4 years ago
Data Science for Marketing Optimization – Case Studies from Airbnb, Lyft, DoorDash

Originally published in Blogboard Journal, Jan 7, 2021.

In this article we’ll look at several case studies of data science being used to optimize marketing efforts at companies like Lyft, Airbnb, Netflix, Doordash, Wolt, Rovio Entertainment.

In the first quarter of 2019, Airbnb spent $367 million on sales and marketing. When you think about this from a technical standpoint, two obvious problems come to mind:

1. How do you scale your marketing processes to be able to spend $300+ million per quarter on ads?
2. Once you have systems in place to spend huge ad budgets, what’s an optimal way to allocate the money?

In this article we’ll look at several case studies of data science being used to optimize marketing efforts at companies like Lyft, Airbnb, Netflix, Doordash, Wolt, Rovio Entertainment.

Summarizing articles from official blogs of these companies, we’ll get a high level overview of marketing automation and then zoom in on the parts where data science and machine learning play their role.

If you read on, you’ll find these three sections:

1. Marketing automation systems – what are they, what subsystems they comprise, where in the process is data science usually applied
2. Performance estimation – why estimating the performance of your campaigns is the fundamental problem in marketing analytics and what is the data science tool set used for this
3. Optimizing bidding and budget allocation – once your marketing efforts are at the scale of hundreds or thousands of concurrent campaigns, it’s impossible to allocate you budget manually in an optimal way. We look at two simple algorithms for budget allocation, shared by DoorDash and Lyft engineers.

Marketing Automation Systems

In large and analytically mature organizations, the optimization piece usually comes as a part of a larger marketing automation system, but as we’ll see it’s not always the case. Allocating budgets manually but aided by data science can be hugely profitable and might be a good first step towards a fully automated workflow.

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