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Managing Customer Data for Analytics (Fourth in a Series)

Customer analytics can help you optimize business processes, but first you must assess how you are managing your customer data.

My recent articles discussed using analytics to increase customer engagement and optimize business practices. In this column, I'll address managing your customer data.

If you want to grow your customer analytics program, you must have a plan for managing customer data in a way that accumulates, validates, and ensures consistency as information is acquired or updated at each stage of the customer life cycle.

Note that this may conflict with your current approach for managing customer master data. In many environments, managing a master customer system involves periodically pulling customer data from the different business-function systems and merging that data into a surviving record that is supposed to represent the "truth" about that customer.

The problem with this method is that attributes, characteristics, and even the fidelity of customer data are all subject to change at different touch points along the stages of the customer life cycle.

In some systems, data attributes are continuously kept up to date, while others are seldom updated. There are data attributes that are necessary for sales and marketing that are less relevant for finance and accounting; customer purchase data informs the behavioral profiles, but not until after you convert the prospect into a customer.

The upshot is that many systems treat all data sources similarly even though the usability of their attributes differs over time and across life cycle stages. This is why I suggest taking a different tack when it comes to master customer data management: align the data models and services for access around the touch points of the customer data life cycle.

If you have the luxury of engineering your customer data management from scratch, first establish the opportunities for improvement and optimization at each stage of the life cycle. Assess the needs associated with the types of analytics implied by those opportunities.

Next, map your needs to the operational and transactional processes that engage the customer at each of the stages. If you manage a single data environment for customer data, and ensure that business function dependencies are not violated, you will ensure that the single customer record stays consistent and up-to-date.

Unfortunately, few organizations have the opportunity to build their customer data management environment from the ground up. In most cases you will have to contend with existing applications and siloed customer data sets. If this is your situation, you must expend some additional effort to use the customer life cycle approach to data management.

Your organization may have deployed many applications intended to achieve operational goals; you must review these to isolate how they support the different stages of the life cycle and understand the dependencies among the uses of customer data. You must then adapt the master data management environment to synchronize the master copy of each customer record with the different application data sets.

This may require some retooling of the existing applications, but remember that your original objective is to augment the existing business processes with analytics, and most of these applications will already need to be modified to incorporate the results of customer analytics.

Customer analytics applications can help you optimize business processes, but attempting to implement analytics without synchronized customer data is a risky prospect. Interleaving the transition to a synchronized master customer data environment will smooth the process for integrating customer analytics.

 

Part 1: Analytics for Customer Engagement

Part 2: Optimizing the Stages of the Customer Life Cycle

Part 3: Planning for Customer Analytics

Part 4: Managing Customer Data for Analytics

 

About the Author

David Loshin, president of Knowledge Integrity, Inc. (www.knowledge-integrity.com), is a recognized thought leader and expert consultant in the areas of analytics, big data, data governance, data quality, master data management, and business intelligence. Along with consulting on numerous data management projects over the past 20 years, David is also a prolific author regarding business intelligence best practices; he has written numerous books and papers on data management. David is a frequent invited speaker at conferences, online seminars, and sponsored websites and channels. David is also the program director of the Master of Information Management program at University of Maryland’s College of Information Studies.


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