Eye level they say is the buy level – this merchandizing tenet for long has been of value to entice customers into making the buying decision. It touches the ‘buying chord’ in a customer, hits the customer eyeballs, and ignites the buying spark – a technique leveraged to make products move from the retailers’ shelf to the customer, resulting in more sales. If getting into the minds of customers is important, reading the customer’s mind is even more important these days.

Stepping into the empowered-customer era, retailers are driven to know what the customers want even before they do. Organizations that achieve remarkable sales performance are the ones that know and read customers’ wants, needs, wishes and buying behaviour with exactness well in advance. By predicting customers, retailers travel an assured path to sell better.

Today, analytics is the sure-fire weapon in the sales arsenal. Sales analytics empowers sales personnel with relevant insights, equips them with comprehensive customer information and allows them to close more deals. These days, sales kits become incomplete or rather toothless without that one potent weapon – sales analytics. That’s giving sales personnel the push they need to understand customers better and sell better.

What does the customer want?

A telesales person decides to target a prospect with a home loan offer, but only finds that he is speaking to someone who has availed one recently. ill-targeted—pitches can only lead to ill will. The telesales person’s efforts would have been better served had the person leveraged the means to assure that he was targeting the right prospect – or know for sure that the prospects he is targeting is looking out for a home loan offer.

Predicting what the customers want even before the customer knows what he wants is the secret to meeting the customer expectation and selling better. Predicting customers is about encrypting that who-when-what scenario time and time again. The proliferation of customer channels also mean that there is a new addition to this scenario, and that’s how the ‘How’ comes in.

With so many different channels giving customers the opportunity to engage with a brand, the ‘How’ component has risen into a significant feature that needs to be entwined with the scenario to predict customers, give them what they want at the time they want, increase sales in the process.  Analysing customer behaviours, creating ‘clusters’ of customers, comparing trends and unearthing sales opportunities, leaning on algorithms to predictively spot new business opportunities and dig cross-selling opportunities are some ways where the prediction engine can prop up sales.

Customer prediction also equips brands to unearth up-sell opportunities for increasing conversions. Models such as Customer Lifetime Value, Customer Segmentation and Propensity to buy are leveraged to offer the right product to the right customer at the right time through the right channel.

What is the next best offer, what are the recommendations?

For Amazon, infusing recommendation engine into its business has really been a move that has paid off. What this recommendation algorithm has done is that it has taken personalization to a new level, tapped into unplanned purchasing possibilities and accelerated sales in the process.

Personalized offers or the Next Best Offer, is all about providing the right recommendation to the customer. Take the case of the Netflix user – the user finds the exact content that he is looking for from among innumerable titles. Netflix knows what it takes to recommend the right titles to the right user, made possible by way of a personalization algorithm.

Leveraging customer data is the secret to making appropriate recommendations. Customer ratings, reviews, comments on products, clicks, page views, cart events, return history and search log give recommendation engines the feed they need to recommend the most-fitting products for a user. With models built to capture buying behaviour, life-event patterns and social media interactions among other things, organizations take the next best action armed with customer 360-degree view and predictive analytics to unearth common behavioural patterns.

Who is the customer about to defect?

When steaming customer interaction, engagement is reduced to a trickle, organizations have a job at hand. Either a bad experience or the lure from other brands can cause a customer to defect. It works to the advantage of an organization to embrace a proactive approach, embed customer churn analytics into their business to get signals about customers who are about to defect. This way, brands can predict customer churn, cozy up to the customer, provide good offers that can bring the customer back into their fold and increase sales of products.

Earlier, sales person at the corner store would know what a customer wants, suggest relevant items and help the customer go through a pleasant experience. These days, prediction engines are arming sales personnel with all the details they need to know about a customer to help them cater to customer needs right in time and improve sales. Whatever be the period, it is important to know your customer, know what he wants in advance to sell better.