Stiddle - Platform Screenshots [Ecom]

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Stiddle Screenshots For E-Commerce Use Case

Below is an example of Stiddle screenshots for an e-commerce use case. To view screenshots of a lead generation & B2B use case, please click here.

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Dashboards

Dashboards provide a flexible way to view and analyze multiple types of reporting data in one place. This can include ad platform insights, attribution insights, product performance, customer event data, custom conversion goals, and sales data. In the dashboard below, we’ll walk through several key sections. Dashboards are composed of sections and widgets. Sections help organize related data, while widgets display the specific metrics and visualizations you choose. Each widget is highly customizable, allowing you to adjust its size, colors, chart type, and underlying data. To add new data, drag a widget from the toolbar onto the dashboard canvas. Widgets can also be expanded to view the metric in a larger format for easier analysis. As we scroll further down in this dashboard example, you’ll notice additional sections. The next section displays performance insights from Google Ads. This data is pulled in near real time directly from Google and has not yet been attributed by Stiddle. This view is useful for understanding high-level performance metrics such as impressions, clicks, and other non-conversion metrics. It’s also helpful for comparing native platform data with the attribution insights collected by Stiddle. In this section, we’re reviewing key sales KPIs. In this example, a Shopify store is connected; however, Stiddle integrates with all major ecommerce platforms (ie. Magento, WooCommerce, etc.). For custom platforms or more advanced configurations, Stiddle also supports webhooks, allowing sales data and events to be sent in real time. This next section is made up primarily of cost-related metrics, including both attributed and platform-reported data. For example, the Cost Per Purchase metric displays a Stiddle icon alongside the Google icon. This indicates that the metric is attributed and calculated by Stiddle. Metrics that do not display the Stiddle icon are reported directly by the ad platform. In this section, we’re reviewing conversion metrics. These widgets primarily focus on sales-related data and the return, filtered by channel. In the example below, the widgets outlined in the red box represent Conversion Value (Revenue) metrics. The widget on the left displays only the Google icon and does not include the Stiddle icon. This indicates that it is a platform-reported metric and has not yet been tracked or attributed by Stiddle. In this case, Google Ads is reporting just over $53,000 in revenue. However, this does not reflect the full conversion value. The widget on the right displays both the Stiddle and Google icons, indicating that the metric is attributed by Stiddle. As a result, the Conversion Value is significantly higher—just over $95,000—providing a more accurate representation of the revenue driven by Google Ads. In the final section, you’ll see Stiddle Web metrics. This section displays website analytics tracked by Stiddle IRIS. While these metrics are similar to what you might see in tools like GA4, Stiddle differs in how data is tracked and ingested. One of the key differences is that Stiddle automatically identifies and removes third-party bot and crawler traffic from all analytics and attribution data. This provides a higher level of accuracy and a clearer view of real user behavior compared to traditional analytics platforms.

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Attribution

Attribution is the foundation for making profitable and scalable GTM decisions. Within Stiddle, attribution insights can be viewed in several ways, including attribution tables, reports, dashboards, profiles, and even audiences. In this example, we’ll focus specifically on Attribution Tables. In the Attribution Table below, we’re looking at two connected channels: Google Ads and Meta Ads. It’s important to note that Stiddle supports attribution across dozens of channels, including PPC platforms, paid social platforms, DSPs, ESPs, phone, email, SMS, and more—including organic traffic and LLM-driven sources (ie. ChatGPT, etc.). If we take a closer look at the table, you’ll notice several key differences compared to an ad platform’s native reporting interface. In some columns, you’ll see a Stiddle icon. Columns with this icon represent metrics that have been tracked and attributed by Stiddle, while columns without the icon are reported directly by the platform. When comparing Google’s non-attributed ROAS metric to Stiddle’s attributed ROAS, the difference becomes clear. Google reports that for every dollar spent on Google Ads, it generated a return of $10.65. However, the Stiddle-attributed ROAS shows that Google is actually responsible for generating $15.65 for every dollar spent. That’s a $5 increase in return per dollar, representing a significant amount of revenue that is not captured or reported in Google’s native platform reporting. It’s important to note that Stiddle does not always report higher conversion values than the platform. In some cases, the opposite is true. For example, when looking at the Meta (Facebook) Ads channel, Stiddle reports a lower return than the platform. For every dollar spent on Facebook Ads, Stiddle attributes $1.83 in return, compared to the platform-reported $1.69. Regardless of whether the attributed value is higher or lower than the platform’s numbers, Stiddle’s goal is to report what actually happened with the highest level of accuracy and transparency. If we scroll to the right of the table, you’ll see additional metrics. Let’s focus on Total Purchases. Similar to the ROAS column we reviewed earlier, the Purchases column follows the same pattern: metrics with the Stiddle icon are tracked and attributed by Stiddle, while metrics without the icon are reported directly by the platform. In this example, there is a significant discrepancy between the two. Google Ads reports 282 purchases attributed to its campaigns, while Stiddle attributes 436 purchases—nearly double the amount. That’s a substantial number of purchases that are not captured in native platform reporting. One of the key advantages of Stiddle is that every insight is backed by underlying data. In cases like this, where Stiddle attributes a higher number of purchases to Google Ads, you can easily validate the results by drilling into the data. To do this, simply select the attributed purchase count—in this case, 436—to view the individual orders and see exactly who made the purchases. You’ll now see a complete list of every purchase order that Stiddle has attributed to Google Ads. This level of transparency goes far beyond what native ad platforms provide, where attribution data is often hidden in a black box. Stiddle displays every individual order, along with the associated customer, email address, and the weighted distribution of attribution credit across touchpoints. To explore a specific customer journey in more detail, simply select the customer’s name. Stiddle will then open a full customer profile, showing all events and every touchpoint that contributed to the purchase. Let’s take a look at a customer profile for a user who interacted with a Google Ad and was attributed accordingly. Within the profile, you’ll see a comprehensive view of the customer’s data. On the left-hand side, you’ll find core identity details such as name, email, and phone number. On the right, you’ll see a series of widgets. In this example, Shopify is connected, so the widgets display an overview of purchase-related KPIs. Depending on your setup and use case, Stiddle can also surface data from other systems, such as Salesforce or additional CRMs. Below this, you’ll find a traffic overview that includes session analytics, form submissions, and any custom conversion goals you’ve configured. In the lower-left section of the profile, you’ll also see location details and additional contextual information. As you continue scrolling, you’ll see a complete timeline of events and touchpoints the customer interacted with before making a purchase. In this example, the customer engaged with several Meta (Facebook) ads as well as Klaviyo email campaigns. Selecting the dropdown arrow expands each touchpoint to reveal additional details. For Facebook interactions, this includes the specific campaign, ad set, and ad. While this level of granularity is valuable, it can be difficult to make decisions at an individual profile level alone. To explore this data more effectively, let’s click into one of the campaigns. When you click on a campaign, ad set, or ad from the profile’s event timeline, Stiddle automatically navigates back to the attribution table and applies the appropriate filters. This allows you to instantly view performance data for that specific asset in context. From here, you can see a complete list of campaigns, ad sets, or ads across every connected channel. For PPC channels, Stiddle also surfaces individual assets and keywords for deeper analysis. Now that we’ve seen the event touchpoints Stiddle is capable of collecting, it’s important to understand how attribution credit is distributed across those touchpoints. As shown in the profile above, this customer interacted with multiple Facebook ads, Google ads, and email campaigns before completing a purchase. To allocate credit across the full purchase journey, Stiddle uses attribution models. These models determine how credit is assigned to different channels and touchpoints involved in a conversion. Stiddle offers several attribution models out of the box to support different use cases, including standard models such as last click, first click, and linear. However, Stiddle takes attribution modeling a step further in three key ways:

1. Stiddle creates custom AI models post-trained on the brands data
2. Gives transparency and explaines how the model was created in plain english using LLMs
3. Accounts for TOF view-though data to understand the impact on BOF conversions

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People Profiles

Stiddle builds profiles for each person who interacts with your brand and creates an activity timeline of events. These profiles are powered by Stiddle IRIS technology, meaning they can be tracked for up to three years without the use of cookies. This is important because cookies are heavily restricted by most browsers—for example, about 60% of people on Google Chrome opt out of cookies (both first- and third-party). More importantly, even for the remaining 40% who accept cookies, most browsers reset them after seven days, so they do not persist long enough to capture the full customer journey. Stiddle profiles create a single source of truth for every touchpoint, event, and conversion a person has with your brand. Profiles are flexible, allowing Stiddle to ingest and track customer data from many sources. For example, you can track any ad or marketing channel, phone calls, form submissions, sign-ups, calendar bookings, purchases, CRM deal stages, chatbots, and more. First, Stiddle generates an anonymous profile for each unique individual who interacts with your brand. While Stiddle may not immediately know who the person is, it begins collecting data such as IP, location, and every touchpoint (for example, clicking a Facebook ad → viewing a product page → adding to cart). If you click on a profile, Stiddle will show you an overview of the person. In the example below, this person is anonymous, so we don’t yet know who they are. However, Stiddle is beginning to gather data piece by piece. In this example, you can see several widgets. For instance, Shopify is connected to this account, so you’ll see an overview of Shopify insights—this is currently empty because the profile is anonymous. Below that, you’ll find an overview of traffic along with conversion and LTV details. On the left, you’ll see the location of the anonymous profile, along with additional information. As we scroll down the anonymous profile, we begin to see the different event touchpoints the person has made. We can see that this profile clicked on a Meta ad, then viewed a product page. Shortly after, another Meta ad was clicked. The moment an anonymous profile provides identifiable information—for example, by filling out a form on the website, making a purchase, signing up, or booking a demo—Stiddle merges the data and de-anonymizes the profile, classifying it as known. Anonymous profiles do not need to provide identifiable information right away. Since Stiddle continuously tracks these profiles for up to three years, it will automatically identify the person when the time is right and merge all previous event and touchpoint history. Now that this profile has been identified, it is no longer anonymous. We can see a full breakdown of insights related to this person’s history with the brand. As we scroll down, you’ll see more events added to the activity timeline. Stiddle’s goal is to capture every single touchpoint someone makes with the brand. Filtering in Stiddle is very flexible for profiles. You can easily filter profiles—both anonymous and known—using hundreds of properties from event data, ad data, customer data, product data, and more.

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Audiences

Audiences are very easy to build and deploy in Stiddle. There are two common types of audiences: Smart Audiences and Static Audiences. Smart Audiences are built based on your customer data and can be generated with a single click. These are dynamically generated audiences created specifically for your brand. Static Audiences are built using filters to define what you want included in an audience. They are highly flexible and can be customized for any use case. Every audience is given a few key metrics, including gross revenue, AOV, and 30-, 60-, and 90-day LTV cohort analysis. You can also easily view an overview of the audience on the right before it’s deployed.

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Reporting

Reports allow you to query through customer data that Stiddle has ingested, including many types of data such as ad channels, marketing channels, custom conversions, purchases, forms, calls, emails, and more. There are two types of reports in Stiddle. First, we’ll take a look at Flows. Below, you’ll see a Flow. This is a report that shows what’s happening throughout a buying journey. For example, in the example below, we can see what people do after clicking a specific Facebook ad campaign and all the touchpoints that lead to a purchase. Filtering allows you to understand the journeys of visitors based on different scenarios. There are many ways to filter, including by location, touchpoint, page, channel, product, purchase, and more. For example, if we filter by Google Ads, the report will show us what people do after clicking a Google ad. By hovering over the path, we can see key metrics—for example, the number of conversions, conversion rate, and drop-off rate to the next step in the journey. By clicking on any of the steps, Stiddle will show a few options—View Users, Expand by Property, and Expand by Pages. Let’s select View Users. By selecting View Users, Stiddle will show all of the profiles that make up that step. You can then easily build an audience from these people or dive deeper into each profile for a more granular analysis. The other common type of report in Stiddle is called a Funnel. Funnels allow you to understand conversions and drop-offs between different steps in a customer’s journey. For example, you might want to understand the drop-off from the home page to purchase and break it down by which channels generated the traffic and purchases. Let’s do that. Below, you’ll see an example of this scenario. We’ve selected the steps, but haven’t yet added a breakdown by source. In the funnel below, it’s showing the drop-off and conversion from home page → purchase. Let’s take this a step further to understand where the traffic is coming from. By adding a breakdown by source, Stiddle will show the top channels responsible. As we scroll down, we’ll see the full breakdown in a table.