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.
In the old (or current) model, by the time the information gathered from his visits is processed, analyzed and the optimal sales approach is determined (e.g. he’d be more likely to respond to a compelling offer on Prius vs. a Corolla); the customer may have very well purchased the car. If that’s the case, a different opportunity exists for the dealer -- he might try to sell ancillary products like an extended guarantee or waterproof formats.
“It’s not just about big data, but fast data,” says Lusher; he’s referring to not only Causata but the emerging and disruptive “Customer Adaptive Enterprise.”
In the past, according to Lusher, customers had to adapt to the way enterprises did business. Not so much anymore. Companies now have to adapt to the way the customer behaves, says Lusher. And that might mean presenting an offer, literally in the moment, when a potential buyer is on a website, physically in front of the sales/service person, on the phone, and so on …
And because customers do business on multiple channels, companies need to be able to reach customers and gather information from all of them, be they the web, call centers, mobile apps, chats, events at stores and so on …
Causata is omni channel and channel agnostic meaning that data can be received from anywhere and everywhere.
Businesses that use Causata then see Predictive Analytics applied to this data. Algorithms determine what a potential customer might do next and what kind of offer might be impossible to walk away from in the moment. This might mean offering free shipping to one customer, an extended warranty for another and a free gift for someone else.
And regardless of whether a customer buys or abandons at the critical moment, Causata’s machine learning algorithms use the data to help companies become smarter and better at predicting future results.
It’s also worth noting that though setting up a new CXM system like this might seem complex, slow, expensive and burdensome, Causata now offers specific purpose-built Industry applications. This means that time to market and ROI can be won quickly because these solutions come pre-loaded with industry specific seed data and applications to leverage hundreds of analytic variables and models for cross-selling acquisition and retention activities. At present offerings are in place for Financial Services, Digital Media & Technology and Communications companies.
Companies in other industries are calling for solutions of their own.
“The market place (for Causata and solutions like it, if there are any) is primed, they’re just waiting for the right tool,” says Lusher. He adds that Causata is ahead of the pack in offering a compelling, comprehensive approach.