AUGUST 202319Software 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 Bluetoothinteraction. 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, and more. This leads to a number of challenges such as consistency of message across channels, launcth 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, and so on. 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 and so on.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.Artificial Intelligence driven Multi-structured analyticsMulti-structured analytics constitutes combining multiple types of data varied in terms of their type and frequency including structured, unstructured, multimedia data, streaming data and so on. 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 and so on. Predictive intelligence, combined with context awareness, semantic technology, voice analytics, and personalization of user needs anticipates user behaviour by drawing upon
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