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 closer to your customers

Find patterns through transaction analysis

Analyze customer transactions, quickly

Perform sentiment

analysis

1 2 3

Get closer to your customers

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

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.

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

Get closer to your customers

Find patterns through transaction analysis

Analyze customer transactions, quickly

Perform sentiment

analysis

1 2 3

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.

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.

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.

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?

Find patterns through transaction analysis

Analyze customer transactions, quickly

Perform sentiment

analysis

1 2 3

Get closer to your customers

Do the analysis we’ve always done, but faster, so we can take action at the right time.

Analyze customer transactions. Quickly.

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.

Business event

Data analyzed

Business event

Data captured

Insight delivered

Action taken

Time • Engaged with customer

Val

ue

• 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

See how HP has helped NASCAR get closer to its fans

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