Data is the power behind every successful organization. It’s the fuel that feeds insights and helps brands continuously generate growth. However, you need a well-structured marketing team to activate the data and keep it humming.
More and more marketers understand the need to embrace a data strategy as a means to more deeply understand audiences and apply insights. And many are eager to build data-savvy marketing teams. But what’s the best way to structure the team for success?
Every business is different, but the top brands use one of three organizational models when building data-driven marketing teams.
Model 1: The center of excellence
The center-of-excellence model gets its name from the central digital expert (or team) who leads it. In this structure, the “center” establishes and documents all guidelines and processes.
For example, a multinational organization with many offices around the world may not be able to support a robust data science group in every market. Instead, it establishes one center of excellence.
Tapestry Inc. is a luxury fashion company that owns three popular brands: Coach, Stuart Weitzman, and Kate Spade. Their Data Labs team rolls up to the head of strategy, who reports to the CEO. The team includes data engineers, data scientists, marketers, and analysts. They’re responsible for the full value chain of customer data for all global touchpoints across the three brands.
“We have to be high-efficiency and high-performance. There is a spectrum of data needs that teams across our business might have,” says Terrence Lai, senior director of global strategy & data labs at Tapestry. The central team not only takes care of advanced data needs like omnichannel analytics, customer clustering, and persona segmentation. “We also work with marketing teams to reconcile budgets and attribution, or do measurement around small features that we might have on the website.”
The center-of-excellence model might be a fit for companies that can’t support an analytics team at every location or division. Larger companies also tend to use the center-of-excellence model, but it can come with a risk: You might lose your connection to local markets.
Model 2: The distributed team
With the distributed-team model, the organization embeds analysts within individual teams, divisions, or locations throughout the company. That way, analysts can gain intimate knowledge of a team’s priorities and processes.
The distributed-team model also allows management to focus on overall goals without getting bogged down in micromanaging data.
Rather than have a central analytics team setting guidelines, you might have both a marketing analyst and a product analyst that aren’t on the same reporting chain. This structure allows for each team to be more flexible, run tests and make tactical shifts as needed.
“We have analysts embedded in teams throughout the company. We identify the core data sets for the company at a high level, and distribute those to the analysts throughout the company in a clean and workable fashion,” says Stasha Rosen, senior product analyst at fashion and lifestyle website Refinery29.
The model allows top management to remain focused on overall goals and strategies without getting bogged down in micromanaging data.
But one downside to the distributed-team model? People may lose touch with the big picture if there isn’t a clear, integrated data strategy.
For companies that are afraid of data becoming siloed, one potential solution is the “hub-and-spoke” model.
Model 3: The hub and spoke
For organizations that have the budget and infrastructure to support it, the hub-and-spoke model is an excellent hybrid approach that blends the best parts of the previous models. In this model, there’s a central point of contact or team, as well as embedded analysts. The expert core team establishes consistent guidelines, tools, and processes. Meanwhile, the analysts within each brand or division implement them and return results to the core team. There may even be a dotted-line reporting structure in place to ensure accountability.
Sprint uses a “quasi” hub-and-spoke model, according to Chief Digital Officer Rob Roy. He first built up a center of excellence through his digital team, then worked to teach, promote, and influence people within Sprint’s various organizations to champion that digital hub. A close partnership developed with the customer relationship team, then it extended to other arms of the business.
The hub-and-spoke model encourages coordination between divisions and central management, while also empowering local teams.
“We worked closely with the network team as well as the prepaid group to do analysis on things like how much we should price phones, elasticity of price, number of handsets that move, and the type of customer that buys a certain type of handset,” Roy says. “And once we showed those teams some very interesting and actionable data, the teams’ leader took it to the CEO unbeknownst to us. It was very well-received and once he saw it, then the floodgates opened.” More support and interest in making data-informed decisions followed.
This model encourages coordination between divisions and central management, while also empowering local teams to innovate, explore, and take risks. For example, a multinational company that sells the same products over diverse markets may want to retain a firm handle on what counts as core data. At the same time, it may also want to rely on local analysts to apply the findings from that data in different markets.
Making it happen
To find the right org structure for your team, you’ll have to think about factors like budget, geography, company size, and culture. Whatever model you choose, creating a data-fueled marketing team begins with breaking down silos and enabling open access to data. People need to be confident in the data itself, plus those who analyze and report on it. Every person on the team must be data-savvy and equipped to put insights into play so your marketing team can run full steam ahead.
For more tips on how to connect marketing teams, data, and technology for success, download The Data-Driven Marketer’s Strategic Playbook.
