B2B Demand Generation & Predictive Analytics

John Follett

In the world of B2B marketing, demand generation occupies center stage. The expectations placed on this process, and those who manage it, are enormous. Marketers are constantly tuning the demand gen process to strike the right balance between the quality and quantity of leads it produces.

Radius and Demand Metric recently completed a study of the B2B demand generation process in general, and the study looked specifically at how predictive analytics impacts the performance of this process that sits squarely in the critical path to revenue. The study discovered that when predictive is part of the process, the effectiveness of demand generation measurably increases.

Some of the key findings of this study include:

  • Less than one-third of study participants report that their lead generation process meets objectives well.
  • Over half of participants with an ineffective lead gen process state that their customer data richness is lacking, and 47 percent report dealing with data accuracy issues.
  • Over 80 percent of participants with an ineffective lead gen process report that data quality has a moderate to significant impact on marketing campaigns or sales efforts.
  • Over 90 percent of participants with effective lead gen processes are able to use their customer and prospect data to execute various marketing campaigns; just 58 percent of those with ineffective lead gen process are able to do the same.
  • Three fourths of firms in the study that target enterprises are able to analyze their data to find new revenue opportunities, compared to firms targeting mid-market companies (52 percent) and small companies (38 percent).
  • Well over half of firms in the study that have an effective lead gen process are also implementing or using predictive analytics.
  • The top factor driving investments in sales or marketing solutions is projected ROI.
  • Marketers who have adopted predictive analytics are more likely to also have an effective demand generation process.
  • Inaccurate or poor quality data impedes effective demand generation as well as the successful application of predictive analytics.
  • More study participants claim to understand predictive analytics well (44 percent) than are actually implementing or using it (11 percent).

It’s no mystery that for B2B firms, the effectiveness of demand generation impacts revenue growth, as this chart shows:

Revenue and DemandGen chart

Several factors were identified in this study that are critical to demand generation process effectiveness:

  • Start well.  The most ineffective demand generation processes experienced the most difficulty in the early phases of the process, with top-of-funnel activities.  It is impossible to make up for lack of richness and poor quality data later in the funnel without somehow obtaining missing contact information and other data that indicates lead quality or propensity to purchase.  Most marketers understand the tradeoff between the quantity of leads collected and the amount of information leads are asked to provide.  A better solution than simply elongating landing page forms exists in the form of third-party, supplemental data that marketers should investigate.
  • Exploit data.  Marketers have or can get a lot of data about prospects and customers, and to get the highest possible return on this data asset, they must segment it to profile their best customers, and then go find more like them.  Likewise, data analysis can direct marketers toward new revenue opportunities with new or current customers, and guide sales and marketing investments.  The tools, data and technology for doing so are accessible to anyone that has the will to do it.
  • Predict the future.  Predictive analytics is to many marketers the equivalent of theoretical physics: only a few, highly educated specialists can understand or apply it.  However invalid, this perception persists.  The reality is that the demand generation process is an ideal pairing for predictive analytics, and commercial solutions exist to help integrate predictive into the process.  The study found that the impact of predictive on demand generation is dramatic.  Predictive analytics provide a powerful lever for improving demand generation process effectiveness.

Demand generation process effectiveness is the top barrier to firms in this study achieving their revenue objectives.  This critical process can generate drag, sending revenue growth down, or it can create lift, helping revenue attainment soar.  Getting the right data at the start of the process, analyzing it effectively and using it predictively represent the flight plan for the highest possible revenue attainment.  To learn more, download a copy of the study report.