Deployment strategies for increasing your data mining ROI
PART 2
Source: www.spss.com
Copyright SPSS, Inc. 2004


What kind of deployment do you need for better ROI?
As you look to increase ROI in your organization, consider how you can deploy data mining results to decision makers, “virtual” decision makers, operational systems and databases. 

Deploying to decision makers. Getting information into the hands of people who can make changes happen is essential. Typically, reports are deployed to decision makers via intranets. But, reporting only enables you to see the past. It’s like trying to drive a car by only looking in the rearview mirror – the faster you go, the riskier it is. In order to increase ROI, organizations need to predict the future as well as understand the past. When decision makers have the tools to look ahead, they have the ability to act quickly at customer touch points or while doing other front-line activities that require quick, accurate actions. For example, loan officers can input data from a new loan applicant into a data mining solution and get a “score,” or customer category, from a model that predicts an applicant’s risk. In this case, the score indicates the applicant’s credit worthiness. Using this predicted outcome, the loan officer is empowered to make better decisions and reduce lending risk. In this situation, deployment enables more decentralized decision making and enforces consistent decision making. Better decisions are the result of models that are based on facts – actual data – then effectively deployed. At times, an overwhelming number of factors must be considered in making a decision. Putting timely, consistent information into the right hands means your entire organization is on the same page and can act more quickly to make better, more informed decisions.

Deploying to “virtual” decision makers. When customers enter a “bricks-and-mortar” store, they are candidates to receive personalized service from another person – a real decision maker. As you deploy data mining models to your Web site, you transform Web pages from a billboard and Web store into a virtual decision maker. Just as a sales clerk can personalize a customer’s visit to a retail store, deploying data mining models enables you to customize service in your Web
store. For example, based on a combination of what you – and your models – know about your customers and their actions or requests at your Web site, your virtual decision maker offers different content, discounts, recommendations, etc. as they shop.

The value of Web deployment continues to grow as the Web evolves. The Web has reached the point of information overload – the problem is no longer making more content available to people, it’s getting the right content to each person. Data mining makes the difference on the Web because models are built on clicks and behaviors, then deployed to deliver the right information without asking your visitors to do anything more than they normally do – just surf your site. So each click takes different visitors to different pages, based on pages they have previously visited, time of their visit and past purchases – in short, who they are and what they want.

Deploying to operational systems. In a manufacturing setting, deployed models may process information coming from a production line. Based on that data, models can predict errors in a process before they occur, instead of reacting to problems after it’s too late. Data mining models can be useful in a number of operational systems, such as your call center, another valuable customer touch point. Here, deploying data mining models can enable call center representatives to provide customers with the same personalized service they find on the Web.

Deploying to databases. Interactions with customers today are multi-faceted. For example, a retailer might interact with the same customer via a storefront, the Web, a call center and a catalog. In order to keep customer information current, savvy retailers store all data in a centralized data warehouse, integrating the customer knowledge they need for effective CRM. So, when a customer’s profile is updated with new information, like recent purchases or updated demographic data, the customer’s score is also updated. This score is used in all types of future interactions. The next time a customer calls, you’ll know if he or she is loyal and profitable or unlikely to provide much lifetime value to your company. 

Start deploying your results
To get the most from your data mining projects, plan for deployment. There are a number of items to keep in mind as you plan your deployment strategy. These checklist items are taken from CRISP-DM, the CRoss Industry Standard Process model for Data Mining. 

Evaluate alternative plans for deployment. Look at the best way to implement your deployment plan, according to the problem you are solving. Get information directly from end users about how they want to use a solution.

Identify possible problems. What might keep you from reaching your target ROI? Look at the resources and skills you need to deploy now and in the future. Examine all the issues throughout the process, from identifying the business problem to model maintenance to understanding how results are used.

Create a monitoring and maintenance plan. Check for possible changes in the environment of your deployed solution. Anticipate internal changes, like a new strategy or external changes like new types of fraud, that make models obsolete. Develop an action plan for responding to these changes.