A 3 Step CRM Data Strategy to Convert Data into Insights


  • Most companies are data rich and information poor. It's a problem that stems from a poor or missing data strategy.
  • A CRM data strategy defines how to manage and convert customer related data into insights and actionable information. For example, the data strategy will likely show how to use customer data to improve customer relationships and thereby increase customer acquisitions, lifetime value and retention.
  • Data has evolved from an information system byproduct to a strategic asset and business currency. But data offers a use it or lose it proposition. So, business leaders must take action to transform their data into their company's most valuable asset.
Johnny Grow Revenue Growth Consulting

Forrester reports that decision makers use about 40 percent of their data (Source: Forrester Analytics Global Business Technographics Marketing Survey) That's in line with an HBR report that found across industries, "less than half of an organization's structured data is actively used in making decisions, and less than 1% of its unstructured data is analyzed or used at all."

However, these results are skewed to larger companies that have more IT staff and business intelligence tools. Small and midsize businesses use even less of their data.

A Customer Relationship Management (CRM) data strategy delivers the vision and roadmap to apply data for business outcomes. What makes this strategy different from a broader data strategy is the focus on customer data and revenue outcomes.

A data strategy for CRM is built on the three tenants of data value, data integrity and data management.

CRM Data Strategy Framework

Data Value

Data Governance

Like any other strategy, a data strategy for CRM should start with executive sponsorship. Governance is about the people responsible for oversight of a strategic objective. In this case, the objective is to convert data into assets that realize specific and measurable value. Data transformation is an enterprise-wide initiative and requires a combination of business and IT stakeholders.

Clear Objectives

Without clear objectives, these strategies have a way of meandering and pursuing technology tangents.

The data strategy must show how data is applied to realize specific objectives. Most objectives fall into one or more of four categories.

  1. Use data to streamline processes and reduce operational costs
  2. Use data to improve operational performance via better decision making
  3. Reduce the risk of data compromise or regulatory compliance failure
  4. Increase revenue generation by new or novel uses of data or data monetization

The strategies for these objectives are different. Process improvements are focused on gaining efficiencies that remove cost. This might include increasing salesperson productivity or lowering the cost to serve customers.

Operational performance improvements may be focused on efficiency, but the bigger payback is based on improved effectiveness, such as using data to improve customer relationships or make decisions that improve revenue or profitability.

Most data strategies focus on technical capabilities. That's important but takes a back seat to focusing on the business impact of data. The strategy must be able to identify how customer data can be used to drive business outcomes such as increased customer acquisitions, customer share and customer retention.

Data Use Cases

Most companies don't know how to fully leverage their data. Rather than trying to imagine use cases for data, a better approach is to start with the company's top business objectives and determine how data can drive those goals.

Revenue is sourced from customers so knowing more about your customers can drive more revenues. Below are some data-driven questions that deliver actionable insights.

  • What 15-20 percent of customers generate the bulk (often 70-80 percent) of margins and profits? This data should be used to create or improve customer share (i.e., uplift), customer retention or customer loyalty programs.
  • What customers share behavioral or firmographic characteristics with your most profitable customer segment, but are not yet in that segment? This data can be used to uplift an entire customer segment. Look-alike segments often deliver the fastest uplift.
  • What 10 percent of customers deliver the majority (often about 80 percent) of the referrals that result in new sales? This data may be used to find look-alike customers or segments to increase customer referrals or advocacy campaigns.
  • What 5-10 percent of customers contribute negative profits? This data may be used to offset financial losses with tools (i.e., self-service portals) that lower cost to serve these customers.
  • What is the customer lifetime value (CLV) by customer segment? This data will advise how much the company should invest in marketing and sales to acquire a customer, and which customers need additional attention for the company to realize the financial potential.
  • What are the optimal prices for products and services (i.e., price elasticity)? This data can deliver an immediate revenue and profit impact.
  • Which products or services are declining in customer value and should be retired? And the flip side of this question, what products or services would customers buy if offered? This data will directly impact sales win rates and the volume of new customer acquisitions. It can also be used to identify intelligent bundles and cross-sell opportunities.

See the CRM data strategy use cases that define how to use customer data to improve customer relationships and revenue growth.

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The prior questions are only a few examples of data that can be used drive revenue growth. The answers to these sample questions also demonstrate a potential data conundrum. Where do you put all these data findings so that they are easily accessible, actionable and measured?

Customer data and generated insights should be visible on the Lead, Account and Contact records in the CRM system. But it's a lot of data, so rather than display many categories of seemingly unstructured data, the data should be appended to the 360-degree customer view and available where you record and display customer insights.

Some users will take the time to review customer data to aid their processes and some won't. So, to maximize these customer insights it's helpful to use CRM dashboards, workflow tools and other CRM information delivery tools to proactively deliver customer content pursuant to defined points in the buyer journey, the sales cycle, the customer service process or other customer facing processes. For example, guided selling workflows can deliver contextual customer insights based on the customer type, sale type and point in the sale opportunity process.

Remember, the most powerful data use cases will surface when the data strategy aligns with the company's business strategy.


Data Integrity

CRM data strategies often wrestle with data integrity issues. The most common challenge is customer data quality. In addition to data being incorrectly entered, customer data depreciates at about 2 percent per month. The challenge is exacerbated when customer data is stored in multiple systems that lack real-time synchronization. If data quality is poor, or if users and managers don't trust the data, then the entire CRM strategy will deteriorate until failure.

This step of the framework is to ensure data confidentiality, integrity and availability (CIA) and regulatory compliance.

Data integrity must fit within the CIA triad. CIA is the underpinning of data strategy and information security. This is a broad body of knowledge well covered elsewhere so I won't dive deep here.

From a compliance perspective, customer data includes personally identifiable information (PII). Privacy, security and compliance (i.e., GDPR, California Consumer Privacy Act) are critical and should not be overlooked or considered as an afterthought.

A CRM data strategy is incomplete without internal
controls to proactively manage CIA and compliance.


Data Management

Data management is the third leg of data strategy.

CRM data management should start with a CRM data architecture that defines the processes that convert raw data into actionable information. This will include defining the methods to acquire, integrate, store, secure, deliver, manage, monitor and measure the value of data. It also includes data rules such as structured formats, naming conventions and data properties.

And to bring ownership to data management you need to assign data stewards to oversee data policies. Data stewards are the owners of their respective data domains and the final arbiter in defining data value, uses and rules.

Once architecture and accountability are in place, you can begin the perpetual but semi-automated job of data maintenance.

Companies get acquired, employees change jobs, staff get new titles, telephone numbers and email addresses change. This is why CRM data depreciates at about 2% per month making data quality a continuous challenge. A quality control (QC), data hygiene or CRM data maintenance program is needed.

Technology may also be needed.

CRM systems such as Microsoft Dynamics 365 and Salesforce have built-in tools to detect and resolve duplicate data. But these tools are relatively primitive and don't really work for customer data that is shared or stored outside the CRM software. These CRM publishers can step it up a bit with Salesforce Customer 360 Data Manager (for B2C) or Microsoft Master Data Services but even these enhancements fall short of more powerful MDM systems such as Informatica, SAP NetWeaver or IBM InfoSphere.

Master data management (MDM) software is much more comprehensive and brings more automation to data maintenance programs. MDM manages core entities, such as customers, vendors or items, and more so manages the processes for acquiring, standardizing, aggregating, synchronizing, consolidating, and distributing data. MDM processes ensure data consistency and deliver a single view of the truth regardless of the number of locations and versions of data.

Poor CRM data quality is most recognized as duplicate, dirty, or missing data. MDM manages these issues by applying rules that prevent incorrect data entry, standardize customer data, consolidate duplicates, and synchronize data so it available at any location.

Data enrichment and effectiveness measurement are essential steps in the data management process.

We’ve covered CRM and customer data enrichment elsewhere so won't repeat that here.

Data effectiveness ensures the data strategy works. It answers important questions, such as are we measuring the right things? Are we getting the right information to the right people at the right time? What actions or benefits occur from the data? How satisfied are recipients of the information?

The Point is This

The most successful businesses are defined by their ability to collect and curate the right data, use data to create differentiating products, services and customer experiences, and apply analytics to make insights actionable at every customer engagement or decision point.

With enough data the company can create models for what-if analysis and predictive analytics. Converting data to actionable information is a complex undertaking for sure, which is why those who succeed will achieve competitive advantage over those who don't.