In this context, events document digital processes and can be tracked to uncover insights into how something (in this case, human users) interact with a digital product, device, or content. 

Tracking events is how a business becomes data-driven in its approach to improving and optimising its business model, whether that be a B2C retailer selling products via a website, a B2B SaaS company selling software subscriptions or something else. 

Events provide information on where users interact with the business and its product or service. 

Deciding what events to track is foundational here, as there are hundreds or potentially thousands of possibilities.

Tracking too many events in one project is cumbersome, often unnecessary, and requires large teams with plenty of experience and resources. Tracking a select handful of precise and useful events is much simpler in practice. 

Formulate Questions for Analysis

Start with questions. Read this post on behavioural data for inspiration of what questions you can raise.

Think of your business model and what you need to improve. Data is there to help understand business problems and discover empirically robust answers that can then be used to optimise products, processes and user experiences. Data helps you understand customers and users without them telling you exactly what’s going through their head when they use your site, or discover your product, etc. 

There are more than a few possible questions here, such as: 

  • How can I increase sales? 
  • How can I increase activation?
  • Which landing page converts the most customers?
  • How can I reduce customer churn?
  • What types of customers are most likely to leave negative reviews? 
  • How many signups do I get in an average monthly cycle? 
  • What is my average customer lifetime value (CLV)

Some of these questions focus more on user properties whereas others focus more on events. 

For example, “What types of customers are most likely to leave negative reviews?” is an investigation into what customer demographics and characteristics correlate with negative reviews. This is a relatively simple task, involving an analysis of user properties (e.g. age, location, gender) and correlating those with negative reviews. The findings might then prompt the development of a strategy to keep those most likely customers from leaving a negative review, e.g. via segmentation and targeted emails to that customer group which incentivise them to leave a positive review or discuss any issues directly with the business. 

Other questions are likely just too broad, such as “how can I increase sales?” Whilst a cross-analysis of all sales channels, comparing various campaigns, adverts, and all sorts of other variables might provide some insight into how sales can be optimised across the entire business, this sort of question should be narrowed down. 

A more focused example that focuses on events would be; “how can I increase sales of my most popular products using social media?”, which might involve tracking sales gained via different social media platforms, testing different content types and ads to compare their marketing ROI. User properties matter little here, it’s all about tracking events as a user is referred to a product from social media. It’s much easier to track single events across channels rather than all channels at once. 

Signals and Answers

Each question is divisible into related events and processes. For example:

  • Events that show user engagement and keep a user retained, e.g. multiple clicks and swipes across various pages and features within an app or product
  • Events that indicate that a user is likely to purchase, e.g. they’re browsing products continually
  • Events that indicate forthcoming customer churn, e.g. they haven’t used the product or service much over a given period of time
  • Events that show a user is not deriving value from the product, e.g. they’re only using a small selection of features available to them

The basic premise is to take real-world situations and break them down into stages that event data can describe. 

Here’s a worked example involving a subscription business model. A burning question many subscription businesses must address is “what can we do to reduce customer churn?” 

Customer churn likely sits around 5% to 7% on average and measuring it is simple. All it is the number of customers that leave or cancel the product each month, reflected in a percentage. 

The first question would be simply “what is our average customer churn?” which would involve taking a cross-section of customers over a month and dividing the lost customers by the total customers x 100. A business with 250 customers that lose 10 has a churn rate of 10/250 x 100 =  4%. 

However, where this gets interesting is working out precisely when most customers churn, and who those customers are. This requires tracking, namely tracking the event data that details when customers leave and their user properties, e.g. their age, location, sign-up date, etc. After tracking when most customers churn each month, this can be cross-checked against the user properties of those churners. 

It’d also be possible to dig into what churners did prior to cancelling, which might involve downgrading their membership tier or not using certain products for a period of time. Once those event signals are picked up, automated emails or messages could be set up to deliver deals or discounts to attempt to prevent people from cancelling their subs prior to the moment they’re most likely to churn. 

Funnels of Events

Choosing interlinked events that sequence together is the best way to approach tracking in many situations, e.g. product or app analysis, sales or marketing funnel analysis, website analysis, etc. 

Pretty much any swipe, click, view, or any other interaction or process can be tracked. If we look at the typical sign-up flow, then we have the following: 

  • The user heads to the page and clicks ‘sign up’. We are ignoring events prior to this, e.g. where they clicked through from (SERPs, social media, ads, etc). 
  • This creates two events, the viewing of the sign-up page itself, and then click on the sign-up button 
  • The user then fills in their information and submits, which creates an event when the process is completed, which contains the event properties linked to the sign-up. But, the page view for the ‘sign-up completed’ page could also be tracked, as could the click on the ‘complete/register’ button at the end of the sign-up.
  • In this case, it’s likely only sensical to track the event that confirms that the user’s data has been submitted to the database. If the sign-up process fails after the user presses the ‘complete’ button then the data is incorrect. A key example here is when a user hits the ‘finish’ or ‘complete’ button but the system highlights an error in their form submission (e.g. passwords didn’t match). 


Acquisition lies within the remit of marketing, advertising and also sales. In a B2B business, some event data concerning acquisition will likely be collected via CRMs like Hubspot (though CRMs do also apply to B2C, of course).

Much of this data allows businesses to build customer personas and conduct demographic analysis. Never assume the demography of your customers – just because you run a B2B consultancy doesn’t mean that all your customers are likely older white-collar C-level executives.

In fact, Think With Google found that non-C-suite businesspeople play key roles in B2B purchasing decisions and that B2B demographics are much diversified now.

Whilst analysing demographics relies more on analysing customers and organisations as entities that are related to each other (e.g. names, addresses, age, etc), thus creating opportunities to create links and correlations, it’s also possible to analyse events to discover where customers came from and how they interacted with your business.

One way you can do this is by using UTM parameters to track traffic from paid ads and other marketing channels, then connecting that to payments data/subs/other conversions events to gauge how well one campaign fares against another. Comparisons can be made in Google Analytics or Mixpanel.

Heap, Mixpanel and Amplitude all provide funnel analysis for acquisition, but Google Analytics is pretty good for organic and Google Ads. Customer data can also be parsed from email suites or CRMs, and then piped into CDPs.


Activation begins after acquisition and refers to when a customer begins to utilise the product or otherwise derive value and interact with its features.

Overall, activation lies somewhere between the remit of marketing and product teams. For example, it refers to when products are purchased or subs are activated, when someone downloads software, plays music, videos, games, etc, books tickets or taxi rides, or uses SaaS products.

Similarly to how UTM parameters can be used to create a closed feedback loop that allows businesses to connect traffic to acquisition and conversions, the same upstream data can be correlated with activation data to reveal what campaigns spur on enthusiastic customers that are quick to derive value from the product.

Downstream, activation events (e.g. if someone is very slow to activate their product) might provide a valuable comparison to churners (e.g. those who activate slowly tend to churn quickly). This creates opportunities to segment customers based on activation event data and target them with emails designed to enhance onboarding and activation, like this one below:

Activation email example

Engagement And Retention

Whilst engagement and retention are not strictly one of the same, they’re obviously related in that engaged customers tend to stick around.

Engagement also helps derive more value from the customers, which is why your Uber Eats or Deliveroo app pings you lots of notifications when you haven’t used it in a while.

Customers need to be engaged right from the get-go, or else they might instantly uninstall and refund. Tracking events that indicate how many customers instantly churn helps businesses develop solutions to prevent that, like sending attractive offers that trigger after a week, two weeks or a month, etc.

Using event data to engage customers primarily revolves around:

  • Personalised email marketing: Onboarding emails welcome new subscribers or customers, which is incredibly important to assist them in activation and to show them what the product actually does. This is particularly the case for complex software, where customers really need to be shown to ropes via tutorials, detailed instructions or in-software messages. Tutorials, videos and other guidance can be be sent via email. Different emails can be sent to different groups of customers as guided by demographics, allowing businesses to segment different groups of customers to engage them in different ways (e.g. a large enterprise customer might appreciate more high-level B2B-centric marketing comms).
  • In-App or In-Software Messages: In-app messages provide cues and guidance when the user reaches some sort of sticking point or bottleneck, with those events triggering an in-app message that helps them. Think of video games where hints pop up when the game has detected that the player cannot work out how to progress.
  • Push Notifications: Primarily for smartphone apps only, push notifications deliver key information and marketing comms directly to the user. Many will be familiar with push notifications that remind them that they added items to their cart and didn’t check out, or otherwise didn’t complete a conversion or activation process.

When it comes to preventing churn, many of the same tactics above apply. To reduce churn, it’s essential to identify who churns and when (which might take a few months of data at least). You might find immediate patterns, e.g. people churn after 2 months after subbing, or people churn on the last Sunday of the month, and these events might also link to customer data (i.e. entity data) like age, location, profession, etc.

By segmenting likely churners and targeting them at the right time with new offers, free gifts, reductions and other incentives, it’s possible to reduce churn and boost overall customer lifetime value (CLV).

Client-side vs Server-side Events

It’s also crucial to understand the difference between client-side and server-side events. Events including clicks, swipes and views don’t usually rely on back-end processes, and are therefore client-side. They occur on the client’s device and are also called front-end events. 

Contrastingly, server-side events interact with a database and rely on backend processes. They are also called backend events. This might be relevant to teams that require access to the front-end or back-end source and should be noted on the tracking plan.

Defining Events and Properties

Next, it’s time to properly define events and properties. This is where the data tracking plan comes together in a less abstract sense. Once you’ve generated questions, identified data tracking as the potential answers to those questions and have decided on what events intersect with those questions, it’s time to define events and their properties so you can begin tracking.

Data tracking plans conventionally revolve around events and entities. Entities are often users whereas events are actions, or tasks, that are performed by entities. E.g. Simon (entity) signed up (event) to the product. Simon, the user, has his own user properties (e.g. age, name, email address). The event also has its own event properties (or metadata), including timestamp, user IDs, email, etc. 

The tracking data engineering process will vary hugely with basic tasks being possible to orchestrate using Google Analytics or natively with social media data platforms combined with website data (in the case of sales or marketing funnel analysis). Otherwise, using a CDP or CDI like Segment, mParticle, Rudder, Mixpanel, Amplitude or Snowplow might be the chief tool required to make sense of complex event data. 

Below is a table of event names and corresponding properties and data types for some basic events. Each event is broken down with event properties and the likely data type, all of which should be noted on the data tracking plan.  

Event properties

Summary: How Do You Decide What Events To Track?

Event tracking involves a good deal of debate and questioning combined with the deduction and distillation of specifically relevant events that can assist in the understanding of that question. From there, it’s about implementing results and findings to optimize products, web design, sales and marketing funnels, user experiences, etc. 

Event tracking is a fact-finding mission, hence why it’s called tracking. Once you’ve got a good quantity of high-quality, usable data, you’ll need to decide how to analyse and understand it prior to implementation.


What are events in data?

Events are transient processes or interactions that have a definitive beginning and end. Downloads, link clicks, swipes, form submissions and video plays are all examples of events. Events have event properties that break down the various components to that event, e.g. a form submission might comprise of an email address, name, date of birth, etc.

What events should I track?

Deciding what events to track for your data project is crucial to strike a balance between detail and simplicity. Too many events will crowd your systems (and your mind) and make analysis and implementation tricky. Too few events may fail to capture adequate detail on your subject.

What is data tracking?

Data tracking is the method and act of retrieving data from a system, either hardware or software and tracking the information provided therein. Tracking derives meaning from the data, allowing data points to be analysed and used for a multitude of commercial and non-commercial purposes.


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