track and identify a user: Cookies or Identity.

• Cookie-based identities which allows us to target a web browser or device.
• Identity-based identities which allows us to target a person.

Most websites use both. But from a technical perspective, identity-based marketing is far superior.

Cookie Based Identities

When you visit a website, that website will store a number of cookies in your browser’s memory or storage.

A cookie is a small file, which contains information (key pairs of data) served by the web server and stored in your web browser’s memory as a text-like file. This file contains useful information that allows the website (or third-party script on the website) to remember who you are, your preferences, or other behaviour for future reference should you visit again at some stage in the future.

Cookie-based marketing also allows a browser user who has been on your site to be marketed to by an advertising service. If you have a third-party script like Google Analytics’ analytics.js installed on your website, you can set it up to also talk to Google Adwords. Google Adwords will then know if this user hasn’t converted in a way that you’d like them to, and will continue to market to that user across its network.

When visiting the AFL.com.au website above, over 30 cookies were dropped on my browser (many of which were third party).

So, cookie-based marketing is useful for tracking users on your own website, with the intent of re-marketing to that user across an advertising partner’s ad network.

The problem with cookie-based marketing is that you are limited to tracking activity on one browser and on one device only. There are also huge limitations around cross-domain cookies authorisation, and the threat of the user clearing their browser’s cache (and your cookies along with it) at any moment.

Identity Based

Identity-based marketing however is built on the presumption that you, or someone else, knows who the user is (i.e. the user has a unique user ID). No matter where the user logs in and on what device, you know who they are. They’re more than a static web browser.

In your own product you have a system of keying user IDs (if your users can log in, you will), you will be able to identify this user uniquely and begin tracking every time they ‘do something’. We’ll get more into how that works (Javascript event-triggers) in the next section.

Identity-based marketing will always be superior in targeted reach. Firstly, because we can be much more confident about exactly who we’re tracking. Secondly, because all of the behavioural data being collected is being linked to them directly, not a browser.

In fact, cookie-based marketing if often associated with the idea of ‘anonymous IDs’, as although the browser needs a string of characters to identify it in code, there’s a strong chance that the user is still anonymous.

So what exactly is being tracked?

Identity Based Advertising

Because we’re always logged in to services like Facebook and Google, they provide a simple example of identity-based marketing. Have you ever noticed when you visit a website that Facebook is able to tell you how many of your friends have ‘liked this page’? It’s because you’re always logged in, they know the website you are on, and they use their javascript to determine (‘identify’) that you’re there.

Google takes this one step further by getting users to log into the Google Chrome web browser itself. This enables them to track movement and behaviour across every website you visit regardless of whether there is any Google javascript sitting on the websites you visit themselves. Whether Google does this or not is unclear, but technically it’s possible.

Most of the events that you will track against browsers (cookie-based) will be clickstream events, with some select event stream events (if at all). Cookie-based collection is often more clickstream-oriented purely because you are more interested in metrics about a user’s session, rather than the user’s profile. Identity-based marketing platforms will often put more effort into tracking the event stream because they can record engagement to a specific user, and engagement with specific features on a page far more easily.

For instance, Google Analytics largely tracks clickstream events, as it is primarily interested in statistics about your session or your client (by the way, “client” means the environment you are on: web, iOS, Windows 10, Android etc). Although it does also allow you to track event stream events in a limited way, you can’t feed a User-ID into GA along with those events because GA is not set up on the basis of user ‘profiles’. It merely groups events by User-ID for some better accuracy, with these user ids actually being browser ids stored in cookies.

In the same way that Facebook and Google can track users as they move across the web, you can use identity-based marketing to track your users as they move across your app or your website.

Now that we know how to track our users, let’s take a look at how we track what they do (their behaviour).

The Event-stream

Let’s start by defining what an event is.

An event is a packet of data that is sent from one location to another when ‘something happens’. It represents a single instance of behaviour taking place on a website or app, it is usually pinned to an identity (even anonymous identities), and the packet of data can contain whatever specific or contextual information you would like to include.

The Click-stream

The clickstream is the recording of a user’s interaction with a website and or other software application in the course of their browsing. The click stream is simply a record of ‘clicks’ or request between documents on a server. Therefore, the most commonly tracked clickstream data is page views.

The event stream is far broader in its application, and far more granular. Where clickstream events are generally collected in the normal browsing of a website, event stream data is specifically defined by the marketer or product manager based on what they would like to track. An example would be the event: ‘User signed up’. This is not browsing activity, so it has to be sent differently using Javascript.

Put simply: the clickstream tracks changes between two pages and the event stream tracks changes in state on a single page.

Choosing your preferred method of tracking all comes down to what you want to track.

Identity-based event tracking uses the event stream because the events are often reflective of a user’s behaviour across a long period of time. With an identity system, you can log these events to a unique user’s profile.

A simpler example of an identity platform is Intercom. If you wish too, you can install the Intercom tracking script on your website. Intercom will begin tracking clickstream events of users, but also event stream events from the same client or other clients. This isn’t cookie-based remember – the script on your website is sending the information directly to Intercom, and if you have a User-ID system, Intercom will record all of the events it receives against the user ID or user profile that triggered them.

In summary, cookie-based identities are designed to assist with click stream behaviour tracking. Whereas identity based javascript identities are designed for event-stream behaviour tracking.

So, how is all of this useful? Behavioural marketing.

The Right Message, to the Right Person at the Right Time

Okay, so now that we have a general understanding of the difference between cookie-based data collection and event-based data collection, it’s time to explain how all of this becomes useful.

To do this, let’s extend the concept of behavioural marketing.

There are many platforms, tools, technologies and people out there who offer up versions of a definition of behavioural marketing. But what is it exactly?

In our opinion, behavioural marketing is best described as:

1. Getting the right message
2. In front of the right person
3. At the right time.

Tracking user identities gives you the right person, tracking their events gives you the right time, and your own creativity (and the context) will help you craft the right message.

If we know who a user is, and we know what actions they are taking on our website or app, we can communicate with them in a much more meaningful way.

Let’s take a moment to explore a quick case study.

Case study: Spotify

Imagine you are responsible for the marketing of the Spotify music application. Spotify is about to release an app for the Apple Watch in an attempt to increase retention on the iOS platform. Your job, as the product marketer, is to announce this new service to your customers.

Let’s use the ‘right person’, ‘right message’, ‘right time’ framework to solve this problem.

First, we need to determine the right person; exactly who should we advertise the apple Watch App to (let’s build a segment, rather than promoting this to all of our users).

The good thing is that you have been tracking users using direct javascript identites along with eventstream events. At spotify, a generic ‘New Session’ event is triggered every time a user starts a new session on Spotify. One of the attributes sent in this event is the device type and operating system. It looks something like this:

event.newSession {
	deviceType: "mobile",
	operatingSystem: "iOS"
}

When you call the identify method and pass through these attributes, the user object is updated with new ‘traits’. Traits are the descriptors of the user, the things about them that make them unique.

Let’s head over to our event-based marketing platform, Vero, and run a simple query agains the user base to help us produce generate the segment. We can do this by selecting all users who have triggered the event ‘newSession’ where operatingSystem was equal to “iOS” and device was equal to “Watch”. Great, we have found ‘the right people’.

Next, let’s consider how we might construct the ‘right message’. The definition of “right message” is the one that converts the highest. The trick here is that because we can be confident about exactly who the audience is, we can be far more targeted with our message.

A message like “Spotify now available on smart watches” is unnecessarily vague for this audience. Whereas, ‘Install Spotify on Your Apple Watch’ is better, and we can do because of a higher sense of confidence that no one will ask about other watches, seeing as these are iOS users. However, this message still lacks the punch required to drive conversion (more on this later).

Seeing as we know who this person is, we also know their playlists and what song they just listened to. We can now incorporate this into the message. For example, “Listen to Avici Levels on your Apple Watch.”

As you can see, a whole new level of personalisation is available when tracking users in this way, and marketing to them accordingly.

Lastly, we need to decide upon the right time to show this add.

For every communication (email, text, display, push) there are two ‘right times’, one you’re in control of as the sender and one you’re not (when it’s opened/received by the recipient). But with event streaming, we can actually control both. Here’s how.

We can use an event to fire this communication at the right time. For example, we could use the ‘newSession’ event to fire an in-app pop-up message containing our advert. In this scenario, the user would open the iOS app on their smartphone and see the pop-up advert instantly.

Or, we could fire this message the first time a user triggers the event ‘newSession’ after having triggered the event ‘playsSong’ where the song’s artist was equal to ‘Coldplay’ and the time of day is between 5pm and 9pm in the user’s timezone.

This is the level of personalisation event-streaming provides behavioural marketing campaigns, and this is why you should build event-based marketing into your Growth Machine.

Getting Started with Event Streaming

Yes, setting up event stream events and tracking users will certainly require technical expertise. But, the good news is that this technology has been around a while and there are lots of tools that will make this easier for you, so long as you understand the concepts and can navigate your way around your app or website’s codebase (message from the future: learn to code).

The post Data and Marketing appeared first on Will Egan.

Software as a Service (SaaS) – AARRR (colloquially called ‘Pirate Metrics’)

This should be familiar for people who live in the startup world. It’s the go-to framework when thinking about consumer behaviour and decision-making. A startup or a business that operates from a SaaS model can only be successful when each of the five AARRR stages are broken down, defined, measured, analysed and then optimised.

Acquisition

Acquisition refers to how your customers find you. There are many potentials channels out there (over 800 at last count), and I highly recommend reading this great article that covers the variety of channels by Gabriel Weinberg (the author of the book Traction).

Weinberg manages to shoot off 19 different channels. Some such as SEO, SEM and display ads might sound familiar, but have you considered community building? Or unconventional PR? There is more than one way fill up the top of your funnel up.

Peter Thiel, legendary Silicon Valley entrepreneur and investor says “It’s very likely that one channel is optimal, most businesses get zero distribution channels to work. Poor distribution – not product – is the number one cause of failure.”

The Bullseye Framework by Gabriel Weiner is another great framework to help you identify what channels are worth testing, in order to find that one scalable channel.

His article is structured whereby the 19 possible channels lie on the outside circle of a darts board, whilst the channels you should be testing lie in the inside circle on the board. The one channel that performs exceptionally well will be the metaphorical bullseye. Have a read.

These channels aren’t only relevant for acquisition however, or getting new users. For example, email marketing is great for top-of-the-funnel activities, but it is also one of the best weapons for retention, or referral. Have you had an email from a business that says, “we haven’t seen you around on our platform lately, we miss you!” These types of drip campaigns can be used across the entire user journey.

When testing acquisition channels, it’s incorrect to think about the funnel in the context of acquisition alone. As a great marketer, your mission is to consider the entire funnel. For example, whilst you might be getting someone onto your website for $0.30 with a display ad vs. $1.10 with Google Adwords the long-term value of these two users may be very different.

At first glance the display ad is a clear winner. But what if Adwords customers are converting 300% more often? Still a slightly worse option. But what if they are retained/repeat users at much higher levels, and even bring in more referrals? This is what you need to be thinking about in context of acquisition, not just the acquisition metrics.

Put simply, acquisition is a set of strategies and channels designed to acquire users. Acquisition is usually the last thing to be built-into most marketing machines. Often, there’s a minimum viable number of daily sign-ups (say 10 or 20) that most product-teams need whilst building the product and building the growth machine.

Activation

The next step is activation. It’s our view that this step is the most important of all. Activation is tied directly to user experience. It refers to the amount of time before a user obtains value from your product.

A business with great activation will see the majority of users who sign up continue to use the product over the long term. Whereas a business with low levels of activation will have much lower rates of usage and retention over the long term (if at all).

A great example of a business with fantastic activation is duolingo. The core value of this website (learn a language) is demonstrated very quickly – literally within a few clicks you’re learning a new language. You don’t even have to sign up!

In this scenario, activation has been positioned before acquisition. Duolingo actually ‘activates you’ before they ‘acquire you’ (so clever). They have you sign up after you get to experience the value of the product. Ultimately, this means activation is 100% (a dream).

An easy way to start caring more about activation is to change your user acquisition metric from ‘number of sign-ups’ to ‘number of activated sign-ups’.

Retention

Growth is all about retention. Don’t bother trying to scale a product unless you have retention. It’s like trying to fill a bucket that has holes in it.

If you truly want to be an awesome growth marketer, please spend some time watching Alex’s presentation on growth. He is a former VP of marketing at Facebook and truly understands the power of retention.

Retention, put simply, is whether or not users come back to use your product a second or more time. What’s the point of acquiring new users and activating them if they’re just going to stop using your product? In terms of total users, it’s a lot easier to keep adding more users to your platform with a growing base, than trying to compensate for all the users who are disengaged and no longer using your product.

If you have low retention, you have big holes in your bucket (and activation is probably the main problem). In other words, when retention is down, it’s not the fact that people aren’t using your product over and over again, it’s usually because they’ve never used it, not once.

For those of you who are starting a business, retention is actually a great sign of product-market fit. If 40% of users are being retained, this says a lot about the value of your product. And in our experience, 40% is actually a good 30-day retention rate.

Retention metrics vary from business to business so it’s really important to consider how you’re going to measure retention. The simplest and certainly most common measurement of retention is monthly active users (MAUs).

A better (but more complex) approach is measuring the difference between the number of times a user used the product, compared to the number of times a user could or should have used the product over the typical time period in which a user should use the product.

In other words, when the user could have or should have used our product, did they?

E.G. When a user needed to book a flight, did they use our platform to do so?
To determine this ‘typical window of usage’ a social media site might be interested in weekly active users (WAUs), whereas an end-of-financial-year accounting platform might have to wait a year to find out if a user is retained.

In it’s early days, Facebook as a social media platform was heavily focused on Monthly Active Users (MAU’s) while other social platforms were mainly measuring total registered users. What’s the difference? Facebook cared more about people actually using the community, versus the number of people in it. Sounds like an awfully familiar homage to core value proposition doesn’t it.

To go further, Facebook had a golden rule for activation in their context of retention. They thought that if a user got to 10 friends in 14 days, they were much more likely to be retained as a user.

It’s important to be mindful of the difference between correlation and causation here though. A similar metric was espoused by the folks at Twitter: users with 20 or so followers were more likely to be engaged over the long term. This sounds like causation, meaning by following 20+ accounts it would cause a user to become engaged over the long term. To encourage/increase this, Twitter introduced a single button during on-boarding that automatically followed 20+ people. Effectively forcing activation.

A quick aside before we proceed, forcing activation never works. True activation involves the user doing the work, not the product ‘showing’ or ‘demonstrating’ or ‘stepping the user through’ the product.

Alas, Twitter’s observation of users who followed 20+ people being more engaged was actually correlation (not causation). Meaning people who were engaged also happened to follow 20+ people. In other words, one didn’t cause the other.

Revenue

This is pretty simple. Are people paying to use your product?

Yes = good.
No = bad.
Any questions?

If you’re a bit of a sucker for psychology like us, there is a lot of cool content out there on pricing, and how humans react to it. Hint: we as consumers make some very dumb decisions.

A cool example is the psychology of decoy pricing on popcorn. You’d be surprised how you can make this work for a range of business pricing decisions.

Referral

The last component of the SaaS funnel is referral. Have you heard of the phrase ‘net promoter score (NPS)’ before? It’s a great way to understand how likely your users are to refer your product to other people organically.

Companies can work out their NPS by asking their users “How likely are you to recommend this business to a friend or colleague?”

The reason why this metric is so important is because referrals are directly correlated to the virality of a product, and how much it will grow organically through word of mouth.

Companies such as Dropbox, PayPal and Uber have all used incentivised referrals as a catalyst for growth. Refer a friend and get $20 in Uber vouchers, or $20 into your PayPal account or even something as simple as 1GB extra storage in your Dropbox!

Incentivised referrals are great. But remember this:

What’s better than incentivised referrals? Inherent referrals, or inherent virality.

Inherent referrals are ones that come about because of the nature of your product or service. There may be a purposeful mechanism behind it, and it is built in to how people would normally experience or use your product. Sharing the product is part of everyday use.

Email and telephone calls, for example, are inherently viral. You can’t send an email without sending it to a recipient.

Inherent virality is so powerful, and if you can discover it, it will certainly be your biggest driver of growth. Consider this, which of the following referral features drove more growth in Dropbox?

• Incentivised referral: share Dropbox and get 1GB for every friend that signed up.
• Inherent referral: share this file/album with a friend.

Now consider which one of these two strategies is cheaper, and which one is the longest-lasting. Remember, it’s the long-term sustainable tactics that we want to identify and include in our Growth Machine.

A word of caution though. Be careful trying to engineer inherent virality in products that don’t inherently have it. Technically speaking, it is a very difficult test to run, and despite the many times we’ve personally tried to implement an inherent virality strategy, it’s almost never worked.

If you do want to do it though, and at least want to test it, ask yourself the following question: ‘where in my product experience would someone naturally share/communicate with another person?’. If you are able to really maximise this point of experience, you don’t need to try and convince users to tell their friends. They’ll want to do it anyway, all you’ll need to offer is the button/or link to enable them to do it. Even better, if your product by nature has a collaborative element to it, your inherent referral mechanism would also act as an ongoing activation (and therefore retention) factor.

e-Commerce Sales Funnel – The Ol’ Fashioned Way

A typical e-commerce website is transaction focused. It has browse, category, product, and checkout pages. eCommerce follows the following formula:

Improve any one of those three dimensions (without negatively effecting the others) and you will increase revenue.

While there is still a lot to this process, many modern e-commerce businesses adopt the SaaS mindset typically with the intention of outperforming rivals.

A site that adheres to the typical e-commerce way of life in Australia is Myer. Much of the site acts like a directory. They hope you click around, then find something that interests you enough to add it to your basket and ultimately convert.

An example of an e-commerce business, that has literally dominated every market it has entered because of its SaaS mindset is Amazon. Their focus on activation and retention is without doubt integrated into the core of their business.

A sure bet you can make about Amazon is that they will try and get as many Australians as possible to make one purchase from their platform. That way they’re signed up, can experience the value Amazon offers, and Amazon has a chance to ultimately retain them as a user.

This is an example of an e-commerce business thinking about activation. An example of Amazon thinking about retention was their introduction of Amazon Prime as a subscription service. This is a sure way to raise the customer lifetime value (CLV) of a user over time.

Over the years, eCommerce has become increasingly reliant on email and pricing as the value drivers in their marketing funnel.

High Consideration Sales Funnel

This funnel is quite similar to e-commerce but has a much longer purchasing lifecycle. This is largely due to the ‘high-consideration’ nature of what’s being purchased (cars, holidays, degrees). This funnel also accurately describes a sales funnel through to lead generation.

In a high-consideration environment, marketing teams will spend much more time in the awareness, interest and evaluation stages of the sales funnel.

Typical examples of products that would have a sales process like this online include enrolling in tertiary study, buying a property, purchasing a car, or booking an expensive holiday.

Few people have access to such large credit facilities that they can afford to purchase these items online. Think about the most expensive thing you have bought online. Maybe a plane ticket to Europe? Rarely would your average consumer spend more than $5k online.

What about a student’s university debt? The customer’s credit facility is actually the government, and you definitely apply for a degree online for most universities. But because it’s such a massive commitment, the mindset of the user will lend itself to the high consideration funnel more than the e-commerce funnel.

A somewhat obvious insight when thinking about your ‘bullseye’ framework for high consideration products is the potential power of high value content. A great strategy in these scenarios is to have well-developed, long form pieces of content for the early parts of your funnel.

The important thing about high-consideration sales is that a transactional approach (like e-Commerce) doesn’t work. The reality is that whether you like it or not, your user is going to go through most of these stages in the path to conversion.

It’s also very important to realise that if you’re not there during the first few stages (awareness, interest and evaluation), don’t expect to be in the game further down the funnel when the prospect is about to buy. Users aren’t going to just randomly show up to your site and purchase (unless your Apple).

In this process, content is truly king.

The post Choosing a Marketing Framework appeared first on Will Egan.

We employ products to do jobs.

Underpinning every Growth Machine is a fundamental understanding of exactly how our product creates value for our customers. Until we understand this, nothing else will make sense to us or our customer.

This article will help you articulate your core value proposition. To do this, we will be using the theories of Christensen, Porter and Foster.

What is a core value proposition?

A core value proposition is an articulate explanation of the underlying problem that your product or service solves for your target market.

Your core value proposition is best defined as ‘the overarching problem your product actually solves.’ It’s the function or feature that makes your company/product unique.

With this said, don’t just jump to the first thing that enters your mind. Think about it more deeply. Ask yourself the following question: ‘when a customer employs my product, what problem are they trying to solve?’

The best value propositions are rarely longer than a line. In fact, the very best might only be two or three words. Here are a few examples:

• “Cheap flights” – Kayak
• “Instant coffee” – Nescafé
• “Healthy burgers” – Grill’d
• “CE/CPD Compliance” – Ausmed

Channel your inner Karl Marx and stay pragmatic. Think about the utilitarian way of describing your company or product. ‘People use our company or product to…’

Your core value proposition (CVP) isn’t designed to be presented to your customers either. Instead, think about them as a simple, verbal articulation for you or your team’s eyes only. Something so simple, that your team could sense-check the work they do against the CVP statement on a daily basis. Much like a null-hypothesis, your team should be able to determine whether or not the core value proposition is being delivered upon.

Case Study: Dollar Shave Club

For example, Dollar Shave Club goes to market with the phrase “A great shave, for a few bucks a month” (nine words). Although this is a very succinct articulation of how they create value, it doesn’t describe the job they actually do or the problem their customers have employed them to solve.

Dollar Shave Club’s job to be done might be described as ‘never running out of sharp blades’ or ‘always having sharp blades handy’ or even more simply put… ‘a cheap, sharp shave’.

Again, core value proposition (CVP) is what you do, not how you do it.

Why does all of this matter? Because if you can’t work out why or what you do for your customers, it’s going to be very hard for your customers to figure it out.

Conveying the right message in your marketing is absolutely critical. The internet is the short attention span theatre. As marketers, we have only a few seconds to capture our target customer’s attention (and just a few more seconds to hold it).

Capturing someone’s attention relies on us being able to quickly and effectively align our product (solution) with their problem.

Life Without Core Value Prop Clarity

Let’s take a moment to imagine marketing without core value proposition clarity.

A marketer’s role is to take a product to market in a way that gets the highest conversion possible. The problem here is that often we don’t actually know what that message is. So, what’s the first thing we do? We hold meetings, draft ideas and stumble through all these conceptual ideas, random notions and biased beliefs about what our product does.

These confabulated ideas are then packaged up into a document, presented and agreed upon in the board room, campaigns are drawn up, user experience and market research is undertaken (often to re-affirm our own biases), design and media agencies are wheeled in and we ‘launch’ this new message or feature to the market.

The numbers look great to start with, as they always do (it’s new), but wain very quickly.

As the numbers deteriorate (purely because the low hanging fruit has been shaken from the tree), most marketers turn to A/B split testing to start identifying, through conversion rate optimization, better ways to optimise this local maximum. Conversion rate optimisation (CRO) is a fantastic process. It involves testing changes to images, ideas, copy, CTAs, offers, asks, layouts against the current version to determine which changes move the needle. (But despite the magic of this methodical approach, taking a pile of garbage and optimising it still leaves you with a pile of garbage.) So, testing begins and the team start optimizising towards the highest converting combination of these features.

Over time, and in pursuit of higher conversion rates, the marketing team begins to present and describe the product in a different light to how it actually works? We call this the local maximum.

The reason why problems like this emerge is because marketers use CRO as a starting point, not the optimisation tool it actually is. This type of siloed approach leads to a product/marketing mismatch.

Ultimately, customers sign up for solution X, while the product team is building solution Y.

Remember, the customer experience erodes when the marketing doesn’t match the product.

The Minds Behind Core Value Proposition

Clayton Christensen – Jobs to be Done

Christensen describes CVP using a concept known as ‘Jobs to be Done’ (JTBD). We’ve already discussed this concept briefly, but let’s explore it a little more deeply.

With JTBD, Christensen poses the following question: ‘what is the underlying job to be done that your product is being employed to do?’

For example, what is the underlying job to be done for a bottle of water? To hydrate? To hold water and make drinking easy? Maybe. Or, could it be portability?

Let’s simplify the question: why would you choose a bottle of water over a glass of water?

It’s light-weight? It’s convenient? How about when you’re heading out for a run, which do you choose? A bottle. Why? Well, are you going to carry a glass whilst you run? No.

The underlying job to be done of a bottle of water is therefore portability.

This means that the people who are in the bottled water business are actually in the portable hydration business. So not only are they competing against other hydration products, but they are competing against other portable water products. Against your own bottle, small bottles, large bottles, water fountains, non-consumption, etc.

“Portability” is a bottle of waters core value proposition.

So if our core value prop is portable water, how can we innovate on that? A clue: it’s not by adding flavour. This is where Michael Porter comes in…

Michael Porter – Porter’s Five Forces

Whereas Christensen can help us work out what the CVP is (and subsequently how new market-entrants can use improved CVPs to disrupt incumbents), Porter helps us understand how the industry might change in its own right. To do this, Porter developed the Five Forces model.

The basis of the five forces is the question of ‘how does an existing player in the market become disrupted’.

There are five ways according to Porter, and consider these not just from a defensive position, but from an offensive position too.

Rivalry amongst existing players – incremental improvements designed to give an incumbent incremental advantage of its competitors.

Threat of new entrants – how undisrupted or stagnant is the industry (construction)? Is it a trending industry (fintech for example)? Are investors looking at this area? What are the setup costs involved in the industry?

Bargaining power of suppliers – how much control do suppliers have over how the industry operates? In the airline industry for example, fuel prices could change any day, causing turbulent (you’re welcome) price wars.

Bargaining power of buyers – how much control do customers have over the product? In the airline industry, not much. But in the recruitment market, probably more.

Threat of substitutes – what other ways could your consumers solve their job to be done? A user’s preference is a strong factor in them preferring a train over a plane (different markets will see this differently), or soda over bottled water. But the job to be done is still the same. Uber is classic example of a new market entrant changing an industry by substitution (both on the driver and rider side).

Porter’s five forces are important in considering the underlying value propositions of the incumbents in an industry, and how you might best address or exploit them. But what happens when we don’t change anything at all?

Richard (Dick) Foster – Incumbents don’t innovate

Dick Foster, the lesser known of the three, explores an industries resistance to change. Specifically how a market’s incumbent strategy defaults to persistence (maintaining the old or failing innovation rather than innovating beyond it or disrupting it).

Foster demonstrates this through a story that dates back to the late 1800s / early 1900s when sailboats ruled the seas. Wooden sailboats were the market incumbent. Then all of a sudden, this new invention called the steamboat started to gather some traction. People were gawking at the idea of lighting a fire on a wooden boat. “Are you mad?” they exclaimed.

In an attempt to prove the viability of such an innovation, the steamboat manufacturers decided to race a steamboat against a sailboat from New York to London. But it took the steamboat three times as long because the sailboat had a really great wind. So the gawking continued.

Curiously enough, what the steamboat companies did next was rather like “growth hacking”, they fixed sails to their steamboats as well. A great interim solution, but something that certainly added to their mocking.

Yet sure enough, over time as the steam-engine technology advanced, the steamboat became faster and faster until one day it eventually beat the sailboat on the New York to London voyage.

Gradually, buyers of sailboats decided steam boats weren’t that bad after all. Why? Because they’re not in the business of buying sailboats, they’re in the business of moving things from A to B as fast as possible. If the sailboat is better today, great. If the steamboat can do that job to be done better tomorrow, even better.

The lesson could end here, but what happened next is certainly worth remembering…

So what did the sailboats do? They persisted with their incumbent technology and began implementing what’s described as a sustaining or efficiency innovation… they added more sails to their boats. It worked. Sailboats were faster again.

Sure enough, a little while later the steamboat was faster again. So, what did the sailboat companies do in retaliation? You guessed it, they added another sail. And on and on it went. Until one day when the sailboat had so many sails on it that it became so unwieldy that it couldn’t be stopped. Flying in from a voyage form New York to London it crashed into the port with such momentum that the entire ship almost ran aground.

This is why a Growth Machine must be built around the underlying job to be done.

Is Core Value Proposition the same as Product Market Fit?

No. These are two different things.

Core value proposition is a simple articulation of the problem you solve only. It’s how your product creates value, but it’s not the product itself.

A product is the combination of a problem and a solution. The solution is what you create and sell, the problem is what your customers have and are prepared to pay to fix.

Once you’ve found alignment with these two things, you now have a product. Product market fit is when you have identified a good enough solution to a common problem that enough people have whereby you could build a company that makes and sells the product. In other words, your product addresses a market that’s large enough to build a business in.

Product market fit might be a familiar concept to you as it thrown around A LOT, and for good reason. Most investors use it to determine whether a business will be successful.

Okay, so what is product market fit then? To fully understand this, we need to look at how problem solution fit and product market fit work together.

The essential foundation for any product or service is that it satisfies a user’s need (a JTBD). Typically, we are well aware of what our problems are as a user (not always though). But that doesn’t mean we are great at understanding how to solve them, and this is where the opportunity lies for entrepreneurs.

If there is a problem and we have (or can develop) a solution to that problem, it’s likely that we have found our problem solution fit.

This might sound obvious, yet there are many examples of people who have had an idea on a whim, spent thousands or millions developing the solution only to find no one has the underlying problem. Their fatal mistake could have been avoided by following these two steps in this precise order.

1. Confirm problem solution fit → Develop a minimum viable product (MVP) to ensure your solution actually solves genuine and common problems your target users regularly have.
2. Confirm product market fit → Once you’ve got problem solution fit, you then need to work out whether enough people have this problem. If the problem is big enough, there should be a decent sized market for what you’re planning on selling. This is the part investors want to hear.

Once we make it past these two checkpoints, we’re almost ready to start scaling! But before we do, let’s take one last look at Core Value Prop through the eyes of our beloved software engineers.

How Engineers Describe Core Value Prop

Engineers are the best at identifying what a business does? Why, because they’re the ones building it. You’ve probably never noticed it, but almost every market-leading product actually only does one thing. This one thing in marketing speak is the ‘job to be done’, but in software development this is described as the core function. (Engineers use the word “function” to describe the things your product can do.) This core function is designed to allow users to access the product’s core value.

Accessing this core value is typically easy for the user and fast in the best products. It may be coming to the front of your mind now:

“Hey, my favourite app is awesome because it does X!”.

This is why Uber Eats is a different app to Uber for taxis.

Now ask yourself: how fast does your product deliver on its core value proposition? The best products do it immediately.

Case Studies

Let’s take a look at some of the best products and try to articulate their core value proposition and correlate it to the core programmed function.

Google

Google’s core value proposition → A search engine that knows exactly what you mean, and gives you back exactly what you want.
Google’s core function → A search function. (Text input bar and ‘search’ button.)

Google has always solved informational based problems by quickly and accurately finding the best information for people (problem solution fit ✓). Oh, and by the way the whole world wants fast answers (product market fit ✓).

Something particularly interesting about Google is that fact that they found product market fit on day one. Take a historical look at Google’s first search product (below), despite the billions of dollars invested in their software development it still looks the same?

Why? Because once you find the interface that delivers core value prop, you want to avoid deviating from it as much as possible. So, what has Google spent all that money on then (asides from free food and beanbags of course). Well, most of it has gone to making the search function faster, more accurate, more comprehensive etc. Google found product market fit on day one.

Uber

Uber core value proposition → Tap a button, get a ride.
Uber core function → Match a rider with a driver. (Text input bar and ‘request Uber’ button.)

Uber solves a multitude of travel related problems. The primary problem being the lack of quick, accessible and relatively cheap transport options (problem solution fit ✓). This is a mass-market issue, validated by the fact that humans have been travelling between locations since day one.

Just like Google, guess what? Uber’s core function hasn’t deviated a whole lot. Let’s go back in time and look at Uber V1.

The core function again is still the same and like Google, Uber has certainly spent a lot of money improving their product. What have they done? Enhance the core function of matching drivers with riders. The application now sources drivers in a more intelligent manner and supplementary features have been built to enhance this core value prop only.

The most poignant example of this is the sheer fact that Uber Eats is a separate app to Uber. Most companies or marketers wouldn’t think like this. They would make ‘ordering food’ another feature/menu item on the existing Uber app. In doing so they would convolute the interface and increase the user’s cognitive load. This would also break the user’s schema, de-skilling them, and resulting in them regressing in their ability or confidence to use the app. You’ll know this is happening if you hear your customers exclaim “they’re always changing it.”

The core function, that big green button is still essentially the same. A monkey could probably slap the screen a few times with the Uber app open and order a ride. Which is awesome and probably an apt metaphor considering drunk people seem to easily be able to use the app.

So what’s the lesson in all of this?

Test your product with drunk people. Oh, and many of the world’s best products start by focusing on achieving problem solution fit. They then design an interface that delivers upon this core value proposition as fast as possible.

The post Articulating Your Core Value Proposition appeared first on Will Egan.

Although search algorithms are changing frequently, the underlying technology behind them does not.

By taking the time to learn the technical aspects of search, you will develop digital marketing skills that survive any passing change or algorithm update. This SEO tutorial will teach you the fundamentals of search engine optimisation for the modern web.

Let’s begin…

Definition of Search Engine Optimisation (SEO)

Search Engine Optimisation (SEO) is how marketers go about making sure that their web pages are both indexed and ranked well by search engines. Ultimately, SEO is the optimisation of your content against a users’ search phrase.

Above all else, SEO is a marketing channel that loosely falls under the category of ‘inbound marketing’. Inbound marketing is a strategy designed to target customers who are looking for the products or services you sell, and are finding their way to you.

Search engines (as a channel) provide a way for these customers to find, visit and ultimately trust you. But as we’ll learn in this article, search engines are also a ‘customer’ in their own right, and have ‘needs’ that must be met also.

Why You Need to Be on Page One

Typically the most popular websites (or the best optimised) will be displayed on page one of the search engine results pages (SERPs). Your goal as an SEO marketer is to get your website on page one. Here’s why…

Research shows that the top 10 ranked websites (on desktop search) get around 70% of all clicks.

As you can see, the number one spot is getting a third of the clicks. This is even further reinforced when you consider there are literally trillions of searches per year. That’s right. Over 1,000,000,000,000 searches. Big number, right? Time to capitalise.

The good news is that it’s not as hard to do as you think. The real skill is perseverance. Most people give up after a few days, weeks or years. Search engine optimisation is typically a long term effort. You need to work hard to get up the rankings (and stay there).

The name of the game is incremental progress, as with most good things in life.

Let’s start at the beginning…

A Quick History of SEO

Today when we think of a search engine, we have a fairly solid notion of how it works. A document index of the web, accessible and queryable via a search bar. But it wasn’t always like this, the first search engines behaved more like directories.

A directory? Yep, a directory. Remember the days of the Yellow Pages? Say you were looking for a plumber, you would open the directory and flip across to the services section and audit the list for a ‘plumber’. The first search engines worked like this too. But the web was a lot smaller then (maybe less than 1,000 pages per country).

Dmoz.org (an early directory) is one example of such a directory (and is still influencing search results today). Using the directory would involve visiting the home page (you would have memorised the URL), selecting a topic, a sub-topic, and then choosing from a limited set of results.

“Only a few options!”, you exclaim, “how did DMOZ know what was important to display?”. Great question. Frankly, a human told them. Plain and simple, a person like you and me maintained the directory.

This short era then gave way to a new kind of ranking system, primarily based on keyword density or frequency. Directory companies realised that they could count the number of relevant words, or ‘keywords’, on a given page and use this to determine the subject matter. So if I went to a directory like Yahoo, the top results for my search ‘plumber in Surrey Hills’ would likely be the pages that listed their Surrey Hills address a few times on each page, and mentioned the words ‘plumber’ and ‘plumbing’ most frequently.

All that was good and well, until SEOs realised it could be gamed. Webmasters (people who own/run websites) began ‘keyword stuffing’ (filling the page with target keywords). White font on white backgrounds was one example (the search engine would see it, but not the user). Thought you’d try this.Meanwhile the sheer volume of content was growing faster than the directories could keep up. There had to be a better way…

In January 1996 Larry Page and Sergey Brin began working on this problem as part of their PhD students at Stanford University, and ultimately developed a concept called Pagerank. Since then, Google has become the gold standard in sorting the world’s information. Let’s consider how they did it.

Links Are People Too

In order to understand why Pagerank is so vital, we need to understand how search engines actually discover content on the web.

It’s easy to perceive Google as an all-knowing behemoth of webpage knowledge. But it’s not. Your website isn’t hosted on ‘Google’ – your website is a group of documents (.html, .pdf, .jpg, .gif) floating around the Internet, hosted somewhere in the world. To be able to search the web, Google must first create an index of the web. An index is basically a list of things. Google creates this index by ‘crawling’ the web.

To do this, Google employs what are commonly referred to as ‘web crawlers’, or ‘spiders’. Conceptually, a crawler is just like you and me – making it’s way through the web by following links. It is a software program, bot or script hosted by Google that visits a site and adds all the links on that site to a list for it to index later. There are more bots on the internet than people.

As you can see the crawler that indexes the web relies on links to be able to move between websites. Links therefore underpin Google’s (and peoples) ability to navigate the Internet. Imagine a webpage with no links pointing to it or from it. No one would find it (including the crawlers). Links underpinned Pagerank.

Back in the early days (early 2000’s) Google wasn’t so great at crawling. Better than the rest, but nothing compared to today. It would take them up to a month to crawl the whole web, then a week to index the web then a week to update their databases on what’s out there. Not the best user experience if the results Google is serving up take that long to be refreshed!

At this point, Google didn’t prioritise crawling the top sites which are hit often, but in order to serve up up-to-date search results, these sites needed to be crawled more often to accommodate for their higher volume of users and therefore queries. Google suddenly realised they should crawl important sites more frequently, and to help determine which sites to prioritise on this way, decided to segment the web based on a page’s ‘Pagerank’.

But how did they know which sites got a higher rank? The more reputable (popular referrers) and relevant (text keywords) links you have pointing to your page, the more that Google’s Pagerank system will consider your page as valuable.

Pagerank → influenced by the number and quality (popularity, relevance) of links to a page. Helps determine how important the website is. It’s how Google and other search tools can tell that cnn.com matters more than the local pub’s website (to some people at least ?).

Why Links Will Always Matter

Because crawlers travel through links, we immediately start to logically see how it is that Google values inbound links so much. Basically, if the world thinks you have great and relevant content, they will link to you, Google will elevate you, and it becomes a self perpetuating cycle. Links links links.

Now we know how Google crawls the web. With spiders, through links, determining Pagerank. This is very important and fundamental to understanding why the concept of ‘link building’ is so crucial. Re-read the last few paragraphs again if you’re still not 100% clear here.

Why Link Building Matters

As you can see, link building matters. Let’s explore why with a quick example.

Imagine your web page is a student in middle school: Tom. If lots of people referred to Tom as the ‘best football player’, this would create credibility for Tom when others asked ‘who’s the best football player’? Once enough people thought Tom was ‘the best football player’, we make him football captain. What does this mean in the context of a search engine? Tom’s now top of the Google pile, because the search tool sees the credibility he has.

So, how does Google know what people are saying about your website? The answer is simple. The anchor text of an inbound link is essentially a vote for your site for that phrase. For example a link to a website about Brent Harvey with the phrase best Australian Footballer will pass credibility to that content for that phrase.

The most difficult thing in this latest development is that links are on other people’s websites. Linking is a key factor in both popularity (how popular is that website) and relevance (what they say about your link). What a web of intricacy.

With this in mind, do you think the phrase ‘all PR is good PR’ holds true, or not?

This sounds all fairly intrusive, doesn’t it? “It’s my website!”, you say, “Google doesn’t know how to best serve my content on mitochondria!” Correct – which is why it is mostly up to you (the SEO optimiser).

Google does however do an amazing job of interpreting your site, we just need to ensure our website is laid out in such a way to give Google’s crawlers an easy path around the site.

Basic SEO Optimisation Strategies

Here are a few examples of this type of basic optimisation:

• Give crawlers a few tips – use a robots.txt file on your web root folder to tell crawlers what to visit and how often. This is helpful if you have legacy content on your website, or if you have a large volume of static pages that don’t change often.
• Don’t put valuable content deep in your site. If you want someone to read about your new lotion product, don’t put it at www.mysite.com/products/fall-products/1176/product-3. Serve it up at www.mysite.com/anti-fungal-lotion.
• Keep everything in the family – don’t split your valuable content between domains or subdomains – blog.mysite.com is a different domain to www.mysite.com.
• • Seriously—don’t split your content between subdomains. Such a bad idea.
  • blog.website.com and website.com are two different sites according to crawlers
• Try to control relevant text in your inbound links, otherwise known as the “anchor text”. For example linking to your article on the newest iPhone should look like this “iPhone 8 review” rather than “the newest iPhone is waaaay too expensive in my opinion”.
• Crawlers interpret text – for example they have only recently learned how to read images and still struggle to read embedded PDFs. Moreover, the sheer resources required to do so may prevent the content from being indexed regardless. If you can’t make your content text, use alt tags on all your non-text content to help crawlers attribute keywords to it.
• Website metadata – This is hidden within the HTML of the website that tells search engines what your website’s title, description and keywords are. Why HTML? Google understands code, so we have to speak their language. Much more on this later.

Present Day Search

Of course, relevancy and popularity are broad ways of describing the many hundreds of factors that search engines consider when ranking your page. It’s fair to assume that Over time, Google has also removed the odd ranking factors/signals here and there. As such, search has naturally become quite complex.

For example, many of the stronger ranking signals Google now considers relate to the user’s experience (UX). How long is someone spending on a site? Do interstitial popups appear? Are they clicking through to new pages? As such, it’s likely that Google and others in the future will continue to look at these metrics alongside links. If they can determine new ways to measure a user’s interest, you can bet they’re going to show it!

This means that the more you pay attention to SEO over time, the better your chances are of getting to the top of page one.

Further Validation

To validate our newfound conviction on the importance of links, lets review some additional evidence.

Understanding Google’s Dependency on Links to Crawl the Web

We know, and have explained from a high level how Google’s spiders navigate their way around the absolutely enormous amounts of information around the web. Via links. If you’re still a bit hazy on the basics of this we highly recommend you watch the video from Google Webmasters we mentioned previously.

Since Google relies on links to crawl the web and index those pages, it goes without saying that links are part of the equation when ranking and returning the most relevant ones first.

Detailed Secondary Research from Credible Sources

One of the most detailed search engine ranking factor reports from Moz went to the effort to analyse 17,600 keyword search results from Google and to interview 150 of the world’s most foremost search marketers.

Their analysis of 17,600 keywords was to show correlations between pages with certain features and how highly they ranked.

What was the feedback from the top 150 search marketers? Surprise surprise, the top 2 influential factors were:

Domain level, link authority features: This is based on link metrics such as quality of links, trust and domain level pagerank.

Page-level link metrics: This is based on page level trust metrics such as quantity of linking root domains, links, anchor text distribution and quality of link sources.

Links on a page and domain level matter. The statistics say so, and so do the experts. The same theme is appearing. The quality and quantity of the links to your page are going to play a massive role in your search engine visibility, and therefore your business success.

Backlinko analysed 1 million search results, guess what they found? “The number of domains linking to a page correlated with rankings more than any other factor”.

Your page is one amongst millions. Being found is essentially expecting a user to find a needle in a haystack. If we take this advice in the context of everything else we’re going to cover, we can supersize that needle.

Primary Evidence

We can also use tools like Ahrefs to show us the linking portfolio of a website and use this to get an idea of how the strength of their links are impacting ranking.

This might be a more advanced step as there are so many factors to consider. However, if you get used to the interface and the information it shows, this and other similar tools will typically show a correlation between backlink portfolio and ranking. Or, in this case, Ahrefs domain rating and URL rating.

Tesla.com, which ranks first for over 11,000 search terms, shows a URL rating of 87 (very, very high) and a domain rating of 69 (high).

UR rating (URL Rating) – Is the strength of a URL’s backlink profile and likelihood it will rank well on Google.< strong>DR (domain rating) – Is the strength of the whole website’s backlink profile.

We can already start making advanced inferences. Remember how we said page level link-based features were the most important from the correlational study? This means if we have a really strong backlink profile to one particular URL we can potentially outrank a site with a very high overall domain score. There is much more to come from us, but if you’re interested now check out Ahrefs blog.

Note: For tesla.com, there is a higher URL score than domain. That’s easy to understand because it’s the homepage. It’s going to be common practice for people who are talking about this company to link to the homepage.

To stress even further how important backlinks are in shooting up the rankings, Ahrefs provides an approximate estimate of the number of links required to make the first page for a certain term. We typed in ‘car’ and this is what we got back.

The point of this section isn’t meant to be a guide on SEO tools, just to emphasise how important links are. That’s the main takeaway here. If you want to learn more about SEO tools we recommend this article on SEO industry experts favourite tools. We’ll be covering this in depth soon so keep an eye out.

Importantly, all of our own inferences are confirmed by the above studies from Moz and Backlinko.

The top three

Ultimately, we are trying to provide the most relevant and useful information to our users, given their original search. Neil Patel has a fantastic article on the 10 most important seo tips you need to know for blogs, which can be boiled into three key areas for across-the-board use.

Readability

It all starts with the question ‘is this a good experience for my users?’.

Chances are the best ‘yes’ for your users is also a ‘yes’ for search engines. You want as little distractions to the user getting what they were looking for as possible, and Google will respond in kind.

For example, consider page speed. A site that takes 5 seconds to load will have a lower user satisfaction and likely a higher bounce rate than a site with the same content that loaded after 1 second. Heck, Strange Loop even reported a 7% drop in conversions with every second of load time (the same article has some great tips for reducing page speed).

The same can be said for your content – the days of keyword stuffing are gone, and you need to write for your users first, search engines second, if you want to engage them. Users aren’t stupid, and Google doesn’t treat them as stupid either.

This point even goes as far as the seemingly innocuous things like your URLs. If your URL is readable and meaningful, users will follow the trail, and Google will follow. Resist the habit to incessantly catalog your website with structures that render your articles as contoso.com/articles/categories/15/article-135. Make that article about your great new feature at the top of the tree – contoso.com/my-great-feature-for-industry, and don’t use underscores in your URL – search engines don’t recognise them (they join words, instead of separating them).

Meta descriptions need to be unique and relevant to each page, as duplicate content is both a wasted opportunity and could also get you penalised by Google. This is because google reads the HTML of your site, not the actual page text we see. To this end though, ensuring that all non-text content like images are appropriately named also helps search engines and users alike find your content. Fight that urge to name your next file mktg_hero_persona1-3.jpg. Yuck.

Lastly, one of the more recent (at time of writing, mid 2017) changes to Google’s algorithm was to explicitly prioritise websites with a good mobile experience. Not only will this get you ranked higher, your chances of users engaging and converting with your content will increase. Ensure that you don’t have one of those m.website.com subdomains (it’s an entirely different website to search engines anyway), and test for the different form factors you expect your users to interact with your content on.

Reputation

As we’ve already covered, it’s important how and from whom you get traffic; focus on outreach channels that are from reputable domains. For instance, if you are building a tech solution for the construction industry, you want to be featured on a website that a majority of the industry body would visit, as opposed to a site that caters to a small subset niche early-adopters. Even better, think of a site that themselves have regular content being posted, like an industry news site, or a non-competition horizontal tech company that requires users to sign on every day.

To the same effect, don’t ever forego including social media into your SEO strategy. The impact that a well considered channel strategy of both the social machine and seo can have on your conversions is too good to pass by. This is especially true of time sensitive events. If something is going ‘viral’, a good indication that it’s important to your user socially is a good indication for the search tools.

That’s inbound, but outbound matters too. Link to other websites with both relevant content and quality content (cough, Neil Patel link above). You want to be known by Google and the user as a site that both is popular and useful to the user’s end-goals.

As far as your actual content goes, be relevant, but don’t be stale. If you’re a blog, this is easy – post engaging content regularly. If you’re a product site, keep your blog on your top level domain, and update your website product pages with updates, change logs, features, new case studies, customer success stories – it’s your job to drive this content for the sake of your users’ attention.

Lastly, for goodness sake, remember what your users are looking for.

Relevance

On that note, how are we supposed to know what people are looking for? Thankfully search engines leave clues for us to digest, and there are a bunch of awesome tools out there to point you in the right direction! Some free, others, not so much. Let’s have a quick think about how we can make sure we’re focusing on the right keywords.

Start off by working backwards, what pain point does my business address? And what does my website say now? Or more importantly, what should it say? You need to have a high correlation between your core offerings and the copy on your landing pages you’re hoping to rank well for. This way we get the right visitors who convert and the search engines see the value you’re providing, shooting you up the rankings, cha ching!

This might sound obvious, but if you don’t have crystal clear value propositions your user won’t stick around to unravel the mystery. Your user will be annoyed by it, and so will Google. Basically, if you’re the best executive recruiter in town you probably shouldn’t be writing content about resume tips for interns.

As a first step, try to put together a list of of the main ways your business helps people and what these people might be searching for. If your business is a Physiotherapy you might alleviate back pain regularly, you might also deal with shoulder and knee pain as your most regular visits. You know you’re great at helping with back pain, even more specifically lower back pain.

A starting point for your content strategy!

Summary

It is a search engine’s job to both crawl and index content, and to provide users with a ranked list based on their search queries. It is your job to prepare and distribute your content in a way that search engines will rank you well and that your users get what they were looking for. That is, in essence, SEO.

Don’t worry if some of this was a bit foreign. This topic is important to master as a digital marketer and we will give you the right stepping stone to get there.

The post Search Engine Optimisation SEO Tutorial appeared first on Will Egan.

How to Avoid SPAM Filters

Assuming you have a legitimate reason to be sending email, and your messages are not spam, it’s kind of imperative that your email makes it into the inbox. After all, isn’t this how the democratic web should work? The good news is that SPAM filtering is at it’s core an algorithm. Moreover, a set of checks are performed on each email to determine whether the message is legitimate or not. This is good news as algorithms are engineered… which means they can also be reverse engineered. Furthermore, the factors being evaluated by the SPAM algorithm are largely in our control (so we can influence them as well).

Factors that affect your SPAM score

Here is a simple checklist of the key factors that affect your email’s SPAM score, starting with those that are most likely to be the culprit:

• Being on a blacklist
• Unclear sender identity
• Missing Reverse DNS Lookup (PTR Record)
• Missing Sender Policy Framework (SPM)
• Missing DomainKeys Identified Mail (DKIM) Policy
• Not having a dedicated IP address*

*Typically only relevant for mass email senders, not one-to-one communications such as Gmail or Outlook.

Now let’s look at each of these factors in isolation. How do we test them? How do we monitor them? How do we fix them?

How to check if your email is on a blacklist

This is both the easiest and best place to start when trying to identify why your emails are being spammed. Put simply, you might be on a blacklist. You can use online tools to check whether you are, I prefer MX Toolbox as it’s extremely comprehensive and it’s free. Here’s a quick demo of how it works. Also, be sure to check both your IP address and your domain name.

Unclear sender identity

Mismatching sender domain and from addresses, or inconsistent sender addresses result in the inbox being unable to identify you as a sender. Email identity is primarily handled by two values, one being your IP address or domain, the other being your from address. Trust is built through either of these over time.

The goal here is to try and send from the same domain and address at all times. If you have to change one of these values, be sure to only change one, not both. Here’s why…

If a user regularly receives and opens email from the address tim@example.com and the sender IP address suddenly changes, the inbox will still trust new email coming from this address as the from address is constant. Similarly, if the from address is changed to tim@updates.example.com but his sender IP remains the same, the inbox will fall back to the IP address and continue to trust the sender. However, if the email from address and the IP address are both changed the inbox will treat this an entirely new sender. Why? Because all links to the original identity have been lost. The IP has never sent to this inbox before, nor has this from address.

You can fix this by either always sending from the same from address, or at the very least always sending from the same underlying IP address. You will need a dedicated IP address to be able to do this (more on this later).

Missing PTR Record

A PTR or Pointer record is used to map a network interface (IP) to a host name. These are primarily used for reverse DNS lookups. They help a mail client identify a sender by tracing back an IP address to the sending domain and matching that with the domain in the sender’s from address. If you know your domain’s IP address you can use MX Toolbox to do a quick reverse DNS lookup or ptr request.

If you don’t know your domain’s IP address, you can use the command line to both find it out and to do a reverse DNS lookup. You’ll need a program like Terminal or Console and the following commands:

nslookup yourdomain.com
dig -x [your ip address]

Here’s a quick demo of how to do this. Notice in the response of the dig command that an answer is returned, this is what we want to see.

If you want to know more about how to add a reverse DNS record or PTR record to your mail server, I recommend reading this article.

Missing Sender Policy Framework (SPM)

Whilst this is not an essential inclusion and is more of a best practice, it will certainly help to give your emails a greater chance of landing in the inbox. The MX Tools provides this great explanation of what an SPF is:

Sender Policy Framework (SPF) records allow domain owners to publish a list of IP addresses or subnets that are authorized to send email on their behalf. The goal is to reduce the amount of spam and fraud by making it much harder for malicious senders to disguise their identity.

You can use the SPF tool on MX Tools to ascertain whether you have an SPF policy in place. Here’s a quick demo of the tool in action.

To add an SPF record to your site you will need to add a text record to the DNS values on your domain name. Here’s a quick example of what that might look like.

v=spf1 mx a ptr ip4:123.456.789.0 a:example.com include:example.com ~all

A deeper explanation of the SPF concept is available on the mailbird website, including more advanced instructions on how to set one up.

Adding a DomainKey Identified Mail record

A DKIM record seems to be becoming increasingly important in recent times. This process basically signs your e-mail with a private key (generated by you), and you generate a DNS record with the public key so any recipient domain can verify if the mail content has been or not forged. It’s basically a mechanism to help protect both email receivers and email senders from forged and phishing email. Further information on this is available directly from the DKIM project website.

For an even more advanced implementation you may like to consider adding a DMARC record. This article will help you do that in just 15 minutes.

Not having a dedicated IP address

Although there are many sceptics out there when it comes to the question of whether a sender needs a dedicated IP address, the pure technical factors at plat leads one to think that it must play a part. A dedicated IP is offered as an additional upgrade by most mail hosts, and even email vendors such as Mailchimp or Vero.

There are few key benefits of having a dedicated IP address. Firstly, you’re 100% responsible for it so the reputation you build on it is yours for the keep. You can take it with you to your next email vendor for example. You can send from multiple from address without worrying about your identity being lost. Also, your not sitting on a server with 100s of other marketers (who know what rubbish they might be sending out, and who knows where they got their lists from).

The post How to Avoid SPAM and Junk Filters appeared first on Will Egan.

How do I know if I have a good retention rate?

Simple, you measure it properly. When I ask most founders or product marketers what their retention rate is their initial instinct is to talk about their monthly or daily active users (MAUs and DAUs). It’s great that they are tracking these metrics of course, but it’s pretty likely that these are not providing an accurate indication of true retention.

In a nut shell, a good retention rate is like the perfect length of string.

Why? It requires a clear frame of reference. The perfect length of string required to tie a bundle of sticks depends on it’s circumference. Similarly, the perfect retention rate of any product depends on how frequently it should be used. We can only answer both of these question once we have some context.

To help explain this further, let’s explore the fundamentals of retention.

The Fundamentals

When it comes to web or mobile applications, a retained user is defined as someone who continues to exhibit a behaviour indicative of ongoing use, over time.

Let’s consider the familiar monthly active users (MAUs) retention metric to verify this framework.

To measure MAUs, the behaviour we are looking for is the act of ‘logging in’. The use over time window has been set to a ‘month’ (30 days). From this we get a group of users who we consider ‘retained’. We then divide the retained group (the numerator) by the total users (the denominator) to get our MAU retention rate. Huzzah, it works.

Retention Rate = retained users / total users

In the formula above, the retained user number is the result of the equation we previously worked out. However, if we include those values in the formula itself, it would look something like this:

Retention Rate = users who exhibit a certain behaviour over time / total users

Let’s push on. The key line here is: ‘exhibit a certain behaviour over time‘. If you can understand this, then you’ll understand what I like to call true retention.

True Retention

Put simply, true retention is the answer to the following question: ‘when the user last had problem ‘x’, did they use our product to solve it? Where problem ‘x’ is the problem your product solves of course (more on this later). Put even more simply, when the user needed to use our product, did they?

As you can see, there are still two parts to this equation. A behaviour, and a time period, yet they are being interpreted slightly differently. The behaviour remains true, it’s knowing whether a user ‘used the product’. However, the time period on the other hand is no longer a specific ‘time window’. Instead, it vaguely refers to the natural period of time it takes for a user to need to use the product.

True retention rate = users who used it when the needed it / total users

True retention is about measuring the completion of a behaviour within the time window that it should naturally take place. The time window is therefore defined as the interval between the user having the problem the first time, and the user having the problem a second time. This could be a short or long period of time.

Retention is About Measuring Problems

Every product solves a problem. Be it getting from A to B (Uber), finding information (Google), or booking accommodation (Airbnb). Our users encounter problems like these in their daily lives. The products we build are focused on solving these problems. The products capacity to do this is directly addressed through it’s core value proposition, more specifically, the primary utility of the product.

A products core utility is ultimately how it creates value, therefore, this is how we should measure retention.

With this in mind, it’s safe to say that our users only need our product when they are faced with the problem that our product solves. Retention is about measuring, surveying, or even guesstimating how frequently our customers are likely to face this problem in their daily lives. In other words, how frequently they need our product. This will be used as our ‘over time’ window for the retention formula.

Let’s use Airbnb as a case study to explore this further.

Airbnb Case Study

The MAU rate of Airbnb is… completely irrelevant. No one goes on holiday every month, so why measure ‘how many people booked accommodation in the past month’? Let’s try and define the true retention rate of Airbnb.

Firstly, we need to know the real rate of holidays that the average person takes each year. According to the ABTA, people take roughly 1.4 holidays per calendar year. We will treat this number as the global average as it sounds about right.

Next, we need to find Airbnb’s total user base so we can work out the total potential number of bookings that could be made. In September 2015, Airbnb has roughly 50 million total users. Let’s assume that by December it had grown to 60 million. Therefore 60 million people taking an average of 1.4 holidays per year would create 84 million trips.

Finally, we need to count the number of users who have ‘booked accommodation’ through Airbnb in the 2015 calendar year. Whilst Airbnb doesn’t reveal their actual bookings, they do reveal the number of nights stayed and average duration of stays. Using recently published Morgan Stanley statistics, we can guesstimate the total number of Airbnb bookings to be roughly 17 million in the 2015 calendar year.

So, we now have our two numbers. Firstly, the total number of accommodation bookings made by Airbnb users on Airbnb in 2015, 17 million. Secondly, the potential number of accommodation bookings made by Airbnb users on any platform in 2015, 84 million. Therefore, the true retention rate of Airbnb in 2015 can be calculated as follows:

17m Airbnb bookings / 84m potential bookings = 20%

The true retention rate of Airbnb is 20%. Moreover, one in five registered Airbnb users booked their 2015 holiday accommodation through Airbnb. Alternatively, four in five Airbnb users did not use Airbnb to book their holiday accommodation in 2015. This is assuming the data I’ve sourced is of course correct.

As you can see, the challenge with looking at the true retention window of Airbnb is that it would take one year for the data to be collected. This long propagation period is the primary reason why most companies look at MAUs. Ultimately, the true look back window may simply be too large. User retention is a long term game though.

More Examples

The key here is to be able to identify the normal usage pattern of your product. The following table provides further examples to help you identify this for your product.

Final Thoughts

The main takeaway message of this article is to determine whether it’s necessary to track a true retention rate alongside your MAU or DAU rates. Some products will not require this. For some, like the last two in the table above, the natural consumption rate may be daily, weekly or monthly anyway. In these circumstances the conventional retention metrics are likely to offer accurate insights.

At Ausmed, we monitor MAUs quite closely. However, we also look at a 90 day retention window. This is because we know that the vast majority of our users undertake at least one educational activity every 90 days. If they have not used our service in the last 90 days, it’s highly likely they are no longer using the product. Therefore, we don’t consider them to be a retained user.

Finally, if you’re working on a product that naturally has long intervals between use, think Airbnb or an annual tax return software as an example, there are ways to reduce the time window. In situations like this, you could consider building features into the product that would encourage more regular use. For Airbnb this might be achieved by promoting local, short stay trips in the user’s home city. For tax return software, this could be achieved by encouraging users to add their tax-deductable expenses as they are incurred (all year round). In both cases, the time window being evaluated for retention would be significantly reduced.

The post What Is a Good Retention Rate? appeared first on Will Egan.

“If you scale a crappy product, you’ll end up with a million problems.”

True. But, how do we know if our product’s crappy (or not)?

The truth is, as founders, marketers or engineers, we inevitably always seem to think the product is crappy. Today, last month, last quarter, even last year. It’s like we forget about all of the great things the product already does, and instead focus on the current issues. Be it a little bug, a UI problem, or a missing feature, the mindset inside most startups is that ‘it’s not ready yet’.

As a result, we defer growth.

“Let’s just build feature X first, then we can focussing on growth.”

This has held me back personally many times. Everytime someone mentions ‘growth’ I ask myself ‘is the product ready’? The answer has to be yes. You cannot defer growth. So long as the product has product market fit.

Product market fit is not usually something us growth marketers talk about, but it’s super important. I’ve searched everywhere for a clear definition. At first I thought a Quora thread on the topic might help. Nope — it culminated in a “you’ll know it when you have it” moment. Next I turned to the gurus. Still no luck:

• “If you have to ask whether you have product market fit, the answer is simple: you don’t.” — Eric Ries
• “Make things people want.” — Paul Graham
• “The only thing that matters is getting to product/market fit.” — Marc Andreessen
• “One metric for Product/Market Fit is if at least 40% percent of surveyed customers (Users) indicate that they would be “very disappointed” if they no longer have access to a particular product or service.” — Sean Ellis

You see my point. None of these statements provided a tangible framework to help guide us towards finding our product market fit. As a marketer, it’s so important to categorically confirm that product market fit is present before scaling growth. In a nut shell, here’s what you need to know:

Product market fit equates to one function done really well.

Sounds simple doesn’t it? Well, it is. Products solve problems. They do this by offering users a function. One core function. That one function is designed to enable users to access the product’s core value. In fact, every other product function is secondary to this core function. It only exists to make the product better at delivering on the core value being offered.

Now ask yourself: how fast does your product deliver on its core value proposition? The best products do it immediately.

See for Yourself

Google

Core value proposition: a search engine that “understands exactly what you mean and gives you back exactly what you want.”
Core function: a search bar and instant results.

Uber

Core value proposition: transportation in minutes.
Core function: one button that lets you ‘Set Pickup Location’.

Facebook

Core value proposition: connect with friends and the world around you.
Core function: find/add friends.

Snapchat

Core value proposition: Snap a photo or a video, add a caption, and send it to a friend.
Core function: take a photo/video.

All of the products above take the user to the core function of their service — the core problem they promise to solve — immediately.

Product market fit is when you have a core feature that delivers on your value proposition so well, that you can increase value simply by improving that core feature.

Think about the billions of dollars Google has spent on its search product, yet the function that users interact with (the search bar) has barely changed.

It is, however, easy to lose product market fit. For example, the transition from Elance to Upwork has made it much harder to hire a freelancer. Similarly, Messenger’s ‘Snapchat’ update has caused major issues around usability of the core value of being able to send a message.

Upwork

Core value proposition: find freelancers to tackle any job, any size, any time.
Core function: …where would you click?

Messenger

Core value proposition: send messages.
Core function: sending ‘snap chats’ to your friends (the big blue button at the bottom of the screen). The top right corner (blue plus) is where you need to click to send a message.

Marketing Considerations

Marketers have the potential to optimise you both towards and away from product market fit. This is because marketers modify your value proposition, through things like A/B testing ad copy, in pursuit of higher conversions.

Underpinning this pursuit towards increased conversions may be a change in the underlying motivation behind why someone signed up. Ultimately, the people who sign up receive a different product experience (functionality) to what they were originally sold.

It is therefore imperative that what marketing is actually selling to the customer, is consistent with what the product actually does. This means you need to be tight around your acquisition messaging, ad-copy and imagery.

The marketers definition of product market fit is “the landing page that converts the highest”. Still unclear? Imagine this scenario…

Marketing runs an advert that promotes your products in such as way that feature y is now being sold as feature x. Customers sign up to get feature x and your product ultimately provides feature y. Subsequently, activation is low and you interpret this as a lack of product market fit. The truth is, your product might be really good at delivering feature y, but marketing has brought you customers that want feature x.

What do you do? Well, marketing needs to find you customers who want feature y, or your product needs to get better at delivering on feature x. You might even want to consider pivoting towards focussing on feature x if demand for this feature seems stronger.

Key takeaway: product market fit must be evaluated against the initial reason a user chose to sign up. AKA did your product do what the landing page said it would? If so, do users use that feature the moment they sign up (demonstrating activation).

Marketing will inevitably optimise for a high converting promise (embedded in your landing page). Your product must deliver on that promise.

We’re currently applying this theory to our work here at Ausmed. We’ve built a framework to track and drive product market fit. I plan to follow up on this post with a second article showing you how we go.

Final thoughts…

When you’re seeking product market fit, identify the core function that represents your value proposition. Focus on making that as straightforward and as easy to use as possible. Do this incessantly until the majority of people who sign up to use your service to do X, are able to do X—and can do it well within 1–5 seconds.

The post What is Product Market Fit? appeared first on Will Egan.

But one test was a stand out success.

In this article, I want to explore that change. It was a feature we called ‘long life login’. It turned out that by automatically logging our users out at the end of a session, we were losing 2% of the active user base every week. Here’s how…

Background

Retention. It’s a hard game. Why? Because the feedback loop isn’t instant. It genuinely takes 90 days to find out how many users are still active 90 days after sign up. Conducting cohort analysis on such a curve can take even longer… years potentially.

Similarly, a single change made in pursuit of greater first month retention takes a month to propagate. You deploy the test, then the lights go out, and 30 days later the results start coming in.

This is all because of time. We can’t speed it up.

Compare this to a normal A/B split test where we can speed up learnings simply by driving more traffic to the test. The question we asked ourselves earlier this year was whether we could do the same for time-based tests? An A/B split test targeting first month retention will take 30 days to resolve. This is fact. But, it turned out there was a faster way to experiment with retention. A way that does provide us with instant feedback. It just requires a different mindset.

New Mindset

When I chat to most companies (startups through to corporates) about retention, they all make the same assumption. That retention is directly linked to the quality of the product itself. This flippant absolving of responsibility of such a key metric is misguided. Sure, it’s true for products like Uber, Snapchat and WhatsApp that stumble across problem solution fit on day one. But, for the other 99.9% of products it’s a compromising belief.

Of course, if the product in question is not genuinely useful and doesn’t actually solve a problem, no amount of retention mastery will save it. But let’s assume you do have a product that solves a problem for some group of users. In this case, it’s likely that the vast majority of your retention will need to be engineered. In other words, product is not the albatross hanging around retention’s neck.

Herein lies the key mindset shift we made, realising that there are two areas to optimise for retention:
1 – Problem based retention, and
2 – Engineering based retention.

In it’s purest terms, a user is driven to use your product by a cognitive or physical trigger they experience in their day-to-day lives. Let’s call this trigger a ‘job to be done’ (Clayton Christensen’s words, not mine). The moment they encounter this ‘job to be done’ their mind starts searching for a product that they can employ, to get the job done. The first product that comes to mind is the one that they believe can best get the job done. Here’s a quick example…

You need to dig a hole. Shovel.

So, let’s assume your user has the problem your product is designed to solve. They whip out their phone, navigate to your app, and open it. This is problem-driven retention in action. At this point the problem based retention engine has worked. It’s done its job. It now hands over a highly motivated user to the engineering based retention engine, which must finish getting the user ‘retained’.

It’s at this very point that the single biggest hinderance to our retention rate was found. The interface between the user and our product… a login form.

Long Life Login

Logging in a pain for most users everyone. Naturally, security is the main consideration here, but truth be told, Facebook has both the most personal data on us, and is the easiest to login. In fact, online banking is really the only service that we appreciate being automatically logged out of.

At Ausmed, we realised pretty quickly that our problem based retention engine was not a problem. Our product is perfectly wrapped around a key job to be done. The moment a user realised they needed our product, they would visit our website or open our app. But, invariably, this strong driver was not leading to sustained retention. We wanted to know more about what was going on here.

To do this, we began tracking behaviour around the login form using event tracking. We defined three events:
1) User attempted to log in
2) User successfully logged in
3) User failed to log in.

The primary question we wanted to answer was ‘what percentage of people who ‘attempted to log in’ yet ‘failed to log in’ did not log in again over the next 7 days?’

In other words, what percentage of the people trying to use our product were being lost due to difficulties logging in? The results were astounding.

Some comments about this graph. Our user base has grown substantially throughout this period, yet the rate has fallen. In March, almost 1 out of 25 active users were being lost every week. Contrast this to December, where only 4 out of 1000 were being lost.

It turned out that an average of 2% of active users were being lost each week. That’s 8% a month. I say average, but some weeks it was as high a 4%. Extrapolated out: if we had 100,000 monthly active users today and did not acquire a single new user for the next 10 weeks, only 81,707 active users would remain. In other words, we were losing 20% of our active users every two months, purely through login.

So, we got to work solving this problem. We made two changes. One that was obvious but also quite technically hard to do, and one that was extremely clever and super easy to do.

How We Fixed Logging-In

1 – We Stopped Logging People Out

Seriously, this will solve the bulk of the problem. In our case the technical changes took some time to solve. Load balancers, caches, token expiry etc.

2 – We Proactively Sent Username Reminders

As we were now tracking events around the login form, we started sending a copy of this event data to Vero. This meant we could begin sending pro-active username reminders (emails). The message in the email was simple: ‘Hey, we notice you’re having difficulty logging in, here’s a friendly reminder of your username. Here’s a (gif-based) demo of how to log in, and here’s a link to reset your password in case need to do that too.’ It worked amazingly well.

This single email resulted in 58% of people who failed to log in, logging in again. It’s incredibly useful and a novel idea—I’ve never seen it done before. Also, it’s worth noticing that not a single person unsubscribed.

For those with a keen eye, the difference between the converted group, and open group is explained by indirect conversion (my next post).

Summary

Nothing else in the past year has improved our retention rate more positively than long life login. Every ten weeks, we were haemorrhaging 20% of our active user base simply by automatically logging them out at the end of a session. Security is important, no doubt. But even a service like Facebook, with so much data on all of us, does not log users out. Neither should you. Proactively helping users who still struggle after you implement long life login is incredibly effective. And finally, I would love to hear your thoughts on this article in the comments box below.

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