AI-Powered Customer Segmentation

Targeting the right customers with the right message at the right time represents one of the most efficient ways to optimize marketing campaigns and achieve a higher return on investment (ROI). And when a customer receives personalized content, there is not only higher probability (s)he will buy the product but also that (s)he will develop loyalty toward the business.

Yet as far back as 2014, Forrester reported that more than 75 percent of digital marketing professionals were already using advanced analytics and clustering techniques to personalize targeting. And 68 percent were already using machine learning algorithms for more personal targeting and interactions. So what else is there to do?

Today’s Challenges in Customer Segmentation

Actually providing the desired level of specificity is easier said than done; marketing teams and business analysts often struggle with:

• Understanding whether the right data exists for the project
• Quickly connecting to and combining data from multiple sources
• Applying machine learning techniques without data scientists
• Having the agility and scale to provide targeted recommendations quickly
• Managing arising data regulation and privacy restrictions

92% of marketers reported using personalization techniques in their marketing, yet 55% of marketers don’t feel they have sufficient customer data to implement effective personalization.

Source

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Dataiku Brings Customer Segmentation into the Age of AI

Dataiku is the platform democratizing access to data and enabling enterprises to build their own path to AI. More than 250 customers across retail, e-commerce, finance, pharmaceuticals, media, entertainment, advertising, and more use Dataiku to massively scale AI efforts.

For marketing teams looking to do more complex and advanced customer segmentation, Dataiku brings:

• An easy platform for connecting to all data across the enterprise, wherever it’s stored.
• Integrated documentation and knowledge sharing for context about where data is coming from and how it should be used.
• Interactive data cleaning and visual data transformation for fast, code-free data wrangling.
• Visual AutoML for code-free (yet explainable) application of machine learning algorithms.
• Customizable features to comply with internal policies and external regulations regarding data privacy, restricting access to sensitive data at the project or dataset level.