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Sales forecasting

Based on quarterly and yearly marketing, sales and product plans, companies need to be able to forecast their sales for the quarter and year both to help forecast their financial performance, but also to be able to make hiring, product, R&D, etc. decisions.

Overview

Forecast next quarter’s/year’s sales based on the marketing, sales and product plan.

TARGET

Number of sales.

Challenge

To correctly model the sales based on marketing, sales and product plans, all of the sales data needs to be aggregated into total sales from the CRM and this needs to be tied into the existing marketing, sales and product plans active during that time. The more detailed this plan data, including the effectiveness of the prior plans needs to be aggregated. Finally, all this data needs to be cleaned, joined and transformed into valuable ML features before going into model training. This pre-modeling prep process can be frustrating and time consuming. We are here to help.

Modeling techniques and libraries

Sales prediction

Build machine learning models using standard techniques (from linear regression to more advanced machine learning algorithms) to predict sales. Using this model, forecast next quarters sales based on the plans from marketing, sales and product.

Package: 
  • Sklearn

Data features

Customer Annual Revenue
CRM
Data Type
Continuous
Target
No
Yes
Customer Location
CRM
Data Type
Categorical
Target
No
Yes
Customer Size (# Employees)
CRM
Data Type
Continuous
Target
No
Yes
GRP for Ads
Marketing DB
Data Type
Continuous
Target
No
Yes
Interactions with Sales
CRM
Data Type
Continuous
Target
No
Yes
Opportunity Stage
CRM
Data Type
Categorical
Target
No
Yes
Opportunity Stage Progression in Days
CRM
Data Type
Continuous
Target
No
Yes
Price
Competitive Marketing
Data Type
Continuous
Target
No
Yes
Purchase Price
CRM
Data Type
Continuous
Target
No
Yes
Total Sales Dollars
CRM
Data Type
Continuous
Target
No
Yes
Total Sales Volume
CRM
Data Type
Continuous
Target
No
Yes

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