Why You Need Customer Analytics

Customer profitability is simply defined as your (revenue – costs) / customers, or your average profitability by customer. In our data-driven world, we need to have deeper meaning on this metric so that it can aid our decision making, shape our program creation, and ultimately improve our outcomes.

Customer profitability analysis is a retroactive look at customer impact on profitability based on relatively simple attributes and factors. It seeks to understand why something happened – how did my customer impact profitability this past month?  This type of analysis could be used to score a customer or grouping of customers by assigning a value measure (e.g., retain, transform, monitor, replace). This perceived value is based on cost to serve vs. value to the customer and used for shaping programs, marketing, product offerings, etc.

Customer profitability analytics seeks to go one level deeper to gain a better understanding of the “why” a group of like customers receives value from products and services offered by a firm. It seeks to look at the value and the health of the customer.

Two Examples

A customer consistently pays their bills and has been loyal for an extended period. However, if the customer is on an end-of-life product that is not as profitable, it would make sense to try to shift them to the modern version of that product. The new product would be more cost efficient for the firm while offering more features for the customer – it’s a win-win for both customer and firm.

Alternatively, you could infer that customers that pay late – or partially pay – possess negative value. Seeking to understand the “why” may show that it is due to the rigidity that is being enforced – payment methods (e.g., seek to add Venmo or Paypal), payment terms (e.g., customer can choose to pre-pay and get a discount), due dates (e.g., allow the customer to choose the day of the month to make payment) – that do not fit well for specific customers.

A Dataset and a Framework

Customer profitability analytics requires two things:

(1) a dataset with the appropriate levels of detail (e.g., metrics and attributes at the customer/transaction level)

(2) a repeatable framework that can be easily modified to add more factors to glean deeper insights

• Stratify your customers – Identify characteristics to get to a working subset of customers. Unless your business is small enough (<100), you will need to group your customers into like groups (e.g., demographics, product selection, region, household, etc.).
• Identify the levers and develop strategies to act upon the data – Once your customer groups are aligned, identify the levers to maximize profitability (e.g., product selection, rigidity in process, etc.), apply the appropriate business process changes (e.g., increase payment methods, replace services, etc.), and seek to target like-characteristic customers.
• Measure, Calibrate, and Repeat – Measure the success of your strategy changes, feed more data into the model, add more levers and/or factors to continue to calibrate, and repeat the process until you have a finely tuned model to maximize profitability.

While it may be challenging to get started, Customer Profitability Analytics adds valuable insights by identifying the “why” and shapes informed decision making to increase profitability. It enables more strategic management of the value exchange between your products/services and your customers, striving for a more symbiotic relationship between company and customer.