Segmenting users is a crucial skill in business analytics. It helps analysts understand different groups within their customer base and tailor their strategies accordingly. In this post, we will discuss how to effectively segment your users and avoid common mistakes.
Why user segmentation is important
Segmenting our users allows us to gain a deeper understanding of our customer base and make informed decisions based on that knowledge. For example, if we segment our users by the number of years they have worked as freelancers, we may discover that those with more experience are more likely to convert to paying accounts. This information can help us tailor our marketing and sales efforts to target the most promising segments of our user base. By segmenting our users, we can also avoid making decisions based on a "fully blended" view that does not take into account the differences between different groups of users. Instead, we can make strategic decisions that consider the specific needs and behaviors of each user segment.
To perform a user segmentation analysis, follow these guidelines:
1. Understand the business, target market, and available data.
As an analyst, it is important to have a thorough understanding of the business, including the target market and data that is relevant to the business. To get started, consider the following questions:
What does the ideal user look like?
What segments exist within the user base (e.g. industry, geographic location, size, age)?
What information about our users should we collect?
Which information about our users are we already collecting?
A good resource for information on the user base is the CEO, who is likely to have deep insights on the target market and ideal user from the company's inception.
2. Collect and organize data on your users.
Once you have identified the data that is relevant to your analysis, gather and organize it in a way that makes it easy to work with. This may involve cleaning and formatting the data, as well as creating any necessary summary statistics.
3. Identify potential segmentation criteria.
Consider the characteristics that may be relevant for dividing your user base into segments. Some common criteria include demographic information (e.g. age, gender, location), behavioral data (e.g. usage patterns, purchase history), and attitudes and preferences (e.g. opinions, values).
4. Analyze the data to identify segments.
Use statistical techniques and visualization tools to analyze the data and identify distinct segments within your user base.
5. Validate and refine your segments.
Once you have identified potential segments, validate them by comparing them to external data sources and seeking feedback from stakeholders. You may need to revise your segments based on this additional information.
6. Use the segments to inform business decisions.
Leverage the insights gained from your user segmentation analysis to inform business decisions such as product development, marketing strategies, and customer service.
Balance Your Approach
When it comes to segmenting your users, you have the option of taking a quick and dirty approach or a slower, more strategic approach. The quick and dirty approach involves manually building a data set of your users using the dimensions you want to analyze. While this method may be faster in the short term, it is not scalable and requires rebuilding the data set each time you want to rerun the analysis.
A better approach is to create a master data source that can be easily reused for future analysis. This data source should ideally be stored in a tool like Tableau or PowerBI, or as a view in your database. By saving the query for this data source, you can easily access and update it as needed. This approach allows you to segment your users more efficiently and accurately, and to make informed decisions based on the insights gained from your analysis.
Mistakes to Avoid
There are several common mistakes that analysts make when segmenting users. These include:
Re-categorizing null values: When some users do not have values for certain dimensions (e.g. age), these values may appear as "null" or "blank" in the data set. It is important to replace these values with "unknown" to avoid confusion when presenting the results of the analysis.
Forgetting to normalize for time-sensitive dimensions: When time is a factor in the analysis (e.g. analyzing user behavior over time), it is important to normalize the data to ensure that each user has had an equal opportunity to perform a certain action. This may involve selecting a specific time frame (e.g. within 14 days of signing up) and only considering users who have had at least that amount of time to complete the action.
Overcomplicating the analysis: It is easy to get carried away and try to answer too many questions at once. Instead, focus on a small number of high-priority questions and build a data set that will allow you to answer them systematically and efficiently.
Creating duplicates: When using SQL to build a data set, it is possible to end up with duplicate records. To avoid this, make sure to visualize the raw data and check for duplicates before starting the analysis. You may also want to confirm that there is only one row per user by checking the count versus distinct count of the user ID.
Failing to validate and refine segments: After identifying potential segments, it is important to validate them by comparing them to external data sources and seeking feedback from stakeholders. Be open to revising your segments based on this additional information.
In conclusion, user segmentation is a powerful tool for understanding your customer base and tailoring your strategies to their specific needs and behaviors. For an easier and more effective user segmentation analysis, Smoothen offers solutions that can help you create a master data source, validate and refine your segments, and make informed decisions based on the insights gained from your analysis. Remember to balance your approach, create a master data source, and validate and refine your segments to ensure the accuracy and effectiveness of your analysis.
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