Boosting Ad Performance: The Data Advantage

In order to frame our problem, we must first outline the history of the digital landscape to highlight just how competitive it has become. In 1991 there was only one website in the world. In 2001 there were 29,254,37 websites. In 2011 there were 346,004,403 websites, and in 2021 there were 1,930,614,923 websites. That means that in the last ten years alone there has been a 487% rise in competition online.

As more websites have emerged, the demand for online advertising has surged as well. Back in 2009, it made headlines that the keyword "mesothelioma" sold on Google for a remarkable $99.44 per click, setting a record for the most expensive paid click at that time. By 2023, however, the average cost of the top keyword, “offshore accident lawyer,” reached $815 per click—more than eight times the cost from 15 years prior. These figures offer a clear snapshot of the sharp rise in online advertising costs, driven by intensifying competition and the market dominance of companies like Google and Meta.

Should You Trust The Algorithm?

A common dilemma for marketers today is that, due to consumer privacy concerns, they are becoming more restricted in who and how they can target people. They are seeing their costs soar, and being told that they should go broad and trust the 100% automated advertising campaigns such as Performance Max to find their audience.

The problems with this approach are:

Lack of competitive edge: When every business in a market uses the same algorithm for their ads, they all end up targeting the same customer profiles, losing any unique advantage unless they increase their budgets or enhance their landing page quality. This focus on spending means that financial power becomes the primary factor in winning customers—which is exactly what benefits the advertisers!

It still takes time: Despite the promises of rapid advancements in machine learning and artificial intelligence, training a sophisticated algorithm remains a time-intensive process. It can certainly take a few weeks in order for tools like Performance Max to optimise towards your audience. This learning period often frustrates marketers and leads to a level of mis-trust in wondering just how much budget should be spent before the algorithm will deliver the results.?

Greedy advertisers: Conversion is often not linear and someone might click on multiple ads served by multiple providers before converting. This is why you can have a scenario in which you have 2 conversions but only one paying customer, as both platforms will claim they were responsible. Essentially meaning that without data to understand which action had the biggest sway, you end up paying twice.

Optimised towards conversion: Quite often, the primary goal of and ad channels’ algorithm is to maximise conversions. Conversions are often set up by the user themselves and tend to include actions such as forms being filled, items being checked out or sales. This focus on conversion rates can drive short-term revenue and demonstrate immediate ROI, but it often fails to capture the broader picture of customer relationships over their entire lifecycle.?

Conversions vs Lifetime Value

Conversions reflect a transactional perspective on customers. New customer conversions often bring in lower dollar values compared to their potential lifetime purchases, as trust must first be established. It is also true that if you are offering some sort of initial deal through advertising, customers may take this up and then never shop with you again! All of this to say that a first interaction with your brand is not reflective of the longer relationship, and so this is why understanding Lifetime Value is so important.?

So to give you an example; Emily clicks on your website today. She will probably buy something quite small for $20. But over time if she trusts your brand, her lifetime value could be a lot higher, say $2000. And so rather than optimising towards the first click or X amounts of $20, you should optimise towards how many $2,000 you can get.?

Even at a very basic level if you figure out your Average Purchase Value (total revenue divided by total purchases) and then Average Purchase Frequency (number of purchases divided by number of customers) your Customer Value will be your Average Purchase Value times by your Average Purchase Frequency.

You would then figure out your Average Customer Lifespan by dividing your total Customer Lifespans by your Number of Customers.?

And so Lifetime Value = Customer Value x Average Customer Lifespan

Even when you are a new business who might not fully understand the lifespan of your customer, Average Order Value can help to change your thinking on marketing spend.

For businesses with data pipelines, it becomes easier to aggregate transaction histories, average order values, purchase frequencies, and churn rates etc and then apply segmentation to uncover patterns. And so your understanding of exactly what drives Lifetime Value becomes clear.

Using Your LTV Model

Once you have modeled your Lifetime Value (LTV), it typically resides in your data warehouse. To use it for ad optimisation, you need to send this data back to platforms like Google and Meta Ads using a process called Reverse ETL.

Reverse ETL takes data, such as modelled LTV values, from the data warehouse and integrates it into external applications. Here, the expected LTV is sent to Google and Facebook Ads as conversion values, allowing these platforms to optimise campaigns based on projected customer value rather than just immediate clicks or purchases. This approach bypasses dependency on browsers or cookies, enabling a more reliable and privacy-compliant match for improved long-term ad performance.

Real-World Comparisons

173tech have used LTV to optimise ads for a few different clients, we wanted to include them here just to demonstrate the impact, no matter your type of business or advertising budget. In all of these cases the cost of acquisition was significantly lowered, giving us our savings number.

Washclub ( A pickup and delivery laundry service )

Initial marketing spend $86k - Saving 72% - Monetary Value $62k

Momentary Ink ( A D2C temporary tattoo company )

Initial marketing spend $2m - Saving 12.5% - Monetary Value $250k

Petlab ( A D2C pet food company )

Initial marketing spend $8.75m - Saving 40% - Monetary Value $3.5m

What these numbers mean in real life is that for the same budget, these brands can attract more customers. In Washclub’s case literally three times as many customers for the same cost. It is also important to note that the savings outlined here only compare to the previous year, while ever the model is running, these companies are not only saving money in lowering their CAC but also attracting customers who are more likely to spend higher amounts with them.?

Conclusion

Optimising advertising is an effective way to showcase the value of data within your organisation. It directly impacts the bottom line, and acquisition is a key focus area for every business. Many clients see savings of 20% or more through data-driven ad optimisation, meaning that for advertising budgets over $75k, this investment pays off quickly.

Connect with 173tech to explore how we can support you in this area.