How To Use SegMetrics 4.0 Reporting

Overview

With SegMetrics 4.0 reporting we wanted to create a seamless and cohesive reporting system that allows users to explore their data from high-level KPIs down to granular details, all within the same UI. This unified approach enables users to start with basic metrics and progress to advanced reporting as their analytics needs evolve, ensuring they get the right insights without friction.

As a user you are now able to move up and down the levels of specificity within reporting without leaving the current screen. So you should be able to go from a top-level KPI, to drill into a specific sub-segment, to get a list of the people in that segment, and then view a custom journey, and back out again, within the same UI. This allows people to explore data more fluidly, without jumping between different pages and reports to get they data they need to optimize their marketing.

Reporting Organization

Reporting is separated into 5 major sections:

Overviews

Overviews are a type of pre-built dashboard that share the most important data that you need to improve your marketing. Each overview represents a stage of the customer journey so you can find the best information to improve your marketing.

Ledger

One of the core tenets of SegMetrics is trust in the data. If we show you a metric, we’ll show the people that are included in that calculation, so you can be 100% confident in all your analytical data. Clicking on any metric in an Overview, Dashboard or Explorer will open up the Ledger window, that shows every data entry that’s included in that metric.

To get to the Ledger, scroll down to a table and select any of the KPI's there. It will pull up all of the users along with more detailed data.

At the top of the ledger you can see what filters are being used to generate your view.

Customer Journey

Sometimes there’s only so much that aggregate data can tell you, and you want to dive into the specifics of each customer’s journey. Clicking on an email address or contact information will open that contact’s full customer journey.

To get to a customer journey, select any of the people metrics (leads, customers, tagged users, etc). You will be brought to the ledger screen. From there, click any of the individual emails and you will be brought to the customer journey. In here you can see and verify every click, tag, and custom attribute associated with that contact.


Explorer

While the Overviews give a solid foundation for optimizing your marketing, sometimes you want to answer very specific questions about specific subsets of your data. Each widget in an overview can be opened up into a full data explorer, that allows you to slice and dice the data in different ways, add metrics, change visualizations, and even save those explorations for later. We will have more information in the future on all of the features of the explorer.

To get to the Data Explorer, select Insights on the left side where all your report types are located. Then select a saved report or create a new one. From there, you can select the data source, attribution model, and visualization you want to see. The visualization will determine which dimensions and metrics you can chose from. Creating the report within the data explorer has the same overall functionality as creating a widget within our dashboards.


Dashboards

Unlike Overviews, which are pre-built, Dashboards offer complete customization with a wide range of widgets and layouts tailored to your specific needs. They’re perfect for aligning teams, highlighting your brand, and focusing on the data that matters most to your business. Whether you’re tracking KPIs, comparing performance across campaigns, or showcasing results to stakeholders, Dashboards give you the flexibility and control to bring your data to life. For more information on how to create a dashboard, check out this article. (INSERT ARTICLE. IT IS READY TO PUBLISH BUT NEEDS TO BE REVIEWED BY KEITH STILL)

Using Overviews

There are a number of features available in the overviews that are different from standard reports & dashboards.


Filtering

SegMetrics allows you to filter your data on any part of the customer journey, whether that’s demographic information about your users, web pages they visited, or actions they took during their customer journey. Filters are applied to all widgets on an overview page. You can also click any Dimension in a table or report that shows the filter icon to instantly add that filter and drill down into that data set.

Attribution Models

Flexible, real-time, attribution models have always been a big part of SegMetrics. With the new reporting system, we’re clarifying the attribution models and standardizing them across all of our reports, so you can be confident in how attribution works in every case.

  • Full Funnel: Allocate 100% credit across all touchpoint in the funnel. Conversions exist in multiple attributions.
  • First Engagement: Prioritize the first touchpoint within a report's time frame, perfect for lead-nurturing strategies.
  • First Click: Identify the initial touchpoint that led to customer acquisition, crucial for top-funnel marketing.
  • Last Touch: Allocate 100% credit to the last touchpoint before a purchase, great for purchase-driven strategies.

All of these Attribution Models are affected by the Filters being used in the report. If you are limiting your report to only ads and look for First Click then only ad first clicks will be returned, even if there was an organic or social click previously.

If you want to apply filters AFTER the attribution model is applied, you’ll want to use Attribution Model Filters

To select your attribution models, select the "full funnel" model as it is currently set as the default. You may then select any of the four total options to change the view your data. Please keep in mind, adding filters to either the funnel or the report itself will change the metrics for each model.

Attribution Model Filters

You can also apply certain page view filters to the attribution models as well. When clicking the XX icon in a table or report, that value will be added to the attribution filters, which are applied AFTER the attribution model. (Normal filters are applied BEFORE).

Read more about Attribution Model Filters below.

Breakdowns

Each table widget shows a breakdown of the report data to help you quickly identify outliers and re-focus your marketing. On the top-right of each widget, you can choose which dimension is being used for the breakdown. Additionally, you can click the explore icon, next to the dimensions, in the top right of the table. This will open up a full explorer for the current widget, while clicking any metric will open up a ledger

KPI Metrics

Each KPI is a metric that is important for optimizing the part of the customer journey that the overview focuses on. Each KPI Metric shows the current value for the timeframe, as well as a comparison to the previous timeframe. Clicking on a KPI will show a graph of the metric over time, usually broken down by some dimension like channel .

Standard Filters vs Attribution Model Filters

Standard Filters

  • Definition: Applying filters before the attribution model is applied means you are selecting which ads to consider based on their raw, unprocessed data. This filter is applied to the complete set of ad data, without yet determining which ad should get credit for any conversions.
  • Use Case Example: Suppose you're interested in analyzing only your Facebook ads. If you filter down to only Facebook ads before attribution, your dataset will only include clicks, impressions, and other metrics related to those Facebook ads. The attribution model will then calculate how much credit these selected Facebook ads should receive for conversions, based on the remaining data set.
  • Resulting Data: The output will show conversions, revenue, and other attributed metrics, but only for the selected Facebook ads. If a conversion involved both a Facebook ad and another channel (like Google), the attribution model would allocate credit, but only the portion attributed to Facebook would be shown in your report.

Attribution Model Filters

  • Definition: Applying filters after the attribution model is applied means you are filtering the data after the model has already distributed conversion credit across all the ads and channels involved. At this stage, the report reflects the fully attributed conversions, and you're simply filtering to view specific ad performance.
  • Use Case Example: If you apply a filter to view only Facebook ads after attribution, you're viewing the ads based on their attributed value. For example, if a user interacted with both a Facebook ad and a Google ad before converting, and the attribution model assigned 40% of the credit to Facebook and 60% to Google, the report would show the portion of conversions attributed to Facebook ads.
  • Resulting Data: The output will show attributed conversions and other metrics, but you're now seeing the portion of conversions that have already been split across different ads or channels. This may mean you're looking at smaller slices of overall conversions, where the credit for a single conversion is shared among multiple ads.

Practical Example:

  • Scenario: A user sees a Google ad, then a Facebook ad, and finally converts.
    • Before Attribution: If you filter for Facebook ads before attribution, you will only see data related to Facebook ad impressions, clicks, and interactions. The attribution model will then decide how much of the conversion to attribute to the Facebook ad within this filtered dataset.
    • After Attribution: If you filter for Facebook ads after attribution, you'll see the portion of conversions that were attributed to Facebook ads. For instance, if your attribution model gives 30% of the conversion credit to Facebook, then that 30% will be shown in your report, along with relevant metrics.

Key Differences:

  • Scope of Data:
    • Before Attribution: You're narrowing down the raw dataset before any credit is assigned, potentially excluding interactions that occurred outside your filter.
    • After Attribution: You are narrowing down the attributed results, where conversion credit has already been distributed based on the full user journey.
  • Impact on Insights:
    • Before Attribution: This filter might lead to an incomplete picture of the user journey since you're excluding ads that could have influenced the conversion but aren't within your filter.
    • After Attribution: This provides a more holistic view, as you're filtering the results after the model has accounted for all relevant touchpoints.

In summary, filtering before attribution focuses on raw interaction data, while filtering after attribution focuses on the processed, credit-assigned data, offering different levels of insight into your ad performance.

1. First Touch and Removing Clicks

In the current design, using an attribution model will also filter out the clicks themselves from the results. For example, if we use a First Touch attribution model, and there were NO First Touch clicks in the date range, then no data will be returned.

2. Last Touch People are not unique, but purchases are In the current design, with Last Touch

attribution, the PEOPLE will be double-counted if they have a conversion in each table row (they are not unique). For example, if a single contact has a purchase that converted on email and another purchase that converted on paid then each channel would have one leads (would be doubled up between the two), but the revenue would be split to the correct channel.

Still need help? Contact Us Contact Us