Application of Decision Sciences

to Solve Business Problems

Retail Industry

Analytics

for Retail

Merchandising

Store-layout Planning

Effective macro space planning is critical for consumers to have a favourable perception about the store’senvironment and to increase sales throughput. Store layout planning shows the size and location of eachdepartment, permanent structures, fixture locations, merchandizing and overall aesthetics in order tomaximise revenue, increase consumer footfalls and conversion rates.

Store layout includes category space allocations and adjacencies in order to optimize overall sales andfootfalls.

It helps in answering business questions like: Which departments will produce the highest traffic? Which departments should be placed adjacent? Where different departments should be spaced to facilitate the deepest penetration and widest

dispersion of consumer flow throughout the store? Where different categories should be placed so as to have maximum impact on consumers? Where the seasonal items should be placed?

House Hold Laundry

Party Needs

Toiletries

Crisps & Snacks

ConfectioneryPet Foods

Beverages CookiesStationary

Magazines

CerealsMilk Juices

Cosmetics

CakesSoft Drinks Bakery Items

Fish & MeatGrocery

Sauce Pickle Canned-Soup Canned-Vegetables

Baskets

Bas

kets

Beer StacksChilled Beers & WinesRed & White Wines Beers & Cigars

Dai

ry

Pro

du

cts

Fro

zen

Fo

od

RTE Food

VegetablesFruits

Entrance

Stairs

Co

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ter

1C

ou

nte

r 2

Co

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Store layout designed for a leading supermarket

Merchandising

Assortment Optimization and Planogramming

Different products and SKUs within a category are assorted based on their profit and revenue contributionand local consumer preferences. It can be assorted for a store cluster or localized at an individual store level.Dividing the products into—core/destination (drive the store sales), complementary/accessory (add-on itemsfor core products), secondary items (not core, but have the potential to develop over time) and impulseitems—also serves as the basis for assortment.

Once the right assortment has been decided for each category, the next logical step is placing them in themost effective manner on the shelf. Planogramming is a widely used technique for the same. It enables theretailers to stock the product, at the right place, at the right time, with the right facing to attract theconsumers and prompt them to buy.

Category1

Category2

Category3

Category4

Category5

Category6

Category7

Shelf space allocation for categories based on incremental revenue per unit space

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Supply Chain

Sales Forecasting

A good demand forecast helps improve sales volume, cash flow and hence the profitability, by optimizinginventory and by minimizing out-of-stock. Besides considering historical data, external factors like changingtrends and consumer preferences, seasonal impact and promotion influences on demand, price changes,different store formats and channels are also considered for more accurate forecasts. Sales forecast in retailis very essential for:

Stock replenishment by categories and SKUs Predicting excess and stock-outs at SKU level and hence minimizing costs Designing store promotion activities and optimizing resource allocation for the same Capitalizing on peak sales weeks: Accurate forecasting ensures right product mix to take full advantage of

operational capacity and peak market demands

Statistical techniques (like Moving averages, Holt Winters, Regression, ARIMA, etc.) are employed to projectfuture demand on a category and SKU level, based on historical data.

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Lead time : It is the time lag between when the order is placed and the point at

which the stocks are available; A lead time of 4 days implies that there should

always be stock for 4 days supply to avoid stock-out scenario

Safety stock is the buffer quantity to cover any unplanned excess requirement

taking into account delivery delays

Reorder point is the minimum level of stock at which procurement should be triggered and quantity of warehouse

stock should never go below this point

If the quantity of warehouse stock is less than re-order point, there is shortfall

Stock

TimeRelease date

Safety Stock

Reorder point

Availability date

Lot size

Replenishment

lead time

Inventory Management

Optimal inventory management is an indispensable function to ensure un-interrupted product supply tomeet the consumer demand. Stock out analysis on a category and SKU level helps in: Optimizing inventory and service levels by streamlining ordering processes Minimizing stock out which leads to loss of sales Handling overstock which results in increased inventory handling costs and cost to liquidate the excess

inventory Maximizing warehouse space utilization Designing store promotion activities and optimizing resource allocation

Concepts of lead-time and re-order point are utilized for inventory planning. Lead time is the time lag atwhich order is placed to the point at which stocks are available. The buffer quantity to cover any unplannedexcess requirement, taking into account delivery delays, is referred to as safety stock. Providing for safetystock, on top of lead time demand, will give the re-order point, which is the minimal level of stock at whichprocurement should be triggered. Warehouse stock should never go below the re-order point. Re-order pointwill assist in deciding what would be the optimal order quantity and when to place an order.

Supply Chain

Vendor Management

For the efficient and smooth functioning of a retail store, various departments have to work in tandem.Mostly these day to day operations are outsourced to vendors. Constant monitoring and evaluation ofvendors is necessary to maintain the smooth functioning of different departments. It enables to controlcosts, drive service excellence and mitigate risks to gain increased value from their vendor by:

Minimizing potential business disruption Avoiding deal and delivery failure Ensuring more-sustainable multi-sourcing, while driving most value from the vendors Improving operational efficiencies, control costs and planning of workforce

Partner Strategic Fit Brand Equity Financial HealthAbility to operationalize

Final Score Status

Vendor 1 9 8 10 7.4 8.75 Pass

Vendor 3 10 9 8 7.4 9.00 Pass

Vendor 3 10 7 6 7.4 7.50 Pass

Vendor 4 10 10 8 10.0 9.50 Underleveraged

Vendor 5 9 7 8 7.4 7.75 Pass

Vendor 6 2 7 6 8.2 5.50 Risky

Vendor Filtration Methodology & Process Flow

It includes vendor identification, recruitment, monitoring, tracking and evaluating the vendors on certain KPIs like:

Pricing: Competitive pricing (comparable to other vendors), stability (low variance), advance notice of price changes

Quality: Compliance with purchase order, conformity to specifications, reliability (rate of product failures), durability, support, warranty

Delivery: Time, quantity, lead time, packaging, emergency delivery, technical support

Marketing

Loyalty Analytics

In today’s competitive business scenario with consumers having a multitude of options, their preferences & buying patterns have been constantly evolving. It is necessary for retailers to gain insights into changing consumer trends & accordingly tailor their offerings.

CRM analytics helps analyse consumer’s transactional and others behavioural patterns to facilitate optimal decisions regarding marketing strategies.

It helps the business to: Identify consumer segments based on demographic, psychographic and purchase behaviour. Design

customized product offerings and marketing strategies relevant for each of these consumer segments. Track these segments over time to study how the industry is evolving.

Closely track and maintain constant touch-point with your most profitable & loyal consumer segments. Identify any signs of attrition in advance and accordingly formulate the right retention strategy

Formulate cross-selling and up-selling strategies by analysing product affinities & associations. Identify the consumer segments which can be targeted for the same. This helps in increasing overall revenue contribution from the same customer base.

They are frequent visitors and prefer

brewed coffee

They visit mostly during weekends, to

sip their coffee over an enjoyable experience

of a football game or a live concert

These consumer have no set routines, and

visit during lunch hours on weekdays, and prefer not to be disturbed over their

discussion. Cappuccino is their preferred drink

They come generally for the desserts &

smoothies, visit usually during evening hours

“Coffee Junkies”“Entertainment

Seekers”“Business over

Coffee““The Sweet Tooth”

16,000 consumers(37.2%) - $178

12,000 consumers(28%) - $60

8,000 consumers(18.6%) - $125

7,000 consumers(16.2%) - $29

M T W T F S S M T W T F S S M T W T F S S M T W T F S S

Mor Aft Eve Mor Aft Eve Mor Aft Eve Mor Aft Eve

Consumer segments for a leading organized retail café chain

Marketing

Pricing Analysis

Pricing strategies are crafted to meet two key objectives: profit and revenue maximization. It helps inidentifying the best pricing strategy for a retailer. Price optimization enables retailers evaluate cost,assortment, margin targets and promotions. It employs predictive modeling techniques for:

Evaluating price elasticity for their private labels and deciding the optimal price points Identifying price gaps/thresholds to decide the optimal price points and associated discounts for

different brands and SKUs while maximizing category sales Determining base, promotion, markdown and discount prices

Identify price thresholds by brands Optimum price corridor for retailer’s own label

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Price index vs. competition Volume share

Optimum price corridor

Marketing

Consumer & Trade Promotions

Trade promotions and consumer promotions refer to different marketing activities implemented in the store,to increase footfalls and to drive sales and profit. The most commonly implemented programs are features,in-store displays, TPRs (temporary price reductions), couponing and loyalty reward programs.Advanced econometric modeling techniques are used to help stores refine their promotion strategies, tounderstand the lift generated by various promotional programs for different categories and the associatedROI. This information is then used by marketers to:

Optimally allocate budget among different promotion vehicles—features, displays, TPRs and couponingwhile increasing category sales and maximizing ROI

Optimally allocate budget for different brands as per their revenue and profit contribution Design programs specific to a category instead of following “one-size fits all” approach

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TPR OnlyFeature OnlyDisplay OnlyFeature & Display

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TPR Feature Display Feature & Display

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Promotion program Spends

Elasticity curve to quantify the sales impact for each promotion Evaluate ROI from different promotion programs

Streaming Sales Data fed weekly or monthly as is available

Promotion Calendar fed into the system periodically

Marketelligent PRISM

µ Display

µ Feature

µ Consumer

µ TPR

Decomposed Lift (µ)

Marketing

Real-time evaluation of promotions

Marketelligent has developed an in-house proprietary tool called PRISM, for continuous monitoring andevaluation of trade and consumer promotions on a real time basis, using the test-control approach.

Identifying the control samples for each of the test group takes most of the time and effort. PRISMminimizes the time required for the same and identifies the control samples on a real time basis, based onhistorical sales trends and outlet demographics.

PRISM uses sales in test and control outlets, to calculate the lift factor for each or combinations of tradepromotion programs. Based on the lift factor, incremental sales and ROI are calculated for each activity. Theeffectiveness of promotions can be compared at different levels – channels, categories, brands and markets.

Marketing

Market Mix Modeling

Usually for marketing, retailers utilize radio, magazines, newspapers and outdoor for creating awareness.Market mix modeling helps managers develop an optimal media investment strategy that provides therequired sales lift and also maximises the returns on investment by media vehicle.

The model aids in: Establishing key relationships between sales and marketing driver inputs Quantifying impact of each marketing driver on sales Optimizing allocation spends across media vehicles to maximise sales Calculating saturation spends for each media vehicle based on diminishing returns Evaluating decay impact, if any, for each of the media vehicles (also called ad-stock)

Decompose sales into baseline and incremental Evaluate ROI from each media vehicle

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Baseline sales Online incr. sales TV incr. sales Daily incr. sales

Online spend TV spend Dailies spend

RO

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Marketing

Market Basket Analysis

Market basket analysis is done to evaluate consumers’ purchasing behaviour and to identify the differentitems bought together in the same shopping session. It uses store’s transactional data and is leveraged forcreating cross-selling opportunities for furthering sales.

It aids retailers in: Product placements--Which products should be placed next to each other Customizing layouts, assortments and pricing, to the local demographic Affinity promotion--Designing more profitable and effective consumer promotions like couponing based

on associated products• Increase the profit from sales of complementary products, which do not sell by themselves• Stimulate trials and increase consumer awareness during launch of new products and variants• Handling excess stock by designing offers among associated products

Support, Confidence and Lift are used to identify the combination of products consumers buy together mostoften.

CONFIDENCE Product 1 Product 2 Product 3 Product 4 Product 5 Product 6 Product 7 Product8

Product 1 100% 25% 9% 6% 18% 2% 28% 31%

Product 2 42% 100% 7% 8% 22% 6% 29% 22%

Product 3 31% 16% 100% 5% 10% 4% 18% 17%

Product 4 35% 29% 8% 100% 28% 7% 26% 12%

Product 5 47% 35% 8% 12% 100% 3% 37% 24%

Product 6 37% 66% 18% 19% 21% 100% 25% 21%

Product 7 45% 28% 8% 7% 23% 2% 100% 25%

Product 8 57% 24% 9% 3% 17% 2% 29% 100%

Probability that Product 8 is purchased given that Product 1 is bought is 31%

Probability that Product 1 is purchased given that Product 8 is bought is 57%

Increasing sales by creating cross-selling opportunities using MBA

Fraud detection & Loss prevention

Fraud and shrinkage is one of the most common challenges faced by retailers resulting in financial andconsumer trust loss. It can originate with consumers, employees, or external sources. Different types of fraudinclude credit-card fault, fraudulent merchandise returns and shrinkage due to shoplifting, embezzlementand human error.

Predictive modeling helps in identifying unusual patterns of purchase and product movements that can helpdetect fraud and shrinkage. It also helps narrow down the categories and sale seasons that are mostsensitive to fraudulent behaviour. The retailer can then take extra precautions to safeguard against lossamong these sensitive categories and shopping periods.

Fraud Multiplier by Industry

Store Operations

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0 1 1 2 2 3 3 4 4 5 5

Housingwares/Home Furniture

Automotive/Motor Vechile and Parts…

Telecommunications or data service…

Flowers/Gifts/JewelrySporting Goods

Computer/Electronics/SoftwareBooks/CDs/Videos/DVDs/Music

Textiles/Apparel/Clothing

Drug/Health & Beauty

Office SuppliesGeneral Merchandise Stores

Hardware/Home Improvement

Toys/Hobbies

Total

Fraud Multiplier

Category Sales Reporting & Analysis

Constant tracking of sales and regular reporting helps the sales force analyse category sales so that they canhave an action plan before the next sales cycle starts. Also, it serves as the base for formulating salesstrategies.

It helps in: Identifying which categories, products and SKUs are selling the most in the store Analysing consumer preferences and buying patterns in the store Evaluating growth potential for product portfolio (categories, brands and SKUs) Planning and managing store promotions Evaluating the performance of the store by categories and SKUs on a regular basis Enabling root-cause analysis in case of sales/profit decline: help identify the epicentre and rectify the

same

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8%

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22%

50%

Color cosmetics

Skin Care

Personal Care

Hair Care

Current MonthMarket Share

YTD Market Share Change

YOY Market Share Change

3 month MOMMarket Share Change

55% 0.1% 0.3% 0.4% 1.0% 3.2%50%

12%

32%

52%

YTD'11 Company1 Company2 Company3 Company4 Company5 YTD'12

Narrowing down on share loss within Hair Care category

52% 4.1%1.1%

0.3%

8.7% 48%

30%

45%

60%

YTD'11 Brand1 Brand2 Brand3 Brand4 YTD'12

Further narrowing down on the brand(s) causing the share loss

Store Operations

Workforce Analytics

Sales force, for a retailer is an equally important asset as the product that they sell. A good, experiencedsales force yields higher consumer satisfaction and hence increased sales. It is therefore critical to optimizethe employee recruitment, training and supervising process. Retailers can use analytics to increaseproductivity and can help enable an effective and sustainable retail workforce.

The advantages of work force analytics include: Acquisition of talent- identifying the most effective employee attributes Skill set mapping- placing employees in the ideal role based on their capabilities Talent building- recognizing employee training needs in key skills and ensuring all employees meet store

standards Improve scheduling effectiveness- based on predictions of when and where consumers are most likely to

shop, analytics can help schedule the most-productive employees appropriately Retention- by understanding the key risk factors that drive attrition, employers can preemptively

mitigate these risks. Improve safety- detect the underlying causes to workplace accidents and rectify

Store Operations

Tracking work-force effectiveness & it’s impact on revenue

Opening of new stores

Site selection is crucial to a retailer and identifying the ideal location to open a new store has to be astrategic decision.Integrating census data, which provides population and income data, along with survey data, providingdemographic, psychographic and competitor store data, and financial data will give the retailer a betterunderstanding on areas with the greatest potential. With this information, a strategic model can be built,which can help determine the best sites and best strategy for that area.

This process helps retailers identify: The ideal location for the stores The type of store format that is needed in a specific instance Whether to remodel or not What merchandising approach to adopt

Strategy &Planning

Identifying the optimum location for a retailer

Tracking Overall Performance

Retailers need to get a bird’s eye view on changing business conditions and emerging trends, and growthpotential based on the sales and profits earned from their stores and categories. Accordingly they can adjustplans and forecasts to meet the new challenges and opportunities.This requires a close monitoring and tracking process of the sales and financial measure of the overall marketand then correlating it with the individual store performance.

It helps retailers: Analyse market trends and buying patterns in the retail industry and identify the gaps and opportunities Evaluate and benchmark store performance on key metrics like traffic counts, conversion rate, sales per

square feet and sales per employee Track sales activity for all outlets by region/sub-region/category Identify profitable categories in various regions

Monitoring & tracking performance across outlets

Strategy &Planning

Sales Performance:

Store Clustering

Retailers need to customize their product and service offering to meet the taste and preferences to diversecultural and demographic consumer segments. Implementing strategies at an outlet level will beoperationally difficult to manage, while an overall promotional campaign and strategy for all outlets, despitebeing operationally more feasible will not be able to meet localized consumer needs. To counter this issue,retailers need to identify stores that exhibit similar demographics, locational proximity, personal income andshopping behaviours of local consumers and device a localized approach to run their marketing activities.

Cluster analysis uses loyalty card transaction data and survey data to identify similar stores that form acluster based on shopper demographic data and their shopping patterns. The retailer is then able to tailorspecific promotional campaigns, assortment, planogramming, pricing and promotion strategies, storeformats, layouts for servicing each of the identified clusters. This garners the retailer better returns on theirstrategies since it is more focused to shopper needs and increases consumer satisfaction due to the“personalized” approach.

Store clusters for a leading mass retailer in the US

Strategy &Planning

Strategy &Planning

Key Value Item Analysis

A few SKUs have a disproportionate impact on consumer price-value perception and can cause consumers toswitch stores when those SKUs are not priced appropriately. These price sensitive items are known as KeyValue Items, or KVIs. A retailer can use this knowledge to have a significant control over the items’ perceivedprice image and thus regulate the store’s image by carefully fixing the everyday pricing and the promotionalpricing.

Key Value Item Analysis blends behavioral data (sales, household penetration, purchase frequency) andattitudinal data (consumer awareness of product, accurate price recall, price differential across similarretailers). The KVIs are identified across categories based on revenue coverage, price sensitivity, salesvolume, and the role and prevalence of the item in the market basket.

By managing true KVIs through aggressive pricing, promotions, wide range availability and correct placement,retailers will be able to: Influence consumers’ overall perception of the store Drive sales and footfalls Gain market share

Factors determining

a KVI

SalesVolume

PriceSensitivity

Revenuecoverage

Sale

Price

Role & Presence in Market Basket

Price differential across retailers

Business Situation:The client, a leading retail chain offering various products across categories, wanted to understand its customers to better plan customized campaigns and promotions with the objective of increasing customer engagement and overall revenues.

The Task:Identify appropriate customer segments based on various factors such as purchase patterns, promotion response and demographics of the customers.

Framework:

Customer Personas:

Analytics in ActionIncreasing Revenues by better Understanding Customers

Client: A Leading Retail Chain

Define & Build customer segments

Segment analysis Customer profile

Identified an appropriate customer base based on the #

of visits and days on books

Built customer segments using clustering algorithms after

treating the outliers

Analyzed the segments and identified the customer

personas in each segment

Got a detailed profile of customer in a segment to

target for promotion

Who?

What?

When?

70% salesfrom FMCG &Staples

Early morningWeekend

Early Morning Weekend Shoppers

Large family High visits

60% salesfrom FMCG &Staples.Multi-categoryshopping

Afternoon to Evening

High sales, large family shoppers

Salariedstaplesshoppers

70% sales from StaplesShops in rice, oil, pulses and flour

Morning to Afternoon1st 10 days

Salaried, Health conscious,

staples shoppers

SalariedLarge family

50% sales-staples, 30%-FMCG. Multi-category shopping

Morning to AfternoonWeekend 1st

10 days

Salaried, large family, weekend shoppers

Low visits Low sales and high margin

45% sales from ApparelsShops in Men’s casual and formal, ethnic wear

Morning to Afternoon Weekend

Weekend, apparel buying shoppers

Single familywith kidsHealthconscious

70% sales-FMCGHigh proportion of baby care and health SKUs

Morning to Afternoon

single/small family shoppers

Discountseekers

50% sales from Home needs.Shops in utensils, bed and luggage

Afternoon to EveningWeekend

Discount seekers

70% sales from Staples & FMCG

Evening

Evening shoppers

The Result:

• Developed relevant Customer personas like discount oriented, large family, weekend specific category shoppers,impulsive buyers, high end buyers, etc

• Customer personas helped the business to appropriately target customers based on the day, time, affinity and categoryof purchase with appropriate promotional offers, leading to incremental revenues

Identify an appropriate

Customer Base

Small to Medium size families

Large families shops mostly in FMCG and Staples

Business Situation:The client, a B2B US-based retailer with presence in North America, Australia, Europe and Middle East noticed a significant increase in % Bad Debt for new prospect acquisitions; from 5.1% of total prospect sales in 2007 to 8.8% in 2011. Business wanted to manage this without compromising on lost sales.

The Task:Design, develop and implement a robust predictive strategy that will help in quantifying the forward-looking risk at a Prospect-level. This will be a quantifiable and reliable benchmark for the business to leverage and decision whether to extend credit, go for credit card pre-payment, or completely avoid a particular prospect.

Analytical Framework:Developed a Risk or Q-Score using firmographics and transaction information on prospects acquired between 2007 – 2011. Upon implementation, each Prospect had a risk score between 1 and 10; with 1 being the most risky prospects; and 10 being the least risky Prospects.

The Result:• Based on validation results; the predictive model was able to significantly separate prospects who paid and who defaulted• Prospects with Q-score = 1, 2, 3 are high risk prospects. These prospects on average have 2.2X default rate as prospects with score = 4 to

10• Recommendations implemented by the business: Review of all orders > $100 for Q-score < 4 and Prepayment for all orders > $200 for Q-

score < 4• Overall Bad debt decreased by 32% in the subsequent year

Analytics in ActionProspect Acquisitions: Lowering Bad Debt by 32%

Client: A leading US-based Retailer to Small & Medium Enterprises

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Defining Modelling universe

Risk Tables

Variable selection and

Model development

Build Model, assigning Q-

scores

Prospects segments with limited data and low bad debts ignored (these segments

were treated separately)

Risk factors calculated for each prospect variable such as location, transacted value, organization size by prospect segment, etc

Variables (factors) with high information value (IV) to risk identified; inter-correlated factors discarded to include most relevant

factor in the model*

Q-score calculated based on model and assigned to each prospect

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Business Situation :US-based manufacturer of personalized gift items; with presence across North America, Australia, Europe and Middle East. They have over 3,000 sku’s on offer; across 8 product platforms and 13 countries. SKU’s are supplied from Far East suppliers, with a 3-month lead time. And almost 60% of Customers purchase gift items for immediate consumption; with the remaining having a deferred shipment date through out the year. In addition, the manufacturer runs SKU-level promotions through-out the year, which result in SKU-specific demand. Critical to have accurate sku-level demand forecast so that all orders are met; while maintaining optimal inventories.

The Task :- Develop a framework and relevant forecasting models for improving the forecast process and accuracy.

- Obtain a robust and accurate SKU-level forecasting for each week over a year.

- Implement the predictive models so that forecasting is improved, inventory levels are optimal and customer satisfaction is improved.

Analytical Framework :The solution was aimed at simplifying the process and improving the timeliness and accuracy of demand forecast:

1. Simplify and automate some of the current processes that were cumbersome and susceptible to human error.

2. Use statistical analysis to learn from historical trends, project future demand, and create an Early Warning System to predict weekly excess and stock-outs at SKU level.

3. Improved the existing process of predicting repeat business using cannibalization models and also provided shipment profiles with insights on patterns of how products shipped out to customers. This helped in placing timely and appropriate Purchase Orders with suppliers.

4. Provided dashboards for measuring forecast accuracy and also performance of shipments.

5. Adhoc analytics to support current forecasting, customer care and marketing decisions e.g. quantifying financial impact of late shipments

The Result :• Better sku-level forecasts and ability to react faster to products becoming hits.

• Less obsolete inventory at the end of the year meant freeing up working capital and reducing waste. Lower stock-out rates also meant better customer satisfaction in addition to revenue. Since repeat customers are their main focus, this factor is critical in preventing unnecessary attrition.

• More scientific approach to forecasting, thereby eliminating any bias in subjective forecasting logic.

Analytics in ActionImprove Demand Forecasts. Sales up by $ 3MM, stock-outs down

Client : A US-based Manufacturer of Customized Gifting Products

Forecast Variance

Sale

s gr

ow

th (P

Y)

Over- forecasted Growing

Under-forecasted Growing

Under-forecasted Declining

Over-forecasted Declining

WC57001A, +, +

TD72601B, -, -

WC87901A, -, -

WC74846A, +, +

WC57001B, -, -

WC59401A, +, +

WC69501A, +, +

WC62545A, -, -

WC93001A, +, +

WA38001A, -, -

WC62545B, -, -

WC58802A, +, +

WC59004A, -, -

WC74846B, +, +

WC30146A, -, -

WD25646A, +, +

WC74903B, +, +

WD06001A, -, -

WC74903A, +, +

WC59401B, -, -

WC59004B, -, -

WC83504A, +, +

WD31503B, -, -

WC80446A, -, -

WC28801A, +, +

WC81001A, -, -

WC83945A, +, +

WC28301A, -, -

WC87901B, +, +

WA85401A, -, -

WC58802B, +, +

WC88102A, -, -

WD11202A, -, -

WC89803A, +, +

WC83945B, -, -

WD01993A, +, +

WC30146B, -, -

WD18006A, +, +

-5000

0

5000

10000

15000

20000

25000

30000

-40000

-30000

-20000

-10000

0

10000

20000

30000

40000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43

MAPE - 23.47%

2UCL

3UCL

2LCL

3LCL

Variance

235386

359051

198157211658.2184196496.3737

0

50000

100000

150000

200000

250000

300000

350000

400000

1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031323334353637383940414243444546474849505152

Plan C

FY_2009

YTD_2010

Regression 1

Regression 2

forecast

• Further, it was found that key style groups and colors remained consistent year-on-year• And that Customer repurchases on deleted style groups and colors were not significantly impacted the

following year• Recommendations made to rationalize marginal style groups and colors, leading to approximately 30% SKU

reduction and significant cost savings in business complexity and working capital.

5% 5% 5% 5%

11% 10% 8% 6%

11% 11% 11% 10%

10% 11% 11%12%

26% 25% 26% 28%

30% 30% 31% 30%

0%

20%

40%

60%

80%

100%

2008-09 2009-10 2010-11 2011-12

13%

34%

64%

44%

15%

36%

57%

49%

0% 10% 20% 30% 40% 50% 60% 70%

Reactivation Continued SKUs Reactivation Discontinued SKUs

Customer Segment1

Customer Segment2

Customer Segment3

Customer Segment4

• Business had over 5000 SKU’s across a few unique Product Platforms. Each platform had SKU’s across many style groups and colors.

• Style groups and colors were investigated for marginal contributions to Revenues.

• It was found that only a few key style groups and colors accounted for over 90% of revenues

Client : A leading B2B Retailer of Personalized Gift Products

Blue

Gunmetal

Red

Black

BurgundyGreen

Rest

Analytics in ActionEffective SKU Management

MANAGEMENT TEAMGLOBAL EXPERIENCE.

PROVEN RESULTS.

Roy K. CherianCEORoy has over 20 years of rich experience in marketing, advertising and mediain organizations like Nestle India, United Breweries, FCB and FeedbackVentures. He holds an MBA from IIM Ahmedabad.

Anunay Gupta, PhDCOO & Head of AnalyticsAnunay has over 15 years of experience, with a significant portion focusedon Analytics in Consumer Finance. In his last assignment at Citigroup, he wasresponsible for all Decision Management functions for the US Cardsportfolio of Citigroup, covering approx $150B in assets. Anunay holds anMBA in Finance from NYU Stern School of Business.

Kakul PaulBusiness Head, CPG & RetailKakul has over 8 years of experience within the CPG industry. She waspreviously part of the Analytics practice as WNS, leading analytic initiativesfor top Fortune 50 clients globally. She has extensive experience in whatdrives Consumer purchase behavior, market mix modeling, pricing &promotion analytics, etc. Kakul has an MBA from IIM Ahmedabad.

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1.212.837.7827 (o) 1.208.439.5551 (fax) info@marketelligent.com

CONTACT www.marketelligent.com

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YOUR PARTNER FOR

DATA ANALYTICS SERVICES

Greg FerdinandEVP, Business DevelopmentGreg has over 20 years of experience in global marketing, strategic planning,business development and analytics at Dell, Capital One and AT&T. He hassuccessfully developed and embedded analytic-driven programs into avariety of go-to-market, customer and operational functions. Greg holds anMBA from NYU Stern School of Business