B2B Customer Segmentation: Customer Tenure Analysis by Firmographics
Last week, I explained how a combination of monthly recurring revenue (MRR) and monthly recurring cost (MRC) analyses of your customers can be used to identify a target segment. In that post, I also shared a step-by-step guide on how to set-up and execute these types of analysis. This week, I will explain how to set-up and execute a customer tenure analysis for the purpose of segmentation.
The purpose of a customer tenure analysis is to identify customer characteristics that are correlated with longer customer tenures. Customer tenure is important as it allows a longer time for acquisition costs to be recouped. This information alone is not enough to identify a target segment, but it can be a complementary data point to support a segmentation model. Generally, this type of analysis is paired with a monthly recurring revenue analysis to get a sense as to which customer segments will have higher average lifetime values.
Customer tenure analysis requires that a product or service has been around long enough to see a significant percentage of first generation customers complete their life cycle. Otherwise, the analysis can only be used to identify segments that tend to have shorter customer life cycles. This can be informative, but other segmentation analysis approaches will often be more informative in these situations.
A Step-by-Step Guide to Set Up and Execute a Customer Tenure Analysis by Firmographics
- Identify the best data source for calculating customer tenure. This will generally be the company’s transactional database. However, it will need to contain lost customers, as these are the data points you are most interested in.
- Calculate customer tenure in months. This can be calculated by finding the time elapsed between a customer’s initial acquisition date and its termination or completion date.
- Now you will need to identify which characteristics you would like to analyze. This will likely be determined by the results of other segmentation analyses as this information will be supplemental evidence.
- Then you will need to identify where you can get this information to append to the customer tenure data source. Since this is a secondary analysis you will generally be able to pull it from the monthly recurring revenue analysis or another analysis informing your segment selection.
- Now you will want to identify tenure groups. Generally, it is best to use similarly-sized buckets as is shown in the example below. You can also use quintiles to divide the customers into buckets. However, this is not as informative about the length of tenure difference, so I recommend using tenure length buckets. This is a way of standardizing the data so that you can try to identify which customer characteristics are most likely to be correlated with long-term customer relationships.
- Next you will need to run counts for each segment by tenure group.
- Now you will want to chart the tenure group counts across each of the segmentation characteristics you are testing to see if you can identify any patterns. Often, the best segmentation criteria are actually developed by looking at trends across one or multiple segmentation criteria. For example, you may find that a series of related sectors all look good and this may suggest to you that a certain type of business model or business is a good target.
- After analyzing the customers across each of these cuts, you should have a pretty good idea as to which of these characteristics has the most explanatory power when it comes to customer tenure. You will want to look for positive and negative relationships. Knowing what is bad is almost as useful as knowing what is good as it can help you prioritize targets.
You can also do customer tenure analysis using regression, but that is a blog post for another day. I recommend reading the OpenView customer segmentation eBook: Finding Your Best Customer: A Guide to Best Current B2B Customer Segmentation if that is what you are looking to learn how to do.
Next week, I will explain how to set-up a win-loss analysis for the purpose of segmentation.