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Developing Data Science Knowledge to Power Marketing Technology

Developing Data Science Knowledge to Power Marketing Technology

Alex Gutman, Bradley Boehmke and Scott Crawford

long line of grocery carts

It’s evident to most professionals that the marketing landscape is changing.

Consumer and brand interactions are increasingly digital, leaving bits of data and insights that have the potential to make future interactions more effective and efficient.

Take, for instance, the personalized shopping experiences—online or in-store—offered by 84.51°, the data and analytics arm of Kroger, the United States’ second-largest general retailer. With more than 10 petabytes of data in its arsenal, 84.51° harnesses data and analytics to connect brands with people—whether a foodie scanning the web for a perfect Friday night dinner recipe, a parent struggling to come up with a week’s meal plan, or a consumer seeking more health-conscious alternatives.

Behind the Marketing Technology

Innovation in marketing technology requires innovation in developing and enabling individuals who can make sense of data.

However, according to a 2017 survey by Capgemini Digital Transformation Institute, “Close to 50 percent of the organizations … studied conceded they have not taken digital talent seriously.” Capgemini’s study further reveals a widening “digital talent gap” and increased demand for members of the digital and data talent pool. This means companies need to not only increase their data science IQ with “data scientists,” which ranked No. 1 on Capgemini’s global demand index, but also improve the data literacy of non-data scientists across the organization, from C-suite to marketing and sales.


Embedding data science thinking into all levels of the organization removes communication barriers between data scientists and non-data scientists by teaching each group to appreciate and understand the challenges the other side faces. Data scientists need better communication skills and improved business knowledge, while business and marketing partners need to improve their data literacy and be equipped to spot opportunities where data science can bring value. It’s only at this point when a company raises its total data science IQ that it can make a paradigm shift with its marketing technology.

So how does a company raise its overall data literacy? 84.51° has developed its own deliberate and structured effort to enable, empower and encourage data science knowledge across all personnel.


Data scientists require a powerful and robust technology toolkit to do their job. This includes R, Python, DataRobot, Kubernetes and a powerful software environment in which to operate these tools. 84.51° “enables” its data scientists by leveraging open source platforms; benchmarking these tools; and providing internal, custom-built applications so that its data scientists have the best toolkit for their data and business problems.

However, there is no expectation for HR personnel to program in Python, nor one for marketing partners to understand the intricacies of these tools. Instead, 84.51° focuses on enabling its non-data scientist personnel by demonstrating the unique capabilities these tools can provide and embedding data scientists within their mission. This allows the data scientist to learn the unique business knowledge of that mission which will enable them to identify areas of opportunity to embed automation and machine learning into that mission’s daily practice. As a result, 84.51°’s business experts gain an appreciation for what data science can do and begin to approach business problems more analytically.


With the speed of technology changes, a company must be deliberate about empowering its personnel to grow. 84.51° empowers its data scientists through a series of internal trainings and a knowledge repository to teach data science best practices; share example code; and provide videos, full-length tutorials and case studies that expose how data science methods power its marketing technology.

To empower 84.51° and Kroger non-data science business experts, an Enterprise Data Science Academy is being developed that will provide them with the right mix of technical depth and business application to become more data literate. This academy includes executive newsletters to share thought pieces, data science roadshows to showcase how data science impacted business, and workshops to discuss common data science approaches and question framing to help restructure business problems to analytic problems.


An organization must deliberately encourage data science thought across all personnel by intentionally shaping the culture around its data and marketing. 84.51°’s people are encouraged to train and mentor others, lead workshops and help spread technical knowledge across the 84.51° and Kroger community. This includes data scientists sharing with non-data scientists and vice versa.

For example, one group of data scientists holds weekly meetings to review and curate data science blogs and podcasts that are worth sharing with non-data scientists. Another group focuses on how to make machine learning results more interpretable and actionable for business problems. Another includes data scientists and managers learning about natural language processing and identifying new company use cases. This community-driven work environment leads to a challenging, fun, rewarding and highly innovative culture.

Changing the Environment

We must prepare for and invest in a future where data science will play a more significant role in marketing technology. As the landscape changes, we must meet demands by enabling our talent with the best technology, empowering them with the right training, and encouraging them to shape the culture and drive innovation with data and science.

Consider taking a deliberate effort to raise overall data science IQ and embed data science thinking into all levels of your organization. By minimizing the data literacy gap between scientists and non-data scientists, communication improves, innovation ensues and new advancements in marketing technology can swiftly be identified and implemented.

Alex Gutman is lead data scientist at 84.51°.

Bradley Boehmke, Ph.D., is director of data science at 84.51° .

Scott Crawford is mission lead – embedding machine learning at 84.51°.