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!

1. Slide 1
Data Visualization: Sales Forecasting
July 19, 2017
At AlgoAnalytics

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. 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. Slide 4
Sales Forecasting

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. 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. 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. 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. Slide 9
Data Visualization: Importance in Sales Forecasting

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. 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. Slide 12
Tools Used in Sales Forecasting

13. Slide 13
Interested in Knowing More?
Contact: info@algoanalytics.com
August 8, 2017