COVID-19 has altered the retail and consumer goods landscape, pushing retail and consumer goods brands to change their business models to accommodate increased consumer spending, higher consumer expectations, and a rapid shift to digital channels.
As consumers spend more time shopping online than ever before, personalization has become a strategic priority for digital marketing teams everywhere. However, effective personalization can only happen with a foundation of data science.
Why Is Data Science Important for Marketing Teams?
Data science has never been more important or accessible for marketing teams, but not every brand understands how to leverage it. When brands build digital strategies around a foundation of data science, their marketing efforts are easier to scale. That enables marketers to create experiences and messaging that grow with the business and evolve to meet changing consumer needs.
Today’s consumer journeys take place across multiple channels and platforms, with more touchpoints and a more complicated path to purchase. For marketing teams to capture this opportunity, they need to implement data-driven processes and decision-making. Doing this in a scalable way requires a foundation of data science.
The Challenges of Developing Data Science Capabilities
Every brand is trying to become more data-driven, but not all of them know how to get started. Collecting, tagging, and managing large amounts of data and extracting actionable insights is a complex task that marketers don’t necessarily have the technical skills to do. This is where data scientists come in – their skill and technical knowledge can help bridge the gap between the data and the actionable insights that marketers need to curate relevant, personalized omnichannel experiences.
Developing internal data science capabilities comes with its own set of challenges. For example, hiring data science talent is a major hurdle. Most data scientists flock to tech giants like Amazon and Google, leaving retail and consumer goods brands at a disadvantage when building data science departments. Enabling these capabilities internally also takes a considerable amount of time, but competitors and consumers aren’t waiting around for this to happen. If brands can’t give consumers what they’re looking for, they’re likely to make the switch to a brand that can.
How Today’s Brands Can Build Data Science Capabilities
Today, brands need to use data science to curate relevant, personalized experiences for their consumers across multiple channels. These three strategies can help businesses build out their data science capabilities and put themselves on the path to personalization:
1. Identify short-term and long-term business objectives.
One key element of successfully using data science is setting specific business goals. For example, a long-term objective might be to implement personalization across channels within one year. A short-term objective for the same business could be increasing checkouts on an e-commerce platform and improving marketing ROI in a single quarter. While every business has different targets, setting the right goals can help brands measure their progress and make adjustments when they feel stuck or encounter challenges.
2. Leverage technology to reduce risk and improve scalability.
Advances in marketing technology mean that brands don’t necessarily have to build an entire data science department to become more data driven. Look for AI solutions with strong ML experts built for non-technical marketing teams.
While AI-powered tools can be used for most businesses, marketers need to pick a personalization solution that fits their specific needs. For example, a direct-to-consumer brand would need different algorithms than a B2B company, so they should find a solution that helps solve their problems and can scale as the business grows.
3. Pick the right partner.
While most brands are interested in leveraging data science and improving their personalization efforts, it can be tough to know where to start. Especially for teams without deep technical knowledge, it’s important to work with a partner whose solution doesn’t require tons of engineering to implement or use on a daily basis.
Integration time is another key element – brands should find out what results to expect after one month or one quarter, then see where they can be after one year. A great vendor understands the whole scope of the personalization journey and can help brands move the needle on their business objectives in a few weeks, not months.
For retail and consumer goods brands, data science should be the foundation of every digital strategy, especially those centered around personalization. The sooner brands figure out how to access and leverage data science in their own organizations, the more likely they are to secure a competitive advantage in today’s e-commerce and digital-first landscape.
