Data Analytics Models for B2B Demand Generation Optimization

Updated: Aug 18


Every interaction a lead has with a brand represents an opportunity to collect data. These interactions, which marketers often call touchpoints, can tell you valuable information about what elements of your marketing programs are working, which drives revenue impact through enabling data-driven optimization of your Demand Generation motions.

With these large volumes of data come an even bigger influx of tools developed to help companies organize, analyze, and train teams to interpret and derive value from it. Unfortunately, many organizations face a daunting gap between their desire to make use of these tools and their ability to actually do so.

One of the factors that contribute to this pain point is that many B2B companies do not know how to evaluate the maturity of their data analytics. There are countless frameworks to reference, but as a general rule, as the complexity of an analytics framework increases, so does the potential value of the insights that are delivered.

As a first step, enterprise marketers need to form a full picture of what their data can do for them. Only then can they make an educated decision about which tools will support the kinds of data analytics models that align with their business objectives.

Here are the categories to look for in your demand analytics capabilities:

  • Descriptive analytics. The most basic descriptive analysis focuses on historical data and is incredibly valuable for reading out the health of the business. Historical data can be measured through period-over-period (WOW, MOM, YOY) analysis of leads, pipeline, or revenue, and allows marketers to look for trends and identify correlations between marketing activities and desired results. Enabling descriptive analytics is only possible with an effective analytics strategy that clearly maps to the organization’s business strategy.

  • Diagnostic analytics. Diagnostic analytics poses the question, “Why did X happen?” Looking for causation rather than correlation, it can take our historic reporting to the next level through techniques that get to the root cause of an observed event by looking at demographic, firmographic, and behavioral variables. Sensitivity and regression analysis models, for example, allow you to allocate uncertainty to different variables through modeling how they affect dependent target variables.

  • Predictive analytics. Identifying factors that improve the probability of success is enormously valuable to marketing and sales teams. These models use the same variables used for descriptive analyses (demographic, firmographic, behavioral) to identify your high-value audience and segment them by leads, industries, and companies. Often, companies choose to work with a third-party platform to provide the base model for predictive analytics, along with other data enrichment that can improve the quality of insights.

  • Prescriptive analytics. The last category takes the last and final step: prescribing the best action based on input variables. A prescriptive analytics model uses real-time signals to direct marketing programs to send the best piece of content, or a sales rep to deliver the most compelling sales message. This takes foundational techniques to the next level because it happens when your audience is engaged with your content in real-time and requires little to no human interaction to optimization sales and marketing activation.

These techniques are data-heavy and require real-time information to provide relevant direction. In addition to considerations for the other types of analysis, you will need to feed your data environment clean, behavior-triggered data to generate the meaningful insights you desire.

For more information on how to set your data up so that it is clean, organized, and actionable, see our blog on best practices for B2B data hygiene.

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