As cookies are phased out, returns analytics will provide additional insights needed for successful targeted marketing efforts.
Personalization is expected to be one of the biggest marketing trends moving into 2024. Forbes explains, “With the vast amount of data available from various touchpoints—be it social media, websites or even physical stores—brands can harness this information using sophisticated analytics. This results in hyper-personalized marketing strategies where content, product recommendations, and even advertisements are customized for individual consumers.”
The issue? Many businesses are still relying on third-party cookies to inform their targeted marketing efforts.
As Google begins slowly restricting website access to third-party cookies in 2024, marketers will lose access to data they’ve previously used to create hyper-focused marketing strategies.
Fortunately, using a combination of zero-party data, first-party data, and returns analytics, marketers can still uncover the valuable insights necessary for successful targeted marketing efforts. That means better customer experiences, higher ROI on marketing spend, and greater net revenue.
2024 Targeted Marketing Hurdles
Data privacy has been a growing consumer concern over the past several years. As a result, the marketing landscape in 2024 will likely be impacted by web browsers phasing out third-party cookies and more stringent privacy regulations. Already, many states and countries outside the U.S. have enacted stricter data privacy and security laws requiring transparent data collection and consent.
Sean Buckley, a data privacy and technology lawyer at Dykema, told CMSWire, “These laws often expand the definition of 'sensitive information' and set higher thresholds for obtaining consumer consent. For example, some new state laws are broadening the scope of what is considered 'sensitive information' to include things like geolocation data, biometric data, and even behavioral data.”
So, how will businesses develop successful targeted marketing strategies with dwindling access to third-party cookies?
Zero-Party and First-Party Data Offer Partial Solutions to Post-Cookie Marketing Strategies
Even before the recent issues with third-party cookies, first-party and zero-party data were reliable sources of information for customer preferences and behaviors.
Zero-party data is willingly provided by customers. Examples of this include the customer’s name, age, location, occupation, brand expectations, product reviews, etc. Loyalty programs or memberships that ask customers to fill out profiles are particularly useful for this type of data collection. First-party data can be gathered from customer interactions (purchase history, email engagement, app usage, etc.).
Since these data are provided by customers or gathered from their interactions, first-party and zero-party data offer more ethical methods of data collection, which can ease concerns about privacy and create opportunities to build trust and loyalty.
However, this can also come with reduced relevance and precision. The extensive information provided by third-party cookies helps create highly detailed, real-time behavioral profiles that are used to develop hyper-focused marketing strategies. Zero and first-party data generate less overall data, and their relevance can quickly deteriorate. That can make it harder to create targeted ads. Plus, incentivizing customers to share data can be challenging.
Cross-Reference Zero and First-Party Data with Returns Analytics For Enhanced Customer Insights
Since returns analytics are gathered directly from customer returns, it’s technically considered a type of first-party data. However, not every business utilizes the wealth of data that can be found within these transactions. Not only does returns data provide a pivotal piece of the customer information puzzle, but neglecting to analyze this data alongside zero and first-party data can leave a lot of money on the table.
Think of it this way. While a product may initially fly off the shelves, a high return rate can still make it less profitable and can negatively impact net revenue. The same thing can be said of customers. A specific group of customers may have a high purchase rate. When that same group regularly returns a product, however, profitability decreases. Analyzing these transactions, however, can uncover unique insights and marketing opportunities.
While zero and first-party data provide reliable customer information, cross-referencing returns analytics with this data offers additional insights into customer preferences, behaviors, and shopping predictions that are vital for targeted marketing. Basically, returns are an important signal of customer behavior and preferences that businesses can’t afford to miss.
For example, imagine that a shoe retailer asks customers to share information about how they use a specific style (running, walking, etc.) and how often they use them. By comparing this information with returns data, businesses can create a more comprehensive understanding of how this particular shoe style is working for different customer types.
Once a business understands which groups will likely have a positive experience with that style, it can more effectively market that product to targeted customers.
Increase Targeted Marketing ROI with Returns Analytics and Machine Learning
While machine learning has technically been around since the 1950s, businesses have only started utilizing it within the last decade. And in the last few years, it’s seen quite a bit of advancement.
When it comes to targeted marketing, its ability to analyze returns data and make predictions is astounding. Machine learning can use returns data to create product-level scoring that can be used to rank products. It can also be used to develop transaction-level scoring that predicts return rates. So, why is that a big deal?
Businesses that use machine learning with predicted return rates, purchase history, product price, and COGs, can better understand the predicted profitability of transactions. This helps marketers understand which customers will provide more value so they can adjust marketing spend for specific customers. Additionally, a combination of product-level and transaction-level scores allows businesses to create automated advertising feeds.
For example, it’s pretty straightforward for Google to show more ads for high-scoring products. When transaction scores are also provided, however, Google can combine that information with its own proprietary data to go out and find new customers who are similar to the ones already receiving high scores.
Prepare Targeted Marketing Strategies for a Post-Cookies Ad Landscape with Returnalyze
Cross-referencing returns data with zero and first-party data will not only assist in the creation of targeted marketing efforts in a post-cookie ad landscape, but it can also lead to better customer experiences, higher ROI on marketing spend, and greater net revenue.
Plus, now you don’t have to navigate this shift on your own.
In addition to access to the Returnalyze Intelligent Dashboard, a partnership with us means we’ll be with you every step of the way. Our experts will help analyze data, identify trends and marketing opportunities, and they’ll even help develop actionable solutions so you can implement this information for maximum impact.
