Datawiz.io case study

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Case study for retail and supermarket chain store: Dynamic repricing, Weekly recommendation, Sales Prediction, Association rules and upsell.

Transcript of Datawiz.io case study

  • Case Study DATAWIZ.IO: GOOGLE ANALYTIC FOR RETAIL / FMCG DATAWIZ, INC.
  • Below are the technology we used in this case and criteria that you need check when you apply the case to your own business use. Marketing Division of Retail, Supermarket Marketing/consulting company TARGET USER Machine Learning, Predictive Analysis, Time Series Analysis MAIN TECHNOLOGY POS/Receipt Data, or, Data from Loyalty program, club card or membership card INPUT DATA TYPE Datawiz.io Case Study
  • Dynamic Repricing DISCOVER POTIENTIAL PROFIT RANGE OF PRODUCT. Weekly Recommendation FAST REACTION TOWARDS MARKET NEEDS AND CHANGES Sales Prediction OVER COME THE SUPPLY DEMAND PROBLEM Association Rules and Upsell MANUPLATE THE KEY DRIVEN PRODUCT AND SATTELITE PRODUCT Business Cases Datawiz.io Case Study
  • Dynamic Repricing DISCOVER POTIENTIAL PROFIT RANGE OF PRODUCT.
  • There is a price-demand dilemma, which is growing price does not alway lead to more profit, because of the decline in demand. Finding out the optimized point of each product on the price-demand curve can help you maximize your profit. Problem: Grow price & maximize profit Case Study: Dynamic Repricing The purpose of this case is to find out when you could grow price for which product and how much you can grow. Price Demand0 D1D2 P1 P2 Recommended Price Current Price
  • System gives prediction on how to grow price for each SKUs. Our solution Case Study: Dynamic Repricing System runs calculation for all receipts and gets the average value of baskets. What we need to do is to find out products inside lower value baskets that are not price sensitive for the customer.
  • System can operate over thousand of SKU on a frequent basis and has ability of growing price by the mean time keeping the demand level. Advantage Case Study: Dynamic Repricing
  • Increase basket size and value Save time Case Study: Dynamic Repricing BENEFITS
  • Weekly Recommendation FAST REACTION TOWARDS MARKET NEEDS AND CHANGES
  • It is complicated to research each products in the store and launch effective marketing compagin. Not to mention to launch recommendation in all SKUs weekly! Problem of weekly marketing compagin Case Study: Weekly Recommendation TONS OF SKUs POS DATA TOO MUCH WORK LIMITED TIME
  • Our solution Our system can answer you the questions by only one click! What to do next week? What to promote in each day of next week? Our recommendation engine is based on machine learning algorithm, association rules and our product tree algorithm. System can automatically build dynamic recommendation models according to different purchase behavoir. Case Study: Weekly Recommendation
  • Promote the right product to right customer BENEFITS Case Study: Weekly Recommendation
  • Sales Prediction OVER COME THE SUPPLY DEMAND PROBLEM
  • Guesswork and experience are the only tools that help you to calculate how much products you should order from suppliers. And it often causes either stortage or over supplement. Customer couldn't buy the product they want and store loses chance to sell. Over supply increases warehouse and employee cost. Problem: Out of storage and over supplement Case Study: Sales Prediction
  • We built prediction modles based on different factors that influence the sales, which include weather, fuel price, currency rate and geographic elements. Solution: Prediction model for future sales The accuracy of prediction is higher than 97%. You can do prediction per month, per week and even per day. Prediction model is built for each product and category that make sure the high accuracy. Case Study: Sales Prediction
  • TAKE CONTROL OF YOUR COST BENEFITS Case Study: Sales Prediction
  • Association Rules and Upsell MANUPLATE THE KEY DRIVEN PRODUCT AND SATTELITE PRODUCT
  • Find key driven product; Use key driven product to bring in more customers; Increase sales of hight margin satellite products. Purpose of applying association rules Case Study: Association Rules and Upsell It helps you to find out which product to promote and the product that drive most of the profit.
  • Satellite / accessory products can bring you more profit! We help you find out them and promote at right time. Our Solution: Case Study: Association Rules and Upsell We run clusterization for all baskets and filter out the key driven products for each basket type. Satellite products with high profit can be marked according to request from store. Except for applying association rules, we invented product tree to extend the effect of algorithm. On the product tree, you can see clearly which SKU can trigger profitable sales.
  • Promote the product that drive most of the profit. BENEFITS Case Study: Association Rules and Upsell
  • YOUR CONCERN IS OUR RESPONSIBILITY Datawiz Inc. www.datawiz.io Gaidatara 1-D suite 302 Chernivtsi, Ukraine +38 050 337 73 53 hello@datawiz.io