Mphasis: Four Top Trends in Cognitive
1. Smart environments with Pervasive Human and Machine Networks
As more devices are getting linked to the internet, connectivity is growing exponentially and getting extended to appliances such as televisions, home appliances, cards etc. For the various connected Internet of things to work, they need to have common language for interaction and data exchange regardless of the type of devices and wireless connection networks. Software standardization initiatives for IoT are designed to be embedded in a vast gamut of devices across manufacturers, operating systems and wireless links such as Wi-Fi or Bluetooth. As devices get connected through common standards, they can work in conjunction, for example a motion of temperature sensor can work with other nearby connected sensors such as gyroscopes, accelerometers, door and window locks, video camera, light bulb or speakers which alert the owner. Smart environments are embedded with sensors which collect data across distributed locations. Sensors enable enterprises to monitor a phenomenon remotely and transmit the information to other sensors or to a control unit. Embedding sensors, controllers, devices and data into the physical spaces of human beings facilitates immersive interactions and multi-modal interfaces, resulting in seamless experiences. Given below are some key enterprise trends from leveraging smart environments:
• Connect and engage end customers accessing products and services via multitude of devices such as mobile, TV, sensors, appliances as well as via multitude of delivery and interaction channels
• Embed sensors into the eco-system and supply chain for enhanced insights and experiences with regard to humans, goods, products and machines across their life cycle usage
• Offer location based experiences, services and payment processing by leveraging bionic sensors and hand held devices
2. Predictive Analytics driven Customer 360
Enterprises need to correlate customer data footprints from across multiple interaction channels and build an accurate customer profile with product recommendations specific to the channels of interaction. This involves complex event processing which co-relates Customer demographics and transactions data, Call Center data, Web browsing behavior data, Online chat data, email Campaigns data such as click through rates, Display Advertising data, Voice data etc. This leads to a number of challenges such as consistency of message across channels, launch of offers across channels. Fragmentation of the channels also means that customer voice and opinion is distributed across the internet and in various forms – blogs, tweets, Facebook update etc. Systems need to in place to keep a real time watch on all this conversation and deliver timely insights to marketing team to respond in a timely fashion. Opportunities exist to break digital silos by combining data such as user reviews with enterprise transaction systems so that every time a customer gave a lower rating, an alert is generated which goes to a customer service agent who then will connect with the customer.
Digital Customer 360 helps generate unified customer insights based on data from multiple sales and interaction channels. Enterprises need to leverage customer footprint correlation engines which takes slivers of customer data from multiple interaction channels and builds an accurate customer profile with product recommendations specific to the channels of interaction. This involves complex event processing which co-relates Customer demographics and transactions data, Call Center data, Web browsing behavior data, Online chat data, email Campaigns data such as click through rates, Display Advertising data, Voice data etc. Given below are some key enterprise trends from leveraging predictive analytics driven Customer 360:
• Build enterprise level Build Big Data correlation engines that generates Customer 360 insights by correlating data from multiple internal and external customer touch points as well as open data
• Create engaging experiences across multiple customer touch points by better understanding of customer behavior using techniques such as text analytics, natural language processing as well as social network analysis.
3. Artificial Intelligence driven Multi-structured analytics
Multi-structured analytics constitutes combining multiple types of data varied in terms of their type and frequency including structured, unstructured, multimedia data, streaming data etc. Big data analytics about people and machines would give us a historical picture of customer behaviour, and known elements that constitute a claims fraud and their evolution. This can be coupled with other techniques such as social data analytics from mining the customer’s social profile, voice analytics of the customer and cognitive intelligence based user profiling and modeling based insights. Cognitive Intelligence can enable insurance companies in analysing contact centre as well as chat data interactions in real time to predict propensity for fraud based on voice, video and text analysis and correlating the same with other similar fraudulent customer behaviors. The long term objective in such scenarios is to build machine learning based intelligent systems which learn on an ongoing basis based on historical pattern based analysis of billions of user and machine data points and predicts events.
AI driven multi-structured analytics is going to impact various facets of enterprise value chain. These could be the search and advertising algorithms, friends, movies and books recommendation algorithms, driving patterns recommendation, money lending related credit recommendations of peer to peer lending platforms, predicting journey times to frequently visited locations etc. Predictive intelligence, combined with context awareness, semantic technology, voice analytics, and personalization of user needs anticipates user behaviour by drawing upon phone usage, user data, and historical behaviour. The power of prediction gets enhanced as more partners such as hotels, car rentals, airlines, banks and retailers share user data. Given below are some key enterprise trends from leveraging Artificial Intelligence driven Multi-structured analytics:
• Generate insights from multi-structured and multimedia datasets, digital footprints of customer interactions and customer intelligence across multiple channels, touch points as well as social networks
• Leverage predictive analytics for better planning, forecasting and decision support (for decisions such as cross-sell, upsell, retention, loyalty management, risk mitigation, fraud detection, campaign management, inventory management etc.)
4. Immersive Multi-modal User Experiences
The future of immersive experiences would involve combining the physical world and an interactive, three-dimensional virtual world. This is achieved by integrating synthetic information into the real environment. Rather than immersing a person completely into a completely synthetic world, technologies need to embed synthetic supplements into the real environment. The need is for technologies which blurs the line between what's real and what's computer-generated by enhancing what we see, hear and feel. Augmented Reality has the potential to enable natural interactions and immersive user experiences by blending physical and virtual worlds.
Immersive user experiences are predicted to grow exponentially with increasing focus on location-based search, search, games, lifestyle and healthcare, education and reference; multimedia and entertainment; social networking, and enterprise applications. Other drivers include the widespread adoption of mobiles and the increasing trend towards the adoption of wearables and sensors which makes the availability of mobile devices with camera, GPS, digital compass, tilt sensors and mobile broadband connectivity. Since immersive technologies intersperses the real and virtual worlds, it presents new challenges in the ways of conventional user interaction design methods. Going beyond the technological complexity of building immersive systems, there is the larger challenge of involving various system stakeholders during the system design process.
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