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Create a different trial experience for each user segment to increase conversions

Source: Growth Bites

Different types of users will have different "Aha" moments. Boost your conversion rate by emphasizing different actions and features to different user-types during their trials.

Initially, MYOB was offering the same trial experience to every user. But when they realized that the aha moment differed according to the type of user, they started tailoring their trials. The result was a 54% increase in their trial conversion rate.
In MYOB's case, one segment was more likely to convert when prompted to try their point-of-sale features, while others were more interested in payroll or banking features. They encouraged each segment to use the relevant feature by prompting users in-app, via email, and (for one specific segment) by phone. The easiest way to segment your users for this is to ask them which use-case applies to them when they sign up. Then survey users or test your educated guesses to figure out which action gives each segment that aha moment. And encourage the action by navigating them there after onboarding, alerting them in-app, or emphasizing the feature via email.

What's trending?

Product Launch Prep: building desirable products

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Conducting thorough research is vital for entrepreneurs when introducing a new product. It enables you to understand your target market, outpace competitors, and address customer needs effectively.

AI in research plays a significant role, offering a clear view of market opportunities for launching new products. It helps with market analysis, product development, and testing by processing large data sets, identifying patterns, and predicting trends.
This real-time data analysis enables informed decision-making, saving time and resources, identifying market gaps, understanding consumer preferences, and tailoring products to meet specific demands.

Here are two examples out of the top 10 research methodologies powered by AI covered in our program:

• Market Research: Gather and analyze data on market size, growth trends, and potential customer segments to understand the market landscape and identify opportunities for your product.

• Competitor Analysis: Research your competitors’ products, strategies, strengths, and weaknesses to determine how you can differentiate your product and gain a competitive advantage.

Discover the free prompt examples for these two research methodologies in the blog below. 👇

When running experiments, should you go higher or lower in the funnel?

Source: Demand Curve

Prioritization is a critical step in the experimentation process.

You can’t test everything. Testing takes time and resources, which are always in short supply.

One piece of criteria we always recommend factoring into prioritization: impact. How much could test findings move the needle on your north star metric—the metric you care most about?

When making that call, it’ll help to think about a test’s funnel stage.

Bottom of funnel

Bottom-of-funnel events—those nearer to the point of purchase, like the checkout process—are almost certainly closer to your north star, so they have a high likelihood of driving impact.

An extreme example: A test that removes the “buy” button from your checkout page will have a drastic effect on revenue (just not the kind you want!).

Prospects at that stage have high buying intent. They’re ready, or nearly ready, to buy.

However, some changes to bottom-of-funnel events might not be as effective because prospects have already made their decisions.

Top of funnel

Top-of-funnel events, like those in the awareness and consideration stages (e.g., landing pages and ads), can sway decision making. And prospects’ emotional investment may be higher at earlier funnel stages, when they’re discovering how your product will help them.

Plus, top-of-funnel experiments are often easier to test and alter, both because sample sizes are bigger (top of funnel gets more traffic) and because the changes themselves are frequently lower effort.

But they’re farther from conversion, they have lower intent, and they run a greater risk of being vanity tests: tests that move the needle on some metrics but not your north star.

Our recommendation: When your experimentation program is new and you’re gaining an understanding of which tests will have the most impact, all else being equal, go lower in your funnel to remove the distance from your north star.

Thank you for reading! ✌️

We look forward to sharing more with you next week. Stay tuned!

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