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Presenters: Chris Doherty and Jeff Manheim

Path to Shopper Analytics. Presenters: Chris Doherty and Jeff Manheim. October 16, 2013. Your shoppers can buy products anywhere. It’s their experiences that can set you apart, . And make you the first choice in their hearts and minds. . Customer’s

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Presenters: Chris Doherty and Jeff Manheim

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  1. Path to Shopper Analytics Presenters: Chris Doherty and Jeff Manheim October 16, 2013

  2. Your shoppers can buy products anywhere It’s theirexperiences that can set you apart, And make you thefirst choice in their hearts and minds.

  3. Customer’s experiences before, during and after the purchase shape their loyalty

  4. Shaping the Customer Experience Building Blocks

  5. Opportunities to Build Loyalty Through every customer interaction Community At-Home • Causes • Altruistic Support • Community Partners • Coupons • Shopping Lists • Recipes On-the-Go In-Store • Target Offers • Navigation • e-Coupons • Payments • Promotions • e-Receipts • Integrated Mobile Engagement

  6. Interaction at Home Kroger – Best in Class • Seamless Customer Planning • Add coupons to Kroger Plus card • Access via Mobile App • Add Coupons from App • Create Shopping Lists Online • Add Items from Flyer to List • Access in Store via App • Recipes • Click to Add Ingredients to list for some Recipes • Digital Coupons Displayed for Ingredients as Available • Opportunity: Integrate ingredients for Meal Solutions recipes with shopping list

  7. Interaction in Store Safeway – Best in Class • Customer Convenience • Targeted Offers • Click-to-Card or Mobile Coupons • Paper-less Redemption at POS • Easy Pay with Loyalty Club Card • Earn additional points • One-Card Shopping • Integration with Fuel • Opportunity: In-Store Navigation and Customer Offers

  8. Interaction On-The-Go CVS – Best in Class • Convenience of Mobile • Targeted Offers • Scan and Refill Prescriptions • Order Photo Prints • Opportunities: Integrate Couponing, Integrate Rewards, Payments Via Card or Mobile Device

  9. Interaction in the Community Price Chopper – Best in Class Making an emotional connection with the Advant-Edge • Savings for Customers • Store and Pharmacy • At the Fuel Pump • Across the Community • Altruistic Support • Scholarships • Grants • Donations • Opportunity: Customers participate in giving; customers choose their own charities

  10. Same Objective, Different Paths

  11. Develop Actionable Strategies To drive loyalty Create Strategies that Deliver Tangible Results • Drive Sales Growth • Identify customers likely to purchase specific products or categories • Motivate shopping in sluggish categories • Proactive Customer Retention • Likely to attrite predictive modeling • Prevent attrition before it actually happens • Support Merchandising Decisions • Pricing / Promotion • Assortment • Support Strategy and Program Development • Offer redemption/ promotion analysis • Brand loyalty and likely to switch

  12. Leverage Shopper Insights To build strong shopper understanding • Marketing • Merchandising • Supply Chain Avoid “Rear View Mirror” Thinking and Leverage Shopper Insights and Predictive Analytics to Optimize Business Decisions

  13. Importance of Shopper Segmentations • Simple • Deciles • Repeating Shoppers • Frequency / Spend • Loyal Level • Intermediate • Demographics • Consumer Engagement • Complex • Price Sensitivity • Lifestyle • Demographics MERCHANDISING MARKETING

  14. Simple Segmentation Example Spend/Frequency • Definition: Grouping shoppers based on a combination of how often they shop and how much they spend FREQUENCY High Low High S P E N D Low

  15. Intermediate Shopper Segmentation Example Consumer Engagement • Definition: Grouping shoppers based on a combination of spend, frequency, breadth of store shopped, promotional response and other non-product scoring metrics

  16. Complex Shopper Segmentation Example Lifestyle • Definition: Grouping shoppers based on what they buy You are what you buy…

  17. Boots The Chemist Unique Segmentation

  18. Shopper Data Example

  19. Metrics Calculated From Available Fields • Trip • Focused Metrics • (Transactions & Time) • Cycle Time (Purchase Cycle) • Repeat Shoppers • Additional Repeats Per Repeater • Trip Conversion • Shopper • Focused Metrics • (Number of Shoppers) • Household Penetration • Household Cross Shop • Exclusivity • Profiling • Basket/Product • Focused Metrics • (Spend) • Loyalty • Basket Measures • Affinity Metrics

  20. Category Management Application Understanding shopper behavior can help answer key business questions such as: • What is the estimated effect on sales, units and category profitability when a brand is removed from the shelf? • How incremental is my corporate brand program? • Are corporate brand program changes influencing shoppers to shop a greater % of the store? • What is the market structure for a category? • Who is buying the portfolio of products in a category and are there any important segments we are not engaging? • When new items are introduced to the shelf where are they sourcing volume from and is it greater or less than expected? • Is there demand for a private brand in a segment and what is the value of the opportunity? • Which items in the category are driving category strategy?

  21. What is a Market Structure analysis? Market Structure analysis identifies the attributes of product in a category most valuable to shoppers • What key questions can a Market Structure help answer? • What attributes of product are more important/less important to shoppers? • How do we optimize the layout and flow of a planogram? • What product groups should have more/less allocated space? • What products should we promote? • How should we position the category to shoppers? • What attributes should we consider for new item development?

  22. Market Structure – Toilet Tissue

  23. What can’t be done with the data? It’s not magic What we can answer: “HOW” shoppers actually behave What we cannot answer: • “Why” can only be answered through conversations with consumers, consumer surveys and primary research It is important to utilize FSC data in conjunction with other data sources WHY?

  24. Thank You

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