In the past, merchandising was considered an art form, as there was no true way to measure the its results.
As online sales grew, a new trend emerged, in which shoppers would do their research in the stores and then make their purchase online, after shopping around online for the best deal.
Now, people-tracking technology is enabling omni-channel retailers to better measure the impact of their merchandising efforts.
This is where big data is making a huge difference.
Embracing a Data-First Strategy to Drive Offline Sales
People-tracking technology, which includes sensors, beacons, video cameras and mobile applications, can provide an endless supply of data on the behaviour of shoppers in retail stores, and a treasure-trove of facts that can be used to enhance the consumer’s experience and maximise the chances of them spending their hard-earned money in a particular store.
People-tracking technology can track the path of shoppers in stores to determine the optimal merchandising and displays for various products and monitor what products and displays the customer sees in order to measure the impact on sales for that customer overall. Retailers should be able to then link this information to loyalty programs and applications, to ensure that products stay top-of-mind with customers so that the in-store experience is not wasted.
A modern business intelligence (BI) platform can help to harness this data and combine it with existing customer data touchpoints to optimise the in-store shopping experience, increase sales across all channels, and make merchandising a data-driven process.
Blind Spots in the Data
However, with data flowing from people-tracking technology, social media platforms, and other sources, retailers have lots of data on customer behaviour coming in from different channels, but no way to seamlessly integrate it for real-time correlation and analysis – which is really the key to making breakthrough insights. This is because most retailers today still rely on traditional data-integration methods, which replicate data via batch-oriented extract, transform and load (ETL) processes. Not only are such methods unable to replicate data in real time, but they are also unable to support streaming data sources.
This means that retailers struggle to achieve a 360° view of customers. While online behaviour is rapidly becoming the basis for many retail marketing campaigns and data programs, retailers struggle to integrate the critical in-store data that would complete the picture.
Also, retailers generally rely on many specialised analytical tools, each optimised for different data sources and set up to answer specific, predetermined questions. This variety further limits the potential insights that retailers can gain.
Clearly, retailers need a proper BI platform that can collate all data, across all touchpoints, and structures it so that it is easily digestible and actionable. To do so, BI platforms need the data as quickly and seamlessly as possible, overcoming any obstacles and bottlenecks in the network.
Data virtualisation is a modern data-integration method that enables BI platforms to access all data in real time wherever it is stored across the company, regardless of geography and inter-departmental data regulations. With real-time access to all data, including people-tracking data, merchandising becomes a data-and-results-driven process, which gives retailers the power to better control their reactions to daily events, make and execute plans in a more informed and logical manner, and make the most of the resources that are available to them.
Harnessing the Competitive Advantage
With data virtualisation and big data analytics, retailers can turn their data sources into a major competitive advantage. The key is making good use of data-driven insights to provide in-store experiences that offer unique value for consumers. Insights based on data from websites, social media sources, point-of-sale systems, mobile apps, supply chain systems, in-store sensors, cameras, and other sources should be used to improve in-store experiences, optimise online experiences, increase customer loyalty, drive cross-selling activities, and increase the effectiveness of promotions.
Using a fast, virtualised data platform coupled with a BI solution, omni-channel retailers can test and quantify the impact of different marketing and merchandising tactics on customer behaviour and sales, use a customer’s purchase and browsing history to identify their needs and interests, and then personalise in-store service for them based on those findings.
Loyalty program members are among the most reliable customers, demonstrating strong wallet-share and repeat purchase rates. They also tend to be very receptive to not only email offers but also to real-time offers through loyalty program mobile applications.
For loyalty program members, data generated by people-tracking technologies can be correlated with online activity and past purchase behaviour to influence purchases. Personalised offers can be generated, both in real time via beacons and mobile applications, and in the future via email or when visiting online stores, to influence the loyalty program member to complete purchases on products that might interest them. People-tracking data can also be used to drive sales, by determining loyalty-customer interest in specific products by in-store locations, then correlating that data with in-store purchases on that day or at that location, to drive re-marketing efforts.
BI, powered by data-driven insights, provides a sophisticated, informed way for retailers to learn more about their customers, engage them with meaningful offers, and present them with personalised opportunities. Ultimately, this results in increased sales and greater customer loyalty, which is a win-win.
Ravi Shankar, senior vice president and chief marketing officer, Denodo