But collecting data about your customers is only the first step and data in and of itself is of limited value. To real value of data for retailers is to better understand their customers.
Unfortunately, many retailers can do more, and as they look to determine what customer data can help them do this, here are five things that every retailer should know about its customers in 2013.
The five things
Location, when used creatively, is a great data point that has many uses. For instance, it can be used to maximize the likelihood of opens in email marketing campaigns, or to better target customers based on seasonal and regional trends in a retailer’s category.
2. Average order value
Most retailers can tell you their overall average order value (AOV), but tracking the AOV on per-customer basis is a worthwhile exercise. Not all customers are created equal, and knowing which ones spend the most, and which ones spend the least, can be a useful data point in determining who your MVCs are.
3. Purchase frequency
Purchase frequency can be a tricky metric to use effectively, but that doesn’t mean it’s not worth knowing. Savvy retailers can use it to identify customers that may be on the verge of becoming former customers. Once identified, these customers can be engaged before the probability of them coming back drops below the threshold at which such efforts are unlikely to be effective.
Identifying the most productive marketing channels is crucial for retailers. After all, some channels are going to deliver customers that have a higher customer lifetime value than others. That, for obvious reasons, should be taken into consideration when deciding where to spend and how much to spend. As such, retailers should make it a point to keep track of where each customer came from and to use this data to perform cohort analyses.
5. Predicted customer lifetime value
Although it’s often calculated incorrectly, customer lifetime value (CLV) is arguably the most important metric a retailer can establish. But forget about CLV values based on historical data. Thanks to the constant march of technology, predictive modeling of CLV is something even smaller retailers can engage in at minimal cost today.
Having versus knowing
It’s important to note that knowing your customers is about more than having data about your customers. Most retailers, for instance, have the location of each of their customers. And metrics like average order value and purchase frequency can be calculated with relatively simple database queries.
But that isn’t enough. Knowing your customers is about recognizing the valuable customer data you have, associating it with each customer in a meaningful and usable way, and making sure that the right people can and do apply that customer data to their decision-making processes on an ongoing basis.