Customer Value and Profitability Data Management  

Customer Value and Profitability

Its finally the shareholder who rules. Customer value and profitability is fine-tuned to maximize the value out of a customer.


Customer Value and Profitability Data Management


Customer value and profitability data is not a production data in true sense, but it still needs to be well integrated with the production data related to the customers. This is needed so that we can track the progress on customer value, post our actions to enhance the same. We also need to keep on re-validating the linkage between the customer value and customer parameters.

Customer Single View

A single view of the customer can be for operational as well as for strategic purposes. You can refer to Customer Data Quality, for some of the challenges related to getting single view. Customer Single View will be covered in much more detail in Master Data Management.

Customer single view has been an ongoing challenge with the organization, as the business become more complex and the organizations grow. The different customer data models, duplicate customer records, incomplete customer data are few examples which need to be managed.

POSSIBILE SOLUTION- One does not have any other option, but to go for customer integration solution. You may choose to have an initiative, which enables you to take decisions (analytic BI focused) and not use the data for operational purposes (operational BI OR Operational Data Store) OR you may go for a holistic CDI solution.

Managing Customer cost data

Unless you have a robust activity based costing platform, you will have a challenge to maintain the detailed cost data to enable you to have complete track of costs, their sources and allocation. If the organizations don’t have an ABC platform, one needs to manage these cost tracking and allocations at broader rules and simplicity. For example, you may like to have your customer services cost tracked and allocated at total level instead of tracking it at product level and the customer segments which are biggest users of your customer service. For example- Unit linked insurance products (due to their investment feature, where a customer can decide on her investment strategy), may be using most of the customer service cost. If you have ABC, you may be able to track the cost of customer services at the level of specific insurance product and allocate the data accordingly. However, if you don’t have an ABC platform, you may come out with broad heuristics for this allocation.

POSSIBLE SOLUTION- Implementation of ABC process and platform. As ABC platform may itself take months and years to mature, you may use heuristics and simpler cost and cost allocation models. One may not like to wait till the ABC model becomes fully evolved.

Managing customer lifetime value and long-term value data

Lifetime Value data does not exist in your production system, but in your data-mining, analytics tool OR in a field CRM tool. The calculation of this data is based upon data taken from source systems, business modeling systems, data mining system, customer service systems and other sources.

One needs to maintain a track of the data, the business rules and models which have helped you to calculate the customer value. The reason is that:

  • These models and rules change pretty frequently.
  • The value calculations may be done on regular basis.

POSSIBLE SOLUTIONS- Customer lifetime value data is extremely iterative and has a potential of keeping on changing. This requires for you to store multiple scenarios (what-if analysis). We would recommend that one can keeps the customer value and profitability data in some kind of separation (like having different cubes), so that it does not mix with the production related ‘actual’ measures. You can find a way to link the production data cubes with the customer value cubes (refer multi-cube architecture in OLAP Servers).

Tracking the progress on the customer value

You would like to keep track on the impact of your actions on enhancement in the value for the customers OR customer segments. This necessitates the connection between the core systems, business modeling systems and your analytics system which are maintaining these details.

NOTE- The overall and key message on the above points is that customer value management is not done by having stand-alone sophisticated modeling systems. It has to be an integrated platform, by which you can have the complete cycle of value calculations, actions, actions execution and tracking the impact of those actions.

Historical Customer Data on Value and related parameters

You need to have lot of historical data to validate your business model for calculating the future OR lifetime customer value.

The key challenge in this effort will be to do the data preparation. An organization may not have detailed cost data, and especially the historical data. For example- if you have recently implemented an Activity Based Costing platform, you may still need the past cost to validate the co-relation between customer tenure and profitability (How customer value changes as the customer tenure increases)

The other time data challenge is to have past data on overall relationship value. You may have come out with a customer data integration initiative only recently, and you may not be in a situation to get the historical perspective on this.

POSSIBLE SOLUTION- You can apply heuristics and simple business rules to generate the historical data. Integrating the historical data (say on overall customer relationship) as well as the current customer data will have its benefits not only customer value calculations, but also on host of other initiatives (like customer services, customer leads...).