SPSS: Analytics Force in Customer Experience Management

SPSS is a well established brand in the analytics market providing high quality predictive analytics technologies. However over the years it has acquired a number of products that has extended its portfolio to include Customer and Enterprise Feedback Management. SPSS new portfolio is called Predictive Analytics Software (PASW) that includes statistics, along with new version of what are called PASW Modeler 13 and PASW Text Analytics 13 that are pretty much self-explanatory but Data Collection provides something much more exciting. Behind what appears to be a fairly generic name is the new version of their Enterprise Feedback Management technology, which in my opinion is one of the key supporting technologies that provides Customer Feedback Management capabilities which is a key component of Customer Experience Management (CEM) and what SPSS recently announced as part of their Voice of the Customer announcement.

For me CEM is about changing what happens during a customer phone call or a visit to a web self-service interface, so the experience is more personalized, more in the context of previous interactions, and delivers a more satisfactory outcome for the customer and the company. To do this requires a lot of information, and it requires that information to be available during the interaction. One of the key pieces of information is the customer’s feelings about the company, its products, and how previous interactions were handled. The only way to acquire this information in a non-subjective way is through customers giving companies feedback through short and to the point surveys. SPSS PASW Data Collections includes the functionality to create such surveys, to create multiple versions of the same survey which can be delivered in multiple forms, through the channel of the customer’s choice, and  collect the responses into a common format for analysis. In this respect the product competes with other providers like ResponseTek and Satmetrix. Through integration with the other products in the suite, SPSS includes this information  into the much sought after single view of the customer.

The collected customer feedback data can be combined with just about any other form of customer information and this can be used to create a more holistic view of the customer. SPSS is able to bring together a broad set of customer information including structured data from business applications and data warehouses, and unstructured content such as forms, letters text files extracted from call recordings, and the very latest, text extracted from social websites and blogs. SPSS is addressing what I have written about with the Unlocking the Voice of the Customer through Text Analytics. This is where the SPSS technologies for modeling, statistics and text mining come into play as all this data can be combined to create as very broad view of the customer, which extends to creating predictive models of what customers might do next and how they might react to different circumstances and offers. But none of this will impact the customer experience unless it is made available at key moments during a customer interaction. To-date SPPS has worked on a number of customer specific projects where this information has been integrated with agent desktop applications such as Oracle Siebel, web-servers to impact self-service, and IM providers to impact what happens during an IM session with an agent

I am on record as saying that CEM is now more important than traditional CRM because CRM is more internally focussed rather than CEM which is outwardly focussed on the customer interaction and experience. Making each and every experience as good possible is therefore more likely to satisfy the customer, keep them loyal and open up the opportunity to cross-sell more products. But taking these customer focused predictive analytics can add significant value to BI efforts as my colleague has written about and SPSS has worked for many years to integrate their technology into others. SPSS just announced that IBM Cognos will embed their PASW Statistics in their core BI platform making it easier to employ predictive analytics. Companies examining how to improve their customer relationships should examine how SPSS is supporting the Customer Experience Management and Customer Analytics needs of your organization.

Mark Smith's blog can also be found here.