ACTIONABLE INSIGHT FOR RETAIL AND CPG PRICING ANALYTICS, CUSTOMER BEHAVIOUR
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1 ACTIONABLE INSIGHT FOR RETAIL AND CPG PRICING ANALYTICS, CUSTOMER BEHAVIOUR
2 Marketing Optimisation Marketing Automation Personalised Experience PLAN BUY MOVE SELL Cross Channel CUSTOMER Retail Customer Insight Initial price Promotions Markdown Products How many? Availability Display Customers Channels For when? What price? Inventory Optimisation Forecasting SUPPLIER Cost to sell Method Promote Communicate
3 CUSTOMER ANALYSIS BUSINESS QUESTIONS POSED For our key customer groups, what is the basket spend trend? Which key basket categories are shrinking? Which customer groups are changing most in terms of spend and value to us over time? What is the level of promotion and deal uptake from our customers? Can I predict how they are going to respond? How do I see a joined up view of customers spend between online and in store? How do our most profitable customers see our range? Which brands are driving business? Visual insight last week and previous period by cutomer type at basket level. Insight sales by time and customer compared to previous years. Price sensitivity and impact on customer purchase pattern. Joining customer data and transactions to see cross-channel patterns. Forecasting which customers will buy which products and the impact of where / how we sell them.
4 PRICING ANALYSIS BUSINESS QUESTIONS POSED How did our customers respond to the promotion last week? Stores compared to online? So far this season which promotional types are working best? How is it affecting our full price basket? How did the last markdowns impact our trading margin? Which are the most profitable? A key supplier of own label is pushing for cost price increase? What will the effect be? Our key competitor is taking market share from us? How do we compare on range and price? Visual insight last week and previous period by customer type. Insight into each promotional type and impact on the customer purchase pattern. Price sensitivity of markdown activity and impact on margin. Price structure perception versus branded forecast sales and margin impact. Immediate insight into competitors range and price position against your performance.
5 THREE STEPS TO SUCCESS BUILD ANALYTICAL MATURITY PRICING EXAMPLE BUSINESS VALUE Pricing Analytics Understand historic pricing impact Markdown Promotions Competitors Modelling Price Impact Apply analytics to future pricing decisions Forecast implications of price changes Price Optimisation Apply pricing optimisation constrained by business pricing rules / objectives ANALYTICAL SOPHISTICATION
6 MACY S.COM COMPANY After more than 80 years in business, Macy s Inc. is one of America s most iconic retailers. With annual revenues exceeding $20 billion BUSINESS GOALS Macys.com needed enhanced customer insight, online and across channels, to effectively measure and understand the impact of its online marketing initiatives on Macy s store sales. RESULTS Increased customer insights, cross-sell and up-sell effectiveness, productivity, revenue and customer satisfaction; An approximately 20 percent reduction in churn rate More than $500,000 savings in productivity annually in terms of FTE time saved. Both best-of-breed technologies, we chose Cloudera s Platform for Big Data for its unparalleled Hadoop management and SAS analytics systems for their deep insight into our data. Karem Tomak Vice President of Analytics Macys.com
7 SHOP DIRECT COMPANY 2million active customers 1.7bn turnover 55% orders on mobile/tablet 97% orders on credit 900+ brands 60,000+ Very.co.uk products at any one time BUSINESS GOALS To build the best personalised shopping experience in the world Alex Baldock CEO RESULTS Expecting 20million in sales as a direct result of personalisation this year. Shop Direct which owns the brands Very.co.uk and Littlewoods, said it has selected SAS as their decision engine partner to drive real-time, onsite personalisation this financial year, with initiatives including personalised homepages and top navigation already delivered. Computer World
8 SOBEYS COMPANY Sobeys is Canada s second largest grocer. $17billion (CAN) revenues BUSINESS GOALS Sobeys seeks to put its customers first and uses advanced retail analytics and data integration to gain practical customer insight and continue to build customer relationships and loyalty and to drive results. Pricing and promotion was being done manually via spreadsheets and home grown tools, and they were driven primarily via intuition RESULTS Increased customer insights, cross-sell and up-sell effectiveness, productivity, revenue and customer satisfaction; An approximately 20 percent reduction in churn rate More than $500,000 savings in productivity annually in terms of FTE time saved. Price and promotions optimisation for 30,000 SKU s across 1300 stores. Approximately 3500 product promotions per week Our goal is to be widely recognized as the best food retailer in Canada. In order to achieve that, we need strategic partners to support us in that goal. SAS is one of those strategic partners, providing our users with the tools to do their jobs well. Clinton Keay Senior Vice President and Chief Information Officer Sobeys
9 IMPROVING PROMOTIONAL MIX CAN DELIVER REAL VALUE Before After Sales on promotion 25.0% 24.4% Gross margin impact m Product availability 89.0% 91.0% Sales increase impact m Source: SAS revenue optimisation, department store chain COST PRICE RETAIL PRICE PROMOTIONAL SPEND
10 OTHER CUSTOMER EXAMPLES Watch manufacturer supplying multiple retailers SAS analyses and recommends assortment and selling price into multiple types of retailers and provides visibility of price list performance V actuals. CPG - Selling into multiple small franchised retail outlets SAS used to understand segmented patterns of demand. Predictive models and local sales information used to recommend what new products or brands should be stocked. CPG promotions analysis SAS used to conduct rigorous statistical analysis of promotions to determine the incremental revenue from a promotion. Using data from promotions at multiple retailers and stores or store-clusters, the impact of promotion mechanic and discount applied can be understood. Online fashion retailer Customers segmented into groups using SAS and promotional performance analysed to understand which promotional methods are most successful for each customer type.
11 WEEKLY OVERVIEW
12 CUSTOMER OVERVIEW
13 RFM AND ORDER ANALYSIS
14 CUSTOMER SEGMENT ANALYSIS
15 PROMOTIONS ANALYSIS
16 PRODUCT ANALYSIS
17
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