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Advanced Analytics Maturity Model For B2B Sales & Marketing

Building an advanced analytics program is a challenging endeavor for any team. It’s a process that requires both the right knowledge and subject matter expertise as well as the right approach. B2C organizations are the clear leaders when it comes to leveraging advanced and predictive analytics to improve their marketing efforts and accelerate growth. And while B2B sales and marketing teams are clearly behind their B2C counterparts when it comes to effectively using advanced analytics, there is an increasing focus on beginning to leverage these technologies.

There are, however, two issues at play. First and foremost the hype around predictive in B2B sales and marketing has been very misleading. Many vendors promoting “predictive” marketing technologies are making false promises and not delivering any degree of true predictive analytics. Secondly, achieving a significant ROI from an advanced analytics program requires a well-designed approach that positively impacts all phases of the customer lifecycle.

The improvements in sales and marketing performance and effectiveness B2B organizations can realize from an advanced analytics program are very real. But a phased approach that’s rooted in driving a quantifiable return on investment must be followed.

An analytics maturity model can be a very helpful tool in giving an organization the ability to not only better understand where they are today but also develop a blueprint for long-term success. A few years back Gartner released an ‘Analytic Value Escalator’ outlining a very logical path that organizations can follow to create a more data-driven approach and capitalize on big data and analytics.

Following a path that begins with descriptive analytics to gain a thorough understanding of what happened, followed by using diagnostic analytics to examine why it happened and eventually graduating to predictive and prescriptive analytics models to look at both what will happen and what needs to be done, makes perfect sense from a high-level perspective. And there is definitely some applicability here when looking at developing an advanced analytics program inside of sales and marketing. However, an approach that is focused not only on incrementally utilizing more sophisticated forms of analytics, but also on delivering business impact will prove more effective.


The two biggest obstacles B2B sales and marketing organizations face when trying to implement an advanced analytics program is poor data quality and the lack of a well designed strategy for using advanced and predictive analytics to drive growth. All too often sales and marketing teams are enticed by shiny new tools that claim to deliver predictive insights. And the end result is almost always cobbled together software tools sitting on top low quality data. In order to truly leverage advanced and predictive analytics as a vehicle for growth a solid foundation must first be built. And developing the right strategy and data infrastructure are the primary components of that foundation.

The right strategy should answer key questions such as;

  • What are the near-term and long-term goals for an advanced analytics program?

  • What key performance indicators should we be focused on improving?

  • What is the current state of our sales and marketing data and what changes need to be made in order to support an advanced analytics program?

  • What analytics models are best suited to address our program goals?

  • How will an advanced analytics program drive alignment and improvement across; sales, marketing, customer success, and even product?

  • How will this effect our current plans and process and how can we best manage what are some inevitable changes?

  • What approach will be most manageable and set us up for long-term success?

And when looking at improving overall data quality and establishing the necessary data-infrastructure several areas should be examined including;

  • How accurate and complete is the data we already have?

  • What 3rd party data sources should be utilized to augment and improve our current data?

  • Do we need to establish a data warehouse for all of our sales, marketing, and customer data?

  • What level of data governance should be implemented moving forward?


When looking at driving improvements across certain key performance indicators, it’s of course important to first understand what’s occurring and why. Enter descriptive and diagnostic analytics. The vast majority of sales and marketing teams are already using some form of descriptive analytics within their CRM or marketing automation platforms to monitor performance. Reporting that delivers analytics on key performance indicators, including; sales pipeline and forecast, opportunity win rates, lead volume, and revenue numbers by time period are table stakes for established sales and marketing organizations.

Understanding why these numbers are the way they are is where many are missing the mark. Diagnostic analytics offers the ability to examine a specific KPI such as lead volume and drill down to understand all of the factors that are effecting lead volume and lead quality. This level of granularity requires increased rigor and sophistication. But when done the right way, will provide data driven answers into what can be done to improve specific KPIs.


Once a complete understanding of the status of a specific KPI has been established as well as all of the factors that are affecting that KPI, an organization can turn to predictive and prescriptive analytics as a tool to drive improvement. Predictive and prescriptive analytics will provide insights and offer answers that sales and marketing leaders would not be able to identify on their own.

By definition predictive analytics is a form of advanced analytics that uses historical data to predict future outcomes and behaviors. Whereas prescriptive analytics provides answers into what will be the most effective approaches and solutions to use to achieve a desired outcome.

In further examining the use case of increasing lead volume, predictive analytics will offer predictions around variables such as what target accounts have a higher and lower likelihood of engaging and converting to qualified leads. Prescriptive analytics will deliver insight into; what messaging will be most effective across different audience segments, what marketing channels should be utilized to maximize lead volume, and even what timing and frequency should be used when targeting prospects.


Most of the discussion around using advanced and predictive analytics to drive growth has been focused on its applicability within the realm of sales and marketing. And that’s a very myopic view that can rob an organization of realizing the full potential of using advanced analytics. The insights an advanced analytics program can provide positively impact an organization’s ability to optimize all phases of the customer lifecycle (acquire > grow > retain).

It’s very easy to get solely focused on increasing top-of-funnel lead volume and new customer acquisition. But when advanced analytics are used to not only accelerate customer acquisition, but also increase expansion revenue, and average customer lifetime value, in addition to reducing churn, organizations create a force multiplier that impacts all revenue generating teams. Applying an integrated approach across sales, marketing, and customer success is a significant undertaking that requires both planning and effective change management in order to succeed. But when effectively implemented this level of cross-departmental integration will not only produce a strong ROI, but will also foster stronger alignment across these teams.

A Horizontal vs. Vertical Approach To Implementation

When looking to follow this maturity model an organization can take a horizontal or vertical approach to implementing a program. A horizontal approach looks at each stage of the maturity model and seeks to drive improvement across all phases of the customer lifecycle before moving on to the next phase. Whereas a vertical approach focuses on a specific business initiative or key performance indicator (i.e; increasing top-of-funnel lead volume) and follows the prescribed path and maturity model for using advanced and predictive analytics to drive improvement for that specific KPI. No approach is universally better than the other and the decision to use either should be a function of the time, budget, and resources an organization has at its disposal.

Creating predictable and scalable revenue growth is the goal of every B2B company. And taking a data driven approach to optimizing all three phases of the customer lifecycle is the path to achieving this. But this data driven approach not only requires developing the right infrastructure, but also a well designed strategy that is focused on creating real business outcomes that positively impact all revenue generating teams across the organization, and most importantly the customer.

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