How Agency Leaders Can Navigate The Ins And Outs Of Data Analytics

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CEO of Agency Management Institute, serving 250+ marketing agencies to help their owners build profitable agencies that evolve and scale.

Creatives don’t always mix well with math and science. If you’re like me, you might have gotten into marketing as a preferable alternative to a more technical career. However, the data revolution is in full swing, and crunching numbers has become essential if you want to run an effective agency.

So, what should you do as an agency leader to make sure your data analysis delivers a solid return on investment? Here are my favorite suggestions for managing the ins and outs of data analytics.

Respect the work and invest in help.

For the uninitiated, it’s easy to jump to the conclusion that generative AI or another software is going to solve data difficulties for free or cheaply. However, that idea is pure fantasy. With data analysis, don’t trust cheap solutions or think you can just figure it out yourself without a significant investment in time (and likely, time you don’t have).

The insights of competent professionals are the only way to get the most out of your marketing intelligence—far beyond what programs alone can do. In fact, companies now recognize the need for multiple specialists to crunch and interpret the numbers across each discipline.

To illustrate, you’ve likely encountered business leaders who erroneously believe that, with a little time and martech software, they can get results as good as your agency. The reality is that staying competitive in today’s crowded field usually requires the guidance of different specialists in distinct marketing arenas, such as content, public relations, paid advertising and branding. Similarly, the range of assignments for effective analytics is becoming just as vast. Your goal should be to eventually grow a robust data analytics team.

What if you have to do it alone for the time being? Connect with agencies similar to yours and glean what you can about which tools they’ve found to be effective and how they’ve used them. Then, experiment and test promising programs. However, you should still hire an expert as soon as you can.

Hire the right people.

How do you decide who to hire? You probably already know that being at the top of Google results is not always the answer to your problems. Whether you’re hiring a data agency or an individual analyst, you have to look in the right spots and use solid criteria to locate a good match.

Look at it like you’re hiring a translator for a language you don’t speak. Rely on referrals from others you trust, the results of past work, and cultural matches that facilitate easy collaboration. You could even decide to upskill an existing team member in data analytics. Supply them with the resources, training and communities they need to become effective. For example, a new analyst should listen to analytics podcasts and attend conferences.

Still, I’ve discovered that outsourcing the work to a different firm can be best. One benefit is that you can collaborate with a proven authority who educates you over time. Another advantage is objectivity. When an outside firm evaluates your clients’ campaigns and initiatives instead of an in-house employee, you don’t look like you’re “writing your own report card.” The perspective of a third party can inspire confidence in you from your clients.

Learn to ask the right questions.

The power of data is similar to that of internet search and, more recently, generative AI. Solutions result from asking the right questions.

Each client has different goals and levels of understanding about what data can do. Numbers are just the raw material, and for many teams, stats and figures will be boring. Learn to ask good questions and your work with data won’t go to waste.

For example, if a client wants to improve branding, determine how you’re measuring that. Just “getting better” or “doing more” are vague and useless qualifiers. Instead, ask the client what good branding measurement looks like and use those markers to determine success. For example, you could establish whether you’re aiming to increase:

• The number of page visits.

• Time on the website.

• Email open rates.

• The number of people willing to come to a webinar.

• Social shares.

Conversely, would the client rather focus on lowering bounce rates, cost per conversion or customer lifetime value? Work with whatever metrics communicate success and lean into that.

Share insights through storytelling and conversations.

Like I said, most of us are in this business because we’re not STEM aficionados. Don’t count on many of your clients being math wizards, either. (If they were, they’d be doing their analytics, wouldn’t they?)

With this in mind, don’t simply send a dashboard to your customers and think you’ve done enough to explain the data. Instead, tell a story that connects the dots. You can do this with the simple formula of saying, “When we execute [this marketing action], we get [these specific results].” Then, discuss with the client how to act in response to the data.

This process of storytelling and discussion makes the information easier to understand. You’ll also communicate your progress more clearly to your clients.

Automate your insights.

This data stuff should make your life easier, right? Get more out of your insights by recognizing the patterns that produce results and by automating a response to take advantage of the opportunity.

For instance, an email campaign that gets a high click rate should have an effective follow-up. Retargeting programs are ideal for this kind of automation, saving you time and conserving your efforts.

Don’t give up.

The expanding world of data analytics can feel overwhelming, but keep at it! Your continued investment in the field will pay off in valuable intelligence that gives you a competitive edge.


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