Customer value management systems make use of the customer data given for a specific period of time. Tracking the data is, of course, useful for your long-term customer journey. However, it may not be useful for the short term. If your company does not have enough traffic, there may not be enough useful data for short-term reports. There are also periods where the customer journey pauses due to factors outside of your company’s control. However, there are challenges where you must track customer value in short-term periods.
Many companies have a business model that is seasonal, and it must be taken into consideration. Revenue can sharply decrease, leaving fiscal quarters without data. In this case, tracking customer value may not be effective for those periods. This can be relevant for tax companies, landscaping companies, or if your business model is attached to the activity of school districts. If you want to pivot your business model or include a new revenue stream to mitigate the decrease in customer purchases, then tracking short-term customer value will be appropriate.
Products or services may change during seasons of the year. Grocery stores that sell produce and department stores that sell clothing frequently have a change in customer preference that is set by the change in season. If you focus on the long-term strategy, it can give you insights on brand loyalty. It may not give you insights on customer churn due to customers lack of preference. Customer data can also be influenced by a change in the economy or government policy. External threats can cause a swift change in consumer finances, product availability, or an increase in costs. Customer purchasing activity can sharply decline due to these factors, making long-term strategy reports rigid and partially irrelevant. You can quickly see the effects of these changes with short-term reports.
It can be appropriate to track customer value in the short-term, if these challenges arise. The decrease in customer value must be considered before restrategizing your customer journey to decrease churn. Machine learning can predict churn with relevant historical data. If there is seasonality in the business model, it will predict the periodical decreases in customer activity. It may not predict external factors from the economy and government policy. Being proactive, you must consider the entirety of the business model to detect a projected increase in costs. In this case, customer value profitability metrics will be important to track the resulting changes. Your customer value revenue levels and your churn rate may be healthy, but your customer profitability may tell a different story.
Tracking customer value on a quarterly basis may not be effective for your strategy, unless you have enough consumer activity or come across a specific challenge that requires an assessment of customer churn. When using machine learning or artificial intelligence models, you must have an efficient amount of data to train the models. A lack of data can cause predictions to be biased and inaccurate. On the other hand, you can train the models for training too much. There are times when the accuracy or precision score becomes repetitive, and the excessive computations can cause an inefficient use of resources.