4 common barriers to collecting first-party data

Last updated: December 20, 2023

In the last few years, first-party data has transformed from a competitive edge to an absolute must-have: According to a new report from PILOT, the National Association of Broadcasters’ innovation arm, broadcasters stand to lose $2 billion if they don’t implement effective first-party data strategies.

But despite these high stakes, companies still struggle to gather and use first-party data. And when they do have it, they struggle to implement it across their organization, let alone use it to improve customer experiences.

41% of marketers at large companies (100+ employees) struggle with data accuracy, per Nielsen’s Annual Marketing Report. And more than half of the brands (56%) said they are “below average,” “average,” or “average at best” at using their first-party data, but only 5% of them believe they are using data to create more relevant experiences for their customers, according to research from Google’s APAC division.

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If that sounds familiar, stay with us: We’re presenting the biggest challenges companies face when implementing a first-party data strategy — and how to overcome them — below:

What are the biggest challenges of creating a robust first-party data strategy?

1. Data is siloed across tools

Companies are taking in more data points than ever. But all that data won’t do you any good if it’s just sitting in a spreadsheet — or scattered across different software tools.

Marketing, sales, and support teams all take in customer data from a variety of sources, and store them in separate databases, each with different and inconsistent naming conventions. For example, your email clicks live in your Email Sending Platform, while your website visits live in your CDP or analytics platform.

As a result, companies don’t have a complete view of their customers — or the information they need to give them the personalized, cross-channel campaigns they demand.

So how can you get your whole team on the same page? Use a CDP.

Customer data platforms take in customer data from every source (print, website, events, mobile, etc.) and compile it into one central database for all teams to use. This way, every team in the business gets a single view of each customer — and they can create more personalized customer experiences for each one.

2. Lack of understanding of data

Much of the reluctance to adopt data comes from an organizational lack of data literacy. Below are some ways to increase data literacy across your company — and maximize the value of your first-party data.

Make sure there’s a clear owner for each channel — or consolidate all your channel data into one CDP: You can’t take charge of your data if you don’t know who’s responsible for managing it. And when everyone in your company is working with data, everyone might think the responsibility is someone else’s.

Ask around your organization to figure out where you’re taking in customer data, from your website and email to events, ads, etc. From there, decide who is responsible for managing and resolving data from each channel, then how it’ll end up in your system of record. This ensures that everyone on your team knows where data is coming from and who’s responsible for keeping it clean and actionable.

Encourage questions about your data interpretation: Take it from a journalist turned content marketer: Learning about data can be intimidating — and it can keep your team members from understanding and acting on it.

Counter this by incorporating data literacy into your onboarding process for every position, not just your engineers. Also encourage questions about how you use your data and why.

By asking questions like “What does this measure?”, “How can we use this to learn more about our customers?”, etc., you encourage your team members to see data as a roadmap for connecting with your customers — not just a series of numbers.

Invest in data cleaning, hygiene and standardization: Data is only as good as the insights you can get from them. If it’s not prescriptive, it’s worthless.

So if your customer data isn’t labeled consistently or your profiles are incomplete across channels? Or you somehow end up with multiple profiles for the same person? You’re going to have a lot of data points that you can’t act on with any reliability.

That’s not going to make it any easier to understand your data.

So before you start shopping form builders or launching a new progressive profiling campaign, you need to ensure that the data you ingest is clean, current and free of duplicates. There are many ways to do this:

CDPs eliminate duplicates before they’re stored in your system with automated identity resolution workflows. For instance, Omeda uses a combination of exact and fuzzy matching to spot potential duplicates and indicates the likelihood that they belong to the same person. Just review the two profiles side by side, then decide whether to merge or keep them separate. That simple.

Invest in a CDP with automated field mapping. This ensures that you’re only taking in the data points that matter most to your business.

Establish a set of “brand guidelines” for data labeling so every data point is uniform and can be easily used across teams. This will reduce the need for data cleaning after it’s collected — and reduce the likelihood of quality issues. (Check out even more data cleaning best practices here!)

3. Ineffective team setup

To capitalize on your customer data, you can’t just pull it in and then put it in the right place. Each team needs to know how they can use data specifically to meet their own cross-functional objectives (i.e, marketing needs to be able to see which referral sources are most effective, they need a single view of each customer, etc.).

This is a big stumbling block for many companies: According to research from Google’s APAC division, companies most often fail to adopt first-party data due to technological or organizational constraints.

Choosing the right team structure helps your data scientists best support your other teams and, in turn, make the most of their first-party data. Choose between two data team setups: a centralized or an embedded model.

In a centralized model, data scientists all work within a self-contained team. They process different collection, cleaning and standardization tasks from each cross-functional department.

Some advantages of the centralized model include:

Using a ticket model, the data team can easily prioritize projects across the company.
In this structure, the head of data typically has more seniority than they would in an embedded model. They get a broader view of company priorities and can serve as a thought leader on the use of data to accomplish broader business objectives.
Data scientists get more peer interaction, which facilitates upskilling and personal development.

But in an embedded model, data-specific employees work within each cross-functional team, like marketing, sales, etc. Here, the marketing, sales, service, etc., teams each have a dedicated team member that can collect and manipulate audience data — then help them use it to improve their specific customer experiences.

Some advantages of the embedded model include:

This eliminates the barriers between the data team and data consumers (marketing, etc.) and increases data literacy across the organization.
Data scientists have to do less context switching between departments, which increases productivity.
Prioritization is clear across each department.

4. Fear of regulatory risk

In the last five years, different states, countries and continents have passed increasingly tight and complex data privacy regulations. With the rules now changing across locations, and fines reaching the hundreds of millions of dollars, many companies find themselves unsure how to use their data to the best of their ability — without opening themselves up to excess risk.

With Omeda, you can see everyone’s consent status in one place, regardless of whether they visited your website, signed up for your emails, etc. So when someone requests to be forgotten, all you need to do is sign into your database, pull up their profile and press delete. (So all your compliance tasks will be registered across all channels right away.)

Using our Audience Search database, you can query audiences based on their consent status and split it based on location. This way, you can easily separate people who are subject to GDPR regulations from those who are subject to American state- and federal privacy laws, etc.

But those precautions don’t fully eliminate the need for a human touch. Consider hiring a privacy and governance expert to establish data privacy compliance guardrails and good data etiquette. (That’s why we established a Privacy and Data Governance team!)

Proven ways to collect first-party data

Now that we’ve outlined (and countered) the biggest roadblocks to data adoption, start thinking about how you’ll begin collecting and leveraging your audience data. Here are some low-cost, high-reward ways to generate customer data — and use it to improve your customer experiences.

Use lead generation forms. For best results, include 3-4 fields on each form rather than overstuffing your lead forms and turning potential audiences away.
Incorporate polls and questions into your virtual events and webinars.
Re-engage inactive subscribers.
Experiment with different design options and CTA placements on your landing pages to see which options generate more submissions (use Olytics, our website tracking service, to easily evaluate different options).
Implement loyalty programs and/or subscriptions to deepen engagement and learn more about your audience’s interests.
Use preference pages and sign-up forms to segment email subscribers.
Use a CDP to eliminate data silos and manage your customer profiles in one place.

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