Make the Journey from Descriptive to Prescriptive Data Decision-Making — and Turbocharge Your RevOps Performance

Would you believe your organization was capable of predicting its own future — guiding your teams to make not just better but the best decisions?

Big data has long since entered B2B selling operations in full force. For good reason too: The right data promises insights that can help your leadership make better decisions and reach (or surpass) your goals. Yet in a 2023 Tamr, Inc., study, more than 250 out of 500 data leaders reported that they struggle to realize true business value from their datasets.

Most organizations’ data analytics remain in the early phases, at the descriptive and diagnostic levels that tell a “what” and “why” story. Simply put, it is like looking through the rearview mirror at what is behind you to determine where you are going. Rather you should be looking primarily out the windshield at the road ahead.

In this article, I want to help you identify your revenue operations’ current data-driven decision-making capabilities and help you enter the more-powerful predictive and prescriptive stages. If you stick with me on this journey, you will get behind the revenue operations (RevOps) wheel with a clear path ahead to success.


Descriptive business analytics look at past data to answer “What happened?” This data is easy to identify, collect and present. With descriptive analysis, you can start to see trends and patterns in your business and with your buyers. But descriptive analysis on its own cannot tell you much beyond what already happened. And for future decision-making, historical perspective is helpful but can also be a large drawback.

  • Year-over- year (YoY) sales numbers
  • Annual revenue report
  • Revenue reporting dashboards

Consider routing a road trip. At the descriptive phase, you would drive your route first, more or less blindly making decisions as you come upon signs, traffic and forks in the road. Then either during your journey or after you reach your destination, you would mark your trip on a physical map and note observations about what you experienced. Clearly this sort of approach is easy to track but it is not the most efficient or productive.

In order to uncover insights from your past data to drive stronger decision-making, your teams need to take that data a step further — into diagnostic analysis.


Diagnostic analytics take descriptive data and asks of it “Why did this happen?” Your teams can identify outlying data, isolate patterns and uncover relationships between different outcomes to drive more-confident decisions ahead with less trial and error.

  • Why did subscription customers churn / renew?
  • What explains this good / poor performance of a new product?
  • Why did YoY sales numbers drop / rise?
  • Why do some deals close fast and others linger?

In the same road trip analogy, with diagnostic insights, you would take that map where you routed your journey and compare your past data with other observations you have gathered on different trips of that same route or alongside the area’s traffic trends, construction plans, etc. These insights would help explain why the route may have taken a certain time or why you encountered a smooth ride or roadblock after roadblock. With these additional insights, you can start to make a better estimation of how long the route may take in future trips and what you might encounter along the way.

The problem is, with diagnostic analysis your teams are still stuck in the past as a means to inform your future decisions. But seeing into the future is becoming all the more possible with stronger datasets and more-powerful data science tools — and with these you can start to practice predictive data analysis.


Predictive analysis starts to engage more-advanced data science to help your RevOps leadership make better-educated guesses to answer “What could or will happen?” Today’s digital tools from machine learning and algorithms to artificial intelligence (AI) and statistical modeling can do the heavy lifting for you. But in order to predict likely future outcomes of business decisions, you need to feed these tools quality datasets. The more-complete the datasets, the stronger predictions you can expect. But predictive analytics still cannot foresee unpredictable human factors or disruptions.

  • Likelihood of customer retention and future sales forecasts
  • Recommendations of product upsells / cross-sells to market segments
  • Highest performing marketing content to consider recycling and re-promoting

Let’s return to our road trip analogy. This predictive stage would allow you to analyze your planned route before you put wheels to road and, based on historical traffic data and other internal and external inputs, get insights into your different goals and concerns, like how long the trip will take and how much stress you may encounter.

Not many RevOp teams operate at the predictive stage because not many organizations have this level of data infrastructure. But setting your organization up for predictive insights is more within reach than you might think. And once you start embracing a stronger data infrastructure, your teams can tap into the pinnacle of data insights — prescriptive analysis.


Prescriptive business analytics can give your leadership an answer to “What should we do?” With a powerful data infrastructure comprising internal and external data and run through machine learning, algorithms and AI, your teams can see all the possible future outcomes of decisions against your key performance indicators (KPIs) — while also helping you understand why these different outcomes may happen.

  • Identifying most-relevant value messages for engaging a specific market
  • Scoring sales leads to prioritize based on purchase intent
  • Sales touchpoint suggestions to best drive a specific target segment through the buying journey to a successful deal

In our road trip analogy, prescriptive analytics is live and on full display in Google Maps. You type your destination into your device, and Google Maps uses a number of data insights, both historical and in real-time, to recommend the best route ahead. Might you decide to take a longer scenic route if travel duration is not one of your KPIs? Sure. But at this stage, you are well-informed to make the best decision for your path ahead with little effort on your end.

This level of confidence and input in B2B decision-making remains rare. But leading B2B organizations are starting to see the value in being able to partner with digital tools to improve their operations, mitigate risks and make the most of their resources.


If your leadership is ready to take your data strategy to the next level, let’s talk. Reach out to our RevOps expert principals to learn how we can move your organization toward predictive and descriptive analytical decision-making.