First-Party Data in Retail Media: How to Build Future-Proof Strategy

With growing concerns over data privacy, first-party data has become a much more preferable option for retailers than any data offered by third-party providers. First-party data is directly from customers via eCommerce platforms, mobile apps, physical stores, and service interactions. This allows businesses to execute full control over the gathered information and be confident in its accuracy. But does it really make sense to use first-party data in retail media? How can retailers leverage its value? That’s exactly what we are going to discuss in this blog post.

Importance of First Party Data for Retailers

We shouldn't undervalue the role of first-party data in the retail industry. Generally speaking, such information can fully transform how brands get to know their audiences and what they can learn about their potential and existing customers. This makes all data-powered efforts much more result-oriented.

What are the key benefits of using first-party data?

• More valuable customer insights. Such data provides retailers with a deep understanding of customer behaviors, preferences, and buying patterns. 
• Enhanced personalization and customer experience. With detailed information at hand, businesses can offer a more personalized experience for their customers, including individualized recommendations, targeted promotions, and even content tailored to the needs of each user.
• Increased data quality. First-party data is typically more accurate and reliable as it directly reflects a customer’s preferences and behaviors. This ensures better targeting and more cost-effective marketing.
• Higher conversion rates. First-party data reflects real customer actions. Thanks to this, retailers can optimize their marketing strategies for higher conversion rates. For instance, sending users the right offers at the right time can significantly improve the chances of a successful sale.
• Ownership and control. In the case of using third-party data, retailers need to rely on external providers for all data-related questions, including security. First-party data is changing the game. It belongs to the retailer. The retailer has fully eliminated all issues caused by external dependencies.
• Competitive advantage. Retailers who leverage first-party data can outperform competitors in terms of customer engagement, sales, and loyalty.

How First Party Data in Retail Media Helps to Personalize Interactions with Customers

By collecting and analyzing first-party data, retailers can create rich customer profiles and segments. Here’s what they can learn:

• Demographic data (age, gender, place of residence, etc.). Such information is typically provided by users during account creation or loyalty program sign-ups;
• Transactional data (average order value, purchase frequency, preferred payment methods, buying trends, etc.). These insights are collected based on the purchase history;
• Behavioral information (purchase history, product preferences, website browsing patterns, and search behavior). This can be learned directly from user interactions with retailers' digital facilities;
• Engagement data (how users interact with emails, push notifications, app activity, and social media channels). To get valuable insights, retailers can rely on specific tools for tracking user interactions.

Based on the defined segments (for example, “frequent buyers” and “new users”), retailers can tailor marketing messages. This can include such steps as sending notifications or offering special deals tailored to the needs of different audiences.

First-party data can also help retailers see how customers interact with their touchpoints and map the customer journey. What exactly can they understand?

• How a customer moves from the first interaction with a platform to purchase;
• How people switch between online and offline channels and how they interact with them;
• When customers typically drop off, etc.

Such insights are important for finding pain points and addressing them. For example, if a retailer sees that a lot of users have abandoned their carts already at the checkout step, it’s highly likely that something is wrong with this procedure. Probably there are tech issues or the process itself is too cumbersome.

Apart from this, first-party data can be helpful for predicting customer purchasing behavior. AI-powered analytics tools can process historical data to define the future needs and preferences of different users. Thanks to such insights, retailers can:

• Provide valuable product recommendations;
• Identify signs of decreasing engagement from the side of customers (and deliver tailored offers to retain them);
• Better understand the future demand to avoid shortages of particular items, as well as overstocking.

Ad Spend Optimization with First-Party Data

Retail media networks (RMNs), platforms owned and managed by retailers for advertising purposes, rely heavily on first-party data. Thanks to getting actionable insights into customer preferences and behaviors, brands can adjust their ad campaigns and optimize their expenses.

What can retailers and brands learn about their ad campaigns?

• Conversion rate. First-party data can show how many clicks an ad generates and how many of them result in successful sales.
• Customer acquisition cost. Retailers can calculate the cost of acquiring new customers. This is possible thanks to detecting campaigns that drive first-time purchases.
• Attribution. Retail media networks can help advertisers and retailers connect ad interactions to sales.

With such information, retailers and brands will better understand how, when, and where different ads perform better. By using such insights correctly, they can eliminate potentially unsuccessful campaigns and focus more on those that target high-value customers (people who can generate the most revenue). This helps to achieve a significantly higher return on ad spend (ROAS).

To minimize ad wastage, it is recommended to combine the following strategies:

• Retargeting campaigns. There is no need to target all customers at once. It makes more sense to focus on those shoppers who are most likely to convert (like those who have recently abandoned carts with a big number of items or those who have already made some purchases).
• Exclusive offers. When you have identified top-tier customers, you can introduce loyalty-based campaigns offering discounts and various bonuses tailored to their needs.
• Cross-selling and upselling. You can recommend complementary products or premium options to your existing high-value customers.

Data Privacy and Compliance

Compared to third-party data, the concept of first-party data ensures a more ethical and compliant framework for data collection.

According to major data privacy regulations, like GDPR and CCPA, businesses must get informed consent from customers before dealing with any data from them. Moreover, customers should be able to control their data and decide what types of info can be used for any purpose. Working with first-party data, businesses can meet these requirements.

Though regulatory compliance is highly important, it is just one aspect of ethical data use. Such transparency is also vital for strengthening relations with customers.

People should understand the data collected and its potential benefits. Furthermore, it's crucial to showcase the security measures, such as encryption and regular audits, implemented to safeguard data. Customers should feel that their sensitive information is safe.

Challenges of Using 1st - Party Data in Retail Media

When starting to deal with first-party data, businesses may face particular difficulties. Based on our practical experience in the AdTech and MarTech domains and a deep understanding of their pain points, we can offer the following solutions to the most common challenges.

Challenge 1. Data integration across multiple channels

Retailers often operate across numerous channels, like mobile apps, websites, and physical stores. A customer might interact with a retailer using at least a couple of these channels. Without proper data integration, it will be challenging to connect all these interactions to create a comprehensive customer profile.

Solution:

One of the most efficient approaches will be to implement a customer data platform (CDP). It will consolidate data from various sources to build unified profiles. If you want to learn more about such platforms, you can follow this link to find more information.

Challenge 2: Data silos

Quite often different teams within one company, like marketing, sales, and customer support, manage their own data sets, which may contain duplicate info. But at the same time, there can be serious gaps in cross-functional insights.

Solution:

We recommend centralizing data storage to address this issue and encouraging teams to share data and insights with each other. You can migrate your data storage to cloud-based platforms so that all departments can access it.

Challenge 3: Real-time data utilization

A lot of companies today struggle to use first-party data in real-time. Due to this, they can miss beneficial opportunities to engage customers at the moments when it is required. For instance, customers may abandon their carts because of many reasons, including short-term glitches. In this case, a timely follow-up email can save a sale.

Solution:

It’s vital to employ AI-powered solutions that are able to act on data instantly. The introduction of personalized recommendations, dynamic content, and automated email and app notifications will help you achieve great results.

Challenge 4: Tech infrastructure issues

Some legacy systems and outdated IT infrastructure may not be able to cope with modern tasks related to data collection, storage, and analysis, especially when data volumes grow.

Solution:

To ensure efficient work with data, you need to modernize your solutions and take care of their scalability. Quite often when our customers come to us with such issues, we suggest migrating their systems to the cloud and implementing microservices. This architecture makes it easier to update and scale business software.

Challenge 5. Data quality and accuracy

Due to various factors, like human errors or unstandardized inputs, data can be outdated or incomplete. For example, if one person created several accounts, the related profiles may be fragmented and incomplete.

Solution:

It is crucial to introduce data cleaning processes to regularly deduplicate and standardize data entries. You can rely on AI to find anomalies in data sets that require curation. Moreover, it is possible to implement tools that will detect fragmented data points and unify them.

Efficient First-Party Data Use: The Success Story

It’s always quite inspiring to analyze the results of those retailers who have already started to use first-party data on their platforms.

Let’s take a closer look at how Glossier, a popular beauty brand, is conquering the market by relying on first-party data. It started its journey in 2014, and since then, the competition in the beauty industry has grown drastically. As a result, even with its customer-centric and feedback-powered approach, the company faced some challenges. It became too expensive to acquire new customers by interacting with them only with traditional advertising channels.

To address such problems, Glossier turned to first-party data. The company started to use direct customer insights to enhance its targeting while also staying compliant with privacy regulations.

What data is collected?

• Website interactions;
• Product purchases;
• Social media engagement;
• User-generated content (UGC).

It’s important that the company get to know its customers not only to build interaction with them via ads or special offers but also to change its products based on the real needs of its audience. 

Moreover, the company transforms its customers into its advocates. Today its sales are greatly powered by online and offline peer-to-peer recommendations.

After introducing its new approach, Glossier reported the following results:

• 2.5% increase in conversion rate;
• 3.2% ROAS;
• 17% increase in open rate;
• 4% click-through rate.

Such figures demonstrate that a correctly developed and carefully implemented strategy based on the use of first-party data can bring excellent results.

Wrapping Up

In the retail industry, first-party data has become a highly valuable asset. Apart from providing retailers with deep insights into customer behaviors, it also enables them to change their interactions with shoppers. Now, businesses can design highly personalized experiences and increase the efficiency of their ad campaigns.

However, the shift to first-party data-centric strategies may be associated with specific tech tasks from data integration and compliance to personalization at scale and real-time campaign optimization. 

Here, the support of a reliable tech partner with relevant experience will be of great help. With the right tools and well-prepared infrastructure, you will be able to unlock the full potential of first-party data and drive your sales to new heights.

FAQs

We Help Unlock Answers to Your Questions

What is first party data in retail?

This term is used to describe information that customers share with a retailer. This data can be collected through interactions with the retailer’s own channels and platforms, like a website or a mobile app.

What is first party data collection?

It is the process of gathering information directly from customers or users through their interactions with retailer’s platforms. For example, this data can be collected from various online forms like account sign-ups or subscriptions.

What is an example of first party data?

One of the examples is all the information, including names, ages, email addresses, phone numbers, etc., that a customer provides when creating an account on a retailer’s website.

What is first party data in paid media?

The data collected from users directly is known to be more precise than any data collected by third-party services. In paid media, first-party data is used to improve targeting, personalization, and overall campaign performance.

What is first-party intent data?

This is data that indicates a user’s interest or desire to take a specific action, for instance, to buy a product, subscribe to a service, or engage with content. Such data is collected by a company directly on its own platforms.