From Back Seat Drivers to Co-Pilots

How Marketers Can Work More Effectively with Data Teams

Do you feel like you’re missing important data fields when trying to execute marketing campaigns? Are you frustrated with the disconnect between your data team’s priorities and your marketing goals? If so, you’re not alone.

As a data-driven marketer, you know what needs to be done to build your brand and meet performance goals. However, when it comes time to execute, you find yourself facing numerous data-related challenges, including missing or broken data fields and issues with data formatting.

If your data team’s priorities often feel disconnected from yours, you’re also not alone. They may be too focused on writing ETL processes, predictive models, and recommender algorithms rather than supporting your marketing and personalization strategies. As a result, you may be unable to execute against your vision, leading to misalignment with your data team.

At Simon Data, many of our clients come to us facing issues like these – for example:

  • When marketers go to build segments, they may be missing critical fields, for example connections between in-app product usage data and channel information around the customer’s first touch with the brand.
  • When looking to optimize channels, marketers are missing important contextual information such as direct mail receipts, campaign channels, or sometimes even basics like email funnels.
  • Marketers may have the data fields they need, but find that they’re seemingly always broken or formatted incorrectly.

Consider the age-old abandoned cart email – what does your data look like if a customer abandons their cart with three items instead of one? Are you tracking all three items – and, if yes, are you able to independently determine which one to feature in the email? The list of data issues impacting marketing productivity goes on and on and on and on.

Oftentimes, when unpacking the divide between marketing teams and data teams we find a deep disconnect in priorities. Consider how often your requests are met with long turn-around times. Or it’s that the data is sometimes there, and sometimes it isn’t. They’re seemingly always “writing ETL (extract – transform – load) processes.” But their long-term strategy and the way they work doesn’t inspire confidence in their ability to support  marketing’s goals.

So what’s really going on here?

In a best case scenario, we often encounter data teams that demonstrate a rough understanding of the data marketers need to support their goals. They’re often focused on ensuring the data is properly collected, QA’d and aggregated to support upcoming campaigns and initiatives. In this scenario, marketers may feel supported, but reality prevails: the data they require to create segments and personalize messaging simply isn’t there. Ultimately, they’re still blocked by their data teams and cumbersome upstream processes.

In a worst-case-scenario, data teams operate off in a distant land building predictive models and recommendation algorithms that solve “problems” that aren’t. Oftentimes, the working relationship consists of talking past each other while marketers focus on campaigns and personalization use cases, and data practitioners are thinking about algorithms, deep learning and GPT4. 

In this environment, marketers are absolutely unable to execute their personalization strategies, and are oftentimes forced to fall back on the limited data assets they have in their systems today.

So either way, marketers still are being held back. But the good news is there are solutions – both technical and organizational – to solving these problems. 

Problem #1: Misalignment around data use cases. 

One of the fundamental challenges in working with customer data is articulating exactly what data fields you need, how they need to be formatted and normalized, and how this all ladders into your marketing strategy. 

For any marketer that considers themselves data-driven, it is critical that you put in time with your data team. Schedule a meeting to understand their processes, tools, and challenges. Learn about the data sources they use, how they manage data quality, and what tools they use. This knowledge will help you understand any limitations and how to work with them more effectively. 

On the flip side, bring them into your strategic planning process. Make sure they have a clear idea of what you’d like to accomplish in the next 3 – 6 – 12 months and a voice in designing the pathway to get there. 

Problem #2: You don’t have the right systems in place to take control of your customer data. 

Marketing technologies are plagued with many data problems.

The way they house customer data is rigid and inflexible

Even once you have the data in your marketing systems, it’s often not quite right, and you still need engineering support to make it usable for campaigns. For example:

  • The data is not in the correct format (ex. a field needs to be a timestamp, but is housed as a string)
  • Data is not standardized across systems, or has many null values
  • Raw values exist, but derived values need to be calculated (ex. they have a ‘first_click_date’ field, but marketers need to calculate ‘days_since_first_click’ which would require some derivation)

Marketing technology wasn’t designed to work with your existing data infrastructure.

Do your systems integrate natively into your cloud data infrastructure across Snowflake, BigQuery, or Redshift? Do they plug into your existing real-time data, or require new custom integrations to get things to work at the speed that you need them to?

Finally, marketing systems don’t actually understand your data.

You may have transaction data in your system, but your marketing platform isn’t going to be able to create an RFM model. They may have all the signals in place to identify at-risk customers, but simply can’t predict churn. 

Introducing Simon Data’s Zero-ETL Initiative

At Simon Data, we believe in a world where much of the work your data team is doing when they’re writing ETL processes can be automated with technology.

Our Zero-ETL initiative is all about removing what we feel are unneeded dependencies that you have today with your data and IT teams. Instead of waiting on these teams to prep, stage, and transform your data – we’ve designed our platform to understand your data as it exists today, and to work on top of the systems and technologies in which your data team has invested.

Zero ETL isn’t a feature at all – it’s a new way of working that’s powered by technology that reduces and eliminates any need to rely on – or even think about! – ETL. The goal is to optimize workflows between data and marketing by making sure the data that marketers need to affect your vision and goals is at their fingertips. 

How does this all work? Well – it’s a result of many things that come together:

  • Native Snowflake and Cloud Data Warehouse integrations – starting with our Dataset Explorer, our platform integrates directly with your data as it exists today. 
  • Deep understanding of your customer’s identity – across digital, offline, on-site channels and more
  • Out of the box modeling with Smart Segments, Smart Insights, Smart Journeys, and more – including RFM modeling!
  • Best-in-class segmentation – once the data is in Simon, chances are you don’t need to reformat it or have your team to do any ETL to massage it into the right form for segmentation, which means you have free reign to build the segments you need. 

At the end of the day, technology won’t solve underlying personnel issues across your organization, but it can change the way teams work together across your business and optimize how work gets done. And choosing the right technology that fits not just your marketing needs but also the data requirements for your business – is a critical path to success.


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Check out our earlier posts in this blog series:

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