Anand Subramanian, Vice President, India, Theorem

Anand Subramanian is an IT Services industry veteran with over 29 years of experience  having worked with  large  IT service companies like Tata Consultancy Services, Wipro Technologies, and Tata Interactive Systems. Prior to joining Theorem, Anand managed India Delivery as a Chief Delivery Officer for Ness Technologies India PVT Ltd. Starting his career as a Software Engineer and eventually pivoting into Delivery Management roles, Anand has been involved in various strategic initiatives including  leading Innovation initiatives throughout his professional tenure. Anand has managed customers including large Fortune 500 companies in their digital transformation journey. He is passionate about utilizing  technology to solve customer pain points and he believes that value-addition is one of the key tenets in any customer engagement.


Over the years, companies have adopted newer ways of marketing, moving away from the traditional methods of marketing. Advancements in Technology have been embraced by Digital marketers to help them achieve, if not over-achieve, the return on investment (ROI) on the marketing spend. Every Chief marketing officer (CMO) have their eye on key indicators around customer acquisition, customer retention and providing customer lifetime value. If these are the key performance indicators (KPIs), how does technology help Marketing Directors and CMOs achieve these KPIs.

Digital marketing is an expertise that revolves around building insights using data and analytics. With the advent of social media and engaging the online audience new kind of data has become available to Marketers. All this humongous data has to be processed to generate insights. This is where technology plays an important role on how to make the best use of this data. Specifically, applying Artificial Intelligence (AI) and Machine Learning (ML) on this data sets helps identify behavioral patterns. Here are some insights that AI and ML algorithms help digital marketers

Customer acquisition: With the growth in the awareness amongst customers and with companies increasing digital marketing spend, customers have more choices than ever before. This becomes a challenge for businesses to remain competitive and continue to acquire more customers for their revenue growth. This is where technology has a role to play and AI can contribute in the different steps of the customer acquisition workflow. From creating awareness, building interest and then converting a prospect to customer AI can help in each of the steps. Optimizing the company’s search engine ranking, understanding customer dropouts, providing seamless website and/or customer support experience, generating interest in the products, providing cross-sell/upsell opportunities are the areas where AI can help businesses reduce the customer acquisition costs. 

Customer Experience: When customers want to make an online purchase the buying process has to be seamless. Using AI-enabled chatbots and using conversational AI, the customers’ online buying experience can be improved considerably. Customizing and personalizing content can be easily achieved through the information captured by the BOTs as well as through the feedbacks or reviews captured post the purchase. The chances of customers coming back to a brand increases significantly when brands customize content based on the context which is where technology can help.

Personalized marketing: Based on customer’s buying behaviours, their interests, and spending habits, different kinds of digital marketing engagements can be enabled. This includes sending personalized messages, notifications, emails, relevant offers and recommendations on brands that the customer is more likely to shop. There are various parameters that will need to be considered before determining these offers. For e.g. considering age, demographics, location, gender will not provide accurate insights on customers’ interests and predict future buying behaviours

Product Recommendations: By using the data collected and implementing intelligent learning mechanisms a recommendation engine can help push products based on their behaviors. Since human cannot process huge amounts of data, this is where technology will play a role to push this information to customers.

Improve customer retention: Customer churn cannot be avoided, however, how do we reduce it. Using technology one can determine how frequently and why customers leave. Customers leave brand due to different reasons. Reasons can be numerous – demographics, feedbacks, customer satisfaction scores, amount of time spent on customer support, etc. Analysing the online customer engagement using AI can help businesses determine their best marketing strategies, production capacity to meet customers’ demands. Once the reasons are identified, using different AI and ML algorithms one can identify when the customer is likely to leave. This will give brands an opportunity to take remedial actions to retain these customers. 

Customer lifetime value: Businesses have come to realize that determining the customer lifetime value is important in order to be successful. To put it in simpler terms, customer lifetime value is the money a customer is willing to spend on a brand. Indirectly, it also shows will a customer become a repeat customer. AI will help businesses determine this value and in turn determine their most valuable customer(s) and help determine ways of how to convert a low valued customer to a high-valued customer. Businesses can then plan their loyalty programs, incentivizing repeat customers using different techniques like more targeted/personalized customer messaging, multi-channel marketing, over invest in repeat and continued buyers, etc.

What to watch-out for: One thing that has been discussed quite often is the authenticity of the prescriptions made by AI tools. How can AI tools be free of biases? An AI bias occurs when the algorithms deliver biased results due to incorrect assumptions. It also depends on the data sets on which the ML is being trained. More often than not ML processes get trained on training data and depending on the data quality the machine delivers the results. Hence it is important to question the AI results without which AI could end up being counterproductive.

Conclusion: AI has become mainstream to large brands and businesses who integrate technology to their business growth. Breaking the different stages of marketing and identifying the relevance of AI in each of these stages will help businesses propel forward their growth. 

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