Attribution

Attribution is a type of analysis identyifing a set of user touchpoints and evaluating the credit that should be given to them in order to reflect their contribution in ultimate outcome. In our case it could be either transaction or any other custom event that ends a customer journey.

Benefits


  • Insight into the channels/methods/sources that result in the highest conversion.
  • Optimized costs on marketing strategies.

Requirements


  • Imported history of events: page.visit and transaction.charge (minimum required history of 30 days, recommended 90 days).
  • Correctly added UTM parameters to the links (medium source).
  • Delivered product feed (optional).

The full list of requirements and the AI implementation schedule you can find at these links.

Main tab


Image presents two bars. The first bar presents 5 enumerated with green circles buttons. The second bar consist of a line chart.
Analysis preview in the form of a line chart
  1. To preview AI Attribution, go to Image presents the Analytics icon Analytics > Attribution.
    Result: The user is redirected to the Main tab in Attribution.

Chart explained


The first chart presents the conversions or revenue (the analyzed element is selected by a user) in a defined period for selectd channels. The users can define the previewed data in the chart:

  1. Channels - Choose the channels which you use in order to reach the customers, for example Facebook, Twitter, Webpushes, Email, etc.
    1. To select a group of channels, click the Channels button.
      Result: A pop-up appears.
    2. In the upper right corner of the pop-up, click Add group.
    3. In the Group name field, enter the name of the group of channels.
      Important: Channels are taken from the UTM parameters of the page.visit events.
    4. Select the channels.
    5. Confirm by clicking Apply.
  2. UTM Medium - The customer conversions are divided into Medium, Source or Medium/Source options. This happens thanks to the UTM parameters in the link through which a customer reaches your site. Therefore, in this place you can specify the UTM parameter.
    1. To select a UTM parameter, click the UTM Medium button.
    2. From the dropdown list, select one.
  3. Device - This option narrow down the UTM parameters into smaller groups that may contribute to additional insights.
    1. To select the device, click the Device button.
    2. From the dropdown list, select one.
  4. Conversions - By default this metric is set to display the number of conversions on the chart, however, users can also select revenue to check the income gained in a particular channel.
    1. To change the metric, click the Conversions button.
    2. From the dropdown list, select a metric.
  5. Path length - Choose the period from which the data will be used to evaluate the chosen metric.
  6. Time range - Determine the time ranges for which the conversion will be counted.
  7. Chart model - Choose one of the models described below.
Chart models explained

  • First-touch - This model gives complete credit to the first channel of the path that contributed to the conversion. Because of that, it gives all the credit on the basis of a single touchpoint, and it will overemphasize a single part of the funnel. The first-touch attribution model does not accredit the campaign that brings conversion nor the interaction after the initial touch.
  • Last touch - This model gives the whole credit to the last channel before the conversion. However, the model doesn’t consider influences leading up to the conversion.
  • Last non-direct touch - This model works like last-touch, but additionally eliminates the limitations of Direct Data. Traffic attributed to Direct is typically defined by marketing analytics as a visitor manually enters your URL at anytime. Every marketing analytics product considers any visitor who doesn’t have a referral source as Direct. A common behavior that gets classified as Direct is traffic from untagged (or improperly tagged) social posts, social ads, or untagged emails. Rather than having its own filter, Direct becomes the catch-all bucket for traffic that does not qualify for any of the other filters.
  • Time decay attribution - A multi-touch model that gives more credit to the touchpoints closest to the conversion. It makes the assumption that the closer to the conversion, the more influence it had on the conversion.
  • Linear attribution - Linear is the simplest of the multi-touch attribution models. It distributes credit by evenly dividing and granting it to every single touch in the buyer journey.
  • Position based attribution - The model is composed of the best features of the linear attribution and time decay models. Position-based attribution allows you to allocate the percentage of a conversion to different channels, based on when the touch occurred (first, last, or in-between). The Position Based attribution is 40/20/40 where 40% of the credit for the conversion goes to the first touch in the date range, the other 40% of the credit for the conversion goes to the last touch and credit for the remaining 20% of touches are split evenly.
  • Sales funnel - Also called purchasing funnel, is a consumer focused marketing model which illustrates the theoretical customer journey towards the purchase of a product or service.
Note: To make full use of a funnel model, you need to provide not only page.visit and transaction.charge events but also product.addToCart events. You can fetch attribution results using API
  • Markov model - This model is one of the most advanced data-driven attribution models. It involves computing the probabilities of transition between different channels in order to find out the probability of conversion when a customer sees your ad in a given channel. To calculate these probabilities, the system uses customer paths that lead to purchase, taking into account channel history order. However, to truly assess the value of each channel, the system calculates the so called “removal effect”. The system estimates how many conversions can be achieved without particular channels and compares them with the total conversion number. As a result, you receive information about the importance for conversion of the deleted channel.
  • Shapley model - This data driven model measures the conversion rate of a given channel and compares it with other channels using the Shapley Value concept from cooperative game theory. To make calculations, it uses customer paths which end with conversion as well as those which were just page visits. We compute conversion rates for all possible customer paths, which consist of at least one channel from a given group.

Table explained


A model comparison table that shows data (in this case conversions) according to the options selected in these fields: Channels, UTM Medium, Devices and Conversions.

Comparison between two models represents the difference between conversions (or revenue) in two models. The system divides the conversion of the first model by the conversion of the second. In other words, the comparison shows how much the conversion of the first model is higher/lower than the conversions of the second model and it is expressed as a percentage of the second model.

You can sort and filter the results for better readability.

  • Click the rows in the Medium column to unfold the results for the elements in the whole group.
  • To filter out results from a specific range:
    1. Hover the mouse cursor over the header of a chosen column.
    2. Click the Filter icon icon.
    3. From the Filter dropdown list, select a range (more than, less than, and so on).
    4. In the text field next to the dropdown list, enter the number.
      Result: The column shows the values that meet the condition in the filter.
Model comparison table
Model comparison table

Adding more comparison models


Add comparison pop-up
An Add comparison pop-up
  1. To add more model comparisons to the table, click Add comparison button.
  2. From the Compare dropdown list select the model.
  3. From the Vs dropdown select the second model which will be compared against the one selected in the first dropwon.
  4. Confirm by clicking the Add comparison button.
    Result: The comparison model is added to the table.

Advanced analysis


In this section, users can preview details about the path customers take right before converting. Based on the selected UTM parameter (source or medium), defined path length and time range, the statistics show:

The advanced attribution tab
The advanced Attribution tab
  • Events in path - This chart shows the number of events in one path. It’s the analysis of the outside traffic right before conversion. Example: According to the chart above, there are over 30 thousands paths that contain only one event.
  • Channels in path - The chart shows statistics about the occurrence of the number of channels on paths.
    Tip: A channel is a transition from one source to the another, for example from an organic to a paid one.
  • Path length in days - The chart shows how long it takes a customer to convert (definition of conversion can vary for every user. A conversion can be a transaction or any other activity performed by a customer).
  • Top paths - The chart shows the most popular channel occurrences on paths.
  • Channel transitions - The table presents the most popular channel transitions.
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