The constant cycle of analysing your assortment, and then overlaying it with choosing your base line pricing, promotional pricing, and personalisation prices to drive sales is relentless in the Retail and CPG world. Deciding to go Hi-Low, Every Day Low Prices (EDLP) or a Hybrid approach, whether your company is going to be a leader vs. a follower, and how easily you’re able to link every activity together is a fine art that most struggle to truly make the most of. Partly because each team tends to work in each own silo, often due to clashes between categories, sub-categories, and SKUs, but also simply due to a lack of time to be able to sit back and take a truly holistic view of what is the right thing to do to drive a profitable business and keep your customers happy.
Working on your overall strategy is one thing, but the key to success is the ability to balance both medium-term strategy and short-term agility, although that’s clearly easier said than done. Planning cycles need to predict shopper behaviour many months ahead of time, especially when it comes to Non-Food, making agility hard to come by. Therefore, as Back to School promotions hit the shelves, Christmas and New Year offers are already locked in and ready to go. Are these long-lead cycles still effective in today’s dynamic Retail & CPG environment, and if we are honest, is it really strategic planning or just refreshing/tweaking last year’s activity to protect Like-For-Like (LFL) sales? The Retailers and CPGs of the future use granular real-time data coupled with advanced AI/ML to reduce planning times and increase the effectiveness, store by store and customer by customer.
Past Performance IS No Longer Reliable ENOUGH
Basing this year’s activity on last year’s data was the go-to adequate response. Then it used to be acceptable enough to cluster and segment stores and customers into broad categories to help manage the analytics and decision-making processes. However, the disruption of the last couple of years has served to underline that the relatively static and stable world this approach was based on no longer exists. Also, clearly an out of town ‘big box’ outlets will perform differently from city centre convenience stores – but the relative sales, unit and margin performances will also differ based on a huge variety of other factors. Data from last year, or even last month, may no longer provide enough insights to plan effectively. Real-time granular data is needed to better understand the true intent of millions of shopper missions in order to deliver the precise mix of products, prices and promotions to delight your customers and grow your business.
Beyond What’s Humanly Possible
Historical data and broad-brush segmentations were used for a reason. It was relatively easy to access the data and reduce the variables to a level at which humans armed only with spreadsheets were able to manage. This approach is no longer sufficient, but thankfully also no longer necessary. Leading Retailers and CPGs are now consolidating data across their entire estates and brands, breaking down departmental silos, and ensuring that everyone has access to the insights they need, as and when they need them. This is despite increasing digitalisation, which has multiplied the streams of data that can be captured, which then need to be prepared and integrated correctly to support rapid detailed analysis, and then actioned upon. Leaders deploying Machine Learning and AI techniques are now able to analyse vast amounts of information from the diverse sources required, revealing key insights that dramatically improve performance right down to each individual store, SKU and customer level.
Automated Decision MAKING
Enterprise data platforms provide the foundation for this level of analytical sophistication. Working with the most advanced Retailers and CPGs around the world, Teradata uses its connected multi-cloud data platform for enterprise analytics, Vantage, to support this level of cutting-edge analytics. For category management, this gives you the ability to connect everything, understanding SKU level cost-to-serve insights, assortment product affinities, price and promotion sensitivity, hyper-personalisation impacts, customer and product loyalty and NPath analytics to identify switching patterns – all in one place, and for the good of every department.
Working with one European grocer, Teradata built data models that use up-to-date granular data from each store to create an opportunity matrix that identifies products for price changes based on business opportunity and risk. It automatically calculates price elasticity based on past sales history, future expected sales, as well as estimating the impact of those changes on other items, giving them risk of cannibalisation understanding. As a result, the Retailer has seen increased margins of between 5-10%.
A global coffee retail outlet used Teradata to combine store sales with a range of other data sets to establish a detailed performance scorecard. Looking beyond simple financial comparisons helped identify best practices that separated high performing stores from the average, irrespective of location and socioeconomic demographics. Then isolated what made the differences, allowing them to set up initiatives and activities to raise the overall performance of all stores.
Stronger Data Foundations
Examples like these demonstrate how granular, real-time data from across the entire organisation can fuel automated decision-making that is both faster, and more precise than traditional planning cycles using predominantly historical data. They also illustrate the importance of speed and scale in delivering insights. Now is the time to up-end traditional calendar-based planning for ranges and promotions that are suitable for broad store/customer clusters, and replace it with agile, event and customer focused responses that can be implemented store-by-store in near real-time, opening the door to more responsive, and higher-margin driving operations.
Agile responses that can react to current market insights and changing customer trends requires the ability to scale, score thousands of models simultaneously, and run them over billions of lines of data in near real-time. This capability is fundamental to the Retailer and CPG of the future. Piecemeal investment in individual departmental or point solutions will not deliver the kind of scale or complexity needed. Leaders who are leveraging Teradata to create shared data cultures that can support the latest data analytics tools and models, with data from across and beyond the organisation to make better decisions, are the ones that are set to continue to gain market share.
Combining the latest data science tools, Machine learning and AI, with the ability to run millions of queries on live data in real time, Teradata Vantage is recognised as the platform of choice for leading Retailers and CPGs to help them make better decisions faster in today’s dynamic market.