Day 11 - User Segmentation
Segmenting users to understand and serve them better ...
Hello learners!
Welcome to the eleventh lesson of the series 30 Days of PM by Crework!
Today, we will talk about a very important part of user research and user research analysis that is used by almost every product team to better understand their user and to serve them well.
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Sorting Laundry
Have you ever sorted your clothes before washing? If you haven’t, maybe you should start doing it now. Separating dark colored clothes from whites or heavies from lights extends the life of your clothing and better preserves their color. Setting loads of dirtier clothes to heavier settings gets them cleaner faster. A small amount of planning and effort before washing your clothes could save you the time and money required to frequently replace the ill-washed clothes.
But, why am I talking about laundry?
Because, if every article of clothing doesn’t responds well to the same washer settings, so why would different users all have the same experience with your product? Users of your product are at least slightly more complex than a polo shirt.
Hence, to serve the users well and to build the best product for their needs, we need to understand exactly what type and categories of users do we have. That’s why we have user segmentation.
User Segmentation
User segmentation is the product management equivalent to sorting laundry.
Grouping your users by shared characteristics reveals insights into how these segments are interacting with your product—and, most importantly, where your product can be improved to provide them with a better experience.
Instead of treating a single user experience as the most important, you should leverage user segmentation to identify and embrace the different ways customers use your product.
How does user segmentation help?
Let’s take an example. Say our product is a new community swimming pool. The top-requested features (and number of requests) are as follows:
Starting blocks | 17
Ping pong table | 15
Lounge chairs | 14
Concession stand | 12
Swim lanes | 12
Branded swim caps | 11
Water slide | 7
Basketball court | 4
We have the budget and capacity to choose three of these features by opening day so we choose the top three.
The opening day arrives and we eagerly await to see who subscribes for an annual membership to my pool with starting blocks, a ping pong table, and lounge chairs.
But to our accountant’s horror, no one subscribes!
Why aren’t people subscribing? Did they lie about their needs?!
The truth is that further analysis would have shown that in order to subscribe, each segment has a few dealbreakers — requirements that if not met, will ensure they take their business elsewhere.
Competitive swimmers need starting blocks AND swim lanes
Teenagers need ping pong AND a concession stand AND a basketball court
Parents need lounge chairs AND swim lanes for lap swim AND a waterslide
By delivering starting blocks, a ping pong table, and lounge chairs, we incidentally met one dealbreaker for each segment, but did not deliver sufficient functionality to be good enough for any segment.
So, by not understanding our user segments and their needs, we missed a chance to build a product that solves the needs of at least 1 segment of user properly.
What should have been done:
In this scenario, the chances of competitive swimmers being the subscribers would have increased.
How do we segment users?
In theory, there are an endless number of characteristics with which you could segment your users. However, in practice, you cannot segment users on just any characteristic; you need to have data that enables the identification of if a user has a particular characteristic.
These are some of the common segmentation model:
Demographic - Demographic segmentation uses demographic attributes, such as gender, language, race, and geography to divide up the user population.
Device - Device type segmentation is particularly useful for answering certain questions, such as:
Do desktop users versus mobile users behave differently across device types?
Are updates to your apps improving the experiences of the different groups of users? Or are updates introducing problems that you haven’t yet identified?
Are there features or capabilities that you haven’t enabled on certain platforms that might meaningfully impact overall user retention?
Acquisition Source - This segmentation is used to analyze differences in users based on where they were acquired. Common sources include organic, google search, and Facebook ads.
Usage Patterns - We can segment users by their usage patterns of the product. Users who give referrals can be very different from users who don’t. Users who do more than 1 payment a day can be very different from person who does none.
Day 11 - Completed ✅
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References:
I am thinking that this is most applicable for a 1-n product, where you already have access to user data. And the goal is also different - its about whom to focus on for any product improvements
For 0-n products, in early stages, imo the segments based on demogs, geo and psychographics are more relevant where the goal is to figure out whom to target
What do you think?