Linkedin Linkedin Linkedin Bookmark and Share

Technology Corner



Customers are the heart of any business. One unshakable rule of Retail business is to "know your customer." In today's business climate, this means using business intelligence (BI) software to analyze complex customer data. With BI, companies can answer a wide range of critical questions about their customer base. The questions can include:

  • What are my company's segment-wise top revenue-generating customers?
  • What are cross-selling / Up-selling opportunities?
  • Which customer segment has have contributed most to revenue growth?
  • Which type of customers look for discounts?
  • Which types of customers have highest number of returns?
  • What types of customers are most profitable?

Business analysts, marketing managers, and other decision makers need more detailed information. They need to ask tougher questions about their customers. They need to delve further into the data to understand how their customers' behavior aligns with their production processes and sales cycles.

In order to improve processes with customer interaction, Retail businesses have introduced customer relationship management systems. These systems collect large volumes of data about customers which contain valuable information that can allow a business to improve its customer relationships and services. Typically, CRM applications focus on transaction recording & reporting what has happened. However, in order to become pro-active and truly shape the future of a business, it is important to predict what customers want and how they will react. In addition to understanding customers, it is paramount for any enterprise to understand how its business has performed at any given time in the past, now, and in the future. However, it is becoming essential that not only is the analysis of business performance done on real-time data, but also actions in response to analysis results can be performed in real time and instantaneously change business process parameters.

Improve merchandise planning and tactics, by leveraging the full potential of customer loyalty data, sales transaction data and store data, with Customer Analytics in Retail. It's designed to help campaign managers; promotions managers, loyalty program managers and other key functions exploit the hidden relationships between products, customers and store data sets. It provides overall assessment on each single customer: profitability, loyalty, buying behavioral patterns (trend). This information modeled and analyzed versus time along with customer profiles enable churns management and monitoring.

Customer Analytics in Retail can answer all of these questions, and more. Customer Analytics in Retail draws critical insights from your sales, customer-centric KPIs like Customer Profile, Customer Behavior, Customer Trend (Buying Pattern) and Customer Loyalty. These metrics are made from the data to create a more complete picture of your customers' behavior and its impact on your business.

Customer Analytics in Retail lets you:
  • analyze customer types and profile individual customers
  • monitor and compare trends in customer type, customer base size, buying, contribution to revenues, product mix, customer ranking, profitability, and more
  • evaluate customer profitability and cost to serve
  • view buying patterns, average order sizes, and number of purchases in a specific time period
  • monitor customer type and customer-specific aging schedules by number of transactions and total dollars
  • assess customer satisfaction by number of adjustments, delinquencies, returns, shipping delays, buying frequency, and trends
  • distribute customer information across the organization for operational management and reporting and analysis needs
  • provide self-service or on-demand reporting and analysis

Customer Analytics in Retail lets you evaluate and rank your most valuable customers, monitor and analyze their overall value to your business, and understand their buying behavior. These insights help you focus your attention on attracting and retaining customers whose behavior will help your organization reach its strategic goals.

Dynamic reports, ad-hoc analysis and powerful metrics answer critical business questions and track customer key performance indicators that are grouped into the following categories:

  • Customer Profiling and Valuation
  • Customer Satisfaction
  • Customer Loyalty

Customer Profiling and Valuation

Defining your best customer involves several factors: the revenue they generate, the frequency of their purchases, the cost to serve them, and more. Analyze each of these factors in isolation or combination to create profiles of each of your customers and evaluate their respective value to your business. Analyze customer profiles by sales channel or by industry segment to identify cross-sell opportunities, new markets, or under-performing markets. Use this information to direct your activities on retaining high value customers.

Customer Satisfaction

Changes in your customers' buying patterns, an increase in their rate of returns, or the length of time they take to pay invoices are all indicators of their satisfaction with your company. Examine these and other indicators to gauge individual customer satisfaction and to identify overall trends that can be leveraged into increased customer value. Identify downward trends to retain customers before they leave.

Customer Loyalty

Encapsulate customer insight in order to build long lasting customer relationships: the right offer to the right customer through the right channel can help maintain high levels of Customer satisfaction. More accurate measurement of customer satisfaction is possible through BI.

Advantages of using Customer Analytics in Retail
  • Derive critical information on customer behavior
  • Sort out critical customer details like top revenue generating customers, most profitable customers, purchase trends at different customer profile level, percentage of return customers and also customer segment with potential bad debt risk
  • Work on key areas appropriately for effective marketing strategy with the information generated
  • Group out the best customers based on factors such as revenue, purchase frequency and services costs and concentrate activities on retaining and increasing number of high-value customers
  • Sort out customer buying trends and patterns, return rates, time to pay & other factors to judge customer satisfaction issues & take appropriate action before they affect your bottom lines
  • Identify fast-moving products and cross-sell scope to align production and marketing force to take benefit of this information in assessing product performance over a segment of customers
  • Understand customer purchase patterns and trends in various market segments and concentrate on weaker areas to improve sales