Developing and implementing data science solutions has become increasingly important for organisations looking to optimise the use of artificial intelligence and its ability to analyse and harness data as way of driving business development and offering attractive returns on investment (ROI). Artificial intelligence and predictive models allow us to identify trends more easily and help organisations pinpoint new drivers of change in order to make quick, well-informed smart decisions.
Data science solutions are having a growing impact on business models across all industries, and the real estate sector is no exception. Artificial intelligence is having a particularly strong impact on the retail sector, offering a greater competitive advantage to companies that use it.
This is because artificial intelligence acts as the perfect catalyst for driving interactions between people and machines, providing a basis for organisations to integrate their key business functions in a way that provides the best possible results. It all starts by asking the right questions. How can we stand out and offer a unique shopping experience, while gaining a competitive advantage? The answer to this leads us on to the next question, data – which data do I need to collect, combine and transform? And then, how can I apply predictive analysis in a way that helps me to achieve my organisation’s business objectives?
To date, most data science applications in the retail sector have focused primarily on developing recommendation engines for consumers, based on their shopping habits, receipt analysis, product price optimisation, inventory maximisation and the use of augmented reality. In fact, some retailers are already applying augmented reality by offering virtual changing rooms in their flagship stores, where customers can choose their clothes and see how they would look on them on a screen, saving both time and effort.
At CBRE, we believe that analysing and predicting consumer behaviour is crucial for identifying potential new areas to expand business and drive ROI. We have identified three potential areas where this could be applied in the future:
The use of social media to predict trends
Free data collection on social media provides us with invaluable information on how people behave and allows us to easily identify trends. Retailers could tap this vast amount of data to combine online and offline consumer experiences. Monitoring Instagram and Twitter could help to identify the most popular products among consumers. Using data by processing natural language and analysing it via machine learning, could help retailers to promote their “best” products in their brick-and-mortar stores.
Customer journey analysis
The recent rise in omnichannel retailing has given consumers a wider range of options when it comes to buying a product. This makes it harder for retailers to understand consumer behaviour and their shopping habits. Machine learning tools could help retailers to identify patterns in their behaviour and provide them with more precise information on what makes their customers buy.
Lastly, predictive models could help to mitigate fraudulent activity both among customers and staff, an offence which causes considerable losses each year. Developing data science solutions could help retailers detect anomalies and calculate sales forecasts for each product. Significant variations from the projected level any given product would be an indication of ‘Suspicious’ activity.
Head of Forecasting & Analytics – CBRE Spain