5 steps to using data to support marketing decisions

5 steps to using data to support marketing decisions

5 steps to using data to support marketing decisions

Speaking for one of our clients at a recent conference, I asked, “What is your biggest challenge when it comes to measurement?” Just some of the things I heard from this room of 150+ marketers were:

“Too many things to measure”

“Making results understandable and applicable”

“Ease of use of analytics platforms”

“Integration between tools”

“What has value now may not have value in the future”

Are these things you felt or thought? Let’s be honest. Most marketing teams find it somewhat difficult to get a clear picture of performance and effectiveness. In fact, in the latest edition of their annual content marketing survey, our friends at the Content Marketing Institute/MarketingProfs found that almost half of B2B marketers struggle to integrate/correlate data across platforms, and 45% said they lack of organizational goal setting KPIs measure against. These are basic things that we know we should be doing, yet even in 2023 marketers are struggling to do.

Challenges facing B2B marketers when measuring content performance

45% of marketers say they lack organizational goal setting KPIs to measure against. Click to tweet

How to use data strategically as a marketer

To be strategic in our marketing efforts, we need to know our audiences, we need clarity about our data, we need to be able to understand our data, and we need to experiment with our data. Without a thoughtful and strategic approach to collecting and measuring our data, we cannot effectively power our marketing decision-making engine.

Here are five things every team needs to do to build a strategic measurement framework and take action based on the actions measured.

1. Justify your efforts with 9 types of audience data

In a survey of 1,000 consumers, SmarterHQ found that “72% of consumers say they only engage with marketing messages that are personalized and tailored to their interests.” They also found that the problem is even more acute with business buyers : “82% of them say personalized customer care influences loyalty.” That shouldn’t come as a surprise – weren’t we all frustrated when we received an ad or an email that was completely irrelevant to our specific needs?

To ensure we have a clear picture of what’s important to our audience, we need to gather information beyond the basics. Does your company collect and maintain customer records that address the following 9 areas?

  • Demographic
  • Geographically
  • Behave
  • psychographic
  • customer relationship
  • channel preference
  • Technographic
  • social media
  • Consent and Preference

2. Unify your data

Our friends at Ascend2 found in the Data Unification & Management survey that 71% of marketers agree that implementing a data unification and management strategy is somewhat or extremely complicated. We have seen this with our own customers large financial institutions To well-known health brands; I’ve heard from so many customers that their data unification process is tedious and spans too many teams.

And yet the truth is: There will never be a better time than today to unify your data. We are all moving towards a more personalized and data-driven future. With this in mind, we must prioritize the process of building a single source of truth that will help our teams both report on impact and deliver more personalized experiences for our clients.

71% of marketers agree that implementing a strategy to unify and manage data is somewhat or extremely complicated. Click to tweet

3. Invest in data quality

I recently received an email from Twitter announcing the name change to X Corp. However, it wasn’t addressed to me, it was addressed to a “Stacy K.” I then received a follow-up email telling me that the first email was an error and that my details were incorrect not compromised. Note (below) that they didn’t even bother to include personalization in this email (this email just says “Hello”):

Example of the quality of email personalization
This type of human error can be reputation-damaging, as it makes your brand look stupid at best and undermines trust at worst. In order to use data to make better decisions, we need to ensure that our data quality is high. To do this, we first have to carry out regular checks of our data:

  • Regularly review our processes and standards for data entry/import.
  • Check the data quality regularly using random samples.
  • Review how our data is used/augmented by our various stakeholders.

Additionally, governance and team training are critical to maintaining data quality. It’s not just about the infrastructure or the data itself, but also about the people who oversee its creation and use.

4. Use AI and machine learning

You may remember Charles Duhigg’s influential article for The New York Times in 2012, in which he revealed how Target’s data science team was able to determine which customers were likely to be pregnant before they even made any explicit baby purchases. They’ve managed to do this through persistence and lots of experimentation, but today AI-powered tools like Optimizely or Persado can make it easier than ever to identify cohorts of customers, retarget them, and even serve webpage copy or ads dynamically based on what’s known. This type of marketing decision doesn’t even require human intervention once the systems are in place.

But even small teams can use publicly available AI research tools to better understand their audiences. You can go into ChatGPT and type in questions like, “Which factors are most important and when?” [your target audience] thinking about buying [your product]?” The trick here is to be specific; The answers you may get for “millennials” will be significantly less specific than for “millennial business owners on a budget”.

5. Test your hypotheses

In addition, the data should not be static. To keep improving and learning from your data, you need to make hypotheses and run tests to see what’s true and what’s not.

When I speak to marketing teams, it doesn’t mean they don’t want to test. Often there is simply a lack of mechanisms to be able to carry out regular tests.

Integrate tests into yours Content Calendar. When each piece of content and campaign has a test associated with it, and the creation of assets to support that test built into the production process, you can learn quickly and consistently with every marketing execution.

Ultimately, to make better decisions based on your data, you need to make sure you have the data that relates to your goals. These are not necessarily things like the number of followers or the number of subscribers. Rather, it can be a holistic view of how engagement and conversion look at each stage of the customer overall.

Rather than looking for clarity in individual numbers or how our content is performing, we need to look for shifts in overall performance and strategic approach to marketing development. To do that, we need more data, but also the tools to help us understand the big picture by allowing us to relate the data to our overall goals.

Want to learn more about how your team can use AI to enrich their day-to-day work? Click here to learn more about our upcoming AI Readiness webinar:

5 steps to AI readiness for marketing leaders

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