How to Increase ROI and Find Your Niche with This Easy Technique 📊

Spoiler: Your highest purchase-contributing cohort might not be your most valuable cohort!

Hello, welcome to the 12th edition of DataPulse Weekly, where we unravel the magic behind data and its impact on our daily lives.

Whether you're an analyst or simply curious about how data shapes our world, you're in the right place.

We're trying something new this week by keeping our newsletter short and sweet. We're experimenting with a condensed version that focuses solely on our most popular section—the data case study. It will be a quick read, but as always, we'll keep it fun and engaging for you.

Now, let’s jump right into today’s data case study.

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Finding the right niche helps you build better and sell more effectively.

What could be a good definition of a niche? Let’s compare these two versions:

1. All people in the world who are enthusiastic about data.

2. People aged 21-27 who have recently graduated or are beginning their careers and looking to build a career in the data domain.

Obviously, the second niche is more specific and clear!

Some products or services thrive by creating everything around their specific niches.

However, businesses often get traction from various types of audiences. Thus, to increase the product's value for their audiences and optimize business efforts, it's essential to understand the audience through a simple technique: user segmentation or user cohorting.

In simple terms, user segments are groups of users who share common characteristics.

For any business, user segmentation helps understand users better, leading to effective targeting strategies, personalized communication, prioritization of upcoming product features, and more.

One way to understand which cohort is most important is to look at the contribution of purchases or sales by each cohort. Simple and easy, right? Maybe!

Let’s delve deeper with an example using basic user segmentation by operating systems and age groups.

Consider a hypothetical consumer health tech company, HealthPlus Daily, which sells a next-gen health tech product. The company segments its users based on the operating system (iOS, Android) and age groups (25-34, 35-44, 45-54). Below are the number of signups and the final number of conversions (purchases) for their product.

Conversion rates here mean the percentage of users who purchase the product out of the total sign-up users.

Let's use a pie chart to understand the contribution of each cohort to overall conversions (purchases) -

Let’s plot another chart for conversion rates by cohorts -

From these charts, it's clear that Android users aged 35-44 (Cohort 5) contribute the highest number of purchases (28.4%) due to their large population size (9k signup users), despite having a relatively low conversion rate (3%). Conversely, iOS users aged 25-34 (Cohort 1) have a high conversion rate (4.5%), making them highly valuable despite a smaller user base (4k signup users).

Understanding which cohorts have a high conversion rate (and not just high purchase contribution) can help in making better business decisions in the following ways:

1. 🎯 Effective Targeting: Focusing more efforts (budget and time) on campaigns targeting iOS users aged 25-34 can result in increased ROI and lower CAC due to higher conversion rates.

2. ✉️ Personalization: Create content that resonates with specific cohorts through in-app notifications, email, and landing pages. For instance, highlight Apple Pay or Apple Watch integration for iOS users.

3. 🔍 Finding Your Niche: Identifying cohorts with the highest conversion rates can help you create custom product features to enhance user experience, especially when launching new products. A higher conversion rate indicates a stronger likelihood of purchases from these users.

Key Takeaway:

Understanding cohorts by purchase contribution and conversion rates offers two different perspectives. Both are equally important. However, focusing on conversion rates helps businesses increase ROI, personalize the experience, prioritize the right product features, and identify the right niche. While this example uses basic segmentation by OS and age group, segmentation can be further refined with demographic, technographic, and engagement data to create more valuable user segments.

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