Table of contents for IBM Big Data Analytics Analyst Insights 2013
- Driving Transformation with Big Data Analytics #BDA13
- Business Value of Big Data and Analytics #BDA13
- Big Data and Analytics: Fueling Competitive Advantage #BDA13
- Using Analytics for Competitive Advantage #BDA13
- Big Data and Analytics Use Cases #BDA13
- IBM Watson Engagement Advisor #BDA13
- An IBM Innovation Panel #BDA13
- Enhancing the Client Experience #BDA13
- Addressing the Analytics Skills Gap #BDA13
- Big Data Inspires Analytics Innovations at IBM #BDA13
- Speed of Analytics: Why Infrastructure and Platforms Matter #BDA13
- IBM Solutions for Insight Driven Marketing #BDA13
- IBM Counter-Fraud Point of View #BDA13
- Closing thoughts on IBM Big Data Analytics #BDA13
A solution-focused session next with Marcus Hearne talking about analytic marketing solutions.
Marketing remains focused on its traditional imperatives – how to focus on customers to maximize the value of each interaction, how to measure effectiveness, what marketing mix to use etc. However the new empowered, social consumer is putting pressure on marketing as people use social networks and mobile devices to find information and get recommendations. Companies are focusing on customers and social channel with CEOs being focused on getting closer to customer and on insight/intelligence from data while CMOs are focused on loyalty and advocacy as well as social media as a channel as well as for brand monitoring. CMOs see themselves as particularly poorly prepared for the data explosion and for social channels. They say that they need to invest in technology, integrate insights into operations and understand what analytics are telling them.
What can marketers do? They can reorganize, add people, improve efficiency or get smarter (using analytics). Focusing on analytics has some real value, with leading marketers that use analytics showing higher revenue growth and higher profit growth.IBM therefore is focused on using analytics to drive a deeper relationship with an increasingly empowered consumer. Consumers go through a research, purchase, use, advocate cycle. Companies go through buy, market, sell, service loop. And these loops intersect when companies sell what consumers buy and service while consumers use. While consumers are researching and advocating, however, they have little interaction with the traditional company activities.
In marketing it is essential to deliver appropriate interactions with customers no matter what channel they are using. Increasingly this is a next best offer or next best action based approach. Marcus went through the elements of these systems but I have a set of posts on this (see this introduction or this one on the elements of a solution). This kind of system requires both better decision management and the full integration of various disparate data sources to drive analytics that improve these decisions.
Marcus identified three kinds of analytics for marketing:
- Customer analytics for acquisition, grow and retain
- Marketing performance analytics to show the return and handle attribution
- Social Media analytics to track loyalty, recommendations and brand
IBM puts these on a continuum. Beginning with social media analytics to measure and respond to customer signals. This transitions into deeper customer insights as part of a 360 degree view to drive a better customer journey. Ultimately it drives data driven marketing decisions around attribution, influential touch points and effective campaigns for improved results.
After this intro, Jason backed up Marcus’ presentation with a demo of a telco scenario. He began by showing how one could use IBM Social Media Analytics to do market research, checking out what kinds of features might be important in a new phone say. Moving on he transitioned to show how you could automated a decision management system for retention offers. This begins with an SPSS Modeler stream that pulls various kinds of data together to drive a predictive analytic model that predicts the likelihood of a customer churning, canceling their service. He used the stream first to build a model and then to batch score a current customer data. In reality of course you would need to make decisions using these scores so he pushed the model into SPSS Analytical Decision Management where he can apply it to customers, apply business rules and optimize the decisions to maximize value by picking the right offer for a high risk customer. Decision Management would let the user simulate the results and, when they are happy, push out deployed Decision Service. This service then drivers offers and interaction in the call center so that the call center rep knows which retention offer to make to whom.
Marcus emphasized the value of adding new data sources like clickstreams, attitudinal data, interaction data etc to the traditional descriptive and behavior data used to drive customer models. He wrapped up by talking about the importance of analytics in marketing performance and analysis.
IBM feels it has four critical differentiators in this space – IBM research for advanced analytics, its domain knowledge from acquisitions and internal growth, industry expertise across all industries and its integrated Big Data platform.