Contents
What are associative conversions
Associative conversions (AC) are intermediate visits to a website that occur through various traffic channels. They do not lead to the primary conversion, such as a purchase, application, or call, but play an important role in the customer decision-making process. For example, a user may visit the site through a link in an email newsletter but does not make a purchase. However, if they later return to the site through a search engine and complete a purchase, the visit from the email newsletter will be recorded as an associative conversion.
Another scenario involves a user seeing targeted advertising on social media, visiting the site, browsing products, but not making a purchase. Later, they encounter contextual advertising that reminds them of the products they viewed. If they revisit the site, read reviews, and then make a purchase through a search engine, the visits from the advertising sources will be considered associative conversions, while the search engine will be the source of the primary conversion.
The importance of associative conversions in the sales funnel
The situation where a customer makes a primary conversion on their first visit is ideal, but in practice, it is quite rare. Typically, users require several touchpoints with a brand to make a final decision, especially for products with a long sales cycle, such as home appliances or real estate. In such cases, customers thoroughly research offers, compare prices, and read reviews.
Often, while users are researching a product, they may land on the company's website through different channels: advertising, social media, retargeting, email newsletters, and media articles. Although not all these visits lead to a primary conversion, each has its significance and influences the final decision of the customer. Therefore, it is important to consider which traffic sources contributed to the purchase decision and what the intermediate steps of the customer were.
Why analyze associative conversions
Analyzing associative conversions allows for a better understanding of customer behavior on the path to purchase. It provides information about which sources customers come from, how many touchpoints they have before the primary conversion, and which channel becomes decisive. This is necessary for a deep understanding of the target audience: where people learn about products and services, what drives them to purchase, and how much time they spend making a decision.
Additionally, analyzing AC helps evaluate the effectiveness of advertising campaigns and allocate the budget correctly. In internet marketing, it is important for all advertising tools to work together. Ignoring associative conversions can lead to improper budget allocation and decreased overall marketing effectiveness. For example, turning off search advertising because it does not lead to conversions can negatively impact sales in the long term.
How to find associative conversions
To obtain the most comprehensive data on associative conversions, it is recommended to use the Google Analytics service. This tool allows for tracking the sources of visits, the sequence of visits, and interactions with the website that led to conversion. However, given the limitations in effect in Russia, it is worth considering alternatives such as Yandex.Metrica. This service provides statistics on traffic sources and allows for analyzing the effectiveness of various channels.
In Yandex.Metrica, you can obtain traffic data, including the number of target actions over a specified period, which will help assess through which channels customers make purchases. Reports on traffic sources will help understand how different channels impact business success.
Attribution models and their connection to associative conversions
When working with associative conversions, it is important to consider attribution models, which define how the web analytics service distributes value among different traffic channels and touchpoints. Yandex.Metrica uses various models: first click, last click, and last significant click. Each of these captures the history of user interaction with the site in its own way and helps understand which sources led to conversion.
By changing attribution models, useful insights can be gained about the customer journey, identifying which channels work most effectively, and optimizing marketing strategy for better results. For example, the "First Click" model allows you to determine where users learned about the company, while the "Last Significant Click" helps identify which channel was decisive before the purchase.