HCL's Retail Analytics

Road to Precision Retailing

Analytics have become one of the most powerful tools available to retailers, and are being used to enable fact-based, insight-driven decision making to manage their strategic, operating and financial performance, and create shareholder value. Retailers today are searching for ways to derive more customer intelligence and operational insights from their overflowing databases

The Opportunity and the Challenge

Today, there is a compelling need to provide the right information, at the right time, to the right decision makers, using the right technology.

The aggressive adoption and exploitation of analytics has led to competitive advantage among some of the world's most successful retailers. Retail Analytics can collect, process and analyze a wide variety of data on retail performance.

The Breadth of Analytical Options for a smarter enterprise

Leading retail executives believe they can achieve true competitive advantage with retail analytics technology by using enterprise-wide approach that involves product, customer and functional boundaries. That is the reason why Analytics is the centre of focus for any profitable enterprise today.

Retailers recognize analytics as key to business transformation and competitive advantage

To sustain and lead in a complex and constrained marketplace, companies have to resort to mathematical tools and techniques to make informed choices from terabytes of data available. HCL can customize analytics offerings to retailers to enable them to do their business profitably like never before.

Based on analysis of what all business problems retailers are plagued with today, HCL has come up with a set of often-asked problem definitions, and attempted to map against those what could be palpable solutions to the defined problems. HCL is well equipped to work with the retailers and implement these solutions.

Marketing Mix Modeling

Problem Definition

The task of measuring returns on the marketing mix has become somewhat more complex as media have proliferated, and as alternative explanations of marketing lift have to be ruled out.

“Half of the money I spend on advertising is wasted; the trouble is I don't know which half.” The old complaint, no longer has to be true - retailers can find out whether advertising works, and how it compares to other marketing tools.

Solution

HCL leverages Marketing mix modeling (MMM) to unearth the driving forces in the marketing environment in order to stay profitable.

MMM defines the effectiveness of each of the marketing elements in terms of its contribution to sales-volume, effectiveness (volume generated by each unit of effort), efficiency (sales volume generated divided by cost) and ROI. These learnings are then adopted to adjust marketing tactics and strategies, optimize the marketing plan and also to forecast sales while simulating various scenarios.

It helps an organization's efforts to measure the change in sales and attribution of the change to various marketing mix elements such as Trade, TV, FSI, Print amongst others.

Benefits

Being able to know what investment works for the brands will potentially save a lot of marketing dollars. Experience shows by deploying the models, 7-10% of promotion dollars can be saved or reallocated to more hard working marketing buckets.

BASEMEDIA

TRADE

OUTDOOR

?Price?Distribution?Competition?Long-term

impact from marketing

?Elasticity?ROI of each marketing vehicle?Marketing activity which drives volumes?Marketing activity driving the consumer

behavior?Impact of operational factors?Financial planning

MARKETING MIX MODELS OUTPUTS

?TV?Print?Radio?Trade ?Outdoor,

etc.

INCREMENTAL

SALES DECOMPOSITION BY DRIVERS

ISOLATING FACTORS THAT ARE IMPACTING INCREMENTAL AND BASE SALES

MODELING SALES

BASE VARIABLES

90.56 * Distribution

-686.35 * Price

32.43 * Seasonality

-0.59 * Competition

INCREMENTAL VARIABLES

6168.18 * Trade

943.95 * Print

5187.29 * TV

0.08 * Radio

SALES =

ANALYTICS SERVICE OFFERINGS

?Targeting?Customer Segmentation Analysis?Customer Loyalty Analysis?Market Basket Analysis

?Marketing Mix Modeling?Advertising effectiveness?Awareness planning

?Product attribute drivers?Replenishment analysis?Quantifying ‘lost sales’?Pack size simulation?Optimization

?Price/Cross Elasticity Modeling?Item Cannibalization Analysis?Pricing Simulation?What-If Scenario Modeling

?Inventory Management performance?Analyzing Inventory Replenishment Policies?Evaluating optimal quantity to order to minimize total variable costs

?Test out store renovation prospect?Finding effect of any store intervention on sales and profits

?How do I target precisely and

customize offerings?

?How do I customize offers to stores?

?How do I justify my marketing spends?

?How do I optimally price throughout the

product lifecycle?

?How do I decide on my assortment

composition so that I can minimize lost

sales?

?How do I optimize

inventory/replenishment and

transportation costs?

?How do I manage demand?

?How do I know whether my retail

innovation will work?

Frequently Asked Questions

Marketing Spend Optimization

Customer Analytics

Assortment Optimization

Price Analytics

Inventory Analytics

Test & Learn

Ser

vice

Off

erin

gs

Customer Analytics - Propensity to buy Modeling/Response Modeling

Problem Definition

Decision makers today believe that getting a clear view of customer preferences and customer behavior with effective Predictive Analytics and Data Mining tools, to identify the customers with the highest propensity to buy new products and services, is imperative for accurate and better customer segmentation. Retailers are striving hard to personalize offers and hence need to identify the targets very precisely.

Solution

Business intelligence technology that produces a predictive score for each customer or prospect hence targets the most likely prospects of a marketing campaign. The offers can be based on extrapolating from past behavior in an ad-hoc manner but a more scientific way to target would-be customers is to put probability scores to each customer from a customer base of millions and choose the most probable ones.

Benefits

?Selection of best target customer base for customer acquisition campaigns

?Achieve higher response rate and reduce marketing Cost

?Maximizing ROMI (Return on Marketing Investment) on campaigns

?Understand the demographics of specific product (category/brand) buyers and design promotions accordingly

Test and Learn for Stores and Customers

Problem Definition

Test and Learn is followed by retailers for randomized testing, to test ideas in a small number of locations or customers to predict impact of retail innovations on a small scale.

Large retailers with multiple stores are uniquely positioned to employ the “test and learn” analytical approach, in which a relatively small sample of stores is used to test whether a particular change or intervention delivers the desired result.

Some of the questions answered are:

1. What impact will the program have on key performance indicators if executed across the network or customer base?

2. Will the program have a larger impact on some stores/customers than others?

3. Which components of the idea are actually working?

Solution

HCL can help in carrying out test on smaller set of subjects (stores or customers) and results can be extrapolated to the entire population.

Benefits:

?Feasibility study at a low cost

?HCL can provide an ongoing test laboratory facility for various tests to be carried out – the results can be used for future reference when similar innovations are envisaged

PROPENSITY TO BUY MODELING – PROCESS FLOW

Scope to increase category base Profile buyers and non-buyers

Combine Data sourcesCreate Analysis sampleCreate Training and validation set

Combination of business unit buyers$ value of purchasesTime frame of buying

Sizing the opportunityModeling

Targeting strategy

?Candidate variables

?Significant variables

?Modeling type (Step wise regression etc)

?Model performance

?Scoring custome

Analysis sample creation

Buyer definition

TEST AND LEARN - UNDERLYING PRINCIPLE

RESULT BASED ACTION TEMPLATE

ACTIONRESULTS

= Goal

< Goal

> Goal

Result based Actions

Result:

Sales Lift > 5%

Result:

Sales Lift = 0~5%

Result:

Sales Lift < 0%

?Rollout

?Re-test the same hypothesis

?Stop/Re-design the test

Test and LearnTest and Learn is a set of techniques carried out by retailers a n d c o n s u m e r - f o c u s e d companies to test out hypothesis that holds business promise. Tests are carried out on a smaller set of subjects (stores or c u s t o m e r s ) a n d r e s u l t s extrapolated to the entire population. These techniques essentially are built on the foundat ion o f ‘Des ign o f Experiment’ theory in Statistics

TEST AND CONTROL METHODOLOGY

PRE TEST POST TEST

NET CHANGE : TEST VS. CONTROL

Locations/groups where test is performed

Control group – no activity performed

Amount of change in test group

Amount of change in control group

Pre Pack Optimization

Problem Definition

Fierce competition in today's global markets, the introduction of products with shorter and shorter life cycles and the heightened expectations of customers have forced business enterprises to quickly respond to customer needs without an increase in costs. This has necessitated the streamlining of business processes by cutting down unwanted activities that add to cost and lead time across supply chain.

Solution

Business enterprises are using more and more of information technology tools to optimize their supply chain. One such tool is Pre-pack Optimization which takes a system-wide perspective in identifying and reducing cost and thereby enhancing the supply chain profitability.

HCL has devised an innovative process to address this issue by treading a middle path between optimization of 'predetermined ratio of sizes combination' and 'pick a pack' options.

HCL has undertaken broad steps to follow in order to identify optimum size ratios, framework to quantify 'unnecessary replenishment”, calculate percentage of units sold on clearance, zero in on stores with highest clearance sales and which had the biggest negative impact on margins, find ways to reallocate the unprofitable units to stores that had stock outs, develop and continuously refine size skew customer look-alike model(s).

Benefits

Market Basket Analysis

Objective

With the introduction of electronic POS data, retailers have at their own disposal an incredible amount of data. MBA (Market Basket Analysis) is one tool that leverages this data and helps retailers understand underlying hidden pattern in customer transaction and use that information profitably.

Solution

Market Basket Analysis unfurls the science that goes behind why certain products are bought together.

The application of market basket analysis is generally facilitated by the use of the data mining tools. HCL Analytics has the expertise to take the regular MBA analysis to the next level by identifying the most profitable baskets, differentiate between the natural rules that are inherent to the stores and patterns induced by promotion, normalize the effect of store attributes like size, footfalls etc to sales and margins and compare stores.

Benefits

In retail, affinity analysis can be used for purposes of cross-selling and up-selling, in addition to influencing sales promotions, loyalty programs, store design, and discount plans.

?Pre-pack optimization takes optimization beyond the retail enterprise to improve efficiencies of its vendors and the retailer's stores

?Retail planners and replenishment analysts can rely on an automated and sophisticated solution to determine pre-pack configurations

?Boost the ability to get the right sizes to each store while decreasing the amount of excess, fringe-sized assortment

?It helps to create a lost sales model and incorporate into current processes

PRE PACK OPTIMIZATION

Is there a third option that is more viable?Create and manage a few more packs, (Pack = Size)

Automate decision process

Predetermined Ration of Size Combinations with a Pack

Pick-a-Pack

MARKET BASKET ANALYSIS

TRANSACTIONAL DATA

Market Basket Analysis analyses customer buying habits by finding associations and correlations between the different items that customers place in their “shopping basket”

Customer 1

Milk,

Bread

Eggs, Sugar,

Customer 2

Milk,

CerealsBread

Eggs, Sugar,

Milk, Eggs, Sugar

Customer 3

Milk, Eggs, Sugar

Customer 4

ANALYTIC ENGINE

RESULTS

?Support

?Confidence

?Association rule

discovery (Simple and

complex rules)

SL

12345678910

BSKcount(X,Y)

LBSK RBSK Total Basket

Support Confidence ExpectedConfidence

Lift MedianS

MedianS

X(Rules Basket)

Y(Rules Basket)L

=C/EC

E=R/T

C=RC/L

S=RC/T

TRLRC

X Y===>

Market Basket Analysis

Hello, I’m from HCL. superhuman capabilities. We make sure that the rate of progress far exceeds the price. And right now, 90,000 of us bright sparks are busy developing solutions for 500 customers in 31 countries across the world.

We work behind the scenes, helping our customers to shift paradigms and start revolutions. We use digital engineering to build

How can I help you?

www.hcltech.com

For more information contact us at: retail.solutions@hcl.com