I recently wrote how IBM is making customer analytics smarter. Since then IBM has run events in North America and Europe to demonstrate how it is continuing these efforts and expanding into other areas. Outside of the customer space you can read how my colleagues assess its efforts: Mark Smith discusses HR, Robert Kugel sees its impact on business overall, and Tony Cosentino addresses it in IT. Our research My focus remains the customer and I have learned more about what IBM is doing in social media, identity reconciliation, visualization, mobile apps and big data.
As part of its customer analytics portfolio, IBM has social media analytics. Going beyond basic social media analytics, the product brings together IBM research assets for demographic, geographic and behavioral analytics, uses its SPSS advanced analysis to support sentiment analysis and segmentation, is capable of identifying influencers and analyzing their social activities, has big data built in, and includes a series of prebuilt dashboards that can be configured to individual user requirements. It works with several
My research into the contact center in the cloud shows that today companies
I continued my update about IBM products at a recent analyst event in London on big data. Many of the topics mirrored what my colleagues have written about in the blogs I identified earlier, but a couple of things struck home to me. When IBM talks big data, it means really big. One case study presentation talked about downloading hundreds of thousands of terabytes of data after a flight so engineers can analyze the plane’s performance. To support this level of processing, IBM has developed new hardware and software tools that make previous data analysis performance look slow in comparison. This scale makes processing of customer data seem simple, and I realized that big data only comes into customer engagement in processing social media posts and call recordings, both of which companies typically have in the hundreds of thousands. In this context it is surprising that although IBM has built advanced text analytics into its products, it has yet to include voice analytics.
The other aspect of big data analytics that struck me as increasingly important in my field is predictive analytics. In this era when the customer is king, predicting customer behavior is the most important business analytics capability according to our technology innovation benchmark to help companies be more forward looking. IBM puts this all into the context that analytics is evolving. First there was descriptive analytics, which told users what had happened, then came predictive analytics, which told companies what might happen, then came descriptive analytics, which tells users how they can achieve the best outcome from their activities, and now we are moving into an era of cognitive analytics, which uses past and current events from as many sources of data as possible to tell users what their next action should be. As IBM adds these abilities to its products, users can process more forms of customer data and build more advanced predictive models; this is something else companies should evaluate.
One of the sessions at the London event focused on making analytics easier to use. IBM demonstrated 20 different ways of visualizing information, not all of which are yet generally available. Beyond that IBM also gave a glimpse of nearly 200 other ways of visualizing data – yes, 200 – that can be configured to match any requirements. As these capabilities become available, users will have access to them on mobile devices, which will support setting up a range new uses on the device. All this might seem excessive, but during discussions on customer metrics I have learned that the form presentation, especially visualization, of metrics is almost as important as the actual numbers; something as simple has having a chart scaled the wrong way around can create a wrong impression. But making visualization actionable will require more improvement by IBM to make the discovery process on it more interactive as my colleague has pointed out.
Another session focused on Watson Engagement Advisor, which I wrote about earlier this year. This recently launched IBM platform allows companies to build smart mobile apps. It is based on the Watson platform, which can intelligently search for information buried in any form of structured or text-based data. It thus enhances mobile apps by connecting customer requests to the information they are looking for and can, for example, lead customers through complex purchases by using rules-based searches driven by context. Engagement Advisor can search publicly available sources of data, so it could be a concern that companies undermine customer trust by demonstrating how much personal data they can access. However, designed correctly these apps have the potential to dramatically improve what customers can achieve through self-service; they have the capability to intelligently connect to a person if a transaction does not complete within the app. It is early days in their adoption, but I recommend that companies track how this kind of analytics could help them innovate in customer engagement.
Over all these sessions I learned a lot about how
Regards,
Richard J. Snow
VP & Research Director