Increase CRM Data Value with These Use Cases and Business Outcomes

Highlights

  • Most companies are data rich and information poor. They understand the value of data but struggle to transform it into actionable intelligence and tangible business results.
  • Research from the CRM Benchmark Report shows companies that implement CRM software without a CRM data strategy use less than 18 percent of their data.
  • Data is a powerful asset if converted to information and made actionable, or it is a cost without benefit if it languishes.
Johnny Grow Revenue Growth Consulting

The Extraordinary Value of Customer Data

In working with customers, it's been our experience that executives understand their data is valuable. However, many lack the know-how to convert data into actionable intelligence that delivers revenue and profit results. Executives don't need technical know-how, but instead proven examples and use cases that drive real business impact.

In this blog post I'm going to share examples of how our clients have transformed data into insights that generated actions that led to direct and immediate revenue growth.

With every data transformation project, we begin with revenue generation objectives so that we can then find the data that will most aid those goals.

Below is a simple chart of four revenue objectives with identified data and insights.

CRM Data Use Cases

It can be helpful to understand how data drives performance improvements by function or department.

Here are some marketing examples.

  • Path analysis. What are the customer paths to purchase? By customer segment and channel? Which have the highest and lowest conversions? Data can demonstrate the combinations of content and channel that accelerate sales cycles, result in the highest conversions and correlate with high value deals.
  • Marketing mix optimization. Data can identify the highest and lowest performing conversions when considering a combination of factors such as lead source, content, offers and channels. It can further compare these factors to sales velocity, revenue volume and margin results. The data empowers the marketer to apply different combinations for each customer segment in order to achieve whatever goals are most important to the company.
  • Customer segmentation. Top marketers know that successful campaigns are rooted in research. Data can be used to dynamically generate macrosegments, microsegments and look-alike accounts which share characteristics of your highest contribution customers. Experience shows these look-alike accounts convert at a higher rate than other segments and increase the size of your most valuable customer segment.
  • Attribution analysis. For multi-channel or nurture marketing campaigns, which of the flights moved the buyer to purchase? Most marketers attribute the entire sale to either the first or last campaign interaction. That's almost never accurate and skews your campaign performance reporting. It takes data to identify which campaign flights worked and which didn't.
  • Suggestive selling. Data identifies what customers and offers will generate the most cross-sell and up-sell revenues. It's happening all around us. Amazon is highly accurate in recommending what products consumers want next. Netflix is highly accurate in suggesting which shows you would like next. My pet food supplier knows when I'm about to run out of cat and dog food and my HVAC supplier knows when my air filters need to be changed.

Data can be put into CRM predictive models to identify offer conversion rates for each target audience, what products can be bundled to achieve higher margins, what product prices will earn the most margins (i.e., price optimization) or what content asset will move the buyer forward in their purchase journey.

Some sales examples.

  • Lead scoring. I've had an interesting history when creating predictive lead score models (link to Analytics Create Predictive Analytics). Most sales managers are looking for another perspective to analyze leads and opportunities to make sure salespeople invest their time in deals they can win and walk away from deals they can't. However, most salespeople have been doubters. They don't believe the CRM system is going to score a lead as well as they can. But often the salespeople are surprised. The predicted score by itself is insufficient to change a salespersons mind. However, when you display the data points between an active lead that is compared to historical leads that have been won or lost and illustrate each criterion where the active lead scores high or low, salespeople really take note. The calculated lead score becomes an insight that changes behavior.
  • Sales reinforcement activities. We can apply data to discover patterns, correlations and coefficients of won and lost sale opportunities. We can then display variances between the known actions of won sale opportunities and existing sale opportunities. For example, if the data shows successful sale opportunities had an average of 11 client engagements (i.e., calls and meetings) and a current opportunity forecast to close this quarter only has 4 client interactions, the CRM system can flag this variance for swift action. Imposing actions that shift losing deals into winning deals delivers big. For some of our clients we have created dashboards that show sale opportunities at risk based on their similarities to the data points that most correlated with historical lost opportunities. This provides sales managers real-time coaching and course correction opportunities.
  • Salesperson time management. Research shows that on average salespeople spend about one-third of their time selling. Data can show activities which don't correlate with sales progress or success so those items can be scrutinized. This identifies opportunities to reduce, remove or restructure tasks and schedules and reallocate salesperson time toward activities that directly contribute to revenue generation.
  • Guided selling recommendations. Data can show the combinations of factors that contribute to wins and losses – by customer type, salesperson, product, competitor, geography, time of year and more. These factors can be orchestrated to deliver contextual recommendations in the CRM system, such as: This offer, collateral, case study or payback calculation has contributed to historical sales success for 10 similar opportunities.
  • Data signals and anti-patterns. Data can identify the absence of data that leads to lost sales. For example, failing to meet with a decision maker, failing to complete essential discovery questions or a prospect failing to ask for references may be signs correlating to lost sales.

Some customer service examples.

  • Call deflection. Incident and ticket analysis can identify the high volume of low complexity calls well suited for customer self-service channels. Tools such as website chatbots, virtual agents, online knowledgebases and self-service portals reduce customer wait time and serve customers 24 by 7. They also dramatically lower cost to serve customers.
  • Proactive service. Resolving issues before they happen is a hallmark of customer service excellence. Data patterns can quickly detect the correlation between new incidents and products, extrapolate to other customers with those products, and proactively outreach with a solution before they incur the failure.
  • Differentiated customer experiences. Another hallmark of customer service excellence is delivering relevant, personalized, contextual and predictive customer experiences. Despite the obvious benefits this goal remains elusive to most companies. Data is the key to achieving the goal. The customer 360-degree view identifies what's most important to each customer, while customer insights identify how, when and why customers make purchase decisions.Data tells us which channels customers prefer, when they like to engage, who they prefer to talk to, the issues they consider important and what content or products they are most interested in. We can also review data to see what customer interaction points do not contribute to an elevated customer experience. Only with data can companies deliver differentiated customer experiences at scale.

Some Assembly Required

The value of data becomes synergistic when data and predictive analytics are linked into a holistic value chain.

For example, in the same way predictive analytics calculate lead scores, they can calculate sale opportunity scores, and once opportunities are scored, they can flow through to an automatically calculated sales forecast.

A more powerful example we've created for several clients is the bottom-to-top roll-up that we call the enterprise performance pyramid. It shows how lower level analytics align and feed higher level analytics to satisfy the company’s top priorities.

Revenue Growth Predictive Model