February 20, 2019
Score 9 out of 10
Overall Satisfaction with IBM Watson Customer Experience Analytics
We are currently testing a couple projects using ML Algorithms in Watson to improve the customer experience. Watson Customer Experience Analytics excels at journey analytics, automatically identifying the pain points of customer experience. We collaborate with business and technology teams and come up with predictive algorithms to get better business results from our existing analytics program. It provides us actionable omnichannel customer behavioral insights and also guides decision making.
- Simple implementation
- Better results
- Easy connectivity
- More knowledge sharing sessions
- UI/UX Improvements
- More tooltips on icons
- Positive: Faster solution
- Positive: Better prediction and service
- Positive: Secure and private
Cognos has exploration but it doesn't have much AI and ML functionality. Watson is much more helpful when it comes to customer experience analytics. Watson gives clarity into end-to-end journeys and effectiveness while also giving the ability to visualize the actual customer experience on digital channels. The macro to micro view gives both the what and the why behind performance. The insights can be used to optimize journeys, boost conversion, and maximize profitability.
It's well suited in building chatbots. We have tried and tested building a chatbot using Watson and it was easy to build and deploy. Watson accepts questions posed by the user in natural language and provides the user with a response (or a set of responses) by generating and evaluating various hypotheses around different interpretations of the question and possible answers to it. Unlike keyword-based search engines, which simply retrieve relevant documents, Watson gleans context from the question to provide the user with precise and relevant answers, along with confidence ratings and supporting evidence.
Evaluating IBM Watson Customer Experience Analytics and Competitors
- Product Usability
- Visualizing, integration, and retrieval of structured and unstructured data for better decisions in an intuitive natural language user interface.
- Creating the most effective targeted marketing campaigns and effective sales strategy by leveraging multiple sources of data from analyst reports, social data, blogs, reviews, and market research, and leveraging Watson’s user profiling, message resonance, and psycholinguistic capabilities.
- Leveraging forward-looking structured and unstructured data to enhance intelligent merchandising and management decisions related to product, pricing, and inventory management
IBM Watson is designed to support business uses across a broad range of industries and functional areas. Watson is not a silver bullet capable of answering every question. In general, the following problems are not appropriate for Watson:
- Complex mathematical computation. Watson can perform only very simple number calculations and comparisons.
- Predictive analysis. Watson Advisors cannot perform predictive analysis or predict the future, because it is designed to extract existing knowledge instead of creating new knowledge. It can only find candidate answers by comparing huge amounts of data and considering their statistical strength.