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Faster, More Accurate Customer Segmentation
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Big Data Analytics
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Maintenance
Services
- Data Science Services
The Challenge
Dentsu Aegis is a media buying company that allocates advertisers’ budgets on campaigns across various media using targeted segmentation. When pitching their services to potential customers, the sales staff recommends specific segments that would be the best to target with a particular campaign to maximize return. After they make the sale, the teams need to be able to deliver on those promises and actually maximize return with effective segmentation. However, the department struggled to quickly provide segmentation recommendations to the sales team. The teams built a data lake to collect data from multiple sources, but actually using the data meant embarking on the painful process of writing new code (Python, Spark, or SQL) every time. Every time they had a project, team members had to write a query, get the results, analyze those results with another tool, and write more code to reprocess and use the data. Without an easy way to replicate past work, each project required them to start their process from scratch, no matter how similar two prospects’ or customers’ use cases were.
About The Customer
Dentsu Aegis Network Ltd. is a multinational media and digital marketing communications company headquartered in London, United Kingdom. It is a wholly owned subsidiary of the Japanese advertising and public relations firm Dentsu. Its principal services are communications strategy through digital creative execution, media planning and buying, sports marketing and content creation, brand tracking, and marketing analytics. Dentsu Aegis is a media buying company that allocates advertisers’ budgets on campaigns across various media (TV, digital, search, etc.) using targeted segmentation.
The Solution
Dentsu Aegis chose Dataiku Data Science Studio (DSS) as the all-in-one tool to bring massive efficiency gains to the data department. Now with Dataiku, the department quickly prototypes ideas for new ways to segment customers. If the segmentation works well, thanks to Dataiku’s collaborative environment, the whole team can easily reuse the models over and over again. They no longer have to write separate queries for similar projects. Instead, the team just prototypes a code-devoted pipeline running directly in Scala on Spark. These efficiency gains free up the team to spend more time devising innovative new ways to segment customers and provide value to the company. For example, they used Dataiku to do predictive machine learning to find common features and weak signals. These weak signals enabled them to define very specific audiences that will respond (or not respond) to certain advertisements.
Operational Impact
Quantitative Benefit
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