Why Aren't More Brands Listening to Their Own Data?
Why Aren't More Brands Listening to Their Own Data?

Why Aren’t More Brands Listening to Their Own Data?


Sheri Cosgrove

Apr 02 2024

Data is the cornerstone of any effective marketing strategy. However, while many companies work hard to obtain actionable data, not every campaign makes good use of it. One study revealed only 47% of companies say they can compete on data and analytics, and just 26.5% say they have established a data culture.  

Below we’ll examine why some brands are slow to embrace data analytics, as well as the foundational strategies today’s digital marketers employ to leverage data for superior marketing performance.

How Data Analytics Unlocks Full Marketing Potential

When properly applied, data analytics can deliver ads directly to qualified audiences through relevant, timely, and personalized messaging — without wasting any excess budget on ads expected to fall on deaf ears. Data also serves as a valuable tool for identifying emerging trends to guide your next campaign and tracking the measurable results of your present efforts.

In short, data is the rock on which the most successful campaigns are based.

Modern advertisers have access to new tech and targeting capabilities built to optimize every phase of the sales funnel, from casting wide nets of brand awareness over new markets down to more dynamic, user-specific promotions (eg. emails urging you to take advantage of free shipping for items left in your digital shopping cart). When data is well-organized and applied through these various marketing channels, customers experience brands that seem to “get” them, their needs, and the things they value in a company.

But tailoring your messaging to resonate with a specific customer segment naturally requires information — who is your audience? How do they spend their time? Which pages on your site do they linger on, and how long does it take them to make a purchase? What do they say about you after receiving their order?

Customers have come to expect a certain degree of personalization from brands and sponsored ads. By understanding customer preferences, behaviors, and demographics (not to mention boundaries when it comes to the use of their private data), companies foster brand loyalty through an authentic, relatable online presence.

Data-Driven Campaign Roadblocks

Despite the obvious advantages of using data analytics to inform marketing decisions, several challenges prevent brands from leaning fully into data-driven strategies:

Data is often complex (at first glance). The sheer volume of data available to marketers can be overwhelming without the right tools and expertise to sift through it all.

Budget constraints. Some businesses see data analytics as an overly expensive endeavor reserved only for the largest corporations.

New processes are tricky to implement. Adapting to any new strategy or workflow requires a cultural shift within a company. Some teams may resist this change due to an attachment to traditional marketing approaches or fear of shaking up their current day-to-day processes.

Data quality concerns. Successful data-driven marketing assumes the accuracy and reliability of the data at a company’s disposal. If this data’s accuracy is in doubt, it becomes easy to disregard its potential value.

Using Data to Launch Your Next Campaign

Companies that ignore or misunderstand data analytics risk sacrificing a significant competitive advantage. Brand marketers must research the right tools and strategies to confidently launch data-driven campaigns and convert their desired audiences.

First, consider your brand’s unique needs and available budget, as well as possible partnerships with marketing agencies willing to share industry-specific expertise and insights to further direct campaigns. As always, advertisers should set clear marketing objectives aligned with distinct, measurable key performance indicators (KPIs) to guide any necessary post-launch optimizations.

Next, invest in training your marketing team to interpret and use data effectively. Data quality is vital for accurate insights, and a well-trained team will be able to filter out inconsistencies or errors — plus, understanding how to extract actionable insights fosters a data-driven culture

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