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Measure channel and campaign efficiencies

We develop media mix models that measure the true contributions of channels and campaigns into traffic, conversions, and other business results. These models combine first-party data, external signals, market-level data, and general observations of the marketing teams to accurately gauge channel efficiencies and provide a scientific foundation for marketing optimization. The technology of the platform allows for budgeting improvements while also increasing the productivity of lead generation and customer acquisition.

Understand what drives visits and conversions

Attribution is one of the most challenging problems in marketing data science. We create attribution models that combine customer data, prior knowledge of the marketing teams, and state-of-the-art machine learning algorithms to provide reliable and actionable insights into the performance of channels and campaigns at different levels of granularity—from the overall conversion numbers to individual customers. Our omnichannel marketing analytics technology provides unparalleled insight into customer activity, thus helping to fine-tune all aspects of the marketing effort.

Optimize marketing spend

Attribution models and channel analytics provide a solid foundation for marketing budget optimization. Our tools add an optimization layer on top of the core attribution models, thus enabling what-if analysis and automatic budget reallocation. Our marketing analytics tools can help you optimize marketing spend in a few quick steps—evaluate our software platform, optimize your marketing spend on ads, and improve your overall performance in a matter of weeks.

Optimize ad bids

Bid optimization is a complex process that involves business objectives, budgeting constraints, inventory or service capacity, and other considerations. We create machine learning–powered bidders that can automatically optimize profits, maintain company ranking in the search results, or maintain certain inventory turnover. Our bid optimization systems take into account a wide range of data and signals, including conversions, marketing activity, seasonality, regional data, and more.
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
Book
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.
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We develop marketing optimization models and platforms for companies from many industries including retail, telecom, video games, and finance
Retail
It is common for retail companies to integrate with a large number of digital media channels, and customer acquisition through these channels is critically important. We help retailers to understand the efficiency of each channel and find channel combinations that are optimal for their digital marketing efforts. We also build dynamic components that help them to reconfigure marketing campaigns in real time.
Finance and Insurance
Our marketing optimization solutions help finance and insurance companies to optimize their marketing efforts around customer acquisition, growth, and retention using the most optimal channels and campaign types.
Telecom
Customer retention is one of the central concerns for telecom companies, and we help them to understand how interaction with different advertising, support, and communication channels influences customer churn. This analysis then helps them to optimize channel usage and campaign design to improve customer retention rates.
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.
Cross-channel marketing spend optimization using deep learning
read moreOptimization 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.

Cross-channel marketing spend optimization using deep learning

read moreOptimization 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.
read moreOptimization 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.
Building a next best action model using reinforcement learning
read moreIn 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.

Building a next best action model using reinforcement learning

read moreIn 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.
read moreIn 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.

We provide flexible engagement options to design and build your own marketing optimization solution. Contact us today to start with a workshop, discovery, or proof-of-concept (POC).

Workshop

We offer free half-day workshops with our top experts in marketing spend optimization and data science to discuss your marketing strategy, marketing channels, and ROI optimization opportunities.

Proof-of-Concept

If you have already identified a specific use case that needs to be solved, we usually can start with a 4-8 weeks proof-of-concept project to deliver tangible results and business value.

Discovery

If you are in the stage of requirements analysis and strategy development, we can start with a 2-3 weeks discovery phase to identify right use cases for marketing spend optimization, design a solution using industry best practices, and build a roadmap.

Stream processing
Fraud detection & prevention
Customer intelligence and personalization
Trade promotion optimization
Revenue and price optimization
Anomaly detection and predictive maintenance
Supply chain optimization

Want to get in touch with us? We are pleased to begin helping you.

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