How to Nail the Hand-Off Between Data and Marketing Teams

Imagine a scenario where an engineering team owns the company’s customer data, and a marketer needs to send a promotional email that evening.

The marketer asks the engineer to export a list of customers with certain criteria which they need before they can send their email. Presumably, the engineer has a long to-do list, and probably doesn’t have time to pull the data, sort it, create the list, and share it with marketing. This results in a delayed response from the engineer and causes the marketing team to cancel their planned email send.

You could probably imagine that scene pretty easily because the situation is all too familiar for most companies. That’s because marketing and engineering roles are strongly defined and heavily siloed, with each having unique permissions and tools to do their jobs with little crossover.

Then why can’t marketing just own the customer data and pull their own lists? Because data can get messy and complicated. Between privacy regulations and unclean data processes, it’s vital that customer data is kept clean, organized, and safe – a responsibility often tasked to engineers and data teams.

But there are ways to aid in the communication between data and marketing teams. By setting  clear expectations, having open communication, and using a customer data platform (CDP), marketers and data teams can work together in a friendly, cohesive, and unified way.

Learning the people skills

The fundamentals of marketing are quite different from those of data science and engineering. So it’s no surprise that each team has unique preferences and terminologies. Sure, you might get an engineer who understands the marketing world. Or you might find a marketer who understands just enough code to be dangerous. But thankfully there are strategies that can help both teams work together while meeting the other’s expectations.

Set expectations and share knowledge

In an effort to save time, marketers might only share their end goal with their data teams. For example, “I need a list of customers who live in Denver.”

But if they explain their larger goal there might be a simpler, and even repeatable solution, that could be utilized for other campaigns. By sharing that you're hosting a series of location-based events it may help the engineers create a more dynamic spreadsheet versus pulling lists on a one-off basis.

Sharing the big picture might also prompt more questions that can ensure the desired result is achieved rather than the initial more limited scope. Similarly, the marketer might not know that piecing together all customers who live in Denver requires the engineer to connect multiple data sets. If the engineer explains what is required on their end, the marketer can be more thoughtful in their approach.

One lesson I learned in a previous role was to ask engineers to bake my request into their schedule. I discovered that our engineering teams worked with two-week sprints, and the earliest they could even look at my request would be two weeks from asking. So, with some careful planning, I was able to submit my request earlier and work within their schedule.

Sure, last-minute requests pop up, but when they are well mitigated, they become less of a burden on the engineering team.

Understand communication preferences

Not only are marketing and data teams siloed, when you add in a remote-first environment, you often have interactions across time zones. Fortunately, there are many synchronous and asynchronous tools at your disposal.

Ask your team how they prefer to communicate. Some might prefer in-person coffee chats (if applicable), others a quick, 15 minute Zoom, and some may prefer just chatting. When you respect each other’s preferences, you set the stage for a smoother hand-off.

Ensure you’re all using the same instant messaging tools. It can be challenging if  your engineers are on Mattermost while your business teams are using Google Hangouts. Having a quick and convenient place to check in and offer one-off requests can keep your communications both concise and searchable.

Consider a project management tool, like Jira or Asana. Each can help keep stakeholders and responsible parties updated on timelines and responsibilities, and serve as a centralized location for project progress. Having one source of truth across all projects means less time is spent checking in, while ensuring that deadlines are also being met.

Identify key stakeholders and project boundaries

When working cross functionally, having clearly documented roles and responsibilities is a huge help. This allows teams to know which members to approach and when, keeping everyone aligned.

Some examples of stakeholders include:

• Project champion - the team lead who holds the power to escalate issues.

  • Get their buy-in by engaging them early and align the project with strategic business goals

• Technical owner - understands the technical architecture and allocates projects to teammates, including engineers and data scientists.

  • Get their buy-in by explaining the technical advantages to them, such as how streamlined processes and fewer engineering requests lead to increased efficiency and improved automation

• Marketing owner - defines campaign requirements and owns downstream activation tools where customer data is used

  • Get their buy-in by showing how collaboration enables access to real-time customer profiles that leads to more personalized customer experiences without making requests to technical teams

As outlined above, there are many ways to encourage a smoother transition between data teams and marketers. When both departments understand each other and share their expectations, it can lead to a harmonious collaboration.

A CDP allows smoother handling of customer data

When customer data is stored across multiple, siloed tools, marketers can’t easily piece it together to quickly understand the customer. 

That’s why it’s complicated and time-consuming for the engineer to pull a list for the marketing email, as noted in the introduction.

When data lives in different tools it becomes inconsistent, hard to trust, and difficult to access and activate. Data teams can spend a lot more time digging for data and matching it than solving their own challenges.

By relying on a CDP like Twilio Segment, you can activate your customer data in a more collaborative environment that improves efficiency and drives better business outcomes.

Unified Data Collection

Segment allows engineering teams to collect customer data once and share it across multiple tools and platforms. This eliminates the need for marketers to request that engineers integrate and maintain each marketing tool individually, which saves time and reduces the workload on the data team. This streamlined data collection ensures that both teams have access to the same data, promoting consistency and accuracy in generating customer insights.

Collaborative Planning

Segment encourages collaborative planning between marketers and engineers through the creation of a tracking plan. This plan outlines key customer events to track, aligning the teams on what data is important and how it should be collected. This collaborative approach ensures that each  team is focused on capturing and leveraging data that drives business growth.

Reduced Dependency on Engineering for Data Insights

By making customer data accessible and actionable for marketers through tools like Mixpanel, Autopilot, and Zendesk, Segment reduces the marketers' dependency on engineering for data analysis and insights. It also gives marketers the ability to experiment with new technologies and evolve/modernize their martech stack with no vendor lock-in. This empowers marketers to independently derive insights, plan campaigns, and measure results, while engineers can dedicate their efforts to improving product functionality and data infrastructure.

Conclusion

When data teams and marketers work together, powerful insights can be discovered.. A CDP like Twilio Segment simplifies data collection, enables easier tool integration, and reduces the dependency on engineers for data insights. With less time spent on data management, engineers can use their time for more strategic, high-impact investments.