Last week I wrote about the need to evaluate your data requirements with some care before embarking on a project to acquire and analyze customer data. It’s all too easy to envision capturing all there is to know about every customer you come in contact with, only to find yourself drowning is a sea of unfathomable and overwhelming data.
Information may be power, but data by itself is not information. It takes insight, understanding, and interpretation to turn factoids into meaningful information, and your success at drawing meaningful conclusions from your customer data is largely dependent on knowing which bits and bytes to accumulate.
Spending the time and energy to really understand the range of information available and how different constituencies within your organization use those various bits of data is a critically important investment in the long-term success of your customer data analysis program.
Once you’ve determined which information would be most useful to know (and limited those tendencies to try to database everything), the next step is to figure out what is already known about your customers.
Caches of Data
In many companies I work with, I find that large stores of customer data exist throughout the organization but are not centralized, commonly known about, or easily accessed. Marketing may well own the “official” customer database, but that is just the tip of the iceberg.
Technical support and customer service are two good places to check first; these departments interact regularly with customers who are having problems, and usually they maintain some form of customer files.
Finance will likely have credit and payment files that can shed light on the financial and reliability aspects of your past and current clients.
The sales department almost always has a proprietary database it guards like the crown jewels. It is rich in customer information and insights but may be segmented by salesperson or territory, making access challenging for nonsalespeople.
What becomes clear very early in the process of building a companywide customer data analysis tool is that the biggest barrier to shared data is none other than good, old-fashioned distrust: Until someone can convince each of these functional areas that its needs, as well as the greater needs of the organization, will be better served by pooled data, the resistance will be significant.
One, large, shared database is many a marketer’s dream — and the nightmare of other departments and individuals who can only focus on the potential for abuse or loss of data.
At this stage in the planning it is extremely important to consider the implications of privacy and security for your customers and the company itself. If a shared database contains credit information, should your customer service temp really have access to that? Do you want that disenfranchised sales rep to carry a full account record on every client to your largest competitor?
Understanding which information about customers will help your staff serve them better and where that same information crosses the line into making the client or your organization more vulnerable requires some careful thought. I rarely see organizations get this balance right the first time, so pick a starting point with the knowledge that you will continually refine access and need-to-know points.
Next week, I’ll look at some companies’ approaches to drawing those “appropriateness guidelines.” I welcome input from readers who have tackled this challenge.
Emily Ma, product director of Tencent’s advertising platform products department, was a keynote speaker at ClickZ Live Shanghai where she discussed the ... read more
In today's multichannel world how can marketers use data to ensure the experience a customer receives is relevant to them?
The terms that customers type into your site search function can help you to gain an understanding of user behaviour and can be used to optimise ... read more