Nailing the sale: How AI can decode the customer life-cycle

Most salespeople will tell you the same thing – no amount of preparation, banter, or efforts can ever match the absolute luck involved in catching a customer at the precise point that they are looking to make a purchase.

Finding a person who is a potential customer is simply one part of a company’s acquisition process; like the salespeople, companies want to talk to the person, precisely when the time is right and get the right deal at the right time.

However, this is very rare – those very salesmen will tell you many stories about the many hundreds and thousands of customers whom they’ve spoken to and ended up wasting ridiculous amounts of time on, but just never made a purchase.

Companies have long known that each customer goes through different stages before actually buying a product and different stages post-purchase as well.

Knowing which stage a customer is in helps companies phrase and frame their communications to them, and this customer life-cycle is a very important part of any company’s sales strategy.

Unfortunately, however, there are no definitive cues to knowing which stage a customer is in.

Salespeople use their instinct and experience, and even the best salesmen can read their customer wrong.

Artificial intelligence, on the other hand, can showcase greater accuracy and increase the efficiency and impact of the company’s messaging to customers.

Many years of experience and countless hours spent trying to sell products to people help hone the salesperson’s instincts.

Subtle body language cues and an instinct trained through trial and error are the weapons in their arsenal that they use to understand the customer and their state of mind better.

Given enough time and experience, salespeople will be better able to tell the customer’s state of mind.

However, with much sales communication and outreach happening over the internet, such instincts are incompatible with our current commercial communication paradigm.

However, AI and Big Data can help fix that.

Leveraging machine learning algorithms and neural networks, AI can detect patterns in the speech and language of the customer with a level of speed, accuracy, and attention to detail that would be impossible for human beings to match.

Furthermore, AI software now has the capacity to learn and continuously reduce its margin of error to near zero within weeks of operation, unlike human salespeople who lack the perfect recall or learning curve which AI brings to the table.

Coupled with information and big data, AI can draw a data model that would describe any potential customer or lead with unbelievable accuracy – and continue getting better and better at it over time.

Knowing what to say and how to say it to an existing or potential customer to convert them and make the sale is the most challenging part of any marketing team’s job.

An important component in determining this message is understanding the customer.

Data-driven models constructed by AI using Big Data can provide the precise inputs and decode the customer’s stage in the life-cycle, while also providing other information about what is likely and unlikely to work.

Further, when it comes to financing and financial products, understanding the customer’s financial situation and providing them the right advice is crucial – the variety, complexity, and risk involved in the finance can make the difference between absolute satisfaction of the customer and completely losing any chance of making subsequent sales.

Using a full data-set about the potential customer and their financial status, AI-driven algorithms can make accurate assessments of their credit history, rating, and throw up the best-suited financial solutions for them.

This level of customization and personalization is extremely hard for a human being to match.

AI can crunch through thousands and millions of data points for any kind of customer – from MSMEs to individuals – and suggest the best investment and fiscal strategy.

AI in Fintech seems like the perfect solution since it helps these companies communicate more effectively with potential clients, offer them the right kind of product based on their history, and also indicate how best to offer great post-sales promotions and products that would help both the customers and the company.

Mutual satisfaction, happiness, and prosperity can be achieved through the use of AI in this space.