By Indranil Mukherjee, Senior Vice President and Service Offering Head, Infosys Salesforce Services
Consumer expectations continue to rise. In the sixth edition of an annual survey of service professionals undertaken by Salesforce, 80 percent of agents said customers were asking for more than they did before. And what do customers want? An earlier Salesforce survey – this time of consumers – named good customer service, immediate attention from service staff, and that companies adapt to their changing preferences, as the top three demands. Meeting these and other customer expectations, a huge body of research says, is crucial for maximising retention, brand loyalty and revenue.
Hence that is what organisations must do if they are to meet their business targets. But it may not be as straightforward as that. A recent report by a well-known consulting firm says consumers’ desire for value amid rising prices is leading to unpredictable buying behaviours. For example, customers are trying to get more for their money, not necessarily by buying less or buying cheap, but by waiting for the best deals on every purchase, or trading down across categories – cutting down in one place in order to spend more in another.
How can enterprises uncover such nuances to give customers what they are looking for? The answer is to deepen their customer understanding with a 360-degree view, predict consumer behaviours based on data analysis and insight, and offer the right product, service, offer or experience at the right time.
Fortunately, today there are a number of AI-based Customer Lifetime Orchestrator (CLO) platforms which can help elevate Customer Life Time Value (CLTV) by helping organisations do all the above within a short time and at a reasonable cost. Starting with gathering data from internal systems, customer-facing channels, and third-party sites, AI ensures that organisations have all the information they need in granular detail about customer preferences and behaviours. Artificial intelligence and machine learning tools analyse this data to understand individual customer requirements and suggest the most suitable products and services. Importantly, organisations can leverage AI-powered predictive analytics to anticipate customer behaviour fairly accurately: referring to the earlier discussion, they would be able to identify which customers are likely to defer purchases until a promotion is announced, which customers will shift to cheaper products, and so on.
Further, intelligent recommendation engines enable enterprises to make highly targeted, contextual offers to customers to increase conversion rates and retention, leading to improvement in CLTV. Another way that AI and ML improve CLTV is by allowing dynamic pricing. They provide key data about demand patterns, rival offers, etc. in real-time based on which businesses can adapt their prices to optimise their own revenues while ensuring customers get value for their money. A well-known example here is ride-sharing apps, which dynamically adjust fares during peak hours or when there is excessive demand (for example, when it rains).
Indeed, AI is also revolutionising how products are priced in the first place. Take the case of automobiles, which used to be priced based on what rival models were charging, plus some gut feel. Today, algorithms consider even unfolding market trends and consumer sentiment to come up with the right price for every model at a particular time, and continue to monitor the market to recommend price adjustments in case of a shift in demand/ supply. By bringing transparency and accuracy to price determination, AI helps automakers optimise both the price of their vehicles, and revenue.
In the normal scheme of things, customer loyalty also translates into higher customer lifetime value. To prevent customers from moving on, businesses should provide smooth, seamless, and fulfilling experiences, starting with onboarding and following it up with interactions that are always personalised and meaningful.
Service has a huge role to play in customer retention – promptly addressing issues and going the extra mile when required can determine whether customers will stay on as loyal advocates or leave to broadcast their ire on social media. Here, AI-powered solutions can support service agents by summarising customer history, fetching information, suggesting resolutions, or even autonomously triggering appropriate responses.
To maximise customer lifetime value, organisations must first deliver great customer value. As customers’ expectations of value increase rapidly, the support of AI is becoming imperative for achieving this goal.
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