Data science Use case
real-time-data-approach

Customer experience with a multichannel real-time data approach

Delivering a seamless customer experience has become one of the most important challenges for retail companies.

Why? Because customers are loyal to companies that provide them with outstanding experience despite having more choices than ever.

To create a good customer experience, the retail companies need to facilitate the transitions between online and in-store experiences. How can a retailer build a good customer experience?

    • The first element to delivering this, is the ability for the retailers to collect and connect data from all departments and follow the journey of their customers in all its purchasing environments (from stocks, CRM, …) in real time.
    • The second element is by meeting the expectations of more demanding customers, through delivering personalised offers.

To illustrate this challenge we will use Ben and the retail company XZW. Ben is a customer of XZW, he is moving out of his parent’s house. XZW has leveraged data from the client services department, it knows that Ben is 25, that he has just changed the address on his loyalty card, and that he buys a new box of coffee every month… XZW data science model automatically displays an offer on Ben’s retailer app: “We have an exclusive offer for a new coffee machine!”

Ben starts his experience by receiving a promotional offer from his retailer, with a 10% discount on coffee machines, and 2 packs of his favourite coffee for free if he buys it… Who could say no?

Ben looks up a few coffee machines on the retailer’s mobile-responsive website, selects his favourite candidates, reads online reviews, and chooses the device of his dreams. The next day, he goes to the shop… and finds out that the machine is out of stock. The last one was sold three hours ago. Now Ben is left with a useless promotional offer, low caffeine levels, and the thought of shopping somewhere else.

To have a  happy and satisfied client XZW should have leveraged data from all departments, to follow, in real time, Ben’s journey in its purchasing cycle. When Ben was buying coffee, XZW should have included its in-store data (sales) with its in-store stock data. They should have displayed it when Ben was visiting the website and using its loyalty card.

Digazu platforms (end-to-end data engineering platform, MLOps data platform and end-to-end data science platform) are designed for real-time reporting and the standardization and industrialization of data science models. With Digazu’s platforms, XZW could have streamed freshly updated data, at any time, incorporating real-time feedback across channels and departments. Ben would not have left the shop empty-handed because the marketing department, knowing what was and was not in stock, would have sent him an alert well in time to ask him if he wished to book the machine.