Guide

Exploiting the value of customer data with analytics

It’s no secret that there’s valuable knowledge contained in customer data – if you can extract it. The challenge facing many organizations is exactly how to deploy analytics to unlock insight from customer data. This online guide offers an executive overview of customer data analytics and customer intelligence, with deployment advice and best practices from top performers.

In this section, find out how customer data analytics helps organizations improve customer experience, service and sales – and learn more about what’s possible with customer data analytics today.

    Requires Free Membership to View


Don't miss the other installments in this customer data analytics guide
* Exploiting the value of customer data with analytics
* Improve customer data analytics: Tips for using metrics, technologies
* Expert advice for developing a customer data analytics program
* Customer data analytics best practices from top performers


An executive’s guide to customer data analytics and customer intelligence

How to get more out of customer data with analytics technology

Attaining true “customer intelligence” is both art and science – requiring attention to technology, including customer data analytics, processes and organizational mindset.

Prior to the recent global melt-down of 2008-2009, many executives believed that marketing, branding, customer interactions and social media investments were mostly about creating buzz around an organization’s product or service offering -- an intangible investment that didn’t require justification. The new post-recession paradigm calls for senior executives and marketers alike to create alignment between key performance indicators (KPIs), marketing performance metrics, corporate goals and shareholder earnings in order to prove value and remain relevant to multiple stakeholders. Hypatia Research LLC studies confirm that more than 50% of marketing departments, sales and business development professionals, and customer service and support teams continue to be tasked and measured in a non-integrated, often ad hoc, fashion. In short, companies actually committed to measuring marketing performance continue to rely on a patchwork of linear and non-integrated metrics.

Marketing executives become champions of technology enablers -- such as customer relationship management (CRM), sales force automation (SFA), marketing automation, Web analytics and customer service, call center and support -- that provide some level of metrics-based reporting inclusive of leads generated, sales converted, website stickiness, point of sale (POS), clicks and click to buy, promotion uplift, customer satisfaction surveys, call center resolution time and/or media placement. However, most if not all of the customer information gathered continues to be stored in various systems -- organized and utilized primarily by the role, function or person who input the data. In fact, this non-integrated approach is one corporate-wide addiction that is one tough habit to break!

Currently, a majority of organizations surveyed report utilizing customer data analytics for five main purposes (see Figure 1). Surprisingly, less than a third of respondents utilize analytics for strategic corporate planning purposes or to identify high-value customers and proactively interact with customers. Nearly half of companies use analytics to retain customers, but if the customers being actively retained happen to fit the profile of “no- or low-profitability customers,” the organization may actually lose money on this effort. Without a digital foundation upon which to conduct comprehensive customer analysis, companies risk pursuing top-line growth at the expense of attaining profit margins. What’s a marketing executive to do?

Figure 1: How customer data analytics are applied: Cross-Industy

[Multiple choice ¹100%]  Source: ©20109 Hypatia Research, LLC

Got a single view of the customer?

Product-driven and service organizations alike have greatly invested in amassing customer, product, service, contract, and transactional data in a variety of “spreadmarts.” Unfortunately, poor-quality or inaccurate customer data drains profitability by costing North America-based companies more than $600 billion a year.

In fact, the last time Hypatia Research LLC managed a large-scale customer data integration and migration project (it was akin to a root canal), we discovered the customer data resembled “garbage in, garbage everywhere.” Without standardized fields, consistent database structures or data quality and governance processes in place, all the analytics, algorithms and business intelligence tools on earth could not supply our client with a believable operational report upon which to base decisions.

Moreover, from the customers’ perspective, receipt of multiple catalog and direct mailings is annoying, filling their mailboxes with redundant and incorrectly addressed media -- a telling sign that the company doesn’t know or care enough to understand their needs, preferences or correct names. Think about the market segments that purchase hybrids, shop organic, care about their carbon footprint and recycle. How likely are they to buy from companies that waste natural resources in pursuit of greater profits?

Our research revealed that top performers, defined by performance metrics[1], more often utilized advanced analytics, rules-based interactions and supplemental customer data. As a result, the top 20% of companies surveyed realized higher returns on investment for each metric tracked.

Figure 2: Ability to exploit customer data 

[Multiple choice ¹100%]  Source: ©2010 Hypatia Research, LLC

Post-recession customer data analytics paradigm

The new post-recession paradigm calls for senior executives and marketers alike to create alignment between KPIs, corporate goals and shareholder earnings in order to prove value and remain relevant to multiple stakeholders. Consequently, successful executives will exploit the value of customer data and deliver key performance metrics along with actionable customer insight and market intelligence.

To create alignment, companies will need to develop role-based competencies and business processes and deploy technologies that empower them to measure how profitably they interact -- and manage relationships -- with multiple stakeholders.

Forward-looking solution providers should be ready to provide organizations not only visibility into customer behaviors but also the ability to analyze, report and measure both transactional and interactional customer information from a bottom-up and top-down perspective. Savvy companies have already realized significant gains from building a culture of high performance marketing.


[1] Multiple, weighted key performance metrics.


About the Author:

Leslie Ament, research vice president and co-founder of Hypatia Research, LLC, is a customer intelligence management thought-leader and industry analyst who focuses on how organizations capture, manage, analyze and apply actionable customer insight to improve customer management techniques, reduce operating expenses, and accelerate corporate growth. Her research coverage includes: business intelligence, media intelligence/search/text analytics, CRM, Web analytics, marketing automation and customer data management/data quality.

Previously, Ament served on management teams and led global marketing and market research groups at Demantra, Inc. (acquired by Oracle), Arthur D. Little Management Consulting, Harte-Hanks, Banta Corporation, International Thomson Publishing (Chapman & Hall, U.K.) and Carnegie Hall, Inc. She is a member of the American Marketing Association, Society for Competitive Intelligence Professionals, Customer Relationship Management Association, DataShaping Certified Analytic Professional, Arthur D. Little Alumni Association, Software Industry Information Association and a board member of the Product Management Association.

Ament completed her doctorate Phi Kappa Phi at the University of Illinois, Urbana Champaign and her master's and bachelor's degrees at Indiana University-Bloomington.


Don't miss the other installments in this customer data analytics guide
* Exploiting the value of customer data with analytics
* Improve customer data analytics: Tips for using metrics, technologies
* Expert advice for developing a customer data analytics program
* Customer data analytics best practices from top performers


 

This was first published in March 2010

There are Comments. Add yours.

 
TIP: Want to include a code block in your comment? Use <pre> or <code> tags around the desired text. Ex: <code>insert code</code>

REGISTER or login:

Forgot Password?
By submitting you agree to receive email from TechTarget and its partners. If you reside outside of the United States, you consent to having your personal data transferred to and processed in the United States. Privacy
Sort by: OldestNewest

Forgot Password?

No problem! Submit your e-mail address below. We'll send you an email containing your password.

Your password has been sent to:

Disclaimer: Our Tips Exchange is a forum for you to share technical advice and expertise with your peers and to learn from other enterprise IT professionals. TechTarget provides the infrastructure to facilitate this sharing of information. However, we cannot guarantee the accuracy or validity of the material submitted. You agree that your use of the Ask The Expert services and your reliance on any questions, answers, information or other materials received through this Web site is at your own risk.