How people are using Big Data - Customers 360

Icustomers360.png’m going to write two blog posts on how HP’s customers are using big data. I’ve split the blog into two because big data use cases seem to break nicely into those that improve our interactions with customers - “Customers 360” as we call it - and those that improve our back-end operations systems - “Operations 360” (I’d like to thank my colleagues in HP ES’s Information Management and Analytics group for this characterisation.

 

Let’s then start with Customers 360. This in turn splits into two :

  1. Targeted marketing : using big data to better market to our customers
  2. Better products and services : using big data to create better products and services
We’ve been using data analysis to do targeted marketing and to create better products and services for years, right? So what does big data bring to the party?  I think it brings four things:
 
1. Micro-transactions
The ability to record and analyse “micro-transactions”. I talked about micro-transactions in a previous post (ref) on the types of data that go into big data. In relation to “Customers 360”, micro-transactions give us a fast and accurate way of knowing what our customers are doing. For targeted marketing, for example, you might record touch streams on user interfaces on mobile devices.
 
1a. Machine to machine
There are a special class of micro-transactions from machines; known as machine-to-machine transactions. These can be used to improve products. Imagine you create a new e-bike that uses the F1 concept of KERS (kinetic energy reuse system). You might take telemetrics from your new e-bikes at night from those customers who allow it. This would tell you exactly how your customers are using the bike, just like gaming companies use click-streams to tell how their customers are using their game. This allows you, quickly and accurately, to adjust your product (which you can do to a large extent because your e-bike has a lot of applications inside it).
 
2. Human Interactions
The ability to understand human interactions. We can look at tweets and mine for sentiment about us as a company or our products. We can do the same on online communities where our products get discussed. For example, a retailer might find out that their competition is selling an item they don’t stock - there is no way that analysis of sales data or even click stream analysis on your web site would show you what you should be selling, but aren’t. In other words, human interactions are another angle to the sales or click-steam data.
 
3. The usual transactions - stored for longer and analyzed more quickly
We can store more normal transactions and analyse them so quickly that we can act on them quickly. Below, we’ll see how a retailer does this to arrange their stores.
 
Let’s now look at targeted marketing and better products and services in turn, using real-life customers to illustrate. 
 
Targeted Marketing
 
guess.pngGuess 
Guess is a US-based (LA, in fact) retailer. They analyze all the sales data from all their retail stores and from their web stores over night, every night. This analysis is then delivered to their store managers on an iPad. The analysis tells the store managers which items are selling well, which are not selling well and may therefore need to be discounted to stop an overstocking situation arising, and which items are selling well with what other items (know in the trade as “affinity analysis”, apparently). 
 
Store managers then use this information to adjust their store layout, and the layout of their store window displays. The web store designers do similarly - affinity item placement on web sites has been proven to improve overall sales. 

 

Why do Guess need “big data” to do this? Because they have huge amounts of information and without big data, the analysis simply took too long. So long that the store managers on the East Coast didn’t have the information they needed before their stores opened.

 

HP.COM 
When was at HP DISCOVER in Barcelona last year, I had a chance to talk to one of the data scientists who work on the HP.COM site. The HP.COM big data team analyses customer journeys thru their click steams. They can pretty accurately predict that when a customer goes to one area, which area(s) they will then go to. This allows them to layout the web site to make the customer’s journey as easy as possible. 
 
They also do affinity analysis - looking for what products sell well with what other products. So, if they design the web site accordingly they should maximise add-on sales. 
 
According to a recent Gartner report (Gartner, September 2013, How people use Big Data), targeted marketing is, at present, the most common use of big data.
 
In order to make it easier for customers to use big data for targeted marketing, HP Software has created a product called HP Digital Marketing Hub (ref). This product allows you to combine transactional data such as CRM, purchase history, web analytics, and email metrics with human friendly data such as call logs, emails, social posts, and comments to get a holistic view of the customer . 
 
Better Products and Services
 
Game Show Network (GSN.com)  gsn.png
GSN.com and other e-gaming providers, use click stream (and now, with tablets and smart phones, touch-stream analysis) to determine what their users think of the products - which parts they like, which parts they find too hard, and which areas they don’t go into. If someone zooms thru levels at high speed, it may indicate that they have a cheat for the game - something that needs to be blocked. Based on this information, companies like GSN.com are able to know quickly and accurately what they need to change about their products.

NASCAR
NASCAR's “product” is more than the race day. It’s the fan-site on the web and the information and feeds they give to FOX Broadcasting. They monitor the sentiment on this site so that they can offer a better experience to their fans.
When it rains, NASCAR use a huge machine to clear the rain from the track. NASCAR assumed customers would hate the period while the “sucker” was doing its thing. No so – analysis of twitter feeds found that the fans found the machine fascinating - how much does one of these machines cost; what training does the driver need; who is driving the machine today; how fast can they take water from the track? So now, NASCAR feeds information about the water sucker to FOX, and FOX continues to broadcast from the NASCAR race, even when water is being removed from the track. 

 
Summary
The modern marketing and product design department has so much more to call on than “old-fashioned" broad-brush structured data (“Fred just bought a product”) and word of mouth feedback. There are micro-transactions including machine-to-machine data and human interaction data in the form of social media, emails, and voice conversations. 
 
Because our products are now global, and because the web makes positive and negative feedback loops against our product so fast, we simply can’t get on a plane and “get out there”. We have to increasingly rely upon a rich set of data to help us target our marketing and improve our products and services.

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About the Author
Mike has been with HP for 30 years. Half of that time was in R&D, mainly as an architect. The other 15 years has been spent in product manag...


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