Close the deal on using CRM analysis to boost customer knowledge

Customer relationship analysis holds the key to improved marketing and customer engagement. But it starts with having the right tools and skills.

As businesses gather more and more information about customers through their customer relationship management systems, CRM technology has become a natural partner of data-hungry business intelligence and analytics tools. Companies can score some big business wins by pairing up their CRM and analytics initiatives -- but there are some best-practices ground rules to get straight first in order to achieve the desired BI benefits.

For organizations seeking a competitive edge, applying analytics to customer data makes perfect business sense, said Mike Gualtieri, an analyst at consulting company Forrester Research Inc. Effective CRM analysis enables marketing managers and customer service workers to treat people as individuals rather than faceless members of market segments, Gualtieri said. That kind of customer knowledge can lead to more-tailored marketing campaigns and improved customer engagement efforts.

But organizations need to make sure they're applying the right kinds of analytics tools to their customer data. CRM vendors may say that their software includes analytical features, but Gualtieri said the functionality they provide pales in comparison to what fell-fledged analytics applications can deliver -- especially when it comes to doing predictive analytics and other forms of advanced analytics.

For example, Gualtieri said one of the best reasons to analyze customer data is to predict customer churn, which involves identifying individuals who are likely to take their business elsewhere so customer service representatives can intervene in an effort to keep them in the fold. CRM software on its own typically struggles to detect customers who are about to defect; it requires a fairly straightforward indication of customer dissatisfaction, such as an angry email, according to Gualtieri. Analytics software, on the other hand, can analyze a broader sample of customer data to find more subtle indications of dissatisfaction.

In addition, a CRM system might be just one of the data sources feeding a customer analytics program. Speaking at a conference held by BI and analytics software vendor SAS Institute Inc. in September 2013, Andres de Armas, senior vice president of customer offers and targeting at Bank of America Corp., said the financial services firm also analyzes customer data from a variety of third-party source systems, including credit bureau databases.

Banking on diverse customer data

Bank of America uses the combination of internal and external data to monitor and optimize its customer outreach and marketing campaigns so offers are delivered through the channels that individual customers are most likely to respond to -- and to ensure that customers aren't contacted so often that they get irritated. De Armas said the diversity of the available data is one of the keys to making the bank's customer analytics efforts successful.

CRM systems do have an important role to play in gathering customer data for BI and analytics applications, though. Gualtieri said most CRM software packages are highly customizable, which enables companies to provide data scientists, statisticians and other data analysts with the information they need to build precise predictive models.

David Loshin, president of consultancy Knowledge Integrity Inc., said capturing data about "every single customer interaction" lets companies move beyond straightforward revenue and profitability reporting and do more strategic analysis aimed at maximizing customer lifetime value by fine-tuning marketing programs and service processes. "Instead of having customers that are aggregated or classified by external demographic variables, like age and where they live," Loshin said, "I can start looking at them in terms of their behavior -- 'My best customers come into the bank every single day and make four deposits a month' -- and devise an engagement model that describes them from that perspective."

Loshin, who also is co-author of the book Using Information to Develop a Culture of Customer Centricity, said the biggest challenge to getting CRM analysis right isn't building the required BI and analytics systems -- it's creating the associated processes and "getting the attitude set up the right way" internally. To achieve that, he advised, it's a good idea to secure top-down support from senior executives, especially if a project involves the use of self-service BI software by frontline call center workers.

Think of the users

Businesses also need to make sure they have the right people using the analytics software they deploy -- and the right support mechanisms for those users. In the case of self-service BI tools, Loshin said customer service reps likely will need ample amounts of training and post-deployment handholding on using the technology. BI teams also might want to simplify self-service deployments by delivering useful information to frontline workers in a way "that doesn't force them to get a graduate degree in data analysis," he said.

More about managing CRM analysis initiatives

Get tips on using self-service analytics tools to instill a data-driven marketing culture

Learn how to use customer data analytics for more than just marketing

See how to avoid crossing the line when collecting and analyzing customer data

For more advanced CRM analytics applications, de Armas said that getting the right data analysts in place -- and keeping them there -- requires a long-term commitment. There's a shortage of skilled analytics professionals who understand how to use technology to help improve customer engagement efforts, he said, warning that organizations that fail to pay such people properly or give them meaningful projects to work on will constantly have to start over.

"Imagine what it's like to lose subject matter experts in the middle of a project like this," de Armas said. "And if the commitment isn't there, you're going to lose the best people."

Done right, though, BI and analytics on well-managed customer data can point the way to making customers happier at each touch-point with a company, Loshin said. And happier customers hopefully become lucrative customers. Customer relationship analysis "is a good example where it's business and it's intelligence," he said. "I'm really looking at what the business is, and I'm trying to use my accumulated and aggregated information about the interactions of that business to gain some additional knowledge that can help me create greater value in the long term."

Ed Burns is site editor of SearchBusinessAnalytics. Email him at eburns@techtarget.com and follow him on Twitter: @EdBurnsTT.

Executive editor Craig Stedman also contributed to this story.

This was first published in March 2014

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