Market effectively by knowing your customer


       Peter Todd.JPG
By Peter Todd, Director of Business Marketing Services for D&B Australia

At a recent Business Intelligence Conference, international IT analyst firm, Gartner Group discussed why Business Intelligence (BI) solutions were failing businesses. The analysts in question, John Van Decker and Kurt Schlegel gave nine key flaws for the failure of BI solutions. Flaw #3 is a problem that particularly resonates:

"Data quality problem? What data quality problem?"                    

"Data quality issues are almost ubiquitous and the impact on BI is significant - people won't use BI applications that are founded on irrelevant, incomplete or questionable data."

But the big question is why securing and maintaining timely and relevant customer data is such a challenge?

There is a general acceptance from C-level executives that Business Intelligence and knowing your customer is a critical priority in this challenging economic environment. For example, global retail bank spending on Business Intelligence (BI) technology will increase from $5.6 billion in 2006 to $9 billion by 2012, according to a global study by Datamonitor. Compliance requirements, the credit crunch and the competitive business environment are causing banks in Asia-Pacific, North America, Europe and the Middle East to devote more of their budgets to BI.

However the Gartner study highlights that effective Business Intelligence is still a challenge. Companies see the business need behind an investment in Business Intelligence but are yet to quite discover how to make this investment work for them.

The reality is that building Business Intelligence does not end with the implementation of a Business Intelligence or data warehousing solution. While these solutions can benefit decision making, implementation of Business Intelligence tools themselves are only one part of the solution.

So what can businesses do to ensure that a potentially heavy investment in BI will provide a return on capital expenditure? Surprisingly the answer is relatively simple and starts with the basic premise of quality data equals quality analysis.
The first step in the process is cleansing and identifying data. To make the most of your existing information an organisation needs to ask three key questions:

Accuracy - is your information accurate, are you capturing the same data consistently across your organisation?

Relevancy - is the captured information held in the right place?

Completeness - How complete is your data set?

Accuracy of data is often seen as the greatest challenge. Based on our experience with an international D&B database, we've found that in just one hour:

  • 200 business telephone numbers will change or be disconnected
  • 54 business addresses will change
  • 92 directorship (CEO, CFO, etc.) changes will occur
  • 74 new businesses will open their doors
  • 7 companies will change their names

And every year in Australia:

  • 128,453 new companies commence trading
  • 4,147 companies change their name
  • 17,425 companies change their place of business
  • 7,802 companies and 4,128 businesses cease operations

This data churn can cause significant inaccuracies within databases. Fortunately these inaccuracies can be detected and corrected. By matching customer information against a current source of business information, customer records can be enriched with the most recent information available. A process like this can occur in real time as the data is being entered or as a batch process overnight, weekly, monthly, or at any other interval based on needs and volume. This will provide an accurate source of data that a business can use.

The second step is enriching the information. Organisations are often surprised by the inaccuracy of the data within their databases. Building a database of comprehensive records does not ensure the quality of the data overtime.
Companies typically identify their best customers based on the assumption that each customer is separate and unrelated. Once records are cleansed and identified, the next step adds value by enriching customer data to add insight and show previously hidden up-sell and cross-sell opportunities. Additional and third-party information provides a multidimensional customer view and increases the business value of customer information.

Corporate linkage can help manage credit risk globally by corporate family tree. Using business credit information in sales and marketing efforts allows businesses to select only the most creditworthy targets for direct mail, telemarketing, and other demand generation efforts.

The third step in this process is learning to manage and share this insight. To achieve this aim a single customer view is needed. This is where technology can come to the fore to integrate all source data and ensure that all information is housed and managed effectively in a central repository.

Ultimately however without following the first two steps Business Intelligence as a solution is unlikely to meet any key objectives. Data quality and enrichment are critical. Without it businesses will continue to suffer from the "garbage in, garbage out" syndrome, except now the problem will be compounded by consolidating bad data from all over the enterprise and synchronising it to all of the downstream systems.

Without this process companies will have issues with finding and resolving duplicate records and with only the organisation's "internal" view to go by customers will miss out on the widely accepted external identifiers, the corporate family tree view, and other valuable demographic and predictive attributes.

The opportunity for any organisation if a BI solution succeeds is great but addressing the issues at the implementation level ignores the fact that ultimately success or failure all comes down to the quality of the data. In the current market companies can ill afford to spend money on projects that do not bring positive returns, particularly when the solution is relatively simple.

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