The cost of dirty data: Strategies for minimizing ad waste and maximizing ROI

Data is a valuable tool for advertising that can help marketers reach their target audience and maximize their return on investment. However, it's important to use data technology tools effectively to ensure that ad dollars are being spent in the right place. When data is not used properly, it can lead to wasted spending and, even worse, damage a company's overall profitability.

The internet has become an increasingly powerful tool for advertisers, thanks partly to the rich data generated from today's connected world. Marketers can use this data to gain insights into their target audience and more effectively reach them. However, it's important to recognize that not all web traffic is created equal. Most web traffic comes from non-human sources, including computer programs (bots) and synthetic identities. This bot traffic can be particularly problematic, as it can lead to a significant amount of wasted spend if it's not properly identified and filtered out.

Despite these challenges, data is still a valuable asset for marketers. By using data technology tools effectively, advertisers can ensure that their ad dollars are spent in the right place and maximize their overall return on investment. So, to be a successful marketer today, it's important to be data-driven and use all available tools to your advantage.

Data x Ads

Historically, most resources devoted to advertising are channeled through third-party platforms that promise to target specific audiences based on advertisers’ desired characteristics effectively. For example, a luxury car manufacturer seeks to market their product to high-net-worth individuals. An advertiser submits a bid for this traffic, assuming that the targeting provided by advertising platforms is accurate.

Third-party advertising technology companies rely on household or individual attributes that define various characteristics, such as net worth, and associate them with the identification data associated with browser pageviews. Identity data is information that can identify a person, such as an email, name, address, phone number, or mobile ID. Once a person is positively identified, they can be linked to offline insights such as demographics and purchasing interests, among other things. Targeting of this nature has been used for years before the internet through traditional mail-based direct marketing, and demographic data sources that can be matched to a household address are available from various sources.

The linkage between the vast volume of internet page view impressions and specific targeting characteristics is facilitated through this identity data. This identity data is generated on a massive scale when people log in and provide their email, browse with their mobile device, or submit an address for product delivery. Identity data can also be generated by computer programs that simulate human behavior.

So, the primary concern is how accurate this data and the associated linkage are. Even real individuals may provide false information or make data entry errors in website forms. While an email can be definitively associated with web page views (factual), the association between an email and a physical address and the corresponding household demographics might be probabilistic (i.e., modeled). If the underlying data is not properly formatted, standardized, fundamentally flawed, or generated by a non-human source, the accuracy of the targeting can be substantially impacted.

Social media platforms such as Facebook grew to tremendous value. They captured a significant portion of the overall media spend because of their superior ability to demonstrate accurate deterministic targeting due to the insights they gained from their users.

As the advertising technology ecosystem developed to address this issue, there was a shift towards the activation of first-party data or data owned by the advertiser, such as data gathered and stored in their customer relationship management (CRM) systems and marketing automation platforms, rather than the data held in "opaque" advertising platforms. By activating this first-party data, advertisers gain greater confidence that they are targeting the right audience because they can upload their identity data, such as email addresses and phone numbers, into these platforms.

How to improve data value

You can do a few things to get the most out of your data. First, consider using data hygiene tools to help identify and correct errors in your data, such as missing fields or improperly formatted contact information. This can help ensure your data is accurate and up-to-date, which is important for making informed business decisions.

Another important step is to validate the identity of the people in your data. This means ensuring that your contact information is current and associated with a real person. There are services available that can help you with this.

In addition to validating your existing contact data, it's important to consider appending additional contact points. For example, some people use multiple email addresses for different purposes. Adding these additional contact points to your data can improve your ability to reach your target audience through different marketing channels.

Finally, consider appending insights data to your first-party identity data. This can help you better understand your target audience and create more effective marketing campaigns. By using these tools effectively, marketers can improve their marketing efficiency and avoid wasting resources on advertising to the wrong people or bots due to bad data.