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Technology

By Sanjay Mehta

The questions can include:• Who are my company’s seg-

ment-wise top revenue-generating customers?

• What are the cross-selling / up-selling opportunities in my business?

• Which customer segment has contributed most to revenue growth?

• Which type of customers look for discounts?

• Which types of customers have highest number of returns?

• Which types of customers are most profitable?

Business analysts, marketing managers, and other decision makers need detailed information regard-ing customers’ tastes, current trends, evolving market conditions, etc. They need to ask tough questions about their customers and delve fur-ther into the data to understand how their customers’ 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 transaction recording and report-ing 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 ad-dition 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 busi-ness 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 inven-tory planning and strategy by lever-aging the full potential of customer loyalty data, sales transaction data and store data, with Customer Ana-lytics in Retail. It’s designed to help campaign managers, promotions managers, loyalty program managers and other key functions exploit the hidden relationships between prod-ucts, customers and store data sets. It provides overall assessment on each single customer: profitability, loy-alty and buying behavioral patterns (trends). This information modeled and analysed versus time along with

customer profiles enables manage-ment and monitoring.

Customer Analytics in Retail can answer all of these questions, and more. It draws critical insights from your sales, customer-centric Key Per-formance Indicators (KPIs) like Cus-tomer Profile, Customer Behaviour, Customer Trend (Buying Pattern) and Customer Loyalty. These met-rics are made from the data to cre-ate a more complete picture of your customers’ behaviour and its impact on your business.

Customer Analytics in Retail lets you:

Customers are at the heart of any business. One unshakable rule of any business is to “know your cus-tomer.” In today’s business climate, this means using Business Intel-

ligence (BI) to analyse complex customer data. With BI, companies can answer a wide range of critical questions about their customer base.

A solution to decode the mysterious ways in which customers move is closer than you think.

Know Thy Customers

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• Analyse 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, delinquen-cies, returns, shipping delays, buying frequency and trends

• Distribute customer informa-tion across the organisation for op-erational management and reporting and analysis needs

• Provide self-service or on-de-mand 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 buy-ing behavior. These insights help you focus your attention on attracting and retaining customers whose be-haviour will help your organisation reach its strategic goals.

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

• Customer Profiling and Valua-tion

• Customer Satisfaction• Customer Loyalty

Customer Profiling and Valuation

Defining your best customer in-volves several factors: the revenue they generate, the frequency of their purchases, the cost to serve them, and more. Analyse each of these fac-tors in isolation or combination to create profiles of each of your cus-tomers and evaluate their respective value to your business. Analyse cus-tomer 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’ buy-ing 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 satis-faction and to identify overall trends that can be leveraged into increased customer value. Firms should iden-tify downward trends to retain cus-tomers 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 lev-els 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 behaviour

• Sort out critical customer de-tails like top revenue generating customers, most profitable custom-ers, purchase trends at different cus-tomer profile levels, percentage of return customers and also customer segment with potential bad debt risk

• Work on key areas appropri-ately 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 and other factors to judge customer satisfaction issues and take appropriate action before they affect your bottom lines

• Identify fast-moving products and cross-sell scope to align produc-tion and marketing force to take benefit of this information in assess-ing product performance over a seg-ment of customers

• Understand customer purchase patterns and trends in various mar-ket segments and concentrate on weaker areas to improve sales

Using Customer Analytics in Retail

Deploy Customer Analytics in Retail to leverage metrics from hun-dreds 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 optimising, integrating, and consoli-dating data into a single view

Visibility – Accurate Reports, on Time

Acting on the basis of trends re-vealed through customer behaviour reports can often mean the differ-ence between success and failure. Acting on positive trends while they occur can drive increased sales, sat-isfaction, and loyalty, while spotting negative trends too late in the game can result in lost customers. Cus-tomer Analytics in Retail lets you identify both positive and negative trends and deliver critical informa-tion and analysis in a format that en-ables quick decisions. Pre-built ana-lytic pathways ensure that the right questions are always asked and the right information is always returned. Sales can access specific customer in-formation such as activity at a partic-ular 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 prod-uct 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 integrat-ed and aligned. Each department needs to understand its respective

area of accountability and the im-pact 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 result-ing 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, enterprise resource planning (ERP) systems, and unconnected spread-sheets across your company. Chang-es 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 custom-ers that everyone in the company can rely on. Changes in customer activ-ity based on sales activity will be re-flected in product performance and customer profile data. In this way, critical customer data is constantly updated and optimised for a consist-ent pool of performance metrics and KPIs.

Typical Customer Dimensions & Measures in Retail

• Regular, normal, occasional customer (based on frequency/dura-tion of visits)

• Professional, academic, teen, household, bachelor (based on prod-ucts bought)

• Service sensitive, price sensitive• Power, normal, entry level cus-

tomer• Demographics, customer type

(business-consumer, mass based)• Average revenue per month, ex-

pected yearly revenue• Use of loyalty programs• Seasonality indexes• Statistically derived clusters

(homogenous groups of customers)

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Customer Analytics in Retail also lets you:

1. Identify good customers by• Turnover • Number of transactions • Profit • Life-time value 2. Identify non returning cus-

tomers3. Identify customers by various

selection criteria:• Purchased product X in the past• More than X transactions in the

past y months• Customers with mobile tel-

ephone numbers• Customers with email addresses4. Identify customers abusing re-

turns policy 5. Identify “promotion friendly”

customers

Key Performance Indicators

• Average sale per Customer/Transaction:

• Total sales for a given period di-vided 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 as-sociate) – selling hours only:

• Actual sales for the store divided by the number of selling* hours dur-ing the same period (*selling hours are used here rather than total labour hours

• Sales per Hour (for store or as-sociate) – total labour hours:

• Actual sales for the store di-vided by the number of labour hours used during the same period

• Time spent in the store: Average time spent by customers in the store can be measured through sophisti-cated 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.

Retail Customer KPIs

• Customer Gross Profit = Cus-tomer sales - Customer cost of goods sold for a period

• Customer Lifetime Purchase Value: Monetary value of each cus-tomer’s life time purchases from the retailer

• Customer Profitability = Cus-tomer 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 purchas-es transactions over a period of time

• Customer Purchase Value: Mon-etary 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 Trans-action 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 KPIs in Retail

• 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 cus-tomers 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 – Prof-it 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 transac-tion – 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 re-

turn customers• Percentage of returning cus-

tomers within measurement period

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Customer loyalty KPIs in Retail

1. Total customers lost

• The total number of customers who do not buy your goods again

• Number of customers includes: the number of first customers and customer loyalty removed

2. The rate of customers lost after first time purchases

• With total customer purchase first time removed/total customer purchases first time

• If this rate is low, that may be due to some causes: your product is not suitable, or the product is good but has not been advertised well

3. The rate of customer loyalty loss

• With total customer loyalty lost/total customers loyalty available

• This is one of the most serious ratios that you need to note: this may happen because products and services became more expensive, or new and better products with com-petitive prices appeared

4. The life cycles of a customer

• Formula: a total relationship with customers/total client relation-ship

5. The rate of customers who return

• The number of customers who are repeat buyers/total customers

• This rate is high that will let you know your products are attrac-tive to customers

6. The rate of new customer

• The number of new customers you gain in a specific period of time

• Any sharp increase or decrease here implies that either the business is expanding or it’s losing customer loyalty

The state of BI adoption in India

Aware ness and adoption of BI among enterprises are definitely on the rise. The maturity of BI adoption can best be seen with the new economy companies, includ-ing those in the retail sector. Cur-rently, most retail enterprises have deployed and stabilised ERP/CRM/SCM or core business (transaction-al) applications and are thus looking for a tool that can leverage the IT investment in these packaged ap-plications. We are also seeing great interest from verticals such as retail for adopting BI for increasing their competitiveness and transparency, respectively.

In India, there is a general aware-ness on the theory and concept of BI. They are spearheading BI adop-tion by going in for separate BI units within the organisation to provide the `right’ product to the `right’ customer at the `right’ time and price. Retail industry is one of the early adopters of BI in India. Cur-rently, the demand for BI solutions is largely being driven by MNCs and large enterprises. BI solutions seem to have gained more acceptance and significance in retail where custom-ers play a pivotal role in the future of the company.

The return on Investment (ROI)

ROI on BI is high and fast. Let’s take an example of a HR Analytics. Data captured through show-card on employee entry and exit can be analysed for actual working hours, thereby tracking the productivity

loss. Retail organisations have plen-ty of employees and such number of un-productive hours can cost a lot. The HR department, equipped with these facts and analyses, can now plan and take necessary action. This is just one area within an HR domain. Likewise, BI can help any retail organisation like any other industry for ad-hoc reporting, dy-namic MIS and complex analyses and take informed and proactive decisions, thus saving time and cut-ting costs. With customer analytics as explained above, BI helps boost revenue. Thus, a high rate of ROI happens quickly on BI.

Total Cost of Ownership (TCO)

Contrary to the traditional BI providers (MNC), operational BI leaders like MAIA Intelligence’s 1KEY have low TCO considering the Enterprise License Cost, IT user involvement during deployment, IT user involvement for support, implementation, training and over-all Business Value delivered. These BI tools provide end-to-end BI with low TCO. The unlimited users li-censing policy helps reduce TCO as the no. of users increase in case of 1KEY. Whereas in MNC’s BI, the TCO would go up as the no. of users increase due to user based licensing policy. Implementation of 1KEY happens as fast as within two days and business users are trained in just few hours. So the TCO is very low in case of BI tools like 1KEY.

State of Adoption

Information technology research and advisory firm Gartner Research says only 30 percent of companies that have deployed BI consider their deployments “very successful” – the vast majority is labeled “somewhat successful.” One reason for this is low levels of user adoption – less than one-third of the potential users of BI tools are using them. Actual usage of BI tools is almost always much less than expected due, largely to the dif-ficulty in learning and using BI tools.

According to Gartner, “Business users must get the data, reports and analysis they need for their jobs from multiple sources and in multiple forms. However, the BI initiatives of most enterprises lack the maturity and depth of deployment needed to meet business demands.”

Most of the existing BI players (traditional BI) are primarily focused on strategic BI alone. These tools are expensive and used by only top tier management level, expert users; the over 85 percent of the business user pyramid is deprived of a BI for MIS, analysis and monitoring / gauge per-formances, which if provided can help them get gain visibility into the business and drive performance and get everyone working towards a common goal.

The Business Intelligence mar-ket is growing and continues to evolve. There’s still plenty of room for growth; it has only penetrated 10 to 15 percent of the known user base, but there is a vast opportunity for business intelligence well beyond today’s known markets.

A Gartner report “Hype Cycle for ICT in India 2008” expects the BI market in India to reach US$ 46.8 million by 2012. India is a huge market for business intelligence and is fast growing with double digit figures, even in this slowdown. The overall BI market in India is at a nascent stage, with a huge uptapped opportunity for vendors to capture. BI can deliver on this promise if de-ployed successfully because it can improve decision making and opera-tional efficiency, which in turn drives the top line and the bottom line. ■

Sanjay Mehta is CEO, MAIA Intelligence Pvt. Ltd

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