Allocating value to each customer allows companies to determine how much money and other resources they want to invest in that particular person or group. Doing so is a critical step for organizations, and probably the first research they should engage in when it comes to customer analytics.
Customer value scores can guide organizations in their strategic and day-to-day decisions, which is why it's imperative to have a standardized, company-wide method to define customer value. It's vital that this definition is consistent throughout the organization and that different departments are aware of the value allocated to a particular consumer. This approach can help ensure consistent treatment throughout the client's interactions with various entities within the brand. For example, a customer who is valuable for a bank's credit card division may have limited assets, making him of low importance to the retail group, and possibly leading to discrepancies in the way he is treated by different departments. Giving customers a single value across the board will help eliminate divergent treatment.
Customer valuation can be based on both historical data and predictive metrics, two methods that complement each other and should be used concurrently to give a detailed picture of both the current and future value of a customer. There is great awareness about the advantages of customer valuation and many organizations are already using customer data to take a snapshot of their clients, giving them an idea of where particular customers stand in the overall customer base. For example, if two customers decide to cancel their phone service, a telecommunications provider can offer them incentives in a bid to keep them with the company. But knowing that one customer has a significantly higher value gives the company the needed insight to make him a better offer, knowing that there will be a bigger return over the long term.
The more granular the data, the better it is, since it will provide you with a more precise picture of each customer. And with a more clear-cut representation, you can make more accurate business decisions, allowing you to invest in, and retain, the right customers. However, being granular can also be costly, so it's important to start with what you already have in place and incrementally increase data and complexity as you reap such benefits as improved targeting and increases in response rates and sales.
While historical data gives valuable insight, and is a good starting point, it should ideally not stand alone. Instead it should be supplemented by predictive analytics of a customer's potential value over his lifetime. This is a critical point because you want to base your decisions not only on the current value of consumers, but also on the value you can extract from them in the future, which is where you can get more from your investment.
Forward-looking measures will tell companies what to expect from a specific customer over the entire duration of their relationship. Some customers might have very high current value but are likely to churn after a short period of time, so their lifetime value is limited. On the other hand, predictive analytics will flag the future value of consumers who are not worth much at present. For example, a college student might not be very valuable to his bank right now, but predictive analytics might forecast a potentially significant lifetime value once he starts working.
Although companies benefit greatly from such analytical work, there are challenges because businesses have to rely on statistical forecasting methods that may introduce uncertainties into the process. However, there are powerful analytical techniques to help companies overcome those hurdles; for example, survival analysis can attach an expected lifetime for a particular customer based on statistical probability. It is also important to keep in mind that the aim is not to be 100 percent accurate with every single customer, but to better allocate resources and improve targeting efficiency across your customer base.
Finally, once customer valuation has taken place, the information should be fed into the different channels or data systems across the organization so people within your company have easy access to it and can use it on a day-to-day basis.
Customer valuation is a great guideline whenever a company has to make a decision on resource allocation or prioritization. It should therefore have a solid foundation and be agreed upon by all stakeholders, so decision-makers can--and will--use the valuation data to inform critical business decisions.
About the author: Hamit Hamutçu is a managing partner with Peppers and Rogers Group. Contact him at firstname.lastname@example.org