Beyond Clicks: How Post-View and Post-Click Analytics Enhance Sales and ROMI

When asked if they engage with online ads, the average internet user might straightforwardly answer, "almost never." Fortunately for advertisers, this isn't entirely accurate. Let's delve into how advertisements truly convert traffic, the intricacies of web analytics, the concept of attribution models, and the crucial role that post-view and post-click analytics play in this process.

The Paradoxes of Advertising

The impact of a marketing campaign on future conversions isn’t always immediately apparent. Advertisers must recognize that the sales funnel is broader than traditionally perceived, with media advertising often operating at the top levels of the funnel. This type of advertising typically doesn’t generate immediate conversions following its launch.

One of the key strengths of media advertising is its indirect or delayed influence on consumer consciousness, particularly for online users. This demonstrates just how effective a campaign can be, even if consumers don’t openly acknowledge it.

To truly understand the effectiveness of marketing messages and refine advertising campaigns, it’s essential to analyze user behavior not only after they click on an ad but also after simply viewing it. This is where post-click and post-view analysis comes into play. To better comprehend these concepts, we must first explore the various attribution models and how post-view and post-click models operate within this framework.

Types of Conversion Attribution Models

Advertisers understand that the path to conversion often involves multiple encounters with different advertisements, such as video ads, banners, billboards, etc. The attribution model determines the value assigned to each of these interactions, helping to optimize advertising campaigns across all stages of the funnel.

Google Ads offers various attribution models, each with its own unique features and applications for evaluating how ads influence conversions.

• Last Click: All credit for the conversion is attributed to the last ad that was clicked.
• Data-Driven: Conversion value is distributed among all interactions that led to the conversion.

Imagine you own the "Smak" restaurant in Kyiv. A user has visited your website several times through ads shown in response to search queries like "Smak restaurant," "restaurant in Kyiv," "restaurant in Kyiv address," and "Smak restaurant in Kyiv reviews." The user eventually books a table after clicking on the last query.

The Last Click model would attribute 100% of the conversion value to the "Smak restaurant in Kyiv reviews" query. In contrast, the Data-Driven model would distribute the value across all the queries.

Choosing and Utilizing the Right Attribution Model

Choosing the appropriate attribution model is critical for analyzing and optimizing advertising campaigns. It impacts the calculation of metrics in the "Conversions" and "All Conversions" columns, as well as bidding strategies.

It’s also important to note that different attribution models work with diverse types of conversions. At Fusify, we have developed a classification that helps accurately validate conversions and lead sources:

• Click-Through Conversion: A conversion occurs when a user sees an ad, clicks on it, and completes a desired action (e.g., purchase, registration) on the advertiser’s website.
• Assisted Click Conversion: A conversion occurs when a user clicks on an ad but doesn’t immediately complete the desired action. They do so later, using another traffic source. This highlights the importance of the initial click, which captured the user’s attention and played a supportive role in the eventual conversion.
• View-Through Conversion (Post-View Analysis): A conversion is recorded when a user sees an ad but does not click on it. However, they later complete a desired action on the advertiser’s website after returning via another traffic source.

Let’s break down how each type of attribution works, starting with post-view analysis.

How Post-View Analysis Works

Post-view attribution, or View-Through Conversion, accounts for ad impressions even if the user doesn’t click on them but later completes a conversion. This is about understanding how ad impressions influence their decisions.

For example, imagine a potential customer visits a website and notices a banner ad for a new smartphone. They see the ad but don’t click on it. A few days later, they remember the smartphone, visit the manufacturer’s website, and make a purchase. The post-view attribution model is designed to capture such instances.

Steps in Post-View Analysis:

1. Ad Impression: The user notices a banner on a website or app. They see it but don’t click.
2. User Tracking: Even without a click, the system, using a pixel tag installed on the site, records that they saw the ad. This is also done using information stored within the user-ID parameter.
3. User Action: Days later, the customer visits the advertiser’s site and makes a purchase or completes another desired action.
4. Data Analysis: The system records the purchase and associates it with the initial ad impression.
5. Effectiveness Evaluation: Based on the data collected, advertisers can determine how many users completed the desired actions after viewing ads, even if they didn’t click on them right away.

Conversion attribution is tied to a specific impression or click within a period set by the account manager, known as the "look-back window." The duration of this window can vary depending on the typical conversion time for the specific product. Google, for example, commonly uses a 10- or 15-day window, but a standard 30-day period is often used.

Within this period, the rule for attributing conversions to clicks or impressions is "clicks take precedence over impressions." Therefore, if a user sees six of your creatives, clicks on one, and later converts, the impressions before the click won’t be counted towards the conversion.

Why This Matters

View-Through Conversion analysis allows advertisers to understand how ads influence conversions, even if the client didn’t click on the banner ad. If the user saw or clicked on an ad within a specified period and made a purchase, the system counts these events. Clicks are prioritized over impressions, meaning if they clicked on the ad, this is primarily considered.

Thus, post-view analysis helps advertisers evaluate the long-term impact of advertising on user behavior. It shows that advertising can be effective even if it doesn’t lead to immediate clicks.

This provides a more comprehensive understanding of how campaigns work and helps in better planning future media activities.

How Post-Click Analytics Works

Post-click attribution (or post-click analytics) considers only those interactions where the user clicked on an ad and subsequently converted. This model demonstrates how clicks on banners directly lead to conversions.

Consider this scenario: A user is scrolling through their news feed on social media and clicks on a local tabloid’s article. They see a video ad for a shoe sale, are attracted to a pair of sneakers, click on a Rich-Media banner or video, and are redirected to the store’s website.

Let’s explore how post-click analytics works by tracing the actions of a potential customer online.

Click on Ad: The user visits a website, sees an attractive ad, and clicks on it. At this moment, the system, using a pixel tag, records that they have visited the site through this specific banner or video.

The pixel works as follows:

1. The script "captures" site users when they land on the target page.
2. The system builds their profiles and tracks their actions on the site.
3. The collected data is sent to a Data Management Platform (DMP), from which the advertiser manages targeted ads, displaying future ads for your product to these users.

This is the first step in the post-click analytics chain.

Tracking On-Site Actions: The user then navigates the store’s website. The system tracks which pages they view, what products they add to the cart, which items they compare, and so on.

Suppose they immediately find the running sneakers they liked, add them to the cart, and complete the purchase. In this case, the system links the purchase to that specific ad click, indicating that the ad was successful, properly targeted, and led to a conversion. Here, we see a classic Click-Through Conversion.

However, users aren’t always so decisive. Hesitation is common in e-commerce. Let’s say the user viewed the sneakers but decided to think it over or check out competitors. A few days later, they return to the site and make the purchase. Since the user returned within the set period (typically up to 30 days), the system still attributes this purchase to the initial ad click. Here, Assisted Click Conversion comes into play.

Finally, the least desirable outcome for advertisers is when the user clicks on the ad, browses a few models of sneakers, but doesn’t make a purchase and leaves the site. The system notes that there was a click, but no conversion. This signals that it may be time to change the landing page design, refine the offer, or provide a discount to those who didn’t complete their purchase.

Why This Matters

Post-click analytics help advertisers understand not only how effective their ads are in driving visitors to the website but also in influencing their subsequent actions.

Post-View and Post-Click Analytics: A New Dimension for Media Advertising

If we only focused on conversions immediately following ad clicks, we would miss an important aspect of display advertising’s influence. Users don’t necessarily need to interact directly with an ad to prove its effectiveness. When a website visitor sees your ad, the brand remains in their memory, even if they don’t take immediate action.

Unlike search advertising, which functions as a pull-marketing mechanism, display ads are almost always associated with push-marketing.

This is why, after implementing Post-view and Post-click analytics, DSP-driven advertising becomes valuable not only for increasing brand awareness and conducting brand recognition campaigns but also for boosting conversions.

By conducting comprehensive campaign analysis, you can assess the cost of a desired action, considering all user interactions. In DMP, not only are post-view data from display ads collected, but other traffic sources leading to the site are also considered.

This approach helps identify KPIs crucial for the client and understand which factors influence the final cost per action (CPA). This provides a more complete picture of campaign effectiveness, enabling clients to optimize budgets and create funnels with more predictable conversion outcomes.

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