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Emerging trends in marketing attribution and analytics for 2023

Emerging trends in marketing attribution and analytics for 2023

The digital marketing landscape is changing drastically, cookie limitations and tracking restrictions are the number-one factors that motivate the industry to develop. Let's review the top 5 marketing attribution trends for 2023 that dominate the industry this year.
Emerging trends in marketing attribution and analytics for 2023 Olga Garina
Emerging trends in marketing attribution and analytics for 2023
9 min read

Table of contents

The digital marketing landscape is changing drastically due to a number of factors. Cookie limitations and tracking restrictions are the number-one factors that motivate the industry to develop and find new ways to ensure that proper marketing measurement and attribution are still possible.

In this article, we’ll review the top 6 marketing attribution trends for 2023 that dominate the industry this year.

Marketing attribution is a crucial process that helps businesses to understand which marketing channels, campaigns, and touchpoints are contributing the most to conversions. It involves identifying user actions that lead to a desired outcome, and then assigning value to each of these actions based on their contribution to the final conversion. 

Top 6 trends in marketing attribution - how the customer journey looks How a moders customer journey can look

We’ll see a lot of new marketing attribution trends emerging in 2023, but these six are the most valuable ones for the industry. Let’s take a look at them to keep abreast.

Machine learning and AI in marketing attribution solutions

With the rise of a cookieless world, marketing attribution faces a new set of challenges that prevent it from showing adequate results. The increasing use of cookie restrictions and tracking limitations has made it difficult to accurately attribute conversions. 

Firstly, observing the complete customer journey from the first touchpoint to the conversion is impossible in today’s privacy-focused digital environment. Even when trying to complete the same task, users often switch between devices, which makes it impossible to assign fair credit to the initial touchpoints. As a result, these touchpoints are usually highly undervalued, and all credit goes to closing channels.

Machine learning and AI in marketing attribution solutions - solving cross-device journeys

Secondly, in recent years, browsers like Safari, Chrome, and Firefox have introduced new features and technologies to protect user privacy by limiting the lifespan and amount of data that a first-party cookie can store on a user’s device. This prevents long-term tracking of users’ browsing activities.

In this context, Machine Learning and AI-based solutions have emerged as a key trend in marketing attribution and measurement for 2023. By using Machine Learning algorithms, businesses can overcome tracking challenges and accurately attribute value to marketing campaigns and channels. 

AI-based tools like SegmentStream’s Conversion Modeling Platform collects tons of behavioural data from the website and feeds it to the ML algorithm. The algorithm then assigns fair value to every website visit using this data. 

SegmentStream evaluates each website visit immediately and calculates the probability to convert in the future, without needing to wait for the actual conversion to happen to analyse it retrospectively. If the conversion probability is high enough, the platform creates a Modelled Conversion, which can be sent back to advertising platforms as a feedback signal. Then, ad platforms use these signals to automatically adjust targeting and bidding algorithms, ensuring higher ad campaign efficiency.

Machine learning and AI in marketing attribution solutions - automating bidding and targeting

Overall, Machine Learning and AI are a huge trend in digital attribution for 2023. These technologies do tons of work under the hood to compensate for the unavailability of tracking data. As a result, overall AI-based attribution tools produce better results than traditional ones do.

Finally understanding what marketing attribution really is

The concept of marketing attribution can be challenging to understand, taking into account the numerous misconceptions surrounding it. One of the most significant misconceptions is the belief that a single traffic source should receive value from a conversion it contributed to. Unfortunately, this kind of thinking is what often leads to failure for many marketers who are trying to use attribution the right way.

Marketers and analysts become overly obsessed with tracking customer journeys, wanting to know every traffic source or touchpoint a user interacted with before converting. They then proceed to distribute the value from the conversion accordingly. However, this approach is only successful if the entire customer journey is tracked from the first to the last touchpoint.

In reality, it is challenging to track every touchpoint, not to say impossible. As a result, no attribution model can assign value to traffic sources that they cannot see, whether it’s a simple one-touch model or a more sophisticated data-driven one.

Marketing attribution, therefore, requires a more in-depth understanding of the customer journey and the different touchpoints that influence the purchasing decision. 

Fortunately, this understanding can be achieved by simply starting to ask the right question about marketing attribution: “How to distribute the total conversion value between the traffic sources according to their contribution to it?”

To measure the contribution of each traffic source, it is essential to analyse user behaviour patterns and identify which actions lead to the conversion. By doing so, it is possible to gain valuable insights into how different website visits contribute to sales.

More and more marketers are starting to get rid of this misconception, which is a great marketing attribution trend for 2023. Yet, they still need help in treating marketing attribution differently, and this is exactly why we’ve built SegmentStream.

Our Conversion Modelling Platform uses a combination of 1st-party website data and a proprietary Machine Learning algorithm to measure the incremental contribution of each website visit to future conversions and assign a proper monetary value to these visits.

Overall, by distributing the total conversion value between traffic sources according to their contribution, businesses can gain a more accurate understanding of which traffic sources drive the most valuable users to their website. This, in turn, can help businesses optimise their marketing efforts to target high-quality users with the highest potential to convert in the future.

Focus on the quality of marketing data

Another important digital attribution trend is the attention to the quality of marketing data. As tracking restrictions and cookie use regulations become more prevalent, the quality of data obtained for marketing analysis may be compromised. 

As you already know, attribution tools may provide inadequate information — they undervalue opening channels and overvalue closing ones since the whole customer journey cannot be observed, and the final conversion rarely happens within the same cookie. 

Poor input data translates to poor marketing analysis results, which could lead to inaccurate budget allocation decisions. For instance, if analytics tools indicate that Facebook Ads generate few conversions, marketers may cut budgets on this channel despite it having a significant impact on buying decisions.

To support this trend on a journey to quality data and precise analytics, SegmentStream offers its help. With the use of ML algorithms, it analyses each website visit and calculates the probability to convert for each of them. This way, there’s no need to desperately try to observe the whole customer journey and stitch all touchpoints together. Marketers receive adequate data on the go which they can use for proper budget allocation and decision-making.

Additionally, data about conversions is sent back to ad platforms as feedback signals, and the signal quality matters. While website conversion is almost always the final goal for e-commerce brands, leads who have completed a form on a website might often end up being disqualified during the sales process. Sales-qualified leads or opportunities from the CRM will be the real goal of the business.

To overcome these challenges, SegmentStream users use a Cascade Value Optimisation approach. This approach involves creating a cascade of different conversions and differentiating them by value, including Modelled Conversions, Website leads with mid-value, and Qualified leads from CRM. By assigning a particular value to each conversion, it is possible to prioritise conversions while maintaining their relevance.

Focus on the quality of marketing data - Cascade Value Optimisation approach

With this trend growing strong, we’ll see how marketers finally find a way to receive quality marketing data despite all data regulations.

Constantly addressing the ongoing changes in privacy

Living in a cookieless world, marketers have no other choice but to find ways how to address all challenges related to data regulations and tracking restrictions. Looking for alternatives and ways to overcome these limitations is also a huge marketing attribution trend for 2023.

The Internet is developing and users want to have a more privacy-centric experience on the web. As a result, companies like Apple, Mozilla, and Google have taken steps to protect user privacy by introducing new features and technologies.

Most browsers limit the lifespan of first-party cookies to a certain period, typically around 7-30 days, after which they expire and get deleted automatically. This is to prevent long-term tracking of users’ browsing activities and protect their privacy. For example, Safari sets a default lifespan of 7 days for all first-party cookies, while Chrome and Firefox allow cookies to persist for up to 30 days by default.

Ongoing changes in privacy - browsers limiting cookies

As tracking restrictions and cookie use limitations continue to evolve, marketers need to adapt their strategies accordingly. One of the ways is to try alternative approaches to attribution, such as Conversion Modelling provides. 

Even if there’s little data about your customers and you can’t observe the whole journey from the first touchpoint to the conversion, this tool uses ML to assign a fair value to non-converting visits. As a result, marketers are still able to understand which traffic sources drive high-quality users to the website without violating any privacy regulations. The users get a safe browser experience, the marketers get invaluable insights.

Moving from retrospective towards predictive analytics

Another noticeable trend is that marketers switch from retrospective analytics to predictive analytics more often. 

As privacy regulations become increasingly strict and data collection becomes more challenging, marketers are starting to shift from retrospective analytics and attribution approaches to predictive analytics tools, such as Conversion Modeling. Let’s compare the most popular predictive analytics approaches to it.

Multi-touch Attribution (MTA) models, which determine and assign the value of each touchpoint on the journey to conversion, have been limited by the inability to observe the whole customer journey. This results in imprecise data and an incomplete understanding of strategic decisions. 

Retrospective analytics - even MTA doesn't provide adequate results

Similarly, Incrementality Testing, which measures the incremental value of a marketing strategy through A/B testing, heavily relies on cookies and requires that the whole journey including the conversion happen within the same cookie, which is impossible now. If this condition isn’t met, the approach can’t provide fair measurement results, and the experiment’s accuracy is limited.

Marketing Mix Modelling (MMM) is useful for retrospective analysis, assessing all budget allocations into digital and offline marketing channels and their performance. However, MMM’s applications are limited to just a few situations, and they cannot help assess the impact of marketing activities on the go or on a micro-level.

On the other hand, Conversion Modelling uses Machine Learning algorithms to assess the impact of all marketing activities and predict the probability to convert for all website visits, even when actual conversions are impossible to observe. It is a great example of a predictive tool that can help when other attribution models and marketing measurement tools give up, such as when conversion paths involve cross-device interactions, or when initial cookies expire.

Switching from underperforming retrospective tools to modern predictive ones is a huge step on a journey to adequate marketing measurement even in a cookieless world, and it’s great to see that more marketers are stepping on this path.

Focus on real-time analytics

Real-time analytics is becoming increasingly important for marketers to stay competitive in today’s digital landscape. Obtaining timely and fresh data can provide a significant advantage when making decisions such as budget allocation. This is a rising trend for marketing attribution in 2023, and it needs adequate tools to support it.

Conversion Modelling is that one tool that can assess the performance of all channels in real time. All website visits are assessed immediately, and marketers can analyse the performance of all channels right away.

The importance of real-time data extends beyond just marketers. Ad platforms also benefit from real-time data provided by Conversion Modelling. When the tool assesses website visits and determines a high probability of conversion, a Modelled Conversion is created. This conversion is then sent back to the advertising platforms as a feedback signal, which is used to train Smart Bidding algorithms to enhance bidding and targeting. 

Focus on real-time analytics - Conversion Modelling for analytics and ad optimisation

The bottom line

As the marketing landscape continues to evolve, it is becoming increasingly important to embrace new technologies and methodologies. All these processes could be observed by detecting the most prevalent trends in the marketing attribution and measurement industry.

The top 6 attribution and marketing measurement trends for 2023 are:

  • Rising demand in Machine Learning and AI technologies;
  • Understanding what marketing attribution truly is and getting rid of the misconceptions;
  • Focusing more on the quality of marketing data;
  • Finding ways to address rising tracking restrictions and privacy regulations;
  • Adopting predictive analytics tools instead of retrospective ones more often;
  • Focusing on real-time analytics to gain competitive advantage.

By understanding the limitations of traditional approaches and embracing new ways of thinking, marketers can stay competitive and make data-driven decisions that will empower growth for the business in the future.

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