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New Survey Reveals the Barriers to Data-driven Marketing

A new report by Decision Tree Labs reveals the barriers to data-driven marketing that marketers face. According to the study, titled Benchmarking Marketing Automation: The Shift Toward Next Generation Lead Scoring & Segmentation, the biggest gap in marketing today is the inability to predict outcomes. Marketers are looking for improvements in the accuracy and predictability of their lead scoring programs to make them more effective. The study surveyed 250 marketing professionals at companies of all sizes to uncover the challenges they face in data-driven marketing and effective lead scoring and segmentation today.

Top findings:

A growing number of companies are lead scoring, or ranking leads based on fit and behavioral activities, but the challenges of having the right tools and defining a good lead are still holding many back

• 44 percent of respondents are currently lead scoring, while 16 percent plan to in the next 12 months.

• The top two reasons cited for not lead scoring is a lack of good tools or education (32 percent) and the challenge of defining what makes a good lead (24 percent).

Marketers give their current lead scoring programs low marks; they do not have insight into buying behavior and are not confident in the quality of their prospect data.

• Respondents give their current scoring programs low marks for effectiveness, with an average grade of five out of a possible score of 10.

• 43 percent do not feel they have enough insights into which attributes actually indicate buying behavior.

• 59 percent are not confident in their lead scoring models because of incomplete or inconsistent data on prospects.

• In fact, nearly half (46 percent) believe one of the top challenge of data-driven marketing today is the inability to connect data across multiple sources.

Predictive modeling ranks at the top of what marketers need for effective lead scoring, yet it’s missing from lead scoring models today.

• The top three factors that respondents use to score leads today are demographics (76 percent), firmographics (62 percent) and activity/response to marketing campaigns (87 percent).

• 58 percent ranked predictive modeling as the most sought after capability to make lead scoring more effective. Close behind were more account-level data (55 percent) and analytics to help determine which attributes should be included in models (53 percent)

Comments on the news

• “The survey data showed marketers are clearly looking for opportunities to become more data-driven, predict outcomes and solve the problem of connecting data from multiple sources to better inform their marketing strategies,” said Tom Olsen, research director for Decision Tree Labs.

• “Data-driven marketing can only come to reality if marketers are able to access the right data and gain meaning from it,” said Shashi Upadhyay, CEO of Lattice Engines. “This report points to a massive opportunity for marketers to discover the universe of predictive buying signals so they can come closer to the vision of data-driven marketing.”

Additional information on the survey

• There were 254 respondents to the study, which was commissioned by Lattice Engines and conducted in August 2013.

• One third of the respondents were from the high-tech/software industry. Professional services (17 percent), manufacturing (13 percent) and financial services (7 percent) were also among the top industries.

• 28 percent of respondents reported annual revenues of more than $250 million, 23 percent reported revenues of $10 to $50 million, and 21 percent with revenues of $1 million to $10 million.

• The full study can be accessed here.

About Decision Tree Labs

Decision Tree Labs is a custom research firm focused on helping companies identify opportunities and gather feedback and insights from specialized markets. Our research provides data points to help executives make intelligent business decisions and support business cases.