A data-driven advantage: shaping smarter communities
Stockland, while known for its bricks and mortar, is increasingly using data to drive better decision making and to enhance customer experiences.
Leveraging its own proprietary data and blending it with data from a variety of other sources, Stockland is harnessing the power of cloud computing, artificial intelligence and machine learning to convert data into competitive advantage.
Jenny van Zyp, General Manager of Data Science and Insights at Stockland, says the company has been on a journey since 2019 and data is now central to Stockland’s strategy execution, enabling sustainable growth through improvements in operations, decision making, and customer offerings.
She points to use new use cases in areas such as predictive modelling in shopping centres, where leasing managers can use analysis tools to understand the overall revenue impact of adding particular types of retail stores into the overall mix.
“We want to get smarter about how we lease our shops and to get the optimal mix into our centres for our customers,” says van Zyp.
“Our leasing executives have deep experience and a good gut feel on what works, but sometimes they just don’t know if that next store they put in is going to saturate the category.”
In response, van Zyp and her team built a machine learning algorithm based on spending data which delivers predicted rent, predicted sales and also – more powerfully – a third metric on the overall impact on the centre.
“This third metric is what really enhances our decision making because we are focused on the long-term, sustainable performance of the centre,” she says.
“It is also important that we build data products that these key managers understand and can use, without them being data experts.”
Another use case is in customer segmentation analysis, which is used to better understand the needs of Stockland’s residential customers.
“This blends our first party data with 24 other data sources to give us 40 unique customer segments across six groups,” says van Zyp.
“One group might be first homebuyers, and then they are segmented according to what kind of first homebuyer they are, their property life stage, and what they are likely to do next and when. These predictive analytics are what enables Stockland to anticipate customer needs and deliver better value propositions and experiences.
Hyper local customer insights at scale gives Stockland a competitive advantage.
There are also significant operational savings in terms of time. Traditional customer research would take up to four weeks to complete, but with the data analysis insights are delivered in minutes.
Another example of using data for operational efficiencies is how Stockland are approaching using new technologies like Generative AI. Stockland is currently running a proof-of-concept project called SARA – Stockland Automated Research Assistant. SARA is a ChatGPT-like automated assistant that Stockland employees can chat with to ask questions about research conducted by Stockland’s Customer Insights team into either existing or prospective customers.
Employees can ask questions like “what features do people expect in inclusive playgrounds?” or “which age demographics are most interested in medium-density living on the Sunshine Coast?”, and SARA can dynamically source and then summarise the findings from the Stockland customer research library.
Stockland is also using satellite-sourced geospatial data to help inform its land acquisition strategies.
The proprietary Stockland Terra application enables a better understanding of potential sites and the geographic and demographic context within which they exist, which in turn delivers insights on potential markets and informs acquisitions decisions.
“Defining the area’s geographical boundaries is the starting point, and then the application tells us about demographics in the catchment, the site’s constraints, competitor activity, and overall site potential, which informs the recommendations made to the Investment Committee” says Jenny van Zyp.
“We can better understand the profiles of potential customers, the kinds of products they want and what is the best price point.”
Stockland has a strong commitment to delivering on ESG, and data analysis is also becoming integral to delivering on these goals.
The execution of our ESG strategy is reliant on data capture and measurement. Accuracy, timeliness and strong governance is required to achieve transparent reporting on how we are progressing towards our ESG targets and ambitions.
“This is a rapidly evolving area,” says van Zyp.
Data, says van Zyp, is the “lifeblood” of companies that want a digital and AI advantage to consistently outperform their peers.
“Our approach is to use data to build products which make a difference to our employees and our operations and create differentiated products and experiences for our customers. As we look to the future, we are exploring new use cases to pilot that will leverage our unique and proprietary data assets, with a laser focus on creating more value for our customers and stakeholders. The key to our success will be how effectively we can blend the deep property and domain expertise that exists across Stockland’s business units with the advanced analytics and AI capabilities we have been building over the years,” she says.