Improve customer data analytics: Tips for using metrics, technologies

Get expert tips for leveraging customer data analytics, and read some examples of common mistakes that companies have made, including a analytics case study.

In this section of the "An executive’s guide to customer data analytics and customer intelligence" guide, readers will find expert tips for leveraging customer data analytics, plus some examples of common mistakes that companies have made.

Readers will also find a customer analytics case study on how Nationwide Building Society improved customer experience with CRM, customer interaction management software and process updates.

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

Implementing customer data analytics requires attention to methodologies, metrics and technologies.

Before trying to secure additional budget or resources, audit all customer-related performance metrics along with the methodologies or formulas used for each. For example, does your organization capture:

*  Campaign metrics based on uplift?

*  Trade promotion results based on single products or multiple product attachment rates?

*  Percentage change in customer complaints?

*  Percentage change in customer migration rates?

*  Customer risk profile?

*  Net promoter score?

*  Customer recency, frequency and monetary (RFM) value metrics?

*  Customer profitability per customer on a quarterly, bi-annual or annual basis?

*  Customer lifetime value (CLV) metrics?

The holy grail of customer analytics is closely tied to a virtuous circle of closed-loop performance metrics -- which is next to impossible to accomplish in a multi-channel consumer environment. However, it is highly possible to improve an organization’s customer analytics processes with a goal of enhancing the customers’ experience and corporate profitability. To do this, organizations must define and standardize performance metrics at the corporate level.

Corporate-level agreement on how metrics are defined and calculated is as necessary as enterprise standardization of data dimensions across data marts. Failure to do this renders any report analysis and insight derived wildly inaccurate. Do you want your senior executives making critical decisions based on highly inaccurate information?

Some examples from our research at Hypatia Research LLC include:

  • An office supply retailer utilized specific marketing metrics to calculate “uplift” from a trade promotion run on printer ink and internally announced the promotion as a rousing success. However, at the profitability level, the company actually lost revenues on the promotion as market-basket analysis revealed that the promotion did not influence shoppers to buy other products with higher profit margins. In short, they paid for expensive media advertising, store signage, direct marketing and reduced the price of printer ink below cost and realized a negative return on both trade promotion investment and product sales.
  • Another company utilized activity-based costing (ABC) as the preferred methodology for calculating product category profitability. However, Hypatia found that one team included the actual cost of both production and raw materials in their calculation while other departments added in an average cost of sales per product SKU.

Many industry associations, market research firms and industry analyst firms publish industry best practices and/or benchmarks that can be used for guidance. Understanding industry norms, maturity models[1] and best practices is a good starting point for organizations that want to improve their customer analytics capabilities.

CASE STUDY: Nationwide Building Society improves customer experience with CRM, customer interaction management software and process updates

Find out how a U.K. financial services firm improved customer experience by focusing on updating processes, implementing CRM and deploying a customer interaction management tool.

For U.K.-based financial services firm Nationwide Building Society (NBS), improving the customer experience meant changing their mindset and operating environment from a transaction-based relationship to one that enhances every customer interaction.

The financial services provider, with £111 billion in revenues, is authorized and regulated in the U.K. by the Financial Services Authority for life assurance, pensions, unit trusts, insurance and regulated mortgage products. Primarily a traditional transaction-based financial services provider, NBS realized it needed to evolve into a more customer-centric organization to remain competitive. Towards this end, NBS focused on the following challenges:

*  Improve customers’ experiences by building stronger relationships through their preferred interaction channel through personalized communications

*  Increase customer acquisition and retention rates

*  Boost up-sell, cross-sell, and sales conversion rates

*  Provide a single 360° view of the customer at employee point of contact for enhanced service levels.

With customer data stored in numerous legacy data-marts, NBS lacked the integration and infrastructure necessary to effectively manage, analyze and apply customer intelligence towards their goals.

In order to improve its customers’ experiences, as well as to increase both revenues and customer satisfaction rates, NBS thought it would invest in a comprehensive CRM solution.

Nationwide takes an operational approach to customer centricity

NBS’s strategy was not limited to merely implementing a CRM system. Conversion from a transactional, product-centric business model to a services-oriented customer-centric culture would require more than merely leveraging CRM technology.

The goal was for all customers to have a consistent experience through whichever channel they chose for engagement -- in person, phone or online. Additionally, NBS wanted interactions personalized using both purchase and behavioral information. “Intelligent prompts” would empower the teller, agent or online interface to determine the best course of action. Customer data would be updated nightly to guarantee currency. Three main types of prompts would provide direction and structure for all communications displayed during subsequent contacts. For example:

  • Sales prompts: “Have you considered converting your overdraft balance to a loan?”
  • Service prompts: “Has your replacement credit card arrived yet?”
  • Data gathering/validation prompts: “May we please check when your insurance is due for renewal?”

NBS created a closed-loop process to manage, analyze and apply customer intelligence using several technology solutions, including customer interaction management software and CRM technology.

NBS now recognizes all customers as individuals rather than account numbers and interacts with them in a more consistent, yet personalized, manner. Improvements in creating a culture of customer-centricity include:

  • A customer interaction management tool is now used by 4,500 employees to support more than 25,000 interactions per day.
  • An average of 10,000 callers per day receive personalized offers via the CRM platform.
  • With an integrated multi-channel contact history, NBS now knows which customers are interacting with them, through which channels, and for which services -- improving their ability to segment, profile, understand and measure customer value.

Importantly, the company has also been able to quantify the results of its efforts, according to Simon Baines, head of customer insight, Nationwide Building Society.

“We are delivering approximately 15% more messages than we did before, at a marginal cost of £0 … and these messages are typically 10 times more effective,” Baines said.

[1] ©2009 Hypatia Research, LLC. “Decision Science & Customer Analytics: Competitive Advantage or Necessary to Compete?”

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


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