Marketing spend optimization

Use advanced statistical models to optimally allocate budgets across marketing channels and measure the true contribution of each channel or campaign. Leverage marketing data to gain insights for potential growth and areas of investment to improve the bottom line.

Our clients

Finance & Insurance
How our spend optimization technology works
Continuous learning
Our spend optimization solutions continuously correlate marketing activity parameters (such as sponsored search bids) with business outcomes and progressively learn the dependencies between them. The established dependency is used to optimize the activity parameters. Your ads could be shown on Google or Facebook, and we can still help with the management of the ads for optimal spend.
Smart experimentation
Parameter optimization in ever-changing environments requires testing and experimentation. Our algorithms optimize the exploration vs. exploitation trade-off and determine the optimal parameters with minimal overhead. Marketing models that explore the environment improve with the increasing amount of data that comes in from your business. Google Analytics can only do so much, and we can come in to take care of the rest.
Resource-aware optimization
Our bidding solutions can use the information from multiple sources such as inventory management and ERP systems to accelerate or decelerate advertising activities depending on the available resources and capacities.
Introduction to Algorithmic Marketing
Introduction to Algorithmic Marketing
Would you like to learn more about the economic and algorithmic foundations of marketing optimization and related problems? We have published a 500-page book on enterprise data science that is available for free download, and there is an entire chapter on campaign optimization and marketing analytics in it.


We develop marketing optimization models and platforms for companies from many industries including retail, telecom, video games, and finance
video game industry icon
Technology and Video Games
Technology companies and marketing agencies often need advanced spend optimization solutions that change the parameters of marketing campaigns in real time. We create dynamic components that help them to provide competitive digital marketing services to their clients.

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Cross-channel marketing spend optimization using deep learning
Optimization methods that work with sequential even data represent a major step forward compared with traditional marketing mix and channel attribution models that work with aggregated data. In this article, we explore how deep learning methods can be used to analyze sequences of customer interactions and how the insights gained from such analyses can be used for spend optimization. We develop a line-up of attribution models ranging from basic last-touch attribution to LSTM with attention and evaluate them using a public dataset published by Criteo. In the last section of the article, we show how these models can be used to evaluate different scenarios for the allocation of marketing budgets.
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Building a next best action model using reinforcement learning
In this tutorial, we discuss how traditional targeting and personalization models such as look-alike and collaborative filtering can be combined with reinforcement learning to optimize multi-step marketing action policies (aka Next Best Action policies). This approach can be applied to a wide range of marketing optimization problems, including promotion optimization, optimization of special offers, data-driven budget reallocation across channels, and so on.
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