A Healthy Data Pipeline: The Key to Solving Ad Spend Waste

Across the board, marketers could be spending far fewer dollars and getting the same levels of business outcomes. Said differently, they could be spending ad dollars more intelligently in digital to increase ROI and create a more consistent return on ad spend. 

August 11, 2022

Ad spend waste is a persistent challenge for digital advertising. Dmitry Nasikanov, CTO at Improvado, discusses that a healthy data pipeline can be the solution to the problem of wastage in advertising. Read on to know more about wiser ad investments with higher ROI.

Back in 2017, a company with one of the largest marketing budgets in the world, P&G, cut its digital ad spend by $200 million and saw absolutely no change in their business. 

Even more mysterious, it saw a 10% increase in reachOpens a new window in the same period. Chase, eBay, Uber and a handful of other major advertisersOpens a new window also cut back on millions of dollars of advertising that year and, in the following year, saw almost no measurable difference in their bottom line. Hundreds of millions of dollars spent on digital advertising were producing zero value for these companies. In other words, it was wasted.

 That was five years ago. Things are different now, right? Not really. Research from IPA in the U.K. last year estimates that between one-third and one-quarter of all advertising is wastedOpens a new window . Another survey from Commerce Signal estimates that 40% is wastedOpens a new window .

See More: Leading First-Party Data Targeting with Consumer Trust

That’s a Lot of Waste

Sure, some of it’s bots or click fraud, but the real culprit is that big numbers of ads and clicks do not translate into more sales, and too many companies just don’t have a real handle on what really works. And even if that spending isn’t entirely wasted, it’s unattributable – which means you still have no idea exactly what’s working and why. 

Across the board, marketers could be spending far fewer dollars and getting the same levels of business outcomes. Said differently, they could be spending ad dollars more intelligently in digital to increase ROI and create a more consistent return on ad spend. 

 The reasons for this boil down to a few things:

  1. Growing data complexity: The average enterprise marketing team uses more than 90 marketing cloud servicesOpens a new window . Some departments have more tools than employees. This alone skyrockets the complexity of marketing data pipelines. The data from those sources must be fetched, normalized, formatted, and combined before further analysis.
  2. Unattributed data and analytics misconfigurations: Even if you’re getting a constant flow of data from your marketing platforms, if your web analytics is broken or its performance degraded, you won’t be able to connect leads directly with ad metrics properly; one set of data is happening on your web destinations while other data is accruing on the ad platform, without the two ever meeting to paint a clear picture. 
  3. Delayed insight into ad spend and effectiveness: Even with expertly integrated marketing tools, the effectiveness of an ad spend largely depends on how quickly you can begin to glean valuable insights in early conversion data. A marketing team might launch their campaigns on the first day of the month but may not see a clear picture of what’s working until the 20th day of the month. 

What’s the cost of those 20 days used to establish a baseline for the campaign every time a new campaign is launched? Your guess is as good as mine. 

The Rise of the Modern Data Stack

It is still common for businesses to use CRMs as a ‘single point of truth.’ It seems convenient that leads can be attributed to marketing data directly in a customer-facing environment. However, routing countless data sources into a CRM requires a lot of attention from data engineers and analysts. On top of them, the inflow of marketing metrics holds many conflicting indicators for a single entry and requires an external system to pre-process and harmonize it. The flat lead data becomes messy, too. Each entry would hold the sources, campaigns, and funnel stage data. It’s plural because it’s the omnichannel era, where you get a lead after a good dozen marketing touches. This further increases the complexity of such a setup, dramatically dragging ROI down.

Data warehouses have stepped in to become the new nerve center of marketing organizations. Rather than scattered information across multiple tools, they serve as a central data storage, making it easier to connect vast amounts of data to BI and Analytics tools for further visualization and analysis. Yet, data warehouses alone aren’t a silver bullet to cut on the data complexity. It’s not about piling up data at a central (or cloud) location — it’s about how you do it.

The concept of the modern data stack is about increasing the effectiveness of data operations at scale. At a high level, it’s a modular architecture comprising data sources, extraction/load software, data warehousing, transformation and data science layers, BI tools, reverse ETL, and destinations (such as CRMs). While it sounds like another swarm of instruments, in reality, they come as maintenance-free pieces of software replacing the in-house data pipelines requiring a lot of attention from IT, Engineering, DevOps, and Analytics.

Shifting towards a modern data stack enables marketers to decrease the wasted ad spend to 20%. It solves the data complexity, allows for precise revenue attribution, and provides CRM users with exactly the knowledge they need, not all the existing data.

Knowledge is power and helps turn 80% of ad spend into action by increasing the pace of data analysis and insight generation. What’s even more important is that a modern data stack is easily scalable, thus allowing to reliably increase spending by 10-70% to meet higher ROI and revenue targets. Increasing ad budgeting is now effective since you have a deeper insight into which sources and campaigns drive the most value. Modern data stack is leading to a changing paradigm in how companies organize themselves around marketing and sales. 

See More: 3 Ways to Solve External Data Integration Challenges

Enter the Revenue Operations Model

It’s been projected that by 2025, 75% of the highest growth companies in the worldOpens a new window will deploy a revenue operations modelOpens a new window — and it’s already happening with market leaders. End-to-end revenue operations and enablement offer a continuous feedback loop across the buyer journey, requiring real-time data and reporting to make it possible. Yet, RevOps also comes with its downside — combining marketing, sales, and customer success sources generate even more data subjected to further analysis. The scalability of a modern data stack comes in handy.

Marketing teams spend a lot of time extracting, loading and transforming data, trying to make sense of it so they can report on what worked and what didn’t. But the time (and money) lost to pulling and then reporting on data can be reclaimed and used to deploy additional campaign experiments to learn, refine and improve revenue flow from your ad spend. 

Modern data stack and revenue operations models will change the way companies organize themselves around marketing effectiveness, making for better ROI and far fewer wasted dollars. 

How are you implementing the revenue operations model to reduce ad spend? Share with us on FacebookOpens a new window , TwitterOpens a new window , and LinkedInOpens a new window . We’d love to know!

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Dmitry Nasikanov
Dmitry Nasikanov

Chief Technology Officer , Improvado

Dmitry Nasikanov is the Chief Technology Officer of Improvado, a no-code data analytics tool by marketers for marketers.
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