When Adapt surveyed more than 100 Australian CIOs in 2019 there were some interesting, if not surprising, results. More than 50 per cent believed they had too much data making it unwieldy and difficult to analyse, nearly 60 per cent struggled to hire good data scientists, and 70 per cent felt that they were behind global competitors as they struggled to generate value from their data and analytics e fforts. Y et d espite t hese difficulties, 95 per cent of Australian CIOs say Analytics is an important part of their strategy, and 54 per cent say it is one of their top 5 priority.

The start of 2020 has been a tough one in our industry, but it is clear that the retailers who will flourish are those who can leverage analytics to deliver delightful customer experiences and optimise their retail operations. Innovating your offer is no longer optional – those who fail to improve will be overtaken by digital disruptors, many of whom are not based in Australia. This latter point is another feature of our digital age – problems that used to be solved on a national basis are now being solved globally, often by software-driven businesses. Uber revolutionized the taxi industry and took it global. Our customers are Uber’s customers, and they expect the same personalized service. So, we must improve, and do it fast, for we are competing with the world’s best – and advanced analytics is central to our competitive arsenal.

The Kmart Group Data &Advanced Analytics team is committed to drive competitive advantage for the group by shaping and delivering better data-led decisions. We work in partnership with all business functions, including Merchandise, Online and Supply Chain. While this presents several opportunities, the challenges relate to identifying high impact use-cases, prioritization of work across verticals, and translation of business requirements. W e d on’t h ave d ifficulty finding work to do, rather we have too much work to choose from and so must focus wisely In the current financial year, we have focused on customer outcomes related to demand forecasting and price optimisation. Our business stakeholders had a problem: they want to get the right stock to the right stores at the right time, so our customers can purchase what they want, when they w ant i t. T his i s c ompounded by demographics, size curves and preferential tastes – twenty men and women will purchase the same drinking glass, but wear different shape t-shirts, in different sizes, and like different colours.

Our data scientists built a machine learning model which we fed with data from all products in all stores historically, and trained that model to recognize the attributes that are important to purchase decisions. After tuning and tweaking the model, we now have a powerful demand forecasting tool that is run on our own ML algorithm to accurately forecast full-price monthly demand for new and existing products during a specified time frame. Our Merchandise Planners are using that tool, supplemented by their own in-depth knowledge of customers and product, and have changed the shipping and distribution of millions of pieces of apparel.

We are measuring the results of this by the increase in happier customers, which we can tell from an increase in full-price sales and reduced clearance markdowns, which comes from improved forecast accuracy. This illustrates one of the key success criteria for any analytics initiative: start with customer outcomes, work backwards to desired business benefits, and identify the features, attributes and use-cases that will give you those benefits and outcomes.

Another analytics use-case came from our buyers and marketers who wanted to be able to identify potential issues before they become real problems. Kmart has a devoted social media following with more than one million followers on each of our official Facebook and Instagram pages, and millions more on fan pages. This is a rich source of data, available in real-time, as fans and followers discuss the good and the bad of their experiences. Our analytics team built a semantic engine that reads this data, parses it and conducts sentiment analysis to see what our customers are saying in the public domain. This allows our buyers and store teams to react quickly to issues before they become large scale, such as the stock levels of a particular product, or to deploy resources to improve availability in a store, or to review the speed of online fulfilment for a category or a geographic region. This solution was scoped, built and deployed in less than a month.

At Kmart we consider data to be a strategic asset that helps us deepen our relationship with existing customers, helps new customers discover our great range of products, and design products and collections that make our customers lives that little bit brighter. We want to use the data we see in the morning to take a decision in the afternoon, or sooner. Any retailer that isn’t doing this now needs to hurry up or they will be left behind. Australian retailers are important to our economy, employing more than one million people, often young and predominantly female. We need to start building our ‘data muscle’ immediately: run some experiments, prove the value, build skills, and develop a data culture and enabling platform. Find early adopters, cultivate them and make heroes out of them. If you don’t want to look after your customers, send them to us and we will.