Only in the last few years has business-to-business (B2B) marketing come into its own vs. the larger and, some would say, more exciting world of business-to-consumer (B2C). Yet, billions of dollars (yes, I mean billions) are spent on marketing and sales annually by firms who sell to other businesses. Traditionally, the vast majority of this money was devoted to fielding the sales groups (direct and/or business partner channels), and a much smaller portion spent on marketing activities. This has changed dramatically in the last 10 years as technology entered the fray. Unfortunately, about 10 years ago, when the first wave of technology hit the B2B scene, it was bought largely on the "speeds and feeds" attributes by IT departments. The net result was a colossal waste of money, and the failure of this investment to meet expectations of sales management. This has been widely documented, and usually falls under the banner of the now discredited term CRM (Customer Relationship Management).
Therefore, the goal of this white paper is to go beyond just the standard technology and provide in-depth information on two additional critical companions to technology - data and measurement - that must be integrated to achieve maximum results in B2B database marketing. One might even view this as a three-sided pyramid to achieve true integration and B2B marketing results.
By carefully integrating technology with accurate data and solid measurement practices, then and only then, can the dichotomous goals of "SELL MORE and SPEND LESS" be achieved in B2B from the implementation of database marketing.
The focus on technology, data and measurement does not reduce or underplay the importance of people in this equation. In fact, many would say that the three-sided pyramid should be people, process and technology, not data. Point taken, but books are written about hiring, training and motivating people and that's not the key issue driving this white paper. Therefore, we will stick to these three ingredients to success.
The quality and accuracy of business data is one of the key differences between B2B and B2C. In addition, the importance of accurate B2B data is critical to success in any direct marketing effort. An axiom to remember in B2B is: "a lousy campaign sent to a great list will outperform a great campaign sent to a lousy list". There are several reasons for this, but the most important one is, if the communication is improperly targeted, it most likely will never reach the individual within a company whose job it is to buy or influence the purchase of the product, or service being marketed. This is particularly true when selling larger companies as the mail room doesn't really care about trying to find the proper person if the communication is improperly addressed. E-mail is no different as it will get "bounced". When calling into companies, the receptionist or administrative assistant will most likely not be helpful if you don't have the name of the right person.
As a result, most experts agree that 50-70% of the success of any B2B direct marketing effort is dependent on the accuracy of the list.
Based on the importance of the data, the first question is, what kind of data? The three types of data that are most actionable are:
- Demographic - facts about the company
- Contact level - names and responsibilities of the people
- Behavioral - actions people take
The Bad News
We performed a data decay study in 1995, and at that time 62% of the individuals had one or more changes in their business card. This compares with 70.8% almost 10 years later - in other words, the decay rate in B2B data has gotten worse! In 1995 approximately 31% of the individuals changed companies, and in 2004 this dropped slightly to 29.6%. The biggest difference from the 1995 study is a 10% increase in movement within a company, as 41.2% of the individuals reported data changes without a company change, compared to 31% in 1995.
The change rate or data decay has become even more of an issue today for B2B direct marketers, than ever before. Those firms who collect and sell data are struggling with this same problem, and the inaccuracies found in their lists are symptomatic of this "data decay".
