May 18, 2016 at 9:14 AM
Machine learning is helping brands narrow the divide between their products and consumers in ways that would appear almost magical only ten years ago. From Amazon's personal product recommendations based on past purchases and browsing habits, to Netflix's uncanny ability to suggest just the right movie title according to your taste in film, data-driven insights are helping companies speak to individual customer preferences, who are demanding more personalisation in their products and engagements. This has moved data analytics from novelty status to an integral part of the marketing strategy, as brands discover new opportunities to communicate their unique selling points.
Read our blog post on how Netflix is using Data Analytics to make movie magic.
UK-based B2B food supplier JJ Food Service is a fine example of how more data-centric marketing strategies have resulted in the company reaching customers in more relevant ways while simultaneously aligning its services to the needs of its market.
Speaking to Customer needs more efficiently
The food service provider has been able to fine tune its marketing strategy and upsell its products to existing customers through its e-commerce site by processing years of transaction data within their machine learning platform. The analytics platform positioned them to provide relevant product recommendations, as well as prepare for product demand and introduce efficiencies to its nationwide operations. Mushtaque Ahmed, the COO, says that the food supplier was able to pre-fill customers' online shopping carts with products they were likely to buy based on historic data, thereby reducing the customers' time spent on their website. This was a crucial aspect for the supplier, as customers typically wanted to minimise the time spent finding the products they needed.
Mushtaque says, “.......the wow factor is huge. Customers are amazed that we can predict so accurately what they need.” Considering that JJ Food Service has a customer base of 60,000 and moves around 5000 orders a day, elevating its ability to service its market through a data-centric approach speaks volumes to the data's transformative potential.
Analytics doesn't mean losing the Human Touch
The company has seen around 60% of its daily orders move away from its call centre to its e-commerce website, which has led to missed marketing, up and cross-selling opportunities. Ahmed comments, "When we moved to the e-commerce portal, we lost that capability of talking to the customer. There was nobody to tell them about anything new that we were doing or to suggest some of the products they weren't buying.” Fortunately, its machine learning platform enabled it to anticipate customer purchases and make customised recommendations based on analysis of historic purchase data.
The company also used the same insights in its call centres to ensure the same recommendations would be made, regardless of whether customers preferred shopping online or ordering over the phone. But most noticeably, the supplier is able to make those recommendations on a per customer basis instead of simply shooting in the dark – something its marketing strategy was lacking.
Taking it a step further, JJ's analytics platform is also able to look at customers' shopping baskets and make further suggestions on products that complement the ones already in buyers' shopping baskets. For example, a customer buying a certain protein or vegetable would receive recommendations on other ingredients that complement items already purchased, or offer combinations of goods at discounted prices.
Although some might fear the rise of machine learning to spell the death of human interaction, the discipline in fact ensures more personalised customer experiences. “The only way to stay successful is to make ourselves more relevant to customers and streamline operations on the back end.” says Ahmed. And machine learning does exactly that by allowing business to re-align their resources to meet the demands of the ever-changing customer.
Image credit: freepik.com
Blog post originally published 24 February 2016