Targeting the Moment of Truth - Using Big Data in Retail


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Presentation on the business challenges of using Big Data in Retail with a bit of storytelling on why this is not new! @The Fifth Elephant conference in Bangalore on 28 July 2012

See the notes for more talking points.

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  • A man is coming back home, tired from work on a late rainy evening. And he walks into his neighbourhood supermarket store. As he is folding his umbrella and trying to get water off his jacket, he walks down to aisle 5. He is looking for that one breakfast cereal box that his 5 year old has agreed to eat tomorrow morning. He reaches the right shelf and starts looking for the cereal box. And he doesn't find it there. That is his moment of truth. And the challenge facing the supermarket retailer is how do I ensure I get the right product, in the right channel and the right price in front of that man, at the right moment.My name is Amit Kapoor and I have been working for the last 11 years as a strategy consultant helping retail and consumer companies address some of these challenges. And today I will be talking about how the retailers can harness the power of big data to use all the signals they have about the consumer to address their moment of truth.
  • Let me start with a confession. I am not a big data guy. In all my consulting life, I could best classify myself as a small data guy. The guy who at best used an excel sheet and data dump from whatever patchwork IT system that the retailer had to try and address the challenges the face.
  • Strategic business problems are what I would call wicked problems - typically ill defined, fuzzy and have several unknown factors. They have cross-functional challenges and you typically have amplifying loops, interdependences, constraints
  • If you go to my small piece of estate on the world wide web called, you will find this written on the header: Structure | Synthesis | Solving | StorytellingYou need to understand the problem and start to structure it - What is the problem questions I would answer. I don't mean data structures, I mean what are the hypotheses about the problem. Then you need synthesize a set of data sets that would help you in test those hypotheses. Then you try solve for those hypotheses by testing them and asking the question "what if" - you trying to use abductive logic. Both the definition of the problem and the solution are not separate and we keep refining and reshaping and sharpening both of them. Then you need tell a story around the same to "make the case" a compelling case from data based hypothesis. Explaining 'what is' is an essential step in building confidence in the recommendation. Learning and changing mental models is needed for implementation and acceptance
  • And that is the most important thing we should remember when we start talking about this wonderful concept of Big Data and try and bring it to an industry - be it retail or any other one. If we look beyond the hype and the hyperbole - What is the busines problems we will solve and how will we make that change happen. SOLVE A PROBLEM - MAKE CHANGE HAPPEN. Hold that thought. That is the key. If we do that we would can take this concept of Big Data and make it work [
  • What are these wicked problems in retail we need – lets setup some context for Retail. [PICTURE 5]First thing that is great about retail is that it is very personal. It is very experiential. Retail is where you come across the “the moment of truth” – you can actually see the product like the man in the starting story in middle of a shelf down at aisle 5 at a Reliance Store or may be dirty IOC petrol pump or branch officer at State Bank of Mysore extension branch. It is very experiential – good or bad – but they tell you a lot about retail and all of you sitting here have experienced it.Second thing about retail is that it all local. If you want to buy grocery on bannerghatta road and you want to walk – you may go to a hypermarket like reliance, or a convenience store like More, or a supermarket like Foodworld/ Benison or a local shop. If you want to drive you may go to a SPAR or Big Bazaar But that is the universe for the consumer and good thing that is the competitive set the retailer has to think about when he is looking at things. It changes a little bit if you do e-tailing – but it will boil down to where people can deliver – still fairly local.Thirdly it is a rapidly growing / evolving sector.The Indian Retail markets Is about $410 billion in 2010 and of that only 5% is organized retailing. When you compare that with the developed market like UK where in food /grocery the top 4 players (Tesco, Sainsbury, ASDA and Morrison) have 65% market share and the top 10 have 85% - the scope of growth is immense. In the challenges are different – you can see the first wave of players in India over the last few years – experimenting with formats, changing layouts, offering for the customers – and you can see there is lots to learn on how this organized retail market will grow. Same is true abroad – growth of big box retailing in the 90s, the challenges of e-tailing in early 2000 and the threat of discounters in late 2000s. Now there is lot of talk about SKU rationalization, local merchandising , sustainability, closed loop shopping experience, store as showroom, community / social shopping etc. Very exciting sector
  • Most retailers would take footfall coming into the store as clients of which a certain percentage would get converted into customers. Then you are looking how big of basket / trolley of products to they and what is the average prices within the trolley. It is easy to see the number one the most important factor is location – that will drive the catchment area and the footfall. That is why you will hear a lot of people saying – retail is just Location, Location and Location. The rest of the factor you will see that it Range, Price, Promotion, Merchandising, Layout all that drive.
  • On the cost side now you are looking merchandise that is the biggest cost – hence you look at gross margin. Then you have employees and then space cost and retail is asset intensive business. You want to generate higher margin / sales per sq ft – that is how retailer measure themselves.
  • Retail is Tough. If you look at the Top 250 retailers (none of them are Indian) – you are looking 3.8%. FMCG would be lower at 2.8%, hardlines would be 5.9%. Fashion is much higher at 7.7% but that is another story. If we look at Q Ratio – which looks at market cap divided by tangible assets – If you have a unique brand, merchandise, format, service which gives differentiation, innovation, loyalty, customer experience and hence better pricing power and profitability you should be greater than 1. You will find that the bulk of the grocery retailers are at 0.8. Most business you can buy them out and money by selling the fixed assets. For fashion / apparel it it 1.6 (Amazon at 5.3, Apple at 4.7, H&M at 5.7, Inditex at 4.2) in 2011
  • So what are the changes that we can make happen which will help change the retailers for the better. They are - can I improve the efficiency of the retail supply operations, can I optimize the demand management for my products - through pricing, promotion, assortment, placement and can I improve the effectiveness of my customer management which includes, marketing, and loyalty
  • So let me tell you two stories from retail that bring these themes out, move from small data to big data and paint this picture of change. BAR CODE [PICTURE 5] ---> POS ---> SIGNALSIn 1948, a local food chain store owner made an inquiry to the Drexel Institute in Philadelphia asking about research into a method of automatically reading product information during checkout. Two graduate students Joseph Woodland and Bernard Silver worked on that problem and would go on to file an patent application with USPTO (US Patent and Trademark Office). The picture you see is the image extracted from the Patent no. 2,612,994 issued to them on October 7, 1952 - which is the first version of the bar code - more technically correct as "bulls eye" concentric circle. Their patent application for the "Classifying Apparatus and Method", describing their invention as "art of article classification...through the medium of identifying patterns".
  • It was only in 1973 that IBM UPC (Universal Product Code) was finally selected by the National Association of Food Chain to become the standardized barcode. And next year, the first product scanned was a 67 cent - 10 pack of Wrigley's Juicy Fruit chewing gum was scanned at Marsh's Supermarket in Ohio. Supermarkets loved it because they allowed them to remove the manual process of removing price stickers. But the citizen revolted as they did not trust the supermarkets and there were legislation brought to get price label . Business Week had this article called "The Supermarket Scanner that failed". It was only in 1980 when the supermarkets like Kmart and Walmart started adopting it that it took off.
  • This led to the POS revolution started and led to the supermarkets getting very efficient at the store operations and inventory management. Then consumer companies wanted to access the same type of information - to understand inventory levels in the stores. So they created Retail Link to share this POS data with consumer companies.
  • So what is the future problem that needs to be solved - Demand Sensing Multi Level and Demand Driven Supply Chain [Simultaneously solves minimum inventory levels across all echelons of the value chain, including plant, distribution centers and retailer locations]
  • There are these two young fish swimming along and they happen to meet an older fish swimming the other way, who nods at them and says "Morning, boys. How's the water?" And the two young fish swim on for a bit, and then eventually one of them looks over at the other and goes "What the hell is water?"
  • In 1977, Paco Underhill who was an urban geographer and anthropologist - started to study . He is the founder of Envirosell, a research and consulting firm that specializes in improving retail environments in order to sell more product. The fundamental research methodology is to track customers as they move through stores; observe (unobtrusively) their browsing and purchasing patterns; and take detailed notes of how long they spend in each area of the store, what they touch and look at, what they buy, how much they spend, etc. etc.
  • Examples:Path Intelligence, sells a phone-tracking technology called FootPath that follows shoppers around malls in Europe.3VR - Use Facial RecognitionRetailNext – Instore Analytics for OfflinePlaceCast - Location based Ad targetingShopkick - Rewards for walking the store
  • Other areas of potential-Marketing – Next Best Offers, Enhance Multi-Channel, Demand Shaping – Assortment, Pricing, Promotion Optimization
  • Challenges face in adoption
  • Targeting the Moment of Truth - Using Big Data in Retail

    1. 1. Targeting the Moment ofTruth: Using BIG DATAin RETAILAmit Kapoor
    2. 2. Big Data Small Data
    3. 3. Wicked Problems
    4. 4. Structure | Synthesis |Solving | Storytelling
    5. 5. MakeCHANGE happen
    6. 6. Retail is Personal Retail is LocalRetail is Evolving
    7. 7. Footfall Customers Items in BasketAverage Item Price
    8. 8. MerchandiseStore Space Employee Central
    9. 9. Margin: 3.8%Q Ratio: 0.8
    10. 10. Efficient [supply]Optimize [demand]Effective [customer]
    11. 11. Point of Sale [POS] Retail Link RFID
    12. 12. Demand Sensing Multi Enterprise Inventory ViewDemand Driven Supply Chain
    13. 13. What is Water?
    14. 14. Observation
    15. 15. The Science [Art] of Shopping
    16. 16. Entry zone twilight We need handsEconomical signage Walk on the right Interception
    17. 17. Don’t see prices [men] No jostle [women]Accessibility [seniors] 2 min checkout
    18. 18. In-store Insight [Video, Mobile, POS]Design – Merchandise – Operations
    19. 19. Next Best OffersEnhance Multi-Channel Demand Shaping
    20. 20. Consumer PrivacyLegacy IT and Mindset Talent
    21. 21. Amit