Poor data, poor customer management

The issue of data accuracy and consistency vexes every organisation on the planet, I would wager. It is a chronic problem which continues to evade overall resolution; 100% accuracy and integrity of customer data is rarely achievable: the world is simply too complex.

Poor management of customer data leads to poor quality, outdated data, higher costs of customer management and customers.

Doug Leather, CEO, Knowledge Factory

Poor management of customer data leads to poor quality, outdated data, higher costs of customer management and customers. However, the cost, extreme difficulty and improbability of getting it absolutely right means many companies abandon the effort before investing too much time and money in it. The approach, it would seem, is for most companies to accept that a certain amount of error is inevitable and they are prepared to live with it.

This is difficult logic to fault. Trying to get data quality 100% is a little like the Greek myth of Sisyphus. You may recall that he was doomed for eternity to roll a rock up a steep incline. Just when he got his rock to the top, down it would roll, and he would have to start all over again.

That`s the lot, it would seem, of any person entrusted with the task of data quality. Scarcely have you finished your data cleansing, consolidation and verification than you have to start the process from scratch; for the rule of the game, as any data quality manager will testify, is that the moment you have completed the task, it is out of date!

It is here that executives will often make a judgment call, based on discussions with actuaries and other risk management specialists, not to spend huge amounts on eternal data rectification, but rather to accept, identify and compartmentalise the risk relative to the quantifiable damage it can do to the business.

Without wanting to seem hyper-critical of organisations and their management teams, it should be apparent that poor management of customer data is not just about the direct and quantifiable impact to the bottom line, but also about the impact in many other, widely divergent, downstream consequences.

One of these is under-performing businesses. As I have indicated in previous Industry Insights in this series, poor customer management has a direct and negative impact on the bottom line.

London-based Qci uncovered the following astounding statistics through international customer management scorecard results. Among the findings from a data perspective, just released, were:

* Only 4% of companies have a robust customer information plan, and 67% have no plan.

* 39% of companies have no information quality standards.

* Less than 10% of companies have customer databases that can drive a contact strategy.

* 29% of companies track the benefits described in the business case used to justify the technology investment.

* 28% have no process to update the database for mailing returns.

* 41% of companies understand the implications of the data protection legislation.

* Only 12% of large companies make customer data available to all frontline staff (call centre, service centre, field sales, technical support, critical customer-facing back-office process).

* Just 20% have well documented customer information.

While it is beyond the scope of any organisation to get its data 100% clean, correct and fit for purpose, it is still incumbent on responsible customer-centric organisations to devote the appropriate time and effort to ensure that they have discharged their full corporate obligation, and that they are as well positioned as possible to maximise customer management opportunities. Here are some guidelines for implementing a customer data plan:

* Create an enterprise customer information management plan: Such a plan requires input and agreement on a wide range of complex issues across functional areas. Added complexity comes from the rate of change in the capabilities available in support of information management and usage, and the mid- to long-term time horizon on which such plans will deliver.

* Obtain resources to support the plan: Customer information management and usage includes a range of skills that are in short supply. So organisations must ensure they have sufficient resources in place to meet current business needs and that succession planning and growth in line with business needs is also considered.

* Build measures of data quality: Organisations should understand and measure the effect of data quality on the range of customer management processes and build business cases for data quality improvement. These measures should include financial data on wasted marketing and sales activity generated by poor data inputs.

* Define a single customer view: When does it need to be an analytic or a real-time view; need it initially be the only customer record or other key data too?

* Collect transaction histories, derive customer value, and use value information: Data held at the lowest level is needed as a basis for analysis, planning, customer management activity and measurement. Organisations should try to hold at least three years` data at transaction level, appended to each customer identifier, including date, product, volume, channel/outlet and margin of each transaction. To decide on acquiring, serving, developing and keeping particular customers, you must estimate their present and likely future value. Data on value must be made available at point of contact, so different actions can be taken depending on customer value.

* Privacy: Observe privacy laws and expectations in storing and using data.

* Account for third-party data (intermediation): Work with intermediaries to resolve issues, and be aware of where you could become an intermediary yourself or who could intermediate between you and your customers.

* Consider outsourcing business processes: The management of large customer databases and their integration into business processes requires substantial investment. Specialist companies that support this area can provide broadly the same solution either in-house or out-house.

* Use the customer data you hold to improve the customer interface(s): CDI (customer data integration) technologies now make it feasible to recognise and welcome customers at almost any point of contact. It is also possible in real-time to extract, combine and analyse data pertinent to that customer for true personalisation.

* Gather and use customer preference and behavioural data to build customer satisfaction and reduce operating costs.

* Ensure that retention activity is driven by all valid data available: Very few organisations capture "reason for loss of customer or product" data and few have a formal win-back programme informed through capturing, combining and analysing all customer history.

The issue of data management is key and of strategic import. The consequences of poor data management are certainly felt at boardroom level.

This overview, while not exhaustive, should be enough to get you going on what is a lifelong journey towards enhanced data quality and improved customer management.