On Customer Insight


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How retailers can build knowledge about customers and their families, in order to support intelligent customer engagement.

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  • Top-down reasoning (deductive)Bottom-up reasoning (inductive)Sideways reasoning (abductive)
  • http://www.salon.com/2013/10/16/amazon_threat_or_menace/
  • http://rvsoapbox.blogspot.de/2010/05/two-second-advantage.htmlhttp://www.business2community.com/customer-experience/2-massive-trends-impacting-your-customers-in-2013-0366486http://www.bbc.co.uk/news/technology-24803378http://www.avinteractive.com/news/49055/amscreen-face-detectionhttp://www.dijitalimaj.com/alamyDetail.aspx?img={308498AC-790C-4280-A622-158E8AFB7591}http://www.octarine.co.za/im-not-a-shoplifter/
  • http://rvsoapbox.blogspot.de/2010/05/two-second-advantage.html
  • http://archives.digitaltoday.in/businesstoday/22021999/cover.htmlSee also Healthcare organizations that have really "gotten it right" have long recognized that the patient is only part of the customer equation. Indeed, the family plays a vital role in the patient's overall perception of the care. The family is also a critical piece of making certain that the patient follows the treatment protocols or standards prescribed to the patient. The family contact is also often the one who completes your patient survey, especially on behalf of older patients.
  • http://sloanreview.mit.edu/article/understanding-your-customer-isnt-enough/http://sloanreview.mit.edu/article/finding-the-right-job-for-your-product/http://rvsoapbox.blogspot.de/2009/05/customer-orientation.html
  • http://www.theguardian.com/news/datablog/2013/oct/31/are-loyalty-cards-really-worth-it
  • http://rvsoapbox.blogspot.co.uk/2008/08/responding-to-uncertainty.html
  • Picture: Wikipediahttp://en.wikipedia.org/wiki/File:Schrodingers_cat.svgAustrian physicist Erwin Schrödinger proposed a thought experiment known as Schrödinger's cat to explore the consequences of uncertainty in quantum physics. If the cat is alive, then Schrödinger needs to buy catfood. If the cat is dead, he needs to buy a spade. According to Elster's logic, he might decide to buy both.At Schrödinger's local store, he is known as an infrequent purchaser of catfood. The storekeeper naturally infers that Schrödinger is a cat-owner, and this inference forms part of the storekeeper's model of the world. What the storekeeper doesn't know is that the cat is in mortal peril. Or perhaps Schrödinger is not buying the catfood for a real cat at all, but to procure a prop for one of his lectures.Businesses often construct imaginary pictures of their customers, inferring their personal circumstances and preferences from their buying habits. Sometimes these pictures are useful in predicting future behaviour, and for designing products and services that the customers might like. But I think there is a problem when businesses treat these pictures as if they were faithful representations of some reality.This is an ethical problem as well as an epistemological one. I have a recollection (I can't find the details right now) of a recent incident in which a British supermarket, having inferred that some of its female customers were pregnant, sent them a mailshot that assumed they were interested in babies. But this mailshot was experienced as intrusive and a breach of privacy, especially as some of the husbands and boyfriends hadn't even been told yet.http://rvsoapbox.blogspot.co.uk/2008/08/responding-to-uncertainty.html
  • http://rvsoapbox.blogspot.co.uk/2008/09/responding-to-uncertainty-2.html
  • http://rvsoapbox.blogspot.de/2010/05/two-second-advantage.htmlhttp://www.srm.de/news/road-cycling/uci-road-world-championships-men-ttt/
  • On Customer Insight

    1. 1. On Customer Insight Richard Veryard October 2013
    2. 2. Contents Our Goal • Building Knowledge about Customers • To Support Reasoning about Customers Sources of Knowledge • Customer Tracking • Customer Profiling • Exploration and Experiment Supporting Reasoning • Managing Uncertainty • Managing Privacy 2
    3. 3. Our Goal: Building Knowledge about Customers to Support Reasoning about Customers Knowledge about Customers in General – Sales History – Behaviour Patterns and Trends – Demographic Categories Knowledge about Specific Customers – Static Classification – Dynamic Classification & Context – Behaviour History – Associations 3 Top-Down Reasoning – Customers with large gardens purchase a new lawnmower every three years. – John bought a new lawnmower two years ago. – Therefore we know he has a garden. – And we predict he will buy another lawnmower soon. Bottom-Up Reasoning – People who bought X also bought Y. – This is a statistical correlation, which may have no obvious explanation or meaning. – But we can exploit this pattern to sell more Y. Sideways Reasoning – People often buy beer and nappies together. – We think this is because parents with small children tend to drink at home. – So perhaps we can also sell them a baby listening kit.
    4. 4. The emergence of bottom-up reasoning At one point, Amazon actually had an editorial team that would personally craft book recommendations to be featured on the home page. But as Amazon became more proficient at exploiting the data it was gathering about user preferences and behavior, the added value provided by real living breathing humans came under existential threat from the automated ―personalization‖ ―P13N‖ team. The fight was brutal, but the end was preordained. The algorithmic power of automatically personalized recommendations boosted sales more quickly than the plodding touch of corporeal bodies. Source: Brad Stone, ―The Everything Store: Jeff Bezos and the Age of Amazon‖ (2013) 4
    5. 5. Retail Becoming Smarter Traditional Retail Innovative Retail Segment customers into fixed demographic categories Respond dynamically to customer’s current context and concerns. Focus on top-down reasoning Combine all forms of reasoning. Based on limited amounts of data. Based on much larger quantities of data. 5
    6. 6. Examples Fixed Segment Context Emma • • • • Married woman. Part-time job. Age 30-35. 2 children. Last week, Emma’s husband announced on Facebook that he was changing jobs. Laura • • • • Single woman Full-time job. Age 25-30 No children Laura has recently stopped buying alcohol, and has switched to fragrance-free toiletries. Insight How does this information help you to provide a personalized service? Fixed data is relatively easy to collect and maintain, but provides little competitive advantage. 6 Are there any clues here that might help you provide a better service? Contextual data potentially has far greater value, but only if you can interpret and respond promptly.
    7. 7. In-Store Customer Tracking • Existing technologies will become cheaper, and new technologies will emerge … • … enabling a huge increase in the volume and granularity of the available data. • Customer device (smartphone) • Customer given hand-held scanning device on entering store • Electronic tags on all goods (RFID) • Cameras and face-recognition • And some other technologies we can’t talk about yet • A retail store can collect a much higher volume of data about the customer's behaviour … • … not merely the items that the customer takes to the check-out but also the items that the customer views but doesn’t buy. • (Subject to privacy concerns.) 7
    8. 8. Network Tracking • All aspects of digital activity may be captured and interpreted. • Comments and complaints on Facebook, Twitter, etc. • Photos of customer wearing your clothes, using your products. • Customer-supplied content and creations (recipes, outfits, room designs, etc) 8 • New ways of identifying customers • New combinations and mashups of material • New ways of tracking friendship networks and influence networks. • New ways of tracking internet ―buzz‖.
    9. 9. Who is your customer? Individual as Customer Family as Customer People buy clothes and household goods for themselves Many items are purchased for use and consumption by a family. • based on their own individual characteristics and preferences People are defined, and their behavior determined • without reference to other family members • not just by their individual characteristics • but also by their positions, roles, and relationships in the context of their families. 9
    10. 10. Customer Profile Elements Individual Permanent (slow-moving) Name and address Demographic category Socioeconomic status Friends and family History (recent dominates older) Purchase history Browsing history Response to retail communications (e.g. promotions and offers) Social network trace Purchased gifts. Social network ―likes‖ and other interactions. Current (fast-moving) Present location (e.g. geolocation) Before/after lunch E.g. now browsing the store with two friends. Planned 10 Network Associations Scheduled trip to China next week. Wedding next year. Graduation party
    11. 11. Understanding what the customer is doing Clayton Christensen • The customer is the wrong unit of analysis for innovators to focus on. • Instead, focus on the job that customers are trying to get done when they use your product or service. • Source: Clayton Christensen et al, Finding the Right Job For Your Product. Sloan Mgt Review 2007 12 His Example • A fast-food company discovered that a significant portion of its customers were ―hiring‖ its milkshakes for an unexpected use: as a food to consume early in the morning, while driving on a long, boring morning commute. • By focusing not on the customer for the product but, more specifically, on what the customer was trying to do – consume a filling food on a boring daily drive – the fast-food company could customize the product for its early-morning milkshake buyers in ways to make it more effective in that function. It also gave the company a greater understanding of its competition — which, in the case of the morning milkshake, ranged from bananas to doughnuts.
    12. 12. Divided Loyalties Clever Retail Tactics Customer Counter-Tactics • There is no point offering generous • Many loyalty cards for different deals to your regular customers. Data retailers. If you shop elsewhere you shows that it is more important to could get more deals sent to you to target offers at disloyal customers – lure you back. those who flit from store to store. • Source: Joe LaCugna, Starbucks director of business intelligence and analytics, 2013. 13 • And some customers even get several loyalty cards for a single retailer. The aim is to trick retailers into giving you better deals by making them think you're shopping much less frequently.
    13. 13. Don’t show off your knowledge Use the “Dog Whistle” approach WRONG BETTER • “Based on your recent purchasing • “Here is an apparently random set history, we reckon you might be of coupons, which just happens to pregnant, so here are some include some baby stuff.” coupons.” But what if you are wrong? And what if she hasn’t told her husband yet? 14 Only those customers interested in childbirth and babies will pay attention to these.
    14. 14. Never forget that a profile is only an approximation WRONG BETTER • We know which customers will be attracted to this offer. • We have a rough idea which customers will be attracted to this offer. • We optimize the offer by excluding • We optimize our learning by sending it to a selection of other all other customers. customers as well. But what if you are wrong? You will never find out. 15
    15. 15. Treat every promotion as a scientific experiment learning opportunity compare compare Customers within target segment who receive offer 16 Customers outside target segment who receive offer Customers within target segment who don’t receive offer Customers outside target segment who don’t receive offer
    16. 16. Managing Uncertainty • John has a garden behind his house. Three quarters of the garden is plain green, presumably grass. • There is an object in the garden visible from a satellite picture. It might be a rusty lawnmower. It has been there all winter. • It is nearly Spring. John may need a new lawnmower soon. • A garden equipment company sends John an attractive brochure of garden equipment, including a range of lawnmowers. 18 Perhaps it is artificial grass Perhaps it is a bicycle. Or a sculpture Perhaps John hires someone else to look after his garden. Perhaps we should verify John’s profile before we send him anything? But it doesn’t matter how accurate John’s profile is, as long as there’s a good chance he might buy something.
    17. 17. The “Two-Second” Advantage • Can the retailer respond to tracking data before the customer leaves the store? • For example, a food retailer might infer from the customer's browsing behaviour that she is looking for her favourite brand of pasta sauce. The shelf is empty, but there's a new box just being unloaded from a truck at the back of the store. • Find a way of getting a jar to the customer before she reaches the checkout, and there's your ―two-second‖ advantage. 19
    18. 18. Privacy versus Customer Service DO Customers allow you to collect and use their personal data So what do they get in return? • • • • Loyalty rewards Targeted marketing Special offers Special events If you are generous with your loyal customers, a larger proportion of customers will be willing to participate. DON’T Take your loyal customers for granted, or make them feel exploited. The legal small print may allow you to insert customer’s photos into your advertisements. But if the customers don’t like it, it’s a bad idea. 20
    19. 19. Recommendations DONT • Don’t be intrusive. • Understand the value of the data. • Don’t force your customers to provide meaningless information. • Build the capability to interpret and use customer intelligence. • Don’t let your business partners manage your customer data. 21 DO • Keep asking new questions.
    20. 20. Where Next? Customer Experience Management Turning Customers into Fans 22 Social Network Engagement Turning Fans into Customers
    21. 21. Contacts www.replyltd.co.uk. r.veryard@replyltd.co.uk 23