I got a briefing from Conductrics recently. Conductrics is a start up delivering an agent-based decision optimization solution focused on next best action or next best message. Designed for a website, mobile application or even a POS it uses machine learning techniques to optimize the behavior of the site/application to meet some overarching objectives – goals and values. It will find values that will optimize for these objectives. The mPath agents handle AB testing, multi-variant testing and machine learning to discover and continually refine the most effective options.

The first step is to identify the decision points within your application. For instance, when you go to a particular page on a website there might be a decision point with three decisions – “which header”, “which offer” and “which image”. For each of these there are various options allowed from which mPath will be trying to select the “best” one.  You also need to define your goal points – places where you can identify that your user/visitor has done something of value. For instance all the goal points may be defined on the sales page with registering for offers and actually buying something being the two goal points.

Generally tools try and optimize or run test on a single decision point at a time and lack any awareness of the structure of the application/site. Conductrics links the decision points together with the goal points. This allows it to drive optimization over the whole site or application. How someone navigates, where they might navigate to for instance, is being considered along with the goals themselves and decisions are being optimized to drive a successful conclusion (as defined by the goals). If all the goal points are on a specific page then messages/content that drives someone towards that page matters a lot to achieving the goal for instance.

To make this work within mPath you set up goals (or an objective function), decision-points (along with the decisions made at that decision point and the available decision options), user attributes and configuration information. The product itself has an online UI for administration and reporting, an mPath server where agent specification, controller, learner and data transformation layers are stored plus a RESTful API supporting JQUERY, Flash and Javascript.

When operating, the server gets requests for decisions from the application or site (each identifying itself as being a request related to a particular decision point) and gets back a set of decisions. In addition, when a visitor or customer hits one of the goal points, the server gets a “reward”. Many sizes of rewards can be defined to allow the balancing of competing objectives – a large reward for a direct lead to a smaller but more immediate reward from referral to a partner for instance. Similarly signing up for a newsletter might be worth something or registering for content while a big reward comes from buying something.

Any kind of content can request a decision from mPath and/or tell mPath when it achieves something of value. Most sites or applications will have many decision points and any given customer interaction will involve many decisions over time and may, eventually, reach one of several potential rewards. When a reward comes in the agent assigns this value to the decision that immediately led to the reward but also to the previous decisions in the chain that led to the successful outcome. It keeps the series of decisions made that resulted in either a reward or no reward and increases or decreases the value of the specific options used at those decision points appropriately. It learns what seems to work and what seems not to from the ultimate rewards received.

To do this mPath applies automated a/b testing and machine learning techniques. The control environment allows users to set the explore rate (what percentage of customers are experimented on to find what works) as well as the size of the control group (the group that always gets the one option that is the default in each decision). This allows a lot of testing early while the models are trained and then only a small amount during normal operation to keep things tuned – and perhaps none at all during times of peak demand. The product has some nice reporting, an easy to use call builder including redirect URLs to make it easy to integrate.

I was impressed by the thought Conductrics have put into the product (especially the use of a linked set of decisions to drive a desired outcome) and their use of machine learning/automated decision analysis to learn what works. I look forward to hearing more about them and seeing their product develop.