Maximise Data To Boost Christmas Sales

The cut-off for online identifiers such as third-party cookies is sneaking up on business owners like Santa Claus. If you want to maximise your gift sales 2023 get aboard the data train.

Anita T is an award-winning marketing consultant with 15+ years of experience.

Christmas is just around the bend, and for many retailers, it's the most important season of the year. To make the most of it in 2023, small businesses in particular need to avoid burning through their ad budget by maximise their own data to optimise campaign delivery.

If your business is not securely collecting customer data yet, it's going to impact your bottom line.

First-party data will help you maximise christmas sales

First-party data is the information collected directly from customers and prospects via your own channels such as your website, app, email, social media following or surveys. It can include email addresses, demographic, behavioural, transactional and preference data that can help ad managers segment your audience, tailor your messages, and measure campaign performance.

Third-party data is declining, impacting campaign performance

The use of online identifiers, such as third-party cookies and app IDs are now declining faster than Bitcoin on a bad day. It's been spoken about for a while, but now it's sneaking up on business owners.

Why should you care?

Audience data impacts campaign delivery (aka performance) on platforms like Meta and Google Ads - the platforms that most businesses are still pretty dependent on. These ad delivery platforms have enjoyed significant growth by capitalising on user data. Now these platforms are needing to be "fed" audience data because they're being restricted on data collection methods. That means your campaign returns are becoming more dependent on the data the business or an ad service manager is able to provide.

get on board by keeping your data above board

First-party data is becoming more and more important as cookies are phased out. Unlike third-party data that is collected by external sources and shared with multiple parties, first-party data is owned by you and reflects the actual interactions of your customers. If it's clean, it's super reliable which is exactly what machine-learning is needing in order to deliver relevant ads to consumers.


Before you go gun-hoe uploading (random) customer data to various ad delivery platforms, all DIY, make sure the data complies with local and international privacy regulations as required. This is well, critical, to say the least, not only for your customers but also your returns.

Quality over quantity

Business owners can have the tendency to focus over list size rather than the quality of the list. Focus on the quality. Data quality will impact campaign performance dramatically, whilst high volume with lower value data will be detrimental. Develop an accurate audience that's fit for purpose, or you risk campaign dollars falling into a sink hole of machine learning.

AI may require volume to learn, but it also requires quality data to support performance.

To ensure data quality, you need to follow some best practices, such as:

  • Work with your team or ad service provider to define clear data collection goals and methods. Consider the kind of data critical to your campaign, how you will use it, and how you will or have so far obtained it.
  • Implement a data validation and verification process. Check that your data is correct, complete, consistent, and up-to-date. Use tools and techniques to identify and correct errors, duplicates, outliers, and missing values.
  • Establish secure data governance and clear privacy policies. Assign some serious roles and responsibilities for data collection, storage, access, usage, and protection. Sometimes this can be managed by an MSP. Follow the privacy laws and regulations of your customers’ locations. Obtain consent and provide transparency and control over data processing.
  • Monitor and evaluate data quality regularly. Use metrics and indicators to assess the quality of your data. Track the changes and trends in your data over time. Identify the gaps and areas for improvement.

Many browsers have already blocked or limited third-party cookies by default. Apple's changes to its mobile advertising identifier (IDFA) dramatically impacted digital marketing campaign performance. Meanwhile, Google responded to tightening regulations in the EU by announcing that they will phase out third-party cookies in Chrome by end of year (2023).

The 2023 cut-off is creeping up quick.

The temptation could be to say that ad platforms like Google or Meta "aren't working for my business any more". I hate to be the one to break it to you, but it may be more of a, 'it's not them, it's you'.

We can adapt. You can too. :)

Data tracking changes mean that businesses that have depended on ad platform's data to-date are needing to do more of the hard yards, piecing together data to support campaign delivery, all whilst respecting user privacy. Privacy tightening is likely to continue, and it's why Google is pushing Google ad managers to chase quality aggregated data.

How to create an audience without cookies

As you might expect, there are developing alternatives to cookies being used across marketing. Industry changes include:

  • Universal IDs. These can actually be more accurate compared to syncing multiple cookies to a user across multiple ad partners. This is quite simply because a universal ID is designed to be as it sounds, universal. An individual is allocated one ID tag. This ID is 'hashed' to comply with data privacy regulations, and industry players like Google have jumped onboard. Read more about it on Forbes.
  • Data pools. These are secure environments where advertisers can share their data without exposing individual user information, usually via encryption. In this space the user can create audience segments based on common attributes.
  • Google Chrome’s Privacy Sandbox is providing privacy-preserving solutions for online advertising by using algorithms and APIs to enable ad targeting.
  • Self-serve ad platforms allow advertisers or publishers to create and manage their campaigns based on their own first-party data or the content of the websites or apps where the ads are displayed. They integrate with other platforms (like AdColony or Adobe Advertising cloud) and don't depend on cross-site tracking.

Small businesses can adapt, too.

Small businesses are typically adopting manual aggregation using spreadsheets to collate data from different sources like Google analytics and their Customer Relationship Management (CRM) system. This can be pretty time consuming and error-prone, so automated is definitely the better option if you can set it up. Look into tools like and Improvado that allows you to sync data from Facebook ads, Mailchimp etc.

However, being this close to Christmas, if you don't have something already set-up, data aggregation tools might need to be your New Year's resolution.

Adapt to first-party data requirements

  1. Determine the data you require to best serve campaign delivery
  2. Review your brand's privacy policy + update as required
  3. Uniform data requirements where you can
  4. Clean up your tech stack to serve 1-3
  5. Pull together your data from your determined sources. Eg. website visits, app usage, customer service, surveys, lead gen' campaigns, social media, your email service provider or CRM system.
  6. Check the data is clean - do email details match multiple users for example? Are some profiles no longer engaged? Shift them to a different list.
  7. Refine and export your audience segment according to ad platform formatting requirements.

First-party data is now a critical asset for retailers planning their campaigns. It requires careful planning, management and adaptation to ensure its quality and security. if you want to boost Christmas sales this year, build and review your audience lists to adapt to privacy changes and support AI targeting needs.

Need advice? Feel free to drop me a line.


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