Marketing optimization

John Wanamaker stated “Half the money I spend on advertising is wasted; the trouble is I don't know which half.” Marketing organizations have a large number of choices about where to spend their marketing budget, from large TV advertising campaigns to individually targeted discounts. Identifying the right mix of campaigns and channels is key to driving effective marketing and maximizing the marketing ROI.

Overview

Being able to predict the volume of sales a marketing campaign will drive, allows the marketing organization to decide on the types of campaigns and the timing of those campaigns during the year.

TARGET

Sales volume or dollars each week.

Challenge

To correctly model the impact marketing campaigns have on sales, all of the sales data is needed from the CRM and this needs to be tied into the marketing campaigns currently active and that recently ended. To truly evaluate the marketing campaign, some measure of the reach of the campaign is needed. This reach data may be from Digital Ads platforms or web analytics databases, or it may be necessary to incorporate measures of how many unique views a billboard or television ad received. As the region the campaign was run is important, external data including regional demographics and economic information is needed. For physical products, data about how widely available the product was in the market is needed. Finally, as competitive marketing and sales will impact your sales, information about this activity should be sourced. 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, predict the impact of a marketing campaign on sales.

Package: 
  • Sklearn

Campaign optimization

Find the optimal mix of campaigns and timing to maximize sales.

Package:
  • CVOXPT
  • pyOpt
  • scipy.optimize

Data features

Average Age
Census Data
Data Type
Continuous
Target
No
Yes
Average Income
Census Data
Data Type
Continuous
Target
No
Yes
Campaign Cost
Marketing DB
Data Type
Continuous
Target
No
Yes
Campaign Dates
Marketing DB
Data Type
Date
Target
No
Yes
Campaign Type
Marketing DB
Data Type
Categorical
Target
No
Yes
Clicks per ad
Web Analytics DB
Data Type
Continuous
Target
No
Yes
Competitor Ad Volume
Competitive Marketing
Data Type
Continuous
Target
No
Yes
Competitor GRP
Competitive Marketing
Data Type
Continuous
Target
No
Yes
Employment Percentage
Census Data
Data Type
Continuous
Target
No
Yes
GRP for Ads
Marketing DB
Data Type
Continuous
Target
No
Yes
Number of Stores carrying product
Inventory
Data Type
Continuous
Target
No
Yes
Number of Stores carrying related product
Inventory
Data Type
Continuous
Target
No
Yes

Related accelerators

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