SegmentStream basics

SegmentStream is a marketing analytics tool designed to tackle the challenges of attribution in an environment where users interact with brands across multiple devices and browsers. This article explores attribution models, including single-touch and multi-touch models, and how SegmentStream’s ML post-click and post-view attribution model provides more accurate insights.

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How do analytical tools track your visitors?

In digital marketing, every analytics tool gathers information about user activities — the clicks they make, the products they purchase, and more. These activities are connected through unique, anonymous identifiers stored in cookies. Think of cookies as digital tags that help recognize and track each visitor’s actions on a website. Consider a hypothetical example from a typical e-commerce website. Here, there are 12,458 unique visitors. Visitor 1 made a single click and did not purchase anything, while Visitor 12,458 clicked 3 times and made a purchase worth $100. However, this does not provide the full picture. The actual number of people who visited the website (“Persons”) was 9,546, while the number reported as “Visitors” was 12,458. Why is that? People tend to use different devices or browsers when exploring your website or products. As each device or browser does not share unique visitor identifiers with others, instead of seeing 1 person in your analytics, you might end up seeing 3 “unique users”. Neither SegmentStream nor any other analytics tool can accurately combine these 12,458 separate visitor journeys into 9,546 cross-device, cross-browser person journeys. This presents a significant challenge when it comes to leveraging attribution tools for accurately evaluating marketing performance.

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Understanding attribution

One of the primary goals of marketing analytics tools is to identify which ads, campaigns, and channels contribute the most to conversions, and ultimately, to revenue. This is where the concept of attribution comes in. Attribution comes in several forms:

• Single-touch attribution model — attributes the conversion to a single interaction, typically either the first or the last event prior to the conversion.
• Multi-touch attribution model — gives credit to multiple interactions or touchpoints that a visitor has with your marketing channels before they convert.

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1. Last non-direct click

By definition, Visitor 12,458’s conversion will be credited to the last non-direct channel they interacted with. In this case, the entire conversion and the conversion value of $100 is attributed to Google / Organic. SegmentStream uses Last Non-Direct Click attribution as its basic setting. Unlike other tools, it is flexible. Not only can it ignore Direct channels, but it can also filter out any other channels you want, like all non-paid ones.

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2. Multi-touch attribution model

Multi-touch attribution models (MTAs) are more advanced alternatives to single-touch models such as last-click or first-click attribution. Instead of assigning all credit to a single interaction, they distribute the conversion value across multiple touchpoints in the customer journey. Below is an example showing how a multi-touch attribution model can split credit for a single conversion across different interactions: Depending on the algorithm used, you might see slightly different attribution results. This distribution depends on the chosen attribution model and how it evaluates each touchpoint’s contribution: SegmentStream provides a deterministic Multi-Touch Attribution model that gives credit to each touchpoint based on active time spent on the website and its incremental contribution to conversion. This approach combines cookie-observed conversion paths with calculated visit scores, delivering a more accurate and enhanced MTA. It redistributes the $100 conversion value between Google / Paid and Google / Organic based on which touchpoint had more impact on the final conversion. However, there is still a limitation: Instagram / Paid will never receive any credit for the conversion.

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SegmentStream Visit Scoring attribution

SegmentStream, like other tools, cannot seamlessly join multiple visitor journeys into a single, unified cross-device journey. However, it applies a unique approach. SegmentStream collects detailed information about website activity that occurs after a click on an advertisement and before a conversion for each unique visitor journey. Visit Scoring Attribution uses this data to evaluate the impact of each visit on the likelihood of conversion, based on active time spent on the website and user path. The higher the observed impact on conversion, the more credit Visit Scoring Attribution assigns to that specific touchpoint — even if the actual conversion happens in a later session or on another device. The Visit Scoring model accumulates all conversions and conversion values from all visitor journeys over a certain period. It then analyzes each visitor journey, evaluating each sequence of events and assigning credit to every meaningful touchpoint — even if a visitor did not register a final conversion. SegmentStream aims to distribute credit as effectively as possible. Here’s what happens in the diagram above:

1. Follow the green arrows. The pre-trained model accumulates all the conversions and conversion values from all visitor journeys over a certain period. In this example, 12,458 visitors generated $12,782.
2. Follow the blue arrows. The ML model goes through all the visitor journeys, analyzes each sequence of events, and assigns some credit to each touchpoint. Regardless of whether a final conversion was registered, SegmentStream aims to distribute the credit as effectively as possible. For instance, Visitor 1 gets $0.5 and Visitor 7,654 gets$35, even without any registered conversions. Conversely, Visitor 12,458 gets only $60, even though a$100 conversion was recorded.

You might be wondering why SegmentStream assigns some value to visitors who did not convert and gives less value to those who actually did convert. Visit Scoring Attribution is the only approach that can assign meaningful credit to the Instagram / Paid channel: Now, adding Visit Scoring Attribution to the models table:

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Addressing touchpoints outside your website

Imagine you have started campaigns on social media and YouTube to increase your brand’s visibility. These campaigns aim to get people familiar with your brand. Often, they get people to interact with your brand in many places, not just on your website. These campaigns do bring clicks and interactions to your website. However, these might appear small compared to the huge number of times your brand is seen or interacted with on platforms like YouTube or Facebook. This difference can make it challenging to understand the true effectiveness of your campaigns if it is not properly considered in your attribution models. In these cases, even the advanced method used by SegmentStream’s post-click attribution model may not completely show the full story. Traditional MTA tools could fall short too. You may sometimes notice that the Visit Scoring attribution model attributes more conversions to Direct or Organic channels than a simple last-click attribution does. Here is an illustration explaining why:

1. Person 8,263 watches a video on YouTube but does not click the link.
2. Subsequently, Person 8,263 opens a new browser tab and directly types in the website name, which is recorded as a direct visit.
3. Visitor 10,123, who is actually Person 8,263, demonstrates such a high level of interest that the ML post-click model attributes a significant amount of credit to the Direct channel.

In such cases, the Visit Scoring attribution model attributes more credit to the Direct channel than to the Google / Paid channel. With the last-click attribution model, the entire conversion would have been credited to Google / Paid. Both attribution models provide an incorrect picture.

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Visit Scoring and post-view attribution: bridging the gap

In a perfect world, you would be able to gather user-level data about interactions with ads via an API, and then incorporate this information into visitor journey analysis. However, due to privacy considerations, governmental regulations, and other limitations, ad publishers such as Facebook Ads or Google Ads only provide aggregated information on a campaign, region, and few other levels. Consider the earlier example where the credit that should have been assigned to the YouTube campaign was instead divided between direct and lower-funnel paid campaigns. It would be advantageous to reallocate this credit where it is due. This led to the development of the Visit Scoring plus post-view attribution model. The diagram demonstrates how SegmentStream reallocates credit from unpaid channels, such as Direct, Organic, and email, as well as lower-funnel campaigns like Brand or Retargeting, to the prospecting campaigns. Typically, a prospecting campaign generates website traffic that the SegmentStream Visit Scoring model can evaluate and assign credit to. The advanced Visit Scoring plus post-view model tracks aggregated engagement metrics from publishers, including impressions, video views, and expenditure. The model then finds correlations between these metrics and the credit allocated by the ML post-click model. A campaign with a strong positive correlation receives more additional conversions, while campaigns with no correlation might receive no extra conversions or even lose some that would be reassigned to highly correlated campaigns. As demonstrated in this example, Campaign 1 will gain considerably more additional conversions, while Campaign 2 may not get any extra conversions or might even forfeit some to Campaign 1. However, how to determine what portion of Direct or Organic conversions should be reallocated to effective prospecting campaigns? The truth is, it is not possible to be certain. While website activity and some aggregated metrics from publishers can be monitored, a large portion of context that occurs beyond the publishers is still missing — such as offline activities, competitor actions, TV/Radio broadcasts, and more. These factors can also influence the credits attributed to Direct/Organic channels. That is why the Visit Scoring plus post-view attribution model includes an adjustment tool. You can create several different attribution models, compare results in reports, and choose the model that best aligns with your business. When incorporating the Visit Scoring plus post-view attribution model into the comparison table, the Google Organic channel receives significantly less value, Google Paid slightly less, while Instagram gains considerably more:

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Wrapping up

• Attribution models are models and they will not always be 100% correct.
• Multi-touch attribution models do not show the full picture because they do not consider customers moving between devices and browsers.
• Using information from beyond your website can make your marketing analysis tool more accurate.
• Having the flexibility to manually adjust your attribution model is valuable. As a marketing professional, you are equipped with a unique understanding of your industry’s specifics, target audience behavior, and ongoing campaigns. The ability to adjust the attribution model manually allows you to apply this expertise and make informed adjustments based on insights that are not reflected in the raw data.