In a business where customer is king, Sanjay Mehta demonstrates how the latest reporting tools can help retailers understand customers and serve them better
1) Who are my company's segment-wise top revenue generating customers? 2) What are the cross-selling/up-selling opportunities in my business? 3) Which customer sgment has contributed most to rvenue growth? 4) Which type of customers look for discounts? 5) Which types of customers have highest number of returns? 6) Which types of customers are most profitable? Business analysts, marketing managers, and other decision-makers need detailed information regarding customers' tastes, current trends, evolving market conditions, etc. They need to ask tough questions about their customers and delve further into the data to understand how their customersí behaviour 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 recording transactions and reporting what has transpired. However, in order to become proactive 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, and compare it with its current status and projections of 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. Companies can improve inventory planning and strategy by leveraging the full potential of customer loyalty data, sales transaction data and store data with customer analytics in retail. Itís designed to help campaign managers, promotions managers, loyalty programme 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, and buying behavioural patterns. This information modelled and analysed versus time along with customer profiles enables churns management and monitoring.
Customer analytics in retail can answer all of these questions, and more. Customer analytics in retail draws critical insights from sales, customer-centric KPIs like customer profile, customer behaviour, customer trend (buying pattern) and customer loyalty. These metrics are made from the data to create a more complete picture of the customersí behaviour and its impact on the business. Customer Analytics in Retail lets you 1) Analyse customer types and profile individual customers 2) Monitor and compare trends in customer type, customer base size, buying, contribution to revenues, product mix, customer ranking, profitability, and more 3) Evaluate customer profitability and cost to serve 4) View buying patterns, average order sizes, and number of purchases in a specific time period 5) Monitor customer type and customer-specific aging schedules by number of transactions 6) Assess customer satisfaction by number of adjustments, delinquencies, returns, shipping delays, buying frequency and trends 7) Distribute customer information across the organisation for operational management and reporting and analysis needs 8) Provide self-service or on-demand reporting and analysis. Customer analytics in retail lets you evaluate and rank your most valuable customers, monitor and analyse their overall value to your business, and understand their buying behaviour. These insights help you focus your attention on attracting and retaining customers whose behaviour will help your organisation reach its strategic goals.
Dynamic reports, ad-hoc analysis and powerful metrics answer critical business questions and track key customer performance indicators that are grouped into the following categories: 1) Customer Profiling and Valuation 2) Customer Satisfaction 3) 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. You can analyse each of these factors in isolation or combination to create profiles of each of your customers and evaluate their respective value to your business. Analysing customer profiles by sales channel or by industry segment will help you identify cross-sell opportunities, new markets, or under-performing markets. You can then use this information to direct your activities on retaining highvalue customers.  Customer SatisfactionChanges in your customersí 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 a company. Examine these and other indicators to gauge individual customer satisfaction and to identify overall trends that can be leveraged into increased customer value. Firms should identify downward trends to retain customers before they leave. Customer Loyalty
Encapsulate customer insight in order to build longlasting 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 Using the data gathered from consumer transactions and analysing it can help you: 1) Derive critical information on customer behaviour 2) Sort out critical customer details like top revenuegenerating customers, most profitable customers, purchase trends at different customer profile levels, percentage of return customers and also customer segment with potential bad debt risk 3) Work on key areas appropriately for effective marketing strategy with the information generated 4) 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 5) Sort out customer buying trends and patterns, return rates, time to pay and other factors to judge customer satisfaction issues and take appropriate action before they affect your bottom lines 6) 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 7) Understand customer purchase patterns and trends in various market segments and concentrate on weaker areas to improve sales.
Using Customer Analytics in Retail
Deploy customer analytics to leverage metrics from hundreds of business questions to resolve three common customer issues:
1) Visibility - Achieved through easy access to customer data and guided analysis 2) Accountability - Achieved through distribution of scorecards 3) Reliability - Achieved through optimising, integrating, and consolidating data into a single view. Visibility - Accurate reports, on time Acting on the basis of trends revealed through customer behaviour reports, can often mean the difference between success and failure. Acting on positive trends while they occur can drive increased sales, satisfaction, and loyalty, while spotting negative trends too late in the game can result in lost customers. Customer analytics in retail lets 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 is 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 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 analysed, 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 integrates sales, product, and customer data into one central source of data and metrics for a complete profile of your customers that everyone in the company can rely on. 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 optimised for a consistent pool of performance metrics and KPIs. Customer analytics can help: 1) Identify good customers by turnover, number of transactions, profit and life-time value. 2) Identify non-returning customers 3) Identify customers by various selection criteria: i) Purchased product x in the past ii) More than x transactions in the past y months iii) Customers with mobile telephones iv) Customers with email addresses v) Identify customers abusing returns policy vi) Identify "promotion friendly" customers.  Key Performance Indicators (KPI) for Customer Analytics in Retail
Profit in retail can only be generated by sales on the shop floor. There are various parameters that are used to figure out the success of a retailer and its performance over time. These parameters are as follows:
a) Average Sale per Customer/Transaction: Total sales for a given period divided by the number of customers or transactions for the same period b) 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 c) 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 d) Sales per Hour (for store or associate) selling hours only: Actual sales for the store divided by the number of selling hours (other than labour hours) during the same period e) Sales per Hour (for store or associate) total labour hours: Actual sales for the store divided by the number of labour hours used during the same period d) Time Spent in the Store: Average time spent by customers in the store can be measured through sophisticated techniques utilising RFID and wireless technologies or manually. Reason for this measurement: there is a direct correlation between the time customers spend in a store and how much they buy. Customer Service
Performance in retail completely depends on the customer, the transactions that take place and the customer satisfaction the customer goes home with. This customer satisfaction will result in later transactions. The performance for any retailer can be measured by the following parameters:
1) Total number of customer claims 2) Customer profitability 3) Cost per delivery per customer 4) First request versus agreements 5) Orders delivered in full 6) Orders delivered on time 7) Documentation 8) Accuracy of the sales forecasting 9) Service performance against standard criteria. Other customer-centric KPIs in the retail industry include: a) Conversion Rate ñ Tracks how many visitors to the store are turned into customers. b) Average sales per customer or transaction ñ Total sales for a given period divided by the number of customers or transactions for the same period c) Inventory store conversion rate ñ The number of transactions in a given period divided by the total number of customers who entered the store during the same period d) Coupon conversion percentage ñ The percentage of coupons that have been used by customers e) Profit per customer visit ñ Profit obtained from each customer visit. This way you can easily set goals for your sales team in order to increase profits f) 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 g) Customers per day/week h) Items per customer i) Average sale per customer/transaction j) Units per customer/transaction k) Conversion rate (customer into sale) l) Percentage of income from return customers m) Percentage of returning customers within measurement period. These measures can help you judge whatís best for your business and your customers. Customer analytics is a very important tool in better understanding the customersí wants and behavior. Only after understanding the customerís behaviour can a retailer strategise and build on top of these results. After all, understanding the customer is work half done and the other half is made easier. The author is CEO, MAIA Intelligence Pvt. Ltd., |
