Use Returns Data to Increase Ad Revenue

Use returns data to learn where, when, and how to successfully advertise products for customer growth and increased net revenue.

The last few years have thrown several curveballs at advertisers. According to a Hubspot survey, “more than 80% of marketers feel that marketing has changed more in the last three years than in the previous fifty.” And it’s not hard to see why.

Ever-changing social media capabilities, data collection uncertainty, changing consumer behaviors, and the rise of AI have added a newfound complexity to advertising. While navigating the intricacies of this marketing landscape may feel overwhelming, returns data provides an invaluable source of information that can be leveraged when developing advertising strategies.

Analytical insights from your returns transactions can reveal advertising pitfalls and opportunities related to customer behaviors, specific advertising channels, ads related to brand, style, geography, etc.

Read on and learn how your returns data can tell you where, when, and how to advertise products successfully for increased customer growth and higher net revenue.

Use Returns Analytics to Optimize Advertising Across Channels

In 2024, advertising across channels is non-negotiable. Between social media, email, video, and audio, marketers have more channels to choose from than ever before. The survey we mentioned earlier found that “more than nine out of ten marketers leverage more than one marketing channel — and 81% leverage more than three channels.”

While the endless possibilities of omnichannel marketing are exciting, optimizing these channels can be a complicated endeavor. Fortunately, the granular data from returns transactions can reveal essential insights for multi-channel advertising strategies.

For example, it makes a certain amount of sense to promote high-performing products across channels since it can lead to higher revenue. However, it’s not just about top-line sales. If that same product has a high return rate, increasing advertisements for that product can negatively impact net revenue. In this instance, pausing or minimizing ads for that product may be necessary until the product return reasons are identified and resolved.

In addition to identifying pitfalls, returns analytics can reveal marketing opportunities by helping businesses determine return reasons across channels. Imagine that the product in our previous example only has high return rates in one particular channel. Depending on the product return reasons (sizing, fit, color, etc.), creating channel-specific ad content that addresses that issue can help reduce return rates and protect net revenue.

 

Improve Advertisements by Brand, Style, and Geography with Returns Data

Determining the focus of advertising efforts is key to a successful marketing strategy. It involves a deep understanding of your products as well as the wants, needs, and values of your customers. Not only can returns data reveal which products are sold, canceled, and returned, but it can also help you understand why.

For example, fit and sizing issues are one of the biggest contributors to customer returns. While it’s not always the case, these returns can often be attributed to a certain brand or style. Analyzing returns data can help you pinpoint which brands or styles are performing well, which have a high return rate, and even which ones lead to customer growth and retention.

For retailers that carry multiple brands, insights like these are essential when determining which brands and styles to promote (or even de-prioritize) in advertising campaigns.

Returns analytics can even help you understand product performance in relation to geography. With the right return management platform, you can determine how well a brand, style, or even category is performing by geographic location. This gives you the ability to focus ad spending in regions where it will be most relevant while minimizing it in areas that tend to have high return rates.

Improve Targeted Marketing and Automated Ad Feeds with Returns Analytics

With dwindling access to third-party cookies, businesses will need to develop new strategies for collecting information for targeted ads. While zero and first-party data are reliable, cross-referencing returns analytics with this data offers additional customer insights that are vital for targeted marketing.

For example, imagine that a shoe retailer wants to advertise a specific style. First, they may gather zero-party data by asking how and how often they use that style. By comparing that information with returns analytics and first-party data, the retailer can develop a more comprehensive understanding of how this particular shoe style is or isn’t working for different customer types. Basically, returns are an important signal of customer behavior and preferences that businesses can’t afford to miss.

What’s more, returns analytics can be used to develop product-level scoring (the average net revenue of a product) and transaction-level scoring (the predicted profitability of transactions) that can then be used to predict return rates.

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 proprietary data to find new customers who are similar to the ones already receiving high scores.

Optimize Influencer Marketing Campaigns with Returns Analytics

While influencer marketing may not be right for every business, there’s no denying that the industry is still alive and well. According to Forbes, “In 2023, the global influencer marketing industry was estimated to be worth $21.1 billion, while U.S. influencer marketing spending growth was expected to outpace ad revenue growth on almost every major platform.”

Between sponsored posts, traditional paid ads, paid ads from creator posts, and more, there are several ways a business may engage in influencer marketing. That doesn’t mean every strategy will be successful, though. This is where returns data really comes in handy.

Imagine your returns analytics reveal that influencers have the highest return rates of all your channels. Now, you can decide whether to invest as much as you planned or analyze and act on why this channel is driving such high returns. Plus, the granular data from returns analytics can help you understand those product return reasons.

Returns data can identify influencers with both high and low customer return rates, evaluate the performance of SKUs or brands, and it can even be used to cross-reference returns data across channels to verify issues. This creates a more comprehensive understanding of which products, individuals, and strategies will be the most successful in influencer marketing campaigns.

Optimize Advertising with Returnalyze for Increased Net Revenue

Before the added complexities of recent years, it was already a full-time job to develop, implement, and assess advertising campaigns. However, simply throwing darts at the marketing wall isn’t an option. That’s why data-informed advertising strategies are essential.

The Returnalyze Intelligent Dashboard gives you access to a number of datasets that can be cross-referenced to uncover advertising issues and opportunities. However, granular data of that nature is only as good as the experts that analyze it. That’s why a partnership with Returnalyze comes with step-by-step guidance and analysis from our data experts.

The data is already there. Let us help you effectively leverage it to develop data-driven solutions that make your advertisements more profitable.

Schedule a demo or contact our team today.