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Data Visualization: Sales forecasting


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Visuals present better and quicker insights when forecasting sales. At a glance business strategies can be planned - time periods, geographic locations, pick variables that can highlight what works or doesn't, where it scores or doesn't, join two or more variables that work in specific geographical locations or don't, etc. All this put together makes data virtualization a very nifty tool to project what can make or break your predictions for sales!

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Data Visualization: Sales forecasting

  1. 1. Slide 1 Data Visualization: Sales Forecasting July 19, 2017 At AlgoAnalytics
  2. 2. Slide 2 Aniruddha Pant CEO and Founder of AlgoAnalytics PhD, Control systems, University of California at Berkeley, USA 2001 • 20+ years in application of advanced mathematical techniques to academic and enterprise problems. • Experience in application of machine learning to various business problems. • Experience in financial markets trading; Indian as well as global markets. Highlights • Experience in cross-domain application of basic scientific process. • Research in areas ranging from biology to financial markets to military applications. • Close collaboration with premier educational institutes in India, USA & Europe. • Active involvement in startup ecosystem in India. Expertise • Vice President, Capital Metrics and Risk Solutions • Head of Analytics Competency Center, Persistent Systems • Scientist and Group Leader, Tata Consultancy Services Prior Experience • Work at the intersection of mathematics and other domains • Harness data to provide insight and solutions to our clients Analytics Consultancy • +30 data scientists with experience in mathematics and engineering • Team strengths include ability to deal with structured/ unstructured data, classical ML as well as deep learning using cutting edge methodologies Led by Aniruddha Pant • Develop advanced mathematical models or solutions for a wide range of industries: • Financial services, Legal, Retail, economics, healthcare, BFSI, telecom, … Expertise in Mathematics and Computer Science • Work closely with domain experts – either from the clients side or our own – to effectively model the problem to be solved Working with Domain Specialists About AlgoAnalytics
  3. 3. Slide 3 AlgoAnalytics - One Stop AI Shop •We use structured data to design our predictive analytics solutions like churn, recommender system •We use techniques like clustering, Recurrent Neural Networks, Structured Data •We use text data analytics for designing solutions like sentiment analysis, news summarization and many more •We use techniques like natural language processing, word2vec, deep learning, TF-IDF Text Data •Image data is used for predicting existence of particular pathology, image recognition and many others •We use techniques like deep learning – convolutional neural network, artificial neural networks and technologies like TensorFlow Image Data •We use sound data to design factory solutions like air leakage detection, identification of empty and loaded strokes from press data, engine-compressor fault detection •We use techniques like deep learning Sound Data BFSI •Dormancy Analysis •Recommender System •Credit/Collection Score Retail •Churn Analysis •Recommender System •Image Analytics Healthcare •Medical Image Diagnostics •Work flow optimization •Cash flow forecasting Legal •Contracts Management •Structured Document decomposition •Document similarity in text analytics Internet of Things •Predictive in ovens •Air leakage detection •Engine/compressor fault detection Others •Algorithmic trading strategies •Risk sensing – network theory •Network failure model
  4. 4. Slide 4 Sales Forecasting
  5. 5. Slide 5 Sales Forecasting Analyse sales and Forecast Plan ahead looking at the forecast Higher profits with better planning ● A time-series is a dataset that has values over a period of time. ● Sales Forecasting is future prediction for sales based on past sales performance (time-series) Why Forecast Sales? Enables objectively looking at future Using the forecasts one can establish policies to monitor prices and other costs Manufacturing industries can plan for production and capacity Retail companies can form basis for marketing
  6. 6. Slide 6 Sales Forecast: Retail Store Chain Problem Definition: Forecast sales for each of the 45 days in future for all stores (~1200) in the chain using the daily sales data for last 3 years. Dataset: Three major data columns, date, Store ID, Sales in USD and store wise competitor data. Steps followed for each store: Analyze data trends and patterns Identify lags to use Create time based indicator variables like weekend flag, month of the year, holiday flag Identify and select significant features Apply regression models to predict weekly and daily sales Combine the weekly and daily model Get Final Forecasts
  7. 7. Slide 7 Pre-Forecasting Data Analysis Daily, Weekly and Monthly Features Holidays’ impact over sales Weekends generally see higher sales Promos and offers Geo location of the store and demographics ● Seasonality in the data ○ Seasonal patterns refers to a fixed period influencing sales like holiday season or a particular month or weekday ● Year over year trends ○ Analyse each years‘ worth data separately to look at the trends ● Correlation of lags ○ How is the target’s sales dependent on the previous sales ● External factors affecting the sales, like offers, weather, etc.
  8. 8. Slide 8 Actual Vs Predicted: An Example for a store ● Mean Absolute Percentage Error(MAPE) : ~13% Measure of prediction accuracy of forecasting methods in statistics. Other Techniques used in Sales Forecasting: ● Fixed, Mixed and Random Effects Models: The term fixed effects estimator (also known as the within estimator) is used to refer to an estimator for the coefficients in the regression model. If we assume fixed effects, we impose time independent effects for each entity that are possibly correlated with the regressors. ● Timekit: The timekit package contains a collection of tools for working with time series in R. Adding Value to Sales Forecasting
  9. 9. Slide 9 Data Visualization: Importance in Sales Forecasting
  10. 10. Slide 10 Four Years Sales Comparison Year wise Sales Weekdays Matched Bubble Chart: Extracting important features from data. Visualizing Patterns: Discerning Sale Trends Employing Data Visualization tools can help predict at a glance the time-periods wherein sales can happen. They can also help narrow down the variables that need to be enhanced and strengthen the areas that show what works well in given situations
  11. 11. Slide 11 Satellite map: Top performing stores in blue color and worst performing stores in red color (bicoastal pattern) is identified Map Your Sales Targets: Satellite Mapping Using the same set of data, plotting using Satellite maps can help the Sales team target areas where they need to push up sales, understand geographic differences better, and thus target worst performing areas to shore up sales. Density Monitoring and Customer Segmentation using satellite mapping to visualize Sales forecasting to enhance Business strategies is the need of the hour!
  12. 12. Slide 12 Tools Used in Sales Forecasting
  13. 13. Slide 13 Interested in Knowing More? Contact: August 8, 2017