I’m developing a search feature for my app and estimating the monthly search frequency. Not sure how to estimate how many of those MAUs (Monthly Active Users) will really use this search feature. According to user research, it will probably be a secondary navigation option, although it is something that has been frequently requested by the users.
It appears from your explanation that you want to perform a cost-benefit analysis. The more important query is: Should you build it? Will search convert more customers at the bottom of the funnel than browsing? Can the conversion be increased in a better way?
Worth performing a faux door test. Simply include a search button that you can trace individual clicks on. Currently under construction, you can prompt a message when a user clicks. Once you’ve run this for a short while, you’ll know the result. Minimal effort, but excellent for learning.
All the Best!
What are you attempting to evaluate? How well your search performed or how frequently people used it?
@PriyaVarma, how frequently its been used.
Estimating the monthly search frequency for a new search feature can be a challenging task, but utilizing your monthly active users (MAUs) can provide a good starting point.
To estimate how many of your MAUs will use the search feature, you can look at industry benchmarks for similar types of apps, and then adjust them based on your user research data. For example, if your app is similar to others in your industry, and those apps have a search utilization rate of 20%, you could estimate that 20% of your MAUs will use your search feature.
However, because your user research suggests that the search feature will be a secondary navigation option, you may want to adjust this percentage downwards. You could look at similar apps with a similar feature hierarchy and see how often their secondary features are used, and then adjust accordingly.
Therefore, it’s important to track usage of the search feature once it’s launched and adjust your estimates accordingly. As your users become more familiar with the feature and its benefits, you may find that the search utilization rate increases over time.
Why is it important to comprehend that? What does it say about the usefulness of the search function if it is frequently utilized but users are unable to find what they are looking for?
That figure will help me inform other estimated KPIs, I hope.
I’d like to add, “X number of people are expected to use it every month, Y number of those X users convert, and that conversion number exceeds the cost of service we’re leveraging for this product, resulting in its success.”
Are the users authenticated? If not, can you look at the number user sessions where search is used and compare that to the total number of sessions for that same period of time?
Quite agree with @Luisneilson, performing a faux door test by including a search button to track clicks and prompt messages can be an effective way to gather data on user behavior and interest in the search feature. This method can provide insights on whether users are finding the search button and clicking on it, as well as how often they are using it.
Additionally, by analyzing the data collected from this test, you can identify any potential issues with the search feature or the placement of the search button, and make adjustments accordingly. This can help improve the user experience and increase the utilization of the search feature over time.
All in all, a faux door test is a low-cost and efficient way to learn about user behavior and gather insights that can inform the development of the search feature.
The evaluation goals for the search feature may vary depending on the specific objectives of the app or business. However, in general, there are two main aspects to consider when evaluating the search feature:
- Effectiveness: This refers to how well the search feature performs in terms of returning relevant results to users. The effectiveness of the search can be evaluated by analyzing user feedback and metrics such as the click-through rate (CTR) and bounce rate. For example, if users are frequently clicking on search results but then quickly leaving the app, it may indicate that the results are not relevant to their search queries. In this case, you may need to improve the search algorithm or provide better metadata for the content being searched.
- Usage: This refers to how often users are utilizing the search feature. Evaluating usage can help determine if the search feature is meeting the needs of users and whether it is a valuable addition to the app. Metrics such as the number of searches per user, the percentage of users who use the search feature, and the time spent on the search page can provide insights into the usage of the feature.
Hence, when evaluating the search feature, it is important to consider both effectiveness and usage to gain a comprehensive understanding of how the feature is performing and how it can be improved.
Knowing both the usage and effectiveness of the search feature is important because they are interrelated and provide different insights into the performance and value of the feature.
If the search feature is used frequently but does not return relevant results, it indicates that users are relying on it to find what they are looking for but are not finding it. This can be a sign of user frustration and dissatisfaction, which can lead to negative feedback and decreased engagement with the app. In this case, it may be necessary to improve the effectiveness of the search feature by implementing better search algorithms, improving metadata, or providing more relevant results.
On the other hand, if the search feature is not used frequently, it may indicate that users are not aware of its existence or do not see the value in using it. This can be a sign that the search feature is not well-promoted or placed in a prominent location within the app. In this case, it may be necessary to improve the promotion and placement of the search feature to increase its usage.
Eventually, understanding both the usage and effectiveness of the search feature can help identify opportunities for improvement and ensure that the feature is meeting the needs of users and adding value to the app.
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