Friday, January 31, 2014

Top Three Reasons why B2B Marketers Struggle with Data Analytics

Why does B2B Marketing Struggle with Data Analytics?

A  report from the CEB (Corporate Executive Board) Marketing Leadership Council on the Digital Evolution in B2B Marketing echoes our own research and indicates that B2B marketers are struggling with implementing data analytics solutions in their firms.  Not Surprisingly, the issue is often one of data quality. This illustration shows that poor data quality and analytics capability leads to a lack of meaningful insight, which in turn can lead to a lack of funding for data quality and analytics efforts.

CEB Marketing Leadership Council Report on the Digital Evolution in the B2B Marketplace, 2012

Lack of Results Leads to Lack of Funding

The result of poor data quality is a vicious circle where marketers cannot get the proper funding for their efforts.  Our research results reinforce the results shown in this graphic but also link data quality to customer performance.  In our recent paper in the Journal of Interactive Marketing volume 27, Issue 1, February 2013co-authors James Peltier, Don Lehman and I present a model with empirical evidence from the banking industry, both B2B and B2C applications.  The article is called Organizational Learning and CRM Success: A Model for Linking Organizational Practices, Customer Data Quality, and Performance.

In the article we measure customer data quality in the organizational context.  We not only show the linkages from customer data quality to ultimate firm performance as measured by growth, we explore this result in an organizational context.  Without the cultural that values analytics and data, data quality cannot be obtained. Culture means not only top management support but parts of the organization working together.

Top Three Reasons Why B2B Marketers Struggle at Analytics

Therefore, the top three reasons I see why B2B organizations cannot achieve analytics success are as follows:

1) Lack of a cooperative culture that supports data quality.
2) Lack of support by upper management for data quality efforts.
3) The resulting poor quality data that leads to poor decisions.

I will follow up this blog post with more information about the link between data quality and customer performance as reported in our research.  I thought the CEB report hit the nail on the head in terms of illustrating the grim consequences of a lack of customer data quality and the conclusions dovetailed nicely with our own research results.   In the meantime, please feel free to contact me with any questions about this material or our article.

By Debra Zahay-Blatz.
You can find Debra on  and Twitter as well as LinkedIn.