Getting Better Insight into the Competitive Retail Sector with Data Analytics

By Alexandra Gray, Head of Research, Mirvac

Alexandra Gray, Head of Research, Mirvac

Few would argue the retail sector is one of the world’s most competitive and challenged sectors. Pressure to remain relevant drives an urgency to innovate with desirable offerings for customers both instore and online, more sophisticated supply chains responsive to speedier demand for goods, and a seamless transaction experience.

In Australia, retailers have been grappling with merging the physical and digital for several years. Around this same time, a number of new big data sets and location intelligence tools have become more readily available. These offer owners and managers of retail centres a sharper, more timely view into our customers, their visitation patterns, and their ever-evolving store preferences. Given landlords are constantly making decisions spanning many years, such as fixed term leases or planning multi-year redevelopments with both tenants and government authorities, a sharper lens gives retailers the advantage of being able to make better, closer to real-time decisions, mitigating risk, and improving returns.

At Mirvac, our ways of learning about our customers are evolving with data analytics and newer, bigger datasets. In the past, there was a heavy reliance on external sources such as demographic datasets and focus group qualitative insights. However, even customers with the same demographics are likely to exhibit completely different behaviours toward leisure, dining out, their brand preferences, and even their affinity for technology adoption. Using psychographic datasets at a micro level gives us greater colour on customer segments.

So too does social media data, which offers an ability to synthesize the voice and attitudes of customers—both positive and negative—in their local community environment.

The availability of reliable expenditure data at a category level has been a step change in gauging the underlying spending patterns of our customers. We can now more accurately understand sales leakage to other retail competitors, including online stores, enabling us to pivot and adjust our retail mix. We are exploring mobility datasets that enable us to zero in more effectively on where our customers come from and the patterns of when they frequent our centres, both day of week and time of day. Understanding the origin of customers and when they frequent, is particularly valuable for trade areas with transient populations including the worker, student or tourist visitor; alongside a resident catchment.

While the benefits of better understanding of our customers is substantial, expanding our capability with data analytics offers additional value. We are exploring ways to target key customer groups, track the effectiveness of campaigns and the true return on investment of campaign spend. By learning with greater frequency and accuracy we can adjust and apply insight across other assets in the portfolio.

Front-end visualisation tools are just as vital as the data itself. While some are available through proprietary portals, more and more are brought in-house for us to consume through business intelligence platforms. Also visualising geospatial information from both open source and proprietary data through cloud-based Software-as-a-Service platforms like Carto means we can build customisable and engaging location intelligence applications, which synthesize huge amounts of data, scalable across the organisation. Information silos begin to break down, internal learnings and our capability with data increases, time is expedited, and cost is reduced as dependency on consultants decreases.

Still the challenges of moving to a data-savvy environment are many. Datasets in mobility, expenditure, social media for example, are relatively new and require careful evaluation and consideration of cost and benefits, given limited resources. They also require embedding in an organization’s digital architecture, strategy, and processes for decision making. New skillsets and talent are required for back-end programming and development work, while front-end development skills and interpreters that liaise with business stakeholders are also just as vital.

At Mirvac, we know there will never be a one-stop shop for data and information. For us, it is about joining the dots. As we make these step changes to using big data and analytics to curate our experience-led retail spaces, a high performing and engaged culture that encourages everyone to ‘be curious’, is the real vital ingredient to success.