December, 20209 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.
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