Smart way of doing sales forecasting with machine learning
The biggest pain point of business leaders, according to Gartner, is demand volatility. This means that it’s difficult, even impossible, to forecast the demand for a particular product in the future. There are frequent fluctuations in buyer decisions based on a multitude of factors like weather fluctuations or social media trends. For business leaders to answer questions like “how many camping gears will a store sell in the next season?” can become as difficult as predicting the weather. As you can end up carrying an umbrella on a sunny day in case of a wrong weather forecast, implications to the business may be acute if proper demand forecasting does not happen. It can lead to both monetary and opportunity losses if you can’t predict what your customer wants in the future. This webinar will address this challenge and along with traditional forecasting, our Data Scientist will talk about how Machine Learning can be utilized for smart sales forecasting.
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Smart way of doing sales forecasting with machine learning
- 1. Smart way of doing sales forecasting with Machine Learning Presented By: Shubham Goyal Data Scientist Knoldus and MachineX Intelligence.
- 2. Our Agenda 01 MachineX Introduction 02 Machine learning in retail Industry and its applications 03 Importance of demand forecasting 04 Problem and Solution 05 Demo
- 3. Who We Are?
- 4. About Knoldus MachineX MachineX Intelligence is a group of data wizards. We are a team of Data Scientist and engineers with a product mindset who deliver competitive business advantage.
- 5. What We Do?
- 6. Image Placeholder An Intelligent Meeting Assistant Application Record Videos View DashBoard
- 10. 10 Enable organizations to capture new value and business capabilities Innovation Labs Consistently blogging, to share our knowledge, research Blogs Deeplearning, Coursera, Stanford certified professionals Certifications Insight & perspective to help you to make right business decisions TOK Sessions It’s great to contribute back to the community. We continuously advance open source technologies to meet demanding business requirements. Open Source Contribution
- 11. Machine Learning in Retail
- 13. Superb Ways AI Can Revamp the Retail Industry
- 14. Eliminating overstocks and out-of-stocks AI helps retailers replenish supplies by identifying demand for a particular product based on ● sales history ● location ● weather ● promotions ● trends ● … and so on.
- 16. Adjusting prices
- 24. Store Item Demand Forecasting
- 26. ● Loss reduced from 80% to 20% ● Increased annual sales by an average of 5 percent
- 27. Demand forecasting Models ● Casual demand forecasting ● Mathematical Advanced linear forecasting ● Machine learning and deep learning
- 28. Steps including in forecasting
- 30. Number of sales by month
- 31. Sales are distributed along the month.
- 32. Sales from sunday to saturday
- 33. Step 2: Prepare the variables ● Day of week. ● Month. ● Day of month. ● Promotion. ● Previous day promotion. ● State holidays.
- 34. linear correlations between each input and the target variables "Sales".
- 35. Step 3: model training and testing analysis
- 36. Problem statement We are given 5 years of store-item sales data, and asked to predict 3 months of sales for 50 different items at 10 different stores. The objective is to predict 3 months of item-level sales data at different store locations.
- 37. Data Explanation ● date - Date of the sale data. There are no holiday effects or store closures. ● store - Store ID ● item - Item ID ● sales - Number of items sold at a particular store on a particular date.
- 38. Demo Notebook
- 39. Thank You ! www.knoldus.com +(91) 1204287693 hello@knoldus.com @Knolspeak Stay in Touch