Cognitive Customer Engagement is Here!
Cognitive computing has matured dramatically in the last few years. It is now capable of fulfilling its longstanding but unrealized promise of assisting humans to make effective decisions on complex issues absent of pre-conceived bias. It can do this based on millions of data points, and use learning of what has, and hasn't been successful in the past. These capabilities are made possible through advances in a number of key technologies:
- Big data capabilities and techniques now allow vast libraries of unstructured data to be accessed, categorized, analyzed, and understood. Decisions can thereby be made that would not otherwise be possible given the limitations of the human mind to ingest information. Analysis can be performed across enormous amounts of data, differing sources and media (e.g. speech, text, images and video).
- Machine learning enables cognitive models to be trained by experts in the field to learn from the data it analyzes and to hone the recommendations it provides to become more and more accurate in its findings over time.
- Humans can now interact with this technology in a natural way, by posing questions as we would to another human, and receiving answers back in the same way.
- This type of computing power can now be accessed in real-time through APIs and other cloud-based technologies, that make it both easily accessible and affordable.
IBM’s Watson is the probably the best known machine learning/cognitive solution available today, based largely on its victory over human opponents on Jeopardy in 2011 https://www.youtube.com/watch?v=WFR3lOm_xhE
Watson has come on leaps and bounds since that TV appearance, and is being used to tackle some of the most complex business and societal problems in existence today, such as cancer treatment, financial services compliance, "internet of things" control and decisioning, customer understanding and complex marketing. It is being used to support industries as diverse as healthcare, financial services, manufacturing and the culinary arts. IBM has invested heavily in Watson, and it is a cornerstone of their strategy. Watson today has significantly more processing power than its predecessor, and has evolved dramatically beyond its question and answer research project roots. IBM’s cognitive product set now include their on-premise flagship product Watson Explorer, as well as Watson Engagement, Policy and Discovery Advisors. In addition, IBM provides a set of APIs that can be accessed in a consumption-based charging model. The APIs cover areas such as tone recognition, personality insights, visual recognition, tradeoff analytics, language recognition, natural language and conversation. IBM also acquired AlchemyAPI in March 2015, adding the AlchemyAPI to its arsenal, which is actually a set of API services in itself.
Other organizations are also investing heavily in cognitive and machine learning solutions.
Microsoft has also funded significant research in the area, and has their own set of capabilities. Branded as Microsoft Cognitive Services, these capabilities include a set of cognitive APIs in the areas of Vision, Speech, Language and Knowledge.
Google’s DeepMind has received recent recognition through the publicity surrounding its program AlphaGo, which was able to beat Lee Sedol, the dominant Go player in the world over the last ten years https://deepmind.com/research/alphago/ The Google prediction API includes language processing, a recommendation engine, pattern recognition, and prediction.
AT&T Speech allows the speech recognition capabilities to be incorporated into digital applications. APIs include speech-to-text and text-to-speech, incorporating natural language and translation capabilities.
Cisco and SparkCognition - focus on using Cognition and machine learning primarily to enhance cyber security capabilities.
"In the future, every decision that mankind makes is going to be informed by a cognitive system like Watson, and our lives will be better for it." - Ginni Rometty, Chairman and CEO, IBM
By bringing together vast volumes of information from public and private unstructured data sources, social media, reference material and journals, as well as structured data from corporate data stores, and combining it with complex decisioning and machine learning, organizations can dramatically enhance their business capabilities. It is clear that the future of customer engagement and interaction in the digital realm and in the contact center space will change dramatically over the next few years:
- Voice of the Customer programs will rely less on survey scores and more on analyzing unstructured feedback, looking at intent, sentiment, emotions, needs, and tone during and after an interaction, and more generally as part of developing a broader understanding of the relationship.
- Cognitive solutions will learn what is effective in helping customers achieve their goals, apply that learning as appropriate, and hone those solutions to further improve the results as time goes by.
- Customer interaction will be increasingly driven by automated concierges that will understand complex dialog forms and interact with customers in natural language effectively and efficiently. This will take the form of chat or voice recognition bots that customers can interact with in a natural way, which will be enabled by Watson or similar technology to effectively help the customer solve their problem or answer their questions.
- Customer interaction (not just content) on the web and mobile devices will increasingly become dynamic and tailored based on cognitive understanding.
- Robotic Process Automation will become enabled with cognitive decisioning and machine learning capabilities to handle more back-office "heavy lifting" and tie together front and back-office activities into an appropriate, efficient and individually customized set of processes.
- Marketing decisioning will move from a campaign-centric model to a true omni-channel approach that will cater to a segment of one based on analysis of the most likely way to drive revenue, a long-term relationship and a positive customer experience for that particular customer. Again, machine learning will be used across information sources to determine what approaches are most likely to be successful. These sources will increasingly include unstructured information including market data reports, social media, customer data, and weather data.
Organizations need to prepare for these changes now. Make no mistake that this move to a cognitive-enabled model will represent a seismic shift in the way that organizations do business. In addition, not having access to the capabilities that machine learning and cognitive solutions provide will put them at a competitive disadvantage.
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