How to Design, Construct and Deliver High Impact CRM Analytics


  • Successful Customer Relationship Management analytics are built on the three pillars of analytics strategy, data strategy and a business intelligence toolset.
  • Smart businesses are defined by their ability to collect and curate the right data and apply analytics to make insights actionable at the point of decision. Smart businesses make better decisions more often. It's a complex undertaking which is why those who succeed will achieve competitive advantage over those who don't.
Johnny Grow Revenue Growth Consulting

The 3 CRM Analytics Architecture Building Blocks

Customer Relationship Management analytics leverage customer data to make decisions that better engage target audiences, grow customer relationships and earn customer loyalty. There are three building blocks that illustrate how to convert data into actionable insights that achieve specific and measurable CRM objectives.

Analytics Building Blocks

Analytics Strategy

In a prior post we shared the 5-step framework to create an effective CRM analytics strategy. This article will drill down to describe some additional aids and show how the analytics strategy should link to business intelligence tools and the data strategy.

Two guides to help design the optimal information reporting strategy are the Analytics Continuum and the PACE model.

The Analytics Continuum illustrates how different types of information deliver progressively more powerful insight. It uses a visual model to show that not all information is equal in value and business impact. When analytics advance from historical to predictive capabilities, they shift business intelligence (BI) from hindsight to foresight and empower decision makers to make more powerful decisions, such as decisions that forecast and drive methods that increase company revenues.

PACE is a business and IT alignment strategy that works particularly well with enterprise business applications.

The PACE model aligns the methods business leaders use to create competitive advantage, described as common ideas, different ideas and new ideas, with the technologies that bring those ideas to life, and are described as Systems of Record, Systems of Differentiation and Systems of Innovation.

PACE Technology Strategy

In a CRM context, there are at least two systems of record. The CRM system is the customer system of record and the Marketing Automation System is normally the Lead system of record. I say normally as sometimes companies use CRM software to manage leads.

Systems of record support basic reporting such as customer-related lists and views. This may include sale opportunity views (i.e., the sales pipeline or forecast report), customer service views (i.e., service requests and open cases) or historical sales reporting. These types of lists or reports are essential but basic. They provide minimal insights and no competitive differentiation.

Systems of differentiation help companies outperform competitors. They use information in more strategic ways to take customers and market share from competitors. They advance their reporting from tracking basic data to helping staff become more productive and better at their jobs. These BI tools may include intelligent dashboards, guided behaviors (i.e., suggested next best actions) and exception-based reporting.

Systems of innovation convert data into insights to aid new business models, new customer markets, new products and services, or new revenue streams. These types of analytics advance from information visibility to predictability. In fact, they make extensive use of customer insights and predictive analytics. These insights outflank competitors to realize new competitive advantages. Where systems of differentiation provide incremental improvements, systems of innovation deliver order of magnitude gains.

The combination of the Analytics Continuum and the PACE model can identify an information strategy and roadmap to achieve business results that would otherwise not be visible.


CRM Tools & Constructs

Sometimes called a toolchain, the analytics applications suite defines the tools to extract data defined in the data strategy and deliver the insights defined in the analytics strategy.

The tools can be directly aligned with the use cases and objectives defined in the analytics strategy and illustrated with the analytics continuum.

  • What happened? CRM lists, views, alerts, packaged reports and dashboards are the tools to display historical results.
  • Why did it happen? CRM dashboard drilldowns, online analytical processing (OLAP) with data warehouses, and custom reports that associate or link variables show patterns, often including causation and correlation, to understand why historical events occurred.
  • What could happen? Artificial intelligence and predictive analytics are the BI tools to project data and advance from visibility to predictability. Data mining tools can also fall into this category as they can detect anomalies, spot trends and take advantage of otherwise hidden opportunities.
  • What should happen? Cognitive analytics, propensity models with confidence levels and next best action recommendations are the BI tools to prescribe the most effective actions.
Analytics Continuum

See the CRM analytics continuum model and how CRM software can deliver progressively higher information value.

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CRM software tools generally support purpose-built functions such as data grouping (i.e., customer segmentation), data updates (i.e., alerts and notifications), data comparisons (i.e., benchmarking), real-time data display (dashboards and reports), self-service analytics (i.e., Q&A analytics such as Power BI) and predictive analytics (i.e., forecasting). Aligning the right BI tool for each use case or business objective is a precursor to getting the right information to the right person at the right time.


Data Strategy

BI starts with data and the strategic use of data starts with data strategy.

Data is the raw material to be converted into information, and then into actionable insights.

Data reaches its potential when it is transformed into predictive analytics that aid users and managers with specific use cases.

That means a good data strategy really doesn't start with data but instead with the use cases for data. The ideal CRM data strategy approach is to start by identifying customer data that will aid company objectives, such as increasing customer acquisitions, customer share or customer retention.

Or aid marketing objectives by identifying data such as digital footprints, social graphs, customer insights, ideal customer profile (ICP) attributes or customer segments that will increase marketing campaign conversions.

Or aid sales effectiveness by identifying data such as buyer journeys, buyer insights or a sale strategy that will increase sales win rates.

Or aid customer service by identifying customer sentiment, customer insights that improve the customer experience, or other data that drives improvements to customer satisfaction. Or use data to identify customer value (i.e., customer lifetime value) so high value customers receive additional investments (service escalations, service entitlements or service level agreements) while low value customers are offered tools (such as self-service portals) that lower cost to serve. Investing equally in all customers limits the resources to retain the most valuable customers.

The point here is to first identify the specific data that can be applied to achieve the highest impact business outcomes. After that the more mechanical processes of harvesting, transforming and delivering data can be performed.