what we can do in Retail analytics

1. Retail Analytics
Bhawani Nandan Prasad
MS Computer Science and MBA
Target Customer in Retail domain:
Retailers in Grocery, Convenience, Specialty, General Merchandise, Health and Beauty,
Deep Discount, Home and Garden, Building Supplies and Grocery Wholesale
1. Retail Product Life Cycle Price Optimization
 Manage & Optimize Prices across the Pricing Life Cycle
 Increase Pricing Precision thru Rules, Science & Strategy
Business Revenue Benefits
 Compete More Profitably
 Shape Shopper Behavior
 Enable Data-Driven Decisions
 Return on Investment $10-20 on every $1 invested
 Forecast Accuracy 91-94%
 Sales Increases 2-7%
 Gross Margin Gains 2-5%
Product life cycles vary across stores, markets and channels and the proliferation of data to be
analyzed is daunting without scientific solutions which can organize the data, make inferences
and create recommendations that are localized and shopper-centric.
• Image enhancing, profitable prices that achieve strategic and financial objectives
• Targeted, loyalty building, measurable promotions that incentivize omnichannel shoppers to
buy more and buy more often
• Effective end-of-life and end-of-season markdowns that clear inventory at the highest possible
margin – not giving away too much too soon or too little too late

2. Life Cycle Price Optimization enables retailers to execute precise, localized life cycle pricing that
maximizes profitability while achieving category, channel, store, loyalty and performance
objectives.
2. Markdown Optimization For Short Life Cycle Products
 Forecast Accuracy 91-94%
 Sales Increases 11-20%
 Gross Margin Gains 5%
 Increase in Capture Rates 4.5-12%
Balances Desired Stock Positions, Exit Dates & Budget Priorities
Escalating markdown budgets have challenged retailers to pare down bloated inventory positions while still
presenting fresh assortments on-time and as-planned.
Across-the-board markdowns have exacerbated the financial impact by further hemorrhaging margin.
Decisions are often emotional and typically do not achieve their objectives because:
• ‘Too much too soon’ causes stock outs & margin loss
• ‘Too little too late’ causes excess stock & impacts sales of new products at full price
• ‘Too everywhere’: Enterprise-wide markdowns cause sales & margin loss

3. 3. Markdown Optimization For Large Life Cycle Products
 Markdown Planning, Strategy, Simulation & Measurement
 Maximize Sell-through & Profitability with Localization
Stop leaving money on the table by taking enterprise-wide markdowns or making
suboptimal decisions for timing, cadence & depth. Markdown Optimization
empowers retailers to clear inventory while meeting sellthrough & profitability goals.
Not just for seasonal or discontinued items anymore, retailers’ leverage optimization
for category resets, overstock reductions, aged packaging, & date-coded product.
• Localizing Markdowns to Shopper Demand & Inventory Depletion Rates enables
retailers to maximize margin & inventory value while improving freshness & velocity
of product assortment.
• Planning, Management & Predictive Analytics take the emotion out of markdown
decisions by enabling process driven markdown execution.

4. 4. Data Mining Analytics for price optimization
Analytic services enable retailers to jump start their Price Optimization
implementation with deep insight into channel, location, and competitor and
product price elasticity.
Once retailers get the right start, these services provide additional value by
permitting clients to increase pricing sophistication and solve complex
problems as they move up the maturity curve.
Evaluating elasticity at the outset permits initial system configuration that
maximizes the effectiveness of:
• Key (Known) Value Items
• Item Investment Classification
• Store Zone Clustering
• Competitive Positioning
• Category & Sub-Category Strategies
Designed to easily adapt to the growing skill sets and sophistication levels of
retailers, Price Optimization and Advanced Analytics enable clients to continually
drive significant ROI as their organizations gain experience, refine strategies
and hone competitive positions.

5. Build data visualization for elastic workbench and Competitive Intelligence
workbench.
5. Key Value Item Analysis
- Scientific Insight into the Items that Drive Price Image, Impacting Pricing
Strategy & Competitive Positioning.
• Evaluation of item elasticity
• Rankings and recommendations of the top items individually or by price
family
• Lists and rankings by pricing zone, & by category or department
• Coverage analysis to determine percent of business included on KVI lists
Analysis including:
– Product count by department
– Sales metrics by department
– Top 500 breakdown by category
– Top 15 items by revenue change
– Top 15 items by quantity change
– Top item grouping metric comparison
– Breakdown of the top 100, 250, 500 items & groups
Austin |

6. For retailers, a small subset of items has a disproportionate impact on consumer
price perception. These sensitive items are known as Key Value Items, or KVIs. If
a retailer can scientifically identify those items and their relative price
sensitivity, it can have significant control over their perceived price image.
Unfortunately, many retailers don’t have the tools and are left with gut feel,
missing opportunities to optimally price the right items. Companies brings
advanced science to supplement a retailer’s intuition for improved performance
and price perception.
By aggressively pricing true KVIs, retailers are able to:
• Influence consumer price perception
• Drive store traffic
• Compare competitive prices more strategically
• Gain market share against their competitors
• Maintain profitability on less price-sensitive items in their stores.
Apply Decision science based analysis
Scientific product segmentation: Identifies and segments items that reflect or
display:
• High sensitivity, or demand response, to price changes

7. • High sales volume
• Ability to drive store traffic
• “Anchoring” position in market baskets
• High scores on price awareness surveys
• Sensitivity to local competitive influence
Design KVI Groups by need: The analysis can be performed at the chain level, by
region, by pricing zone, or by category.
Validate and compare: Our recommendations can be used as a stand-alone item
group or to augment and validate a retailer’s own KVI item list.
Coverage analysis: Evaluate the fraction of items, by count and by revenue, that
are included in KVI lists for optimal coverage scenarios.
6. Assortment & Space Optimization to increase sales
- Sales Increases: 2-7%
- Gross Margin Gains: 2-4%
- Increased Customer, Traffic Count, Larger Shopper Baskets, Return on
Investment Within 2 Months
This is Predictive analytics Assortment-aware Store Space Optimization.
• Recommends reformats, remodels and refits to ‘right-size’ locations and assortments
within space
• Simulates ‘What If’ scenarios to evaluate strategy
• Breaks down silos between Assortment and Space planning through simultaneous
optimization
• Ensures proper capacity, facings and SKU presence
• Predictive optimization – identifies “size of the prize” before making changes.
• Incorporates 3rd party Market Data to identify competitive gaps and opportunities
• Determines fair share of market and identifies category growth opportunities
• Provides guidance to store reinvention
• Augments and integrates with current assortment and space planning tools

9. 7. Market Basket Analytics
Deep Insight into Shopper Behavior - Impacting Forecasts, Recommendations &
Decisions.
Retailers leverage Market Basket Analysis to provide a window into shopper
behavior, revealing how they select products, make spending trade-offs and
group items in a shopping cart.
Understanding how baskets are built can help retailers merchandise more
effectively by leveraging market basket dynamics in pricing and promotion
decisions. Analysis of transaction-level sales data can reveal deep insights into
shopper buying behavior and answer questions such as:
• What products tend to be purchased together?
• Are shoppers more willing to buy multiples if incented?
• What baskets are the most profitable to my business?
• What buying behavior characterizes my most profitable shopper?
Understanding basket-level dynamics allows retailers to make better decisions
related to base and promotional pricing enabling them to:
• Improve cross-selling opportunities across categories
• Up-sell to better or more profitable brands within purchased categories
• Drive store traffic with the right offer & incentive
• Improve sales with in-store displays by co-locating the right items
• Understand the holistic impact of promotions & price changes
• Improve performance of multiple-purchase offers (e.g. 2 for $2)

10. 8. Optimization Intelligence Reporting & Analysis
• Trends
Trend reporting at all levels across multiple data sets
• Effectiveness Measurement
KPI impacts
• Weekly Comparisons
Highlight sales, top performers & margins
• Competitors
CPI, prices, gaps & opportunities
• User Adoption & Value Measurement
Recommendation acceptance rates & impact of optimization on results
• Data Exception Reports
Data quality & processing errors
• Price Change Monitoring
Monitor price change results across multiple price zones
• Scenario Review
Review scenario configurations & associated key metrics
• Executive & CM Dashboards & Reports
• Identify & respond strategically to sales trends & competitor prices
• Workflow driven analytics for promotion & markdown planning, simulation,
execution, monitoring & effectiveness measurement
9. Promotion Strategy, Planning, Simulation & Measurement Analytics
Sales Increases 1-12%
Gross Margin Gains 5-20%
Unit Movement Growth 1-9%
Promotional Calendar
• Calendar or Grant view
• Filtering capabilities
• Annual promotion planning
• Visibility into campaign, events & promotion
Planning & Management
• Intelligent dynamic workflow

11. • Dynamic & systematic versioning
• Standard & alternate hierarchies
• Strategy planning and modeling
• Supports all vehicles & types
• Event creation & delegation by role
• Offer-level strategy configurations
• Supports complex promotions
Optimize
• Event & offer versions
• Visual layout configurations
• Planning matrix, vehicles & types
• Vendor trade funds
• Inventory aware
Data & Analytics
• Real-time data & model update
• Deal collection & repository
• Simulation visualization & version comparison
• Forecasts impacts & basket cross-effects
Execution & Measurement
• Approval & event status visibility
• Forecast roll ups versus budget & actual
• Actionable Performance Intelligence reporting
• Campaign effectiveness measurement
10.Social Commerce - Drive Engagement and Amplify Customer Conversations
• Leverages social media by delivering promotions amplified through Facebook
• Drives the acquisition of new customers by utilizing loyal customers’
social connections
• Measures social promotion campaigns and events, for social reach and
provides an ROI for every channel and at every touch point
• Modular: Deploys one or multiple social programs to support business
objectives
• Integrates with loyalty systems/programs

12. • Online to In-store: Integrates online, in-store, social and mobile touchpoints
and channels
• Redemptions at Channel of Choice: Checkout online or in-store at POS
• Integrates with Promotion Optimization solution: Determines items and
optimal offers and drive optimized promotions into social and ecommerce
11.Cluster Analysis- Scientifically group stores for optimal pricing alignment
Benefits:
• Optimally grow profit with the right price, for the right customer, at the right
store
• Maximize financial opportunities while recognizing regional perceptions
• Localize pricing to become more consumer-centric
• Compete more effectively against the relevant competition
• Improve store-specific operations with insight into the point of diminishing
returns
Scientific Store Clusters:
Identifies and segments stores by evaluating:
• Consumer price sensitivity
• Geography
• Store-area market data, including population density and competitive price indexing
(CPI)
• Demographics of customers including income level, family structure, ethnicity, and
employment
• Competitive presence, including proximity of primary and partially competitive stores
• Distribution center proximity and relative cost to serve
• Retailer-defined business rules
Service Deliverables
Store Cluster Analysis provides retailers actionable insight into the best store
clusters. The service deliverables include:
• Store elasticity analysis
• Cost-benefit analysis of opportunity versus number of clusters
• Recommended store assignments for each cluster scenario

13. • Demographic and competitive factor analysis of dominant factors in cluster
selection
• Demographic and competitive profiles of recommended cluster groupings
• Incorporation of business rules into cluster analysis
• Pricing consultation to leverage desired strategies to price optimization
configurations