Improve Return on Ad Spend with Better Data

All the money you put into your ad campaigns won’t get you far without good data. How is ROI impacted by bad data?

Today’s digital marketers understand the importance of customer data and take advantage of instant information to make stronger connections that drive business growth at every opportunity.

Because of this greater emphasis on data, it’s more important than ever to put data quality at the forefront of the industry. Numerous studies have shown just how much advertising ROI is impacted when data quality is poor. 

Let’s dive in a bit deeper and look at these key considerations:

  • What is return on ad spend?
  • Data quality and ROI, by the numbers
  • Why bad data is cost-inefficient
  • The importance of a good data source

What is return on ad spend?

First, let’s take a brief look at what return on ad spend (ROAS) means. This term is another way to measure ROI that focuses on a specific aspect of the business (advertising). ROAS is an important metric that measures the revenue earned from advertising versus its cost.

ROAS shows how effective your advertising efforts are and helps you understand where dollars are being wasted. You want ROAS to remain high, which shows that the effort and money you’re putting into your campaigns are paying off with significant returns for the business. 

To calculate ROAS, divide your conversion value by the cost of advertising. (The conversion value is the revenue earned from each conversion.) The result is a guide to the ROI of that spend.

If you track these numbers, you’ll begin to see that data quality goes hand-in-hand with improving ROI.

Data quality and ROI, by the numbers

It’s easy to say that good data means better ROI. But what do the numbers actually tell us?

So, what is it about bad data that makes it so costly?

Why bad data costs you money

Poor data quality quickly takes its toll on business ROI and ROAS. For marketers, one bad data signal can infiltrate databases and systems, kicking off a domino effect of bad information. When targeting customers based on bad data, those targets become missed opportunities for connection and conversion. These unfortunate effects result in missed sales and lost revenue. 

What’s more, bad data can cause lots of inefficiencies in marketing operations. When business decisions are being made by data analysis, that data must be high quality. Otherwise, decisions will not be based on the latest or most accurate information. This leads to inefficient processes that have to be identified, traced, and resolved later, wasting a lot of time and money on top of the mistakes already made.

The importance of a good data source

Even though data quality is clearly connected to ROAS, many marketers still struggle to find good data. According to Gartner’s Marketing Data and Analytics Survey 2020, poor data quality is still one of the top three reasons why some marketers are not entirely comfortable relying on marketing analytics for decision-making.

The first step to overcoming this obstacle is simply recognizing how much bad data can impact ROI. Next, organizations should establish a data quality management team that oversees key changes like eliminating data silos and integrating the latest data quality tools and technologies.

Another way to focus on data quality is to get right to the source. Bad data can quickly result from too many data sources or a disingenuous source. These dishonest sources are common now that the world runs on big data.

Using a data source you can trust, like BDEX’s Data Exchange Platform (DXP), is a significant part of a successful data quality strategy. Our data as a service (DaaS) platform and identity graph lead the market in quality, comprehensive data. Our BDEX ID Check filters out bad data from the platform so you know you’re always getting the latest and more accurate information. 

To improve your data quality strategy, get in touch with the BDEX team and get started with our data solutions.