Sales and Marketing Data Analytics: A Framework for Becoming Best-in-Class

Over the past several years, data science and data analytics have become top priorities for the C-Suite. Historically, these capabilities were adopted primarily by manufacturing and operations groups. Sales and marketing teams were rarely able to take advantage of analytical approaches, even though their decisions arguably have the most direct impact on revenue.

That is changing. The widespread adoption of CRM systems (over 90% of corporations) and marketing automation platforms (over 55% of B2B companies) has created an enormous foundation of sales and marketing data. The organizations that learn to analyze this data effectively will gain a measurable competitive advantage.

The Case for Data Analytics in Sales and Marketing

When you analyze the details of your CRM and MAP data, you unlock the ability to understand the buyer's journey through your sales system and the lead's journey through your marketing system. Connect these systems, and you gain visibility into the full customer lifecycle from lead creation through win or loss.

One of our customers put it memorably: "Data is the new bacon." The opportunity for sales and marketing teams to embrace data analytics and become best-in-class is significant, and the penalty for ignoring it is falling behind competitors who do not.

What Does Best-in-Class Actually Mean?

The term gets used loosely, so let us be precise. For the purposes of sales and marketing data analytics, best-in-class refers to companies in the top 20% as measured by revenue growth and profitability growth, using the Aberdeen Group's definition.

The business case is compelling. Aberdeen Group research (May 2017) on "Approachable Analytics" found:

- Best-in-class companies (top 20%) achieved 20% customer growth and a 15% profit increase - Rest-in-class companies (remaining 80%) achieved 7% customer growth and a 6% profit increase

That is a 13-point gap in customer growth and a 9-point gap in profit improvement. The research was clear: either you embrace the opportunity, or your competition will.

The Pathway to Best-in-Class

Getting there requires understanding and implementing three layers of analytics, each building on the one before it.

Layer 1: Descriptive Analytics (Hindsight)

Descriptive analytics answer the question: what happened? These techniques organize and summarize your historical data to establish a baseline for your business. Most organizations have some level of descriptive analytics through CRM reports and dashboards.

This layer is necessary but insufficient. Knowing what happened last quarter does not tell you what will happen next quarter or what you should do about it.

Layer 2: Predictive Analytics (Insight)

Predictive analytics answer the question: what is likely to happen? These tools use historical patterns to make forecasts about future performance. Machine learning models, statistical forecasting, and simulation techniques all fall into this category.

Predictive analytics transform your data from a rearview mirror into a forward-looking instrument. You can identify which deals are likely to close, which accounts are at risk of churning, and which marketing programs are generating the highest-quality pipeline.

Layer 3: Prescriptive Analytics (Foresight)

Prescriptive analytics answer the question: what should we do? These techniques, primarily optimization, provide a specific actionable plan to achieve a desired business outcome in the most effective way.

This is where the real competitive advantage lives. Prescriptive analytics do not just tell you that pipeline is soft next quarter. They recommend exactly which marketing programs to increase, which territories to prioritize, and how to allocate your budget to maximize revenue at minimum cost.

According to Gartner, achieving predictive and prescriptive analytics capabilities provides the highest value to organizations. The challenge is that higher value comes with higher difficulty, which is why many organizations consider augmenting their CRM and MAP with a third-party analytics platform that specializes in go-to-market analytics.

Why the Layers Must Be Integrated

The critical insight is that these three analytical layers cannot operate independently. Each builds on the previous one.

Your data is the raw material. Descriptive analytics organizes that data into a foundation. Predictive analytics adds a layer of forecasting on top of that foundation. Prescriptive analytics adds the capstone by using the predictions as inputs to an optimization model.

You cannot build an optimal plan for the future without first understanding what happened (descriptive) and what is likely to happen (predictive). Skip a layer, and the output is unreliable.

A recent Gartner report echoed this point by highlighting that predictive outcomes must flow into prescriptive models. We take it further: it is the harmonization of all three layers that creates what we call Optimized Analytics.

Making Analytics Approachable and Actionable

Advanced analytics only deliver value if they are actually used. Two characteristics determine adoption:

Actionable: The solution must provide specific actions for the organization to take, not just charts and graphs. A dashboard that shows pipeline is declining is descriptive. A recommendation that says "increase outbound campaigns targeting financial services accounts in the midwest by 30% to close the pipeline gap" is actionable. Approachable: The average business user must be able to arrive at the best solution without help from a data scientist. If your analytics require a PhD to interpret, they will be ignored by the people who need them most: sales reps, marketing managers, and revenue leaders making daily decisions.

Getting Started

If your organization is still primarily operating at the descriptive analytics level, here is a practical path forward:

1. Audit your data. Ensure your CRM and MAP data is reasonably clean and consistently entered. Perfection is not required, but a baseline of data hygiene is essential.

2. Connect your systems. Integrate your CRM and MAP to enable end-to-end visibility from lead creation through revenue.

3. Add predictive capabilities. Start with pipeline forecasting and lead scoring models that leverage your historical data.

4. Introduce prescriptive recommendations. Once your predictions are reliable, layer in optimization models that recommend specific actions.

5. Measure and iterate. Track the accuracy of your predictions and the impact of your prescriptions. Continuous improvement is what separates best-in-class from the rest.

The companies that master this progression gain a sustainable competitive advantage through better decisions, faster execution, and more efficient resource allocation. The gap between best-in-class and the rest will only widen as analytical capabilities continue to advance.