Striking The Right Balance Between Marketing and Data Science

Data-driven marketing gives brands a competitive edge in the marketplace. Odd Morten Sørensen, Martech Evangelist and host of the Learning Martech podcast, sheds light on the key challenges and successful strategies for marketing teams working with data.

Reading Time: 5 mins 


  • Striking The Right Balance Between Marketing and Data Science (One-voice Odd Morten Sørensen ) Mug shot

    Drawing parallels between data science and marketing, Odd Morten Sørensen, Martech Evangelist and host of the Learning Martech podcast, says, “Investing in technology should always be viewed as a way to amplify the capabilities of your people, not replace them.” 

    Defining the podcast as a resource he wishes to have had in his time learning about the martech landscape, Sørensen touches upon the primary challenges a marketer faces today when working with data. Through the podcast, he brings to the martech community a multitude of resources about topics ranging from customer lifetime value to marketing automation and tech stacks.  

    Data Science and Marketing: Challenges and Strategies

    A huge inflow of data is a gold mine for marketers, but it comes with its challenges. For one thing, while data is the key to understanding the customer, one needs a certain amount of analytical finesse to make sense of it. This is what makes it a necessity for marketers to work with data scientists to derive meaningful insights from the data. “What I’ve often seen is that marketers and data scientists seem to speak different languages, and it doesn’t matter how exceptional each party might be if they don’t understand each other properly,” says Sørensen. He adds that teams that solve the communication gap will be better able to leverage the insights.

    The stumbling blocks don’t end here. Once the coordination with the data science team is settled, the next stage is to decide what you do with the data and eliminate the barriers to it. What most marketers choose is to offer hyper-personalisation to their customers with the help of the data available. Brands can use data analytics and machine learning algorithms to analyse customer behaviour, preferences, and interactions across various touchpoints to deliver hyper-personalised experiences, says Sørensen. The primary hurdles, he says, include “data privacy concerns and technical skill gaps due to the complexity of integrating multiple data sources and algorithms.”

    Brands are working actively to resolve the issue of data privacy. With the withdrawal of third party cookies, the average customer now exhibits increased awareness about their data rights. They want to be in the loop about who they are sharing their personal information with and how it is being utilised to cater to them. As far as they are not kept in the dark, most customers are more than willing to share their likes and preferences in return for hyper-personalised experiences.  

    Data drives Results with Metrics

    The key to a successful personalisation strategy is to let data take the driver’s seat. Brands can inform marketing campaigns with customer segmentation followed by behavioural analysis. Sørensen suggests two kinds of segmentation for basic customer profiles: demographic and psychographic (interests, values, attitudes). “For behavioural analysis, focus on metrics like purchase history, website interactions (shown intent, conversions, checkout drop-offs, etc.), and engagement with emails and social media to tailor the marketing campaigns effectively,” he adds. 

    These metrics, combined with first or zero party data shared by the customer, make for the perfect mix of data. However, when dealing with multiple data sources, which is the case with most marketing teams, there can be the issue of data quality and silos. Deriving accurate insights from this deluge can be tricky and a type of misdirection if not handled at the right time. Sørensen emphasises that marketers should implement data validation checks and regular audits, besides ensuring that data is collected from reliable sources. 

    For data silos, he adds, “Marketers should promote data sharing across departments into a cloud data warehouse to unify disparate data sources, and integrate and use a platform that leverages their cloud data warehouse as a composable CDP to get a 360-degree view of the customer.”

    The best outcomes can be achieved when human talent works in well-oiled coordination with tech resources. “Investing in technology should always be viewed as a way to amplify the capabilities of your people, not replace them,” says Sørensen. With technologies like artificial intelligence (AI) and machine learning (ML) replacing tons of jobs, the key to success for any business leader is to form a balance between tech investments and people resources. This would help ensure maximised output from the potential carried by both sides. 

    “A well-chosen tech solution can automate basic tasks, freeing up human resources to focus on creative, strategic, and relationship-building activities. These add greater value to the business,” Sørensen concludes. 

    Sørensen is a keynote speaker at Rise Up, a first-of-its-kind summit for practitioners and aspiring CMOs looking to make their mark in the world of martech. It will take place on September 13 – 14, 2023, in Dubai, UAE, and will be co-located with Martechvibe’s flagship conference – Vibe Martech Fest


    More Like This