CRM Analytics Use Cases to Increase Customer Acquisitions, Share and Retention


  • Customer Relationship Management analytics improve customer interactions and drive better and more timely decisions by more people in the company.
  • To maximize effectiveness they must support use cases that show how data will be used to increase customer acquisitions, customer share and customer retention.
  • Improved decision making is one of only four sustainable competitive advantages because better decision making never loses value, is not easily copied by competitors and is not displaced with new technologies.
Johnny Grow Revenue Growth Consulting

How CRM Analytics Increase Customer Acquisitions, Share and Retention

Every good CRM strategy and program starts with a common question. Most companies and CRM consultants think the question is "how do I best implement this CRM strategy or program?" But the first question isn't how, it's why.

Why Not How

Before implementing CRM business intelligence or analytics, you need to first answer the question, 'why should I implement them?' Why invest the time and money? How will this change staff behaviors, customer relationships and company growth? Because if it doesn't do these things you don't need to consider it further.

The short answer to the essential questions is that CRM analytics provide the data-driven, fact-based information to make customer decisions that support company policy, align with customer objectives and aid company growth. In contrast, gut-based, subjective decision making is far more inconsistent and costly to the company. Even small improvements in decision making deliver significant financial benefits.

The long answer to the questions is provided in customer uses cases that align with the company's priorities, are solidified in policy or methods, measured, and improved upon. There's no definitive list of high impact CRM analytics use cases but based on over 30 years of implementation experience and years of qualitative research done as part of the CRM Benchmark Report, we'll share several examples that can deliver substantial financial impact.

How Analytics Improve Customer Acquisitions

Can you forecast which new leads will ultimately close, which new customers will be the most profitable, or which marketing campaigns will produce revenues three quarters from now? You can with predictive analytics.

Correlating customer data among the sale opportunities that are won, and contrasting that with correlated data among sale opportunities that are lost always reveals differences that can be applied to update your Ideal Customer Profile (ICP), adjust your sales process, and better allocate scarce sales time toward the sale opportunities most likely to succeed.

A similar process can determine the attributes of your most profitable customers, often measured as customer lifetime value (CLV). Investing more in high CLV customers, and less in low CLV customers helps the company allocate investments toward the highest payback. For example, marketing can steer more of their advertising and campaigns toward the best fit and most valuable customer segments which will deliver higher conversions, shorter sales cycles and more profitable customers.

Another opportunity for improved sales performance is to identify data patterns related to won and lost sale opportunities and then compare the results to individual sales reps an active sale opportunities. Data will show what top producers do differently than their lower performing peers.

For example, data may show that sales reps with below average close rates also have a below average number of prospect interactions during the sales cycle, pursue less qualified sale opportunities (i.e., those with lower lead or opportunity scores or accounts that don't align with the ICP), or grant discounts more frequently to compensate for weak sales strategies.

Data patterns detect these conditions, send alerts to sales managers for real-time, pinpoint coaching opportunities and even provide links to methods or Playbook plays to implement quick course corrections.

How Analytics Increase Customer Share

Customers are not homogenous. Treating them all the same will reduce effectiveness in customer engagement and relationships, and in turn customer sales and tenure. Business analytics can be used to segment customers by behaviors, purchasing patterns or revenue potential. These segments can be applied to company policies (i.e., high profit customers get faster credit approval, higher credit limits, extended payment terms, elevated customer service, etc.) and business development programs such as target account selling, account-based marketing (ABM) or precision marketing campaigns.

In the book, Converting Customer Value from Retention to Profit, research demonstrated that for many companies, 20 percent of customers are responsible for 80 percent or more of profits. While this is not a big surprise to most executives, what is surprising is the small number of companies that can identify their 20 percent and act on the results. Without this information, companies underinvest in customers that would otherwise grow company profits and overspend on customers that won't.

The CRM Benchmark Report found the three measures most influential to increasing customer share are customer lifetime value, customer satisfaction (as measured by CSAT or NPS) and customer engagement.

Most companies don't track these measures because the metrics are not available in their CRM software. However, these are easy metrics to create. But even when calculated, what actions do you take based on the values?

At a minimum, customer segments aligned to ICP and high CLV should get increased focus and resources because the additional investment will translate to higher company profits.

CLV can be supplemented with indirect contribution to the company, such as customer referral value and customer influencer value. Referrals are generally extremely qualified leads and incur shorter sales cycles. Customer advocacy, especially in social channels, can be particularly powerful as customers trust other customers far more than they trust brands.

Look-alike modeling is another data technique whereby analytics sift through customer history or apply customer intelligence data to identify customers that align with high CLV but are not yet delivering this level of profit to the company. These customers can be segmented and pursued with relevant promotions and nurture campaigns to increase their CLV realization.

On the flip side, it's essential to lower the cost to serve customers that deliver little to no value. Customer self-service options such as online knowledgebases or virtual agents are two of many options.

Other techniques to increase customer share include customer experience management (CXM) and customer satisfaction (CSAT) programs. CXM uses customer data to deliver personalization at scale and differentiated customer experiences. Because customer satisfaction scores almost always correlate with RFM (Recency, Frequency and Monetary) value, this data tells you when you need to implement customer relationship or promotion programs to drive repeat sales, up-sell or cross-sell.

How Analytics Lower Customer Churn

Bain & Company, in conjunction with Earl Sasser of Harvard Business School, analyzed the costs and revenues over the life of customer relationships. They showed that in industry after industry, the high cost of acquiring customers made many customer relationships unprofitable during their early years.

Only in later years, when the cost of serving loyal customers fell and the volume of their purchases rose, did the customers realize big profits. They also found that increasing customer retention rates by 5 percent increased company profits by 25 to 95 percent.

Business intelligence tools can increase customer retention at scale. They can examine customer history to identify patterns of behaviors that most typically occur just before customers churn. These patterns can then be monitored so when they exceed a threshold score, an alert can be dispatched to an account manager or other person to take quick action. Customer churn prediction works. We know because we've implemented it with clients for many years. And for many companies, improving customer retention is their top business growth strategy.

Customer Churn Prevention Data Transformation Framework

An alternative to churn prediction models is customer health scores. Health scores are calculated based on customer behaviors. They generally include data points such as the volume of customer service tickets, the number of unresolved or escalated tickets, customer sentiment, accounts receivable DSO (days sales outstanding) and changes in RFM. When health scores fall to an unacceptable level or take a sudden turn, triggers in the CRM system initiate actions to remedy the situation. Scores and triggers vary by customer type and segment.

See the CRM Analytics use cases to increase customer acquisitions, customer share and customer retention.

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