Omniture acquires Offermatica

Wow.

I go away for a fortnight and Omniture goes and buys a company behind my back. I guess that shows how much they consult me!

OK. Without wanting to sound the corporate lackey, I have to say I think this is a smart deal. Omniture appear to be moving towards the wonderful "site optimisation platform" that they've been looking for.

What have they bought? Its a company with proven success at creating and selling an application that wraps up the natty problem of how to make actionable analytics.

So where does that leave Omniture? Well marketing have come up with a wonderful tagline (que image I dont have yet)

AB + MVT + BT / Web Analytics = ROI.

Firstly, lets establish what (AB+MVT+BT) provides a customer. Lets ignore the AB bit and just talk about its more sophisticated cousin MVT and the different approach of BT and where you would apply either.

Broadly...

BT is based upon customers behaviour and aims to optimise a key tagline, banner, piece of content. You use it when you're really not sure what content you should be targetting at any one time.
MVT is based upon customers reaction to a single page. Its optimising the arrangement of content or indeed subtle differences in a tagline. Ideally, it's used when you know exactly the product, process or content on a page you need, you're just trying to refine it to make sure its presented in the best way - forms is an ideal situation, becuase by that point in a "conversion" process all customers are likely to behave similar hence the lack of need to segment the customer base.

Between them, that covers a lot of onsite optimisation (I'm ignoring campaign optimisation for the sake of this blog). The main areas left for onsite optimisation that I reckon Omniture need would be a rules engine and a recommendation /onsite search engine.

What applications would I buy next if I was Omniture trying to improve the Site Optimisation portfolio?

Rules engines are required because having smartly applied rules that can be deployed easily, understood clearly by business and provide an awful lot of benefit. (In an Omniture situation, a rules engine would be performing a similar function to a complicated CMS)

For instance, if you are launching a new product, have new content related to that product and have marketing spend related to that product, you do not need to waste any time "training" an analytic model. It just makes sense that things line up, have people coming from a certain piece of marketing going to a certain piece of content. In fact, in many "straightforward" businesses you would find examples of marketing activity and site content rules that would get you an awful long way.

This is actually something I've found in lots of work done with offline companies where the application of sophisticated analytics is a little more mature than the online environment. Rules, business knowledge and good management will get you an awful long way and often analytics is really best placed on top of the rules in one of the following ways:

1. to optimise, enhance or make incremental improvements on existing business processes
2. to discover new, as yet unknown patterns in customer behaviour which ideally are feedback to guide business strategy.

Obviously, if you have 50,000 peices of marketing activity, and 10,000 products, managing rules for all of that would seem impossible - but if you can segment the products and marketing activity in 20 segments with clear goals and an estimation of value, suddenly the task doesn't seem that hard. With these segments not only would you have less rules to manage but you would have a clearer understanding of marketing and product strategy that would have a dramatic step change in effectiveness.

Search/Recommendation Engines : I've already mentioned recommendations in this blog so wont go too far into this - The reason I have grouped these things are that they simply return a rank set of results based on a customer interaction. In the case of search engines, its a list of ranked content based on a given keyword. In the case of a recommendation engine, its usually a list of ranked products or content based on some website activity (like looking at another product). The main difference is how you do the ranking of delivered content. In a search engine, a user often has a specific thing in mind, the search engine must return as close a match to that thing as possible (PULL activity). In a recommendation engine, its usually more a "push" from the organisation and the results returned are not usually so directly matched, its usually more about making connections based on historic behaviour (behaviour and product activity + customer segment) (PUSH activity).

Heres an example - if I search for Canon IXUS on Google, I want to find things about exactly that, most probably a review or site with the cheapest price in my case, but still, things that have a direct relationship with the terms used.
However if I've just added to my basket a Canon IXUS on a site I dont really need to be recommended a Canon IXUS by the engine, I've seen that already. Better to recommend complimentary products (possibly bought by other customers with the Canon IXUS) or alternative products. Anyway - lets not get into how we do this ranking of content or products in either search / recommendations, the point although the algorithm is different is the mechanism for deployment (action -> return results -> customer selection) is exactly the same and that is something I reckon Omniture should be getting into.

ttfn