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Big data, big revenue
The document discusses the importance of big data in transforming marketing strategies by enhancing customer retention and informing purchasing decisions through social intelligence. It highlights challenges such as the gap between understanding data needs and achieving measurable business value. Key insights include the need to focus on objectives rather than just data tools to realize the potential financial benefits of big data.
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Big data, big revenue
- 1. BIG DATA, BIGREVENUE Why big data should be changing the way we market Enabling the Data & Information Culture
- 2.
- 3. Half the moneyI spend on advertising is wasted; the trouble is I don’t know which half. John Wanamaker
- 4. Buying Influences areDifferent
- 5. “…when it comesto purchasing decisions, the most influential recommendations come from people we actually know…” Josh Cantone, Who are the real online influencers? Reach Resonance Relevance
- 6.
- 7. How ‘social intelligence’can guide decisions; McKinsey Nov 2012
- 11. Avis Lifetime value= current + potential value Develop Retain 360° view Maintain Nurture Current Value Potential Value
- 13. £ (million) SupplyChain Inventory Management Cooling 6 100 50 100 60 40 20 20 80 0 Tesco Demand Management Annual Savings
- 14.
- 16. “We’ll be sendingyou coupons for things you want before you even know you want them.” Andrew Pole, Target
- 17.
- 19.
- 20. Big data =lots of small data
- 21.
- 22.
- 23.
- 24. “Building out Big Data capabilities too often becomes the end goal itself”. What you need to make Big Data work: The pencil: Matt Ariker, Forbes CMO Network Article
- 25. “…most significant obstacleto big data efforts… is the gap between the need and the ability to articulate measurable business value” Analytics: The real-world use of big data in financial services
- 26.
- 28. “ … thekey is to focus on the big decisions for which if you had better data, … you’d make more money.” David Court, McKinsey, 2013
- 32. Focus on objectives, not tools
- 33. Who can dowhat? When? Where? How?
- 34.
- 35. http://www.linkedin.com/company/master-data-management @Gary_allemann gary@masterdata.co.za +27 11 485 4856 www.masterdata.co.za
Editor's Notes
- #3 Customer Engagement Who are your customers? Where are they? What do they want from us? What’s the best way to contact them? When’s the best time to talk to them? Customer Retention What influences our customers’ loyalty? What can we do to promote loyalty? How do we keep them coming back? Optimise Marketing Which channels offer us the best value? Where do we get the best return? Where should we focus our spend? How did that last campaign do? Test Measure Analyse
- #5 Crowdtap, from http://mashable.com/2012/06/13/influence-marketing-infographic/
- #6 REACH RESONANCE RELEVANCE Crowdtap, from http://mashable.com/2012/06/13/influence-marketing-infographic/ Influence the people that are influencing your audience
- #7 http://www.linkedin.com/today/post/article/20140219191358-127250022-content-and-data-is-at-the-center-of-everything
- #8 How ‘social intelligence’ can guide decisions; McKinsey Nov 2012
- #12 Implemented customer segmentation by value: lifetime value model offered added value to top segment good offers (value) to bottom segments = loyalty at both ends 360deg customer view aids customer service and informs marketing to ea segment Mined data to discover areas where could add extra value for extra market share
- #13 3rd largest retailer in the world Began using data to inform business decisions 1990 First store-brand customer loyalty card “Clubcard”
- #14 Supply Chain: stock, depot, delivery, supplier purchase optimisation; out-of-stock on special offers reduced by 30% Inventory: stock levels shown on Google maps; instore camera tech eg “broccoli cam” Cooling: Central mgt of temps; data aligned with Google maps; remote diagnostics so techs arrive with right parts Demand: Use of weather forecasts and regional sales patterns; hot day in Scotland = different sales and stock req from hot day in South of England Big data is also a powerful, effective force in the multi-channel strategy that Tesco sees as central to understanding the future of consumer retail behavior. This strategy addresses the consumer’s desire to use physical stores, mobile devices, and desktop computers in combinations. For example, a consumer could use an internet kiosk in the store to order an item for collection in the store the next day. A mobile device could be used to order groceries to be delivered at home. These combinations of retail channels require the company to understand not only the purchasing patterns of individual consumers, but also their channel preferences and logistics requirements.
- #16 American retailer Used big data analytics to devise a “pregnancy prediction” score based on buying patterns Sent relevant coupons to expectant mothers throughout pregnancy Cornered “baby-on-board” market using “heightened focus on items and categories that appeal to specific guest segments such as mom and baby.” (Greg Steinhafel, President: Target)
- #18 [A] man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation. “My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?” The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again. On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.” Read more: http://www.businessinsider.com/the-incredible-story-of-how-target-exposed-a-teen-girls-pregnancy-2012-2#ixzz2tDCFvr9O
- #19 American “superstore” chain selling office products, technology and furniture Largest retailer in the sector Named one of “World’s Most Ethical Companies” in 2013
- #20 Occasional customer of OfficeMax One purchase of paper since daughter’s death in car crash Company bought 3rd party data to use for targeted marketing
- #22 Quality Validation Reporting Business Rules Governance
- #23 Moore’s law also applies to data Relational databases reaching capacity and scaling limits
- #24 Exponentially larger rate of production
- #25 Formats outside scope of traditional relational dbs Internet of things
- #26 This is not an IT problem: You know what you need; IT doesn’t. You can’t always wait for IT to understand. You need to be able to get the data yourself!
- #28 Analytics: The real-world use of big data in financial services, Report by IBM Institute for Business Value and Saïd Business School at the University of Oxford, May 2013
- #29 Can be like looking for a needle in a haystack
- #30 Throwing more hay on the stack won’t help you find the needle
- #31 McKinsey Chief Marketing & Sales Officer Forum (2013-07-09). Big Data, Analytics, and the Future of Marketing & Sales (Kindle Locations 121-123). McKinsey & Company. Kindle Edition.
- #37 Access Usage Storage Maintenance Retirement
- #38 Access Usage Storage Maintenance Retirement