John Squire is Chief Strategy Officer of Coremetrics, an IBM company. He discussed his company's new enterprise analytics product and digital data exchange as well as marketing challenges with data today.
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JS: IBM acquired Coremetrics in July of 2010 and then Unica in August 2010 Both companies had a web analytics solution in its respective portfolios.
It was the primary product for Coremetrics and it was a key component of a larger multi‑channel campaign business for Unica.
Within about a month after the acquisition of Unica, I was put in charge of the web analytics portfolio for all of IBM and asked to understand how we could integrate these two market‑leading technologies.
I don't say that lightly because it's been pretty well chronicled in the analyst community that the Unica product is market leading as is the Coremetrics one, and another solution out there, the Adobe [Omniture] technology, has been recognized as having leading functionality. And, we all were on different vectors.
Can you talk a little bit about that differentiation?
The unique thing about the Unica technology - the brand name for it is NetInsight - is its ability to bring offline or multi‑channel data into a completely ad hoc, open data warehouse from which you can build advanced types of analytics - and it’s integrated into the Unica campaign product.
Coremetrics is known for ease of use and natively built applications that run on the same exact data across all clients. We felt like that's the way you scale.
In November 2010, we made the decision that we're going to have one single web analytics application for on-demand solutions or cloud solutions and we're going to have it out by July 2011. And, in July we announced it. Now, all of our clients are on one platform and have the capabilities to use all those applications we built.
Is there a fit with [IBM predictive analytics software] SPSS here?
There are two things that become interesting around SPSS. A large number of our IBM clients use SPSS to do things in terms of predicting and modeling data.
Where SPSS gets interesting for our existing clients is the data structures inside of IBM Coremetrics that are standard across all the clients.
As we move forward, a client can say, "I use SPSS. I know what the data structure is. Now I can start to use that web data to model things."
The second piece is that, now, the IBM Coremetrics team can start to build different data structures that automatically feed SPSS models. It's a two‑way street because a lot of clients have designed highly‑configured models and they just need standard data sets that they can always count on.
It's not. The reason why we call it this is we canvassed our clients and talked to them about a variety of different ways to describe what we're doing for them.
All of them came to the point where this is a hub of their digital data that they own, and that they want to choose which partners they will syndicate or distribute their information.
Also, clients want to better distribute their data to their internal systems -that's probably one of the bigger areas that we've heard from our clients is that ability to exchange or integrate information from their web system into all of their internal datamarts in order to drive other activities in their business.
I realize there's going to be questions around whether we're exchanging broad, anonymous data or if it's advertiser‑only data. As I've always said, we fall very heavily on the advertiser or the publisher side, in terms of helping them manage their own information that they own, and making determinations on where that data should flow to. We're not mingling third‑party data pieces together.
What does the Digital Data Exchange and enterprise analytics address for the marketer? Why should they care?
They address two different needs of a marketer. For the digital data exchange, it eliminates the marketer's need to think about how they do digital asset integration, and how they get customer experience information –first, to use for analysis, but the most important piece is how to use that information to find the best partner, network, or technology that's going to allow them to reach their target audience. So I think it takes away that everyday concern around, "How do I do all of these technical integrations?" - the “putting the pipes together.” And gets them into, "OK. What are the business objectives? How do I solve for reaching the right customer at the right time, with the right message?"
I think that's the top level message for marketers for a digital data exchange.
On enterprise analytics, [the key benefit] is the ability to see in real-time across all of your properties and see how your business is performing in the aggregate as well as by any ad hoc hierarchy that you want to see. A quick example on that so you can understand this –for a client that's in the health and beauty space, they may have 10 different brands that they manage, but those span 20 different countries.
They're trying to understand how their business is performing across all those countries, and how each brand is performing individually in each country -and seeing all that in real time.
In that sense, on a publisher's side you may have lots of different publishing sites and you're trying to understand: are the visitors common across all those sites or are they different visitors?
Today, that's a difficult roll up and I don't see a lot of people doing it in real-time. We've made it so it's one, single tag and everything else is done in the database.
The agency is going to be looking at enterprise analytics as a place where they're going to want to get a log in from our end customers.
This is going to give agencies the ability to have a “cockpit” of information and also configure the information in just about any format they want.
Another way agencies will find this appealing is that it can design the exact metrics and reports that they want, and then publish that as the gold standard across all the other sites.
So now, all of the agency analytics folks will have the exact same report. The KPIs will all roll up and look exactly the same. You can do better compare and contrast. I think that's a big win for the agency.
Finally, the exports are standardized. Agencies can have complex steps of reporting and analytics and ways in which they want to roll it up into a variety of different dashboards. Because they're all building their own dashboards, they're all building their own analytics practice. This will pump it out in the exact same format across all properties that they work with.
So I think you're going to see some really interesting uses of this going forward and the more advanced agencies are going to see this potentially as a competitive weapon to walk into an IBM client and say, "We already know the data structure. We already have our own set of ways in which we manage programs. We can plug this in in a day to a few days and it will run across every property you have. We can show you how our data practice can beat any other agency data practice.”
By John Ebbert