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Bringing AI to Marketing

The evolution of AI, machine learning, data science, and big data have been an increasingly integral part of the transformation of many industries, and marketing is no exception.

To compete and stay relevant in this rapidly changing world, businesses need to embrace the idea of using data and AI for their marketing activities. Data-powered technology has made it possible for marketers to build a clearer picture of their target audiences than ever before, and in the hotbed of all this lies marketing AI.

Some companies originally built around data and its applications are already making AI powered by data a reality. However, these companies are the exception rather than the rule. For most organizations, the journey to leveraging AI isn’t as natural, but still as critical to keep them competitive and profitable.

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Marketing in the Age of AI

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Understanding the Use Cases for AI in Marketing

Marketing Attribution: Marketing attribution works by measuring the effectiveness of different strategies as part of a digital-only campaign across one specific device (for instance, what sort of ad resulted in a customer purchase). While attribution isn’t new for marketing teams that have dealt with it in the past, ML-based attribution (and thus AI) is more accurate than the old methods when dealing with the digital world and its vast number of channels. ML algorithms have the capability to interrelate all the times a consumer has been exposed to your marketing and determine the exact contribution of each channel in bringing the customer to conversion.

Combatting Churn and Increasing Loyalty: Churn analysis (also known as attrition modeling) helps prevent profitable customers from leaving without getting a chance to retain them. Churn analysis relies on careful preparation of customer data, examining available customer history, and taking snapshots at various points in time of the experience. It compares the different snapshots to get an idea of whether something has changed during their time as customers. Finally, a machine learning algorithm associates notable changes to customer actions, such as leaving or staying in your customer base. Used with a lifetime value model, a churn prediction model can promptly and automatically react when the risk of losing important customers appears.

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Recommendation Engines: Recommender systems are the digital heirs of marketing research. Instead of conventional research methods such as questionnaires, recommenders use historical data together with customer profiles, and they immediately satisfy customer needs through targeted suggestions. Some recommenders rely on simple processes, while others require demanding math. However, all recommender systems are embedded in some customer process, such as a website that sells goods, as they directly interact with your customers by pointing out products you want the customer to discover. Meanwhile, the recommender system automatically learns to understand a customer’s needs better.

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Marketing use cases are often the lowest-hanging fruit and can unlock the path to Enterprise AI for the entire company. That’s why today’s top marketing teams are setting the example by diving into machine learning, proving its worth, and thus leading the AI revolution.

„Exciting new measurement technologies hold the promise of lifting marketing effectiveness to new heights but, in the real world, no marketer can thrive on technology alone. The savviest marketers realize that making progress on the measurement journey involves mobilizing both their teams and their technologies, which leads to better business outcomes, whether that’s revenue, profitability or customer churn.“

– John Grudnowski, Digital Marketing Expert at Bain & Company

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Dataiku and Marketing AI

The opportunities for implementing data-powered marketing have grown incredibly. However, even though data is widely available, it’s often scattered in silos (private databases or other sources that lack easy access), and teams may lack the tools to retrieve and integrate the information you need. To extract maximum value, data must be at the hands of everyone in the business to use it to its full potential for every possible use case — that’s where Dataiku comes in.

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Dataiku is one of the world’s leading AI and machine learning platforms, supporting agility in organizations’ data efforts via collaborative, elastic, and responsible AI, all at enterprise scale.

Dataiku is for:

  • Chief marketing officers who view data as fundamental to marketing, for better decision making, generating actionable insights from marketing AI activities, and helping the organization move forward in their Enterprise AI journey.
  • Marketing analysts: Dataiku’s visual platform and automated ML features cover the whole data pipeline, making data science and machine learning an extension rather than a limit to marketing analysts‘ current capabilities and skillsets.
  • Marketing analytics leaders for harnessing a transparent yet structured environment in which they can not only track individual and team performance, but also become a vector for team collaboration, personal growth, and skill-set amplification.
  • Marketing data teams to connect to, explore, prepare, build, automate, and deploy machine learning-based models, applying them in different marketing AI use cases.
  • Other marketing and sales teams, who can use Dataiku’s point-and-click data preparation and modeling features to bootstrap their own data exploration or collaborate with data teams to deploy larger AI projects.
  • …and more — Dataiku is flexible yet robust, built for everyone in the data-powered organization.

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