Digital advertising provides the ability to reach people wherever they are with timely and relevant messages. When it comes to understanding the effectiveness of these ads, advertisers have come to expect a direct and complete view of the customer journey, from awareness through conversion.
But the customer journey has become more difficult than ever to measure. People oscillate between multiple devices before buying a single item, which can lead to a disjointed view of consumer behaviour. Even on a single device, different browsers yield different paths to purchase.
At the same time, successful online measurement has been heavily reliant on cookies that log useful information about what happens after a person has clicked an ad. However, whether due to cookie restrictions in browsers or blind spots from cross-device shopping, there are increasing scenarios where it’s no longer possible to observe whether a conversion has taken place. Increased privacy regulation has also imposed strict guidelines for data collection by region.
The result of these challenges is a complex digital landscape with gaps in media measurement.
The key to unlocking accurate measurement
How are you supposed to accurately assess the effectiveness of your marketing investments when there are so many potential blanks in the customer journey? This is where conversion modeling comes in.
Conversion modeling refers to the use of machine learning to quantify the impact of marketing efforts when a subset of conversions can’t be observed. For example, when measuring conversions across devices, there may not be cookies available to link these devices. In this case, you’ll be unable to attribute some of your conversions to the corresponding customers who interacted with an ad. If no modeling techniques are employed, this attribution problem will leave holes in the customer journey, prohibiting you from fully understanding your customers’ paths to conversion. But with a modeling foundation in place, observable data can feed algorithms that also make use of historical trends to confidently validate and inform measurement.
Modeling enables accurate measurement while only reporting on aggregated and anonymised data. This unlocks a full, privacy-centric picture of your customer behaviour, ensuring that your performance doesn’t suffer just because direct measurement isn’t always possible.
A necessity in a cookieless world
Without conversion modeling in place, it’s not just the measurement framework for one campaign that’s affected. There’s an impact on the overall health of your business. Are your campaigns performing well relative to one another and collectively? Is your advertising meeting your target revenue goals? Without a complete view of performance and a strong infrastructure, it will be very difficult to confidently answer questions like these.
Accurate measurement is the essential foundation upon which your ongoing learnings, decisions, and optimisations are built.
Conversely, if modeling is built into the structure of your measurement solutions, it can provide an essential safeguard that automatically fills gaps using data-driven signals customised to your campaigns. Accurate measurement is the essential foundation upon which your ongoing learnings, decisions, and optimisations are built. There’s no choice but to get it right so that you can continue to improve your business outcomes over time.
Let machine learning understand and fill gaps
For years, marketers have experienced firsthand the power of modeling to do more with less data. For conversion data specifically, it’s been employed as a tool to bridge the measurement gap between devices and across the online-to-offline divide.
Now, as gaps increase in the online world, website conversion measurement is able to benefit from these years of automation expertise. What’s more, richness and reach of data remain must-haves for reliable modeling. This means leveraging high-quality data with a comprehensive view across platforms, devices, browsers, and operating systems. Scale should be your top priority when evaluating the right measurement provider for modeling accuracy.
To create an accurate, aggregate view of customer behaviour, machine learning can analyse current observable signals, such as device, date and time, and conversion type, and model across active campaigns. Having this baked into measurement capabilities, powered by a robust data set, removes uncertainty and ensures that reporting automatically benefits from modeling. This automation is particularly important as measurement expectations advance at different paces globally, and you must be ready to withstand quick changes to conversion observability.
Having a strong online infrastructure is important for creating a data-driven environment for modeling and mitigating further conversion loss, even as industry changes abound. The key to achieving this is to implement solutions that can help increase the amount of observable data for your campaigns. Marketers have long known the importance of tagging for trustworthy conversion measurement, and this remains true across platforms. By proactively taking advantage of tools like Google Tag Manager or global site tag on your website, you can ensure that your infrastructure is set up for measurement success on Google Ads and Google Marketing Platform. To this end, you’re not only capturing more conversion data, but you’re creating a stronger foundation for improved model quality when gaps do occur. These tools provide a robust foundation for ongoing measurement and confident optimisations.
We’re in the midst of a measurement evolution, and global ecosystem changes are challenging marketers to be forward thinking and privacy focused.
We’re in the midst of a measurement evolution, and global ecosystem changes are challenging marketers to be forward thinking and privacy focused. This is an opportunity to double down on the power of data-driven marketing so you can continue to capture a complete and accurate view of your business performance, now and tomorrow.