Transformative power of marketing decision engines

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by Jacob Solomon Mar 14, 2023 News
Transformative power of marketing decision engines

In the past decade, there has been a steady shift in the way that businesses make business decisions.

In the traditional set-up, enterprises build specialized teams to solve for customer data, and other related topics.

Realizing the importance of each of these teams and their components, enterprises look for proven expertise to solve these challenges in their teams. It takes more time for enterprises to go from data to business benefits. The involvement of multiple teams and hand-off points make it difficult to maximize the benefits from use cases.

Marketing Decision Engines attempt to collapse the multiple components into one solution by improving an enterprise's ability to capture maximal value from use cases and help accelerate the time to value realization.

Retention campaigns can be used on an existing customer base. Bringing disparate customer data together and scoring the existing customer base would be required.

The customer attrition scores are fed into the marketing tool to select the list of customers for the campaigns. A team would analyse the results to see how the campaign has performed.

This approach is slow and notScalable due to the proliferation of customer data, complexity of marketing tools and technologies, and drastic changes in customer behavior and journeys.

These challenges are being solved by a unified decision engine solution. There are six critical components of a decision engine.

It takes some upfront thinking to determine what the enterprise wants to achieve through its engines. This could include discovering data, identifying top scenarios, and developing business cases.

The infrastructure refers to the environment for analyzing and making decisions with the necessary data and tools.

In order to bring relevant data from across the enterprise into the decisioning environment, it is necessary to integrate various internal and external data sources.

Marketing analysts can use this to do their job. Audience selection, campaign design, rules set-up, choice management, test set-up, and customer journey orchestration could be included.

Developing machine learning models, maintaining model repository, deployment of analytical models, continuous tuning and enhancements of the models are some of the things that are done in the field of analytic and algorithm.

It is possible to integrate the decision engine environment with various marketing channels for real-time delivery of marketing use cases.

This decision engine solution provides wings to marketers to do more, and do it better, so let's look at our retention use case to see how this can be used. The process of scoring the customer data is completely automated. The data is available to the marketer. The front-end interface can be used by the marketer to set up their campaign and execute it. The results are available as soon as practical for the marketer to make a decision on the next set of actions. The time to market campaigns is reduced and the odds of achieving desired business outcomes are improved.

The marketer can now think beyond the current campaign. Data integration with the brand's website and app would allow the marketing team to identify customer triggered events such as browsing a product page, adding to a shopping cart, and taking immediate actions. There are enormous opportunities to identify customers and prospects with similar online interests and behaviours as that of existing high value customers, and to bring them to the brand's online properties and offline stores.

Business results have been seen with decision engines.

52% increase in the online conversion rates driven by a product and offer personalization engine for a multi-channel beauty retailer 90% improvement in the cycle time associated with setting up and running outbound marketing campaigns for an automobile retailer

200% lift in the engagement rate through content affinity and personalization models for a media organization

The decision engine solution seems to be too complex for marketers. The components don't have to be in place on the first day. The enterprises that took part in the experiment made investments in their decision engine foundations. As more use cases open up for them, they continue to build on that foundation.

The time is right to implement marketing decision engines.

The views are of the author.

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