How to centralize data and get closer to your audience
Gathering first-party data could be as simple as creating clearer consent banners and a better user experience. Angus Hamilton of Search Laboratory explains how.
When it comes to analyzing first-party data, it's about focusing on the individual customer / Paul Kramer via Unsplash
Finding innovative ways to utilize first-party data and respond to the tension between effective optimization, personalization and privacy is increasingly important for future-proofing digital strategies.
Talk of centralized data sets and using machine learning to analyze and predict user behavior can seem daunting. But the key is to take small and manageable steps toward this ultimate goal, each step providing incremental improvements in performance.
In recent years, there have been several regulatory, technical and behavioral changes in the industry. GDPR and CCPA are being enforced more rigorously following complaints by privacy campaigners, while technology changes such as the demise of third-party cookies and Safari’s Intelligent Tracking Prevention (ITP) have made tracking more difficult and less accurate.
These changes have raised general awareness around data privacy, meaning consumers are less likely to willingly share personal data.
Offer a value exchange
The first step to overcoming privacy hurdles is building a strategy to collect (quality) data about prospects and customers in a secure, consented manner. Implementing transparent consent platforms and privacy policies will encourage users to share data in a ‘value exchange’ where they get something back from providing their information.
This could be a simple change to make your consent banners clearer and more concise, or offering incentives for users to share their data with downloadable content, discounts, or a better user experience once they’re logged in.
Not all users will consent or log in to your site, and ad blockers (and ITP) will prevent all cookies from working in the way they previously did. It is therefore important to accept these gaps and ensure the data you do have is as rich as possible. Modeling can fill in the blanks where needed.
Utilizing innovative tools for better attribution
A range of technologies have been introduced to ad and analytics platforms designed to help gather information while remaining ‘privacy-safe’.
Before implementing more complex solutions, ensure you are taking advantage of tools such as Google’s Consent Mode, Server-Side Tag Manager in Google Analytics 4, Google Ads’ enhanced conversions, and Facebook’s Conversions API.
Analyzing existing data sets
As you collect first-party data, it’s worth reviewing any existing data sets in your business that could be used to immediately optimize your campaigns. For example, if you sell subscription products, a one-off analysis of revenue data may identify those with the highest lifetime value, which could be used to amend bidding strategies accordingly.
Alternatively, reviewing profit margins on products could help identify those that drive the most profit rather than revenue, and are therefore the most valuable to the business. These are changes which could be implemented in a one-off adjustment to conversion values in your ad platforms.
Joining online and offline leads
The next step toward centralizing data is to join up online and offline data sets. This involves finding an identifier which can be shared between digital marketing platforms and back-end customer relationship management (CRM) and content management systems (CMS). Typically, this will be an encrypted ‘user ID’ of some description; the more successful you have been in encouraging users to share their data, the more accurate this process will be.
Once joined up, you will have a thorough set of data on which to base your online marketing decisions – for example, optimizing users with the best lifetime value. It can also help identify whether leads on your website convert in the way you expect, allowing you to move away from focusing on the number of leads, and towards understanding and attributing their value.
Modeling and machine learning
At this stage you can start to introduce machine learning models to predict behavior based on historical information about existing customers, allowing you to identify which of your current customers are most likely to return, which new prospects are likely to purchase, and even which will become valuable clients.
This can be used to provide data to automated bidding systems, ensuring you attract the most valuable customers. The same data can help shape better and more seamless digital experiences with tailored messaging and site navigation, crafting a bespoke customer journey.
Evolving attribution in this way makes it possible to optimize prospecting toward a return-on-investment target. You’re no longer running prospecting and presenting the results in form fills and transactions; it’s about understanding the value of each user or individual visit, and optimizing based on their predicted value to the business.
Implementing these steps moving forward
With the speed at which data and privacy are changing, doing nothing to your strategy means things will only get more difficult. More gaps will open up in your data and with less reliable information, automated bidding platforms will start to make less-effective decisions, resulting in less successful campaigns.
Using the above steps naturally guides your business down a path of maturity. Once you begin to understand what behaviors create the best and most profitable customers, you can then increase marketing efforts to this audience. This is a significant process that takes time; the key is to start small and scale in complexity.
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