Over the past few years, there’s been a significant rise in data-driven journalism. This trend makes sense, as our lives become more and more engrained in technology, the more data that exists to tell stories about our behavior. For marketers, this trend is being reflected in content and embracing it is incredibly important in order to differentiate your brand from the rest of your competitors.

This, however, also comes with its own set of challenges – namely gathering and analyzing data. Most companies have some sort of data science department, but responsibilities and alignment may differ. For many content marketers, getting time with a data scientist is precious and getting support is difficult. At Zocdoc, we’ve been fortunate to be able to create a handful of data-driven articles thanks to the support of our data science team. Here are a few best practices to keep in mind to ensure your partnership with data science is productive, successful and mutually beneficial.

 

1. Ask them what they’re interested in

Especially if you’re just starting out with this partnership, there’s no better way build alignment and trust than to work on a project that is interesting for both parties. For us, data around patient reviews was an area that was both interesting for our data science team as well as our prospective doctors. Therefore, an article around what really motivates positive and negative patient reviews was a natural fit.

 

2. Understand their priorities

While some companies may be fortunate enough to have a dedicated data scientist to support marketing initiatives, many do not. If your data science team is lean with a lot of competing priorities on their plates, ask them what types of projects they’re working on. There may be a way to kill two birds with one stone.

 

3. Give plenty of lead time

Again, if your data science team is lean, they may be pulling data for you as a favor in addition to their workload. Keep this in mind and always give plenty of lead time for your ask. Depending on the request, I like to give 3-4 weeks of lead time. If your request is something that will need to be very timely, such as a look back analysis around a holiday, still give them plenty of heads up. They may be able to work ahead and build a query in advance.

 

4. Give context around the data you’re asking for

Whenever you’re sending a request, provide detail around the question you’re trying to answer. This is important because data scientists know your company’s data better than you do. Knowing this context will influence how they pull it.

For example, you may think you need data that looks back an entire year, but a data scientist may know they actually have enough data points looking back just a month of time to support the point you’re trying to make. Trimming the scope this way will then ultimately save time in the analysis.

 

5. Be very specific in the data you want

In addition to providing context, it’s helpful to be very specific in the type of data you want to include. For example, instead of “geographic split” say “metropolitan statistical area.” Again, they may have a better idea of how to pull this data, but providing this specificity and taking a first pass at the parameters makes their job easier. 

 

6. Clearly communicate roles + responsibilities

Be clear on what you as a marketer can and cannot do. If there’s a gap in roles and responsibilities between marketing and data science teams, look for a partner who can help. For us, data science doesn’t always have the bandwidth to analyze the data, but they’re happy to help pull the information for us. We then send the data to Priceonomics, who analyzes the data for us and draws out key insights.

 

7. Allow them to have input in the data portrayal

Once you have the draft of the article, share it with the data science team so they can give input into how the data is portrayed. You want to ensure the integrity of the data remains intact, so it’s important to keep them involved at this stage.

 

8. Let them know when the data is published

Putting yourself in the data scientist’s shoes, it probably feels like they do all this work for marketing and then nothing comes of it. Even though the project or article is driven by marketing, they’ve still invested time and energy. Be sure to share the final product with them to let them know their work did not go to waste.

When one of our data-driven articles was picked up by Business Insider, I made sure to email our data science team and give a shout out to the data scientist who helped pull the data.

 

9. Be appreciative and thank them for their help

As you should with any partner, knowledge the time they spent helping you and be appreciative of their time.

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