Key Takeaway: Segmentation is a super power when it comes to optimizing your product, because it allows you to personalize communication strategies to the realities of your users. Using tools like AWS Pinpoint, you can quickly get started with robust segmentation practices and multi-channel engagement. This means that you can not only automate sending the best messaging to the right groups, but do so based on specific user behaviours that you can choose.
Segmentation is one of the most valuable tools you can use to understand your users. It is unlikely that your users are all going to your product for precisely one thing, in fact depending on your business, your product may satisfy a whole slew of use cases. Further, when it comes to touch points with your users, we tend to segment around demographics and not behaviours. This makes sense as an initial strategy, but it doesn’t scale, at least not truly. This is because if you are segmenting your users based on age and location, you are operating under a lot of general assumptions, rather than thinking about what your users are actually doing.
Segmentation from a data and analytics perspective should first and foremost start with user behaviours and go from there as this will give you the best chance of providing value to your audience. Think about it, are you more likely to solve a problem for someone by bucketing them in with other “21-39 year old city-dwelling working professional”, or by looking at how often they are doing certain key actions in your product?
A similarly typical mistake in thinking about your audience is simply segmenting around products. While this makes sense at a high level and certainly in terms of aligning teams internally, it often negates what your users are actually doing. This restricts understanding of your users and can actually make you miss key areas to focus on when you are trying to grow your company as a whole.
For example, if you have a fitness tracking app and you separate out the running functionality and the biking functionality when creating dashboards, you might be missing learnings on users of both. Let’s say you have a user, Nathan, who is currently a runner and a cyclist. If all your dashboards separate running and biking, since you think of them as two separate offerings, you may count him in two separate buckets and thus not as a single user. This can erroneously bucket him as “occasional users” in one bucket and “power users” in another, but not as a single user with dynamic use cases. The learning here that might not be immediately obvious is understanding why a user is a power user in one bucket and not in another – is this simply because they bike more than they run, or is it because the running functionality of your app is lacking? Being able to hone in on users first and products second allows you to understand your audience holistically across products. Segmenting users solely by feature will typically create insight siloes, preventing you from understanding holistic user behaviour.
AWS Pinpoint is a lesser known product by Amazon that is very effective at segmenting and targeting users with appropriate messaging based on behaviours. As a multi-channel customer engagement tool built around segmentation, an incredibly useful aspect of Pinpoint is being able to apply if/else logic to user behaviours to allow you to separate users not by how we have generally thought about them as different demographic groups , but by their behaviours. Further, you can parse out users by whether or not they are active, which means that you can hone in on activation strategies for the right group and focus on unique engagements for those who are already active users.
Going back to our fitness app example, AWS Pinpoint can help in targeting a user who is generally active to understand their level of engagement with a new functionality. With Pinpoint, you have the ability to track the journey from first push/email/sms to understand interest and intent, all the way into user behaviour within the product. For example, you can hone in on Nathan, our fitness app active user, and understand that he in fact is interested in bike tracking functionality but drops off when he has to register his bike make and model. Since we were able to understand Nathan as an active user, especially when it comes to tracking his runs, we can target messaging to his needs, such as functionality updates more in line with his holistic app behaviour rather than sending him inactive user messaging around the biking functionality and active messaging around the running functionality, with no specificity in understanding his use cases.
With AWS Pinpoint you can automate your user segmentation and their subsequent touchpoints. Specifically, Journeys tool can be used to chart out different journeys and subsequent touch points, depending on a user’s interactions with the previous message.
Importantly, this can be changed at any time, giving you the flexibility to adapt as you learn more. This low lift approach to communicating with your customers takes the guesswork out of trying to match specific user behaviours to touchpoint preferences and allows you to connect with your customer in a way that is more conducive to their needs. With AWS Pinpoint you can stream events into your analytics tool of choice to be able to chart full user journeys rather than falling into the typical analytics problem of the segregation of marketing and product. This opens up possibilities in getting to your “aha” moment as a company a lot more quickly and meeting your users where they will find the most value.
If you’re curious about AWS Pinpoint and what it can do, you can utilize their sandbox here to see if it is a good fit for your company. If you’re already taking advantage of S3 buckets or other products in Amazon’s portfolio, implementing Pinpoint is even more straightforward. You can also stream AWS Pinpoint events to Kinesis which offers an even more streamlined way to understand your users, offer them unique and relevant messaging and ultimately elevate your growth strategy to the next level. If you’d like us to help out with any of the above, just drop us a line at email@example.com and we’ll make sure that you set up your customer engagement stack properly!
Jul 7 • 9 min read