how to get closer to your customer using big data

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  1. 1. Get closer to your customers with Big Data Mike Shaw Director, HP Software Marketing #mike_j_shaw
  2. 2. Find patterns through transaction analysis Analyze customer transactions, quickly Perform sentiment analysis 1 2 3 Get closer to your customers
  3. 3. Find patterns through transaction analysis Analyze customer transactions, quickly Perform sentiment analysis 1 2 3 Get closer to your customers
  4. 4. Determine clusters from social media interactions First step: Look for clustering Twitter Community web sites What is social media clustering? Customers talking about a similar subject Forming clusters and monitoring sentiment
  5. 5. Then monitor the sentiment of the clusters Do people like our products, or not? How do these sentiments trend over time? Once clusters are identified, they can be analyzed for sentiment.
  6. 6. This central person has churned All these connected people may now churn Analyzing social media interactions, we find that some people are central. They have lots of connections and interact a lot. If one of these centrally connected people churns (switches mobile service provider, for example), they may well take those people close to them with them. Social network analysis
  7. 7. Get closer to your customers Find patterns through transaction analysis Analyze customer transactions, quickly Perform sentiment analysis 1 2 3
  8. 8. Bought skirt Buy shoes 54% probability Finding affinities Statistically, humans are quite predictable. If you buy Product X, there is a good probability that you will, at some time, buy Product Y as well. Or, when you are in Area X in a game, there is a good chance you will want to buy Virtual Weapon Y.
  9. 9. Affinities can occur over a long time period For example, if I buy a house, I will probably buy paint, a dishwasher, a new TV, new lights, etc., within the next four few months.
  10. 10. Retailers love affinity analysis because it allows them to increase the average transaction value per customer. And, it allows them to increase the loyalty of customers.
  11. 11. We all know about recommendation engines. Recommendation engines need personal profiles. And it is customer transactions that are used to build up a view of your personal preferences. Using customer transactions for recommendation profiles New model skis? New ski jacket? Latest goggles?
  12. 12. Find patterns through transaction analysis Analyze customer transactions, quickly Perform sentiment analysis 1 2 3 Get closer to your customers
  13. 13. Do the analysis weve always done, but faster, so we can take action at the right time. Analyze customer transactions. Quickly.
  14. 14. A customer is about to make a purchasewe can help them through our timely analysis. This has much more impact than telling them a week later, We know you liked this last week. Fast analysis gives clothes retailer Guess store managers the insight required to arrange their stores optimally before the customer walks through the door in the morning.
  15. 15. Business event Data analyzed Business event Data captured Insight delivered Action taken Time Engaged with customer Value Customer has left the store Data latency Analytic latency Decision latency If we can provide insight while the customer is making a purchase decision, this is much more effective than providing the same insight once theyve left the store. The time/value of insight
  16. 16. See how HP has helped NASCAR get closer to its fans Learn about HPs vision for the future of analytics Big Data 20/20 Know your customers 100% better with targeted marketing See the big picture in Big Data Find out more or fill out the info form on the next page
  17. 17. Get the insight you need to take action: www.hp.com/HAVEn
  18. 18. Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without notice.