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 with 1KEY BI. 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 with 1KEY BI can answer all of these questions, and more. Customer Analytics in Retail with 1KEY BI is an integrated set of reporting and analysis functionalities that 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 with 1KEY BI 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 with 1KEY BI 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 1KEY BI.Advantages of using Customer Analytics in Retail with 1KEY BI
 
• 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 salesUsing Customer Analytics in Retail with 1KEY BIDeploy Customer Analytics in Retail with 1KEY BI to leverage metrics from hundreds of business questions to resolve three common customer issues:

• Visibility—achieved through easy access to customer data and guided analysis
• Accountability— achieved through distribution of scorecards
• Reliability— achieved through optimizing, integrating, and consolidating data into a single view

Visibility— Accurate reports, on time

Acting on trends in customer behavior; whether in sales, product, or customer profiles; can often mean the difference between success and failure. Acting on positive trends while they are happening can drive increased sales, satisfaction, and loyalty. Spotting negative trends too late in the game can result in lost customers. Customer Analytics in Retail with 1KEY BI let you identify both positive and negative trends and deliver critical information and analysis in a format that enables quick decisions. Pre-built analytic pathways ensure that the right questions are always asked and the right information is always returned. Sales can access specific customer information such as activity at a particular customer over a certain period of time. Marketing can study trends in product lines. Finance can easily extract trends in sales, gross margins, revenue, and other relevant statistics. Users can drill down by customer, product margin, or revenue by product line, and get the most up-to-date results within minutes rather than days or weeks.

Accountability— Customer metrics for all

Companies derive maximum value from their customer base when accountability for sales, production, and customer profiling are integrated and aligned. Each department needs to understand its respective area of accountability and the impact that its particular metrics have on other areas. Customer Analytics in Retail with 1KEY BI supports company-wide alignment through scorecards that display metrics and KPIs. Employees can proactively manage their areas and see how accountability for other areas is distributed throughout the company. Performance issues can be identified and analyzed, and resulting insights communicated to those responsible. This ensures that tactics are aligned with strategic goals across the company.

Reliability— Turn data into action

Sales, product, and customer data often reside in a variety of databases, ERP systems, and unconnected spreadsheets across your company. Changes in one source are not reflected in another, leaving customer facing employees to work with outdated or inaccurate information. Customer Analytics in Retail with 1KEY BI integrates sales, product, and customer data into one central source of data and metrics for a complete view of your customers that everyone in the company can trust. Changes in customer activity based on sales activity will be reflected in product performance and customer profile data. In this way, critical customer data is constantly updated and optimized for a consistent pool of performance metrics and KPIs.

Typical Customer Dimensions & Measures in Retail

• Regular, normal, occasional customer (based on frequency / duration of visits)
• Professional, academic, teen, household, bachelor (based on products bought)
• Service sensitive, price sensitive
• Power, normal, entry level customer
• Demographics, customer type (business-consumer, mass based)
• Average Revenue per month, expected yearly revenue
• Use of loyalty programs
• Seasonality indexes
• Statistically derived clusters (homogenous groups of customers)

Customer Analytics in Retail with 1KEY BI

Identify good customers by
• Turnover
• Number of transactions 
• Profit
• Life-time value

Identify non returning customers

Identify customers by various selection criteria

• Purchased product x in the past
• More than x transactions in the past y months
• Customers with mobile telephone numbers
• Customers with email addresses

Identify customers abusing returns policy

Identify “promotion friendly” customers

Key Performance Indicators for Customer Analytics in Retail with 1KEY BI

• Average Sale per Customer/Transaction:
• Total sales $ for a given period divided by the number of customers or transactions for the same period
• Units per Customer/Transaction:
• Total number of units sold in a given period divided by the number of customers or transaction for the same period
• Conversion rate: The number of transactions in a given period divided by the total number of customers who entered the store during the same period
• Sales per Hour (for store or associate) – selling hours only:
• Actual sales $ for the store divided by the number of selling* hours during the same period
• Selling hours are used here rather than total labor hours
• Sales per Hour (for store or associate) – total labor hours:
• Actual sales $ for the store divided by the number of labor hours used during the same period
• Time Spent in the Store: Average time spent by customers in the store can be measured through sophisticated techniques utilizing RFID and wireless technologies or manually. Reason for this measurement: There is a direct correlation between time customers spend in a store and how much they buy.

Retail Customer KPIs

• Customer Gross Profit Customer GROSS Profit = Customer Sales – Customer Cost of Goods Sold for a period
• Customer Lifetime Purchase Value – Monetary value of each customer’s life time purchases from the retailer
• Customer profitability – Customer Profitability = Customer Sales – (Customer Returns – Customer Cost of Goods Sold + Customer Promotion Expenses + Activity Based Cost of Servicing Customer) for a period
• Customer Purchase Freq Count – Count of customer purchases transactions over a period of time
• Customer Purchase Value – Monetary value of each customer purchase during a period with an average value for all purchases for the period
• Customer Reference question – A rating from 0 to 10 that indicates if the customer would recommend the store
• Customer Sales by Segment – This formula is dependent upon defining customer segments (based on age, education, lifestyle, income and other factors) and associating individual customers to specific segments
• Customer Service Staffing – Face to face customer service staff count / total staff count
• Visit to Buy Ratio – Sales Transaction Count per period / Visit Count per Period
 

Customer Service

•  Total number of customer claims
•  Customer profitability
•  Cost per delivery per customer
•  First request versus agreements
•  Orders delivered in full
•  Orders delivered on time
•  Documentation
•  Accuracy of the sales forecasting
•  Service performance against standard criterion

Other Customer-Centric KPI’s in Retail with 1KEY BI

• Conversion Rate – tracks how many visitors to the store are turned into customers.
• Average sales per customer or transaction – Total sales for a given period divided by the number of customers or transactions for the same period
• Inventory Store conversion rate – The number of transactions in a given period divided by the total number of customers who entered the store during the same period
• Coupon conversion percentage – Percentage of coupons that have been used by customers
• Profit per Customer Visit – Profit obtained from each customer visit. This way you can easily – set goals for your sales team in order to increase profits!
• Units per customer or transaction – Total number of units sold in a given period divided by the number of customers or transactions for the same period
• Customers per day/week
• Items per customer
• Average sale per customer/transaction
• Units per customer/transaction
• Conversion rate (customer into sale)
• Percentage of income from return customers
• Percentage of returning customers within measurement period

Customer loyalty KPI’s in Retail with 1KEY BI

1. Total customer lost
• The total number of customers do not buy your goods again
• Number of customers includes: the number of first customers and customer loyalty removed

2. The rate of lost customers lost after purchasing first time
• With total customer purchase first time removed / total customer purchases first time
• This rate is low that may be due to some causes: your product is not suitable, good product but not good advertising

3. The rate of customer loyalty loss
• With total customer loyalty lost / total customers loyal available
• This is one of the most serious ratio that you need to note: the causes may include products and services more expensive, new better products with competitive prices appeared

4. The life cycles of a customer
• Formula: a total relationship with customers / total client relationship

5. The rate of customers back
• The rate of customer purchase up 2 / total customers
• This rate is high that will let you know your products are attractive to customers.

6. The rate of new customer
• The number of new customers you gain in a time
• The high rate prove that: either you’re expanding a business or you are lost customer loyalty

Visit http://www.maia-intelligence.com/customer-analytics-retail.htm for more details on Customer Analytics in Retail with 1KEY BI.

Posted on October 5th, 2009 by Sanjay Mehta
Filed under: Business Intelligence

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