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Get closer to
your customers
with Big Data
Mike Shaw
Director, HP Software Marketing
#mike_j_shaw
Find patterns
through
transaction
analysis
Analyze
customer
transactions,
quickly
Perform
sentiment
analysis
1 2 3
Get clo...
Find patterns
through
transaction
analysis
Analyze
customer
transactions,
quickly
Perform
sentiment
analysis
1 2 3
Get clo...
Determine
clusters from
social media
interactions
First step: Look for clustering
• Twitter
• Community web sites
What is ...
Then monitor the
sentiment of the clusters
Do people like
our products,
or not?
How do these
sentiments
trend over
time?
O...
This central person
has churned
All these connected
people may
now churn
Analyzing social media
interactions, we find that...
Get closer to your customers
Find patterns
through
transaction
analysis
Analyze
customer
transactions,
quickly
Perform
sen...
Bought skirt
Buy shoes –
54% probability
Finding affinities
Statistically, humans are quite
predictable.
If you buy Produc...
Affinities can occur over a long
time period
For example, if I buy a house, I will probably
buy paint, a dishwasher, a new...
Retailers love affinity
analysis because it allows
them to increase the
average transaction value
per customer.
And, it al...
We all know about
recommendation engines.
Recommendation engines
need personal profiles.
And it is customer
transactions t...
Find patterns
through
transaction
analysis
Analyze
customer
transactions,
quickly
Perform
sentiment
analysis
1 2 3
Get clo...
Do the analysis
we’ve always done,
but faster, so we
can take action at
the right time.
Analyze customer
transactions. Qui...
A customer is about to make a
purchase—we can help them
through our timely analysis.
This has much more impact
than tellin...
Business
event
Data
analyzed
Business event
Data captured
Insight
delivered
Action taken
Time
• Engaged with customer
Valu...
See how HP has helped
NASCAR get closer to its fans
Learn about HP’s vision for
the future of analytics
Big Data 20/20
Kno...
Get the insight you
need to take action:
www.hp.com/HAVEn
© Copyright 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change without n...
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How to get closer to your customer using big data

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How to get closer to your customers using big data to perform sentiment analysis, find patterns through transaction analysis and analyze customer transactions, quickly.

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Transcript of "How to get closer to your customer using big data"

  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 we’ve 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 purchase—we 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 they’ve left the store. The time/value of insight
  16. 16. See how HP has helped NASCAR get closer to its fans Learn about HP’s 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.
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