If it happens to me, it happens to you. You type the URL of a business that you e-frequent into your browser and watch the site load. The content that shows up on the landing page is generic, and it doesn’t offer you anything that you don’t already own or have decided not to buy.
Someone Isn't Listening
My bank, for example, promises to give me US$ 200.00 if I open a checking account. Nice, except that I already have a checking account with them. (Should I close my account and open a new one?) The site also offers US$ 500.00 in travel rewards if I apply for a credit card. Again, not for me, I already have a credit card with the bank. (And, by the way, I didn’t get a red cent when I opened the account.)
My sentiment from coming to the site — not too good; in fact, I feel kind of ripped off.
This doesn’t bode well for the bank. The next time I become frustrated with my bank because its website goes down, I have a long wait when I call customer service, or they make a mistake, I may head for greener pastures.
Add to that that I think my bank is kind of dumb. Can’t they think of a way to incentivize me to sign up for something I don’t already have? They certainly have enough information about me. I go to the site to do my e-banking, pay my bills and transfer money between accounts. Since I seldom use cash, they know exactly where I shop and what I buy. They should be able to come up with something unless they simply don’t care about my business or know who I am. (And no, I don’t clear my cookies.)
And get this, if I physically went to the bank a few times each week, like I do the bank’s website, Mr. Smith, the bank manager, would probably be able to make better recommendations to me than the bank’s current CRM and CXM solutions.
That’s bad. Really bad.
In fact, in the age of Big Data, Predictive Analytics, Machine Learning, Social Graphs and the like, it’s inexcusable.
Can it be that we have the data and we have the technology, but that we don’t know how to bring them together or use it?
Causata is a next generation CXM vendor whose software helps companies personalize experiences and offers by leveraging Big Data, “Identity Graphs,” predictive models and machine learning to pull together and analyze a comprehensive set of customer touch points to build unique, meaningful relationships with customers or potential customers says, Kevin Nix, the company’s president.
And data around those touch points can come from anywhere a customer, or potential customer, has visited. This includes first party data (including anonymous data) from website visits, “footprints” traced from a customer’s journey to or from a website, digital data like clicks and email opens, and data that a given business may own about an individual like name and address, purchase histories and, when applicable, even data gathered from calls to a call center and visits to retail stores.
With Causata, all of this data is gathered over a timeline and used to build an “Identity Graph” which gives a comprehensive picture of who you are, how you behave, what your preferences are and what you respond to. When predictive analytics are applied to this information, businesses have a pretty good idea of what offer should be made to you at the time you are most likely to buy.
Causata is listening
Working in the Here and Now
And while one might be tempted to argue that companies have been capable of mining customer data and predicting future behavior for years, the truth is that, when it comes to consumers, few did in an effective way, says Nix.
Not only that, but the ability to process huge volumes of data (a.k.a. Big Data — as many as 100’s of terabytes) couldn’t be done in an affordable, timely manner until recently. And even when it was done (with a smaller volume of data) the results were almost always a week to a month old and therefore less likely to be accurate in the present moment.
The difference between yesterday’s CXM systems and Causata is that traditional solutions were “always backward looking” says Nix; in other words, they looked at what a customer did in the past and made suggestions based on that. Causata looks at what the customer is likely to do in real time (within 50 milliseconds), at the very instant that he makes the decision to buy or abandon.
There’s an important distinction between these approaches, explains Cater Lusher, a research fellow and research analyst at Ovum. He gives an example of a customer who visited a Toyota dealership and website a week ago.
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