The past decade has witnessed tremendous influx of technology across all aspects of business operations. Organizations are increasingly turning to automation, artificial intelligence and robotics to bring about a transformation in their revenue generation capability, reduction in costs and increase in efficiency.
Indian banking leaders like ICICI and HDFC have piloted using humanoids/robots to bring about operational efficiency by intelligently automating many of the backend processes and customer facing areas. On a similar note, global banks like Mizuho and Sberbank use virtual assistants to enhance customer experience. Technological advances are increasingly being viewed as game changers in the ever evolving business landscape.
A quick scan of technological advancements over the past year throws up multiple terminologies - Artificial Intelligence, robotics, humanoids, virtual assistants, chatbots.. the list goes on. All these are completely interconnected. A Deloitte report (Tech Trends 2017: The Kinetic Enterprise), uses the term Machine Intelligence (MI) that encompasses all these and more. MI includes all areas that use machine learning algorithms, be it cognitive analytics, artificial intelligence, robotics driven automation, humanoids etc. Similar to all other functions, MI also has a tremendous potential when applied across marketing initiatives.
Organizations have been using MI in varying levels of maturity, across their marketing functions. Retailers have been aggregating data across various customer touch points, analyzing them to understand customer preferences and priorities, correlating them to buying propensity, incorporating location specific data and using machine learning algorithms to make conscious decisions on the most relevant offers that can be targeted to the consumers.
A retail giant in UK has been piloting the use of beacons that transmit personalized in-store ads to customers, based on their precise location in the shop and analysis of shopping habits. Few global banks have also been analyzing website access behaviors, customer support center conversations, text and visual inputs through machine learning algorithms to understand the frequency and likelihood of banking product purchase. These are further used in marketing campaigns to push the right product recommendations to customers at the right time.
Some banks have moved a step further to incorporate sensor technologies in their branches to identify a customer when he walks into the bank and personalize the service rendered to him, thereby enhancing customer retention. While instances of MI usage in B2C scenario are very much visible, B2B space has also been demonstrating traction when it comes to adoption of MI. Marketing initiatives that involve disseminating content across large segments of probable customers, expecting few to respond based on relevance, is a thing of the past. Personalization of messages, content and products using aspects of MI has already taken off and can be seen in some form being used by multiple marketing teams.