You may have seen our previous post about attribution analysis – the process of identifying which advertising channels convince consumers to make a purchase. Attribution matters because most big-item buyers – like those looking to purchase heavy equipment – get the majority of their pre-purchase research online from multiple sources, rather than coming into your dealership for the information. The complexity of today’s self-directed consumer sales journey makes attribution an essential element of lead intelligence that can significantly inform your advertising strategies.
In other words, attribution analysis can help you know if a equipment-buyer was most influenced by ads, reviews, paid-search, listings on 3rd-party marketplaces, social media, some other “touchpoint,” or potentially a combination of all of the above. That knowledge can then help you decide how to best allocate your marketing dollars. Yet while it’s easy to say that you want to know which channels should get credit for a sale, there are many different methods for assigning credit to a source.
The 7 primary models of attribution are typically classified into three categories – memory attribution, single-click attribution, or multi-click attribution – and they each come with their own set of assumptions, benefits, and drawbacks. To help you best understand the various approaches to attribution, we’re breaking down the 7 models for tracking equipment consumers’ path-to-purchase:
Memory Attribution: Asking a buyer – either in-person in the dealership or online via a post-purchase questionnaire – where they heard about your dealership, vehicles, or services. This model assumes that the most impactful touchpoints will be the ones consumers actually remember. However, it is very likely that they will not remember every source they encountered and/or won’t be able to accurately describe the degree to which each channel influenced them. This method provides some information for those putting forward no other attribution effort, but because it is virtually impossible to know if self-reports are providing the complete picture, the data you gain from memory attribution is not reliable enough to significantly inform your marketing and advertising strategies.
Single-Click Attribution (Last-Click, First-Click)
Last-Click Attribution: Tracking and giving full credit to the last source a buyer visited before coming to your dealership to make a purchase. This model assumes that – regardless of how many touchpoints they encountered previously – the most recent channel is of sole importance, because it was the one to fully convinced the buyer to make a purchase. This approach is more consistently precise than memory attribution and has traditionally been popular among marketers, but – as a single-click model – only offers a partial view of a consumer’s path-to-purchase.
First-Click Attribution: Tracking and giving full credit to the first source a buyer visited before coming to your dealership to make a purchase. This model assumes that, regardless of how many touchpoints they encountered afterward, the initial channel is of sole importance, because it was the one that convinced the buyer to begin their purchase journey in the first place – without the first source, none of the later touchpoint encounters may have ever happened. However, this is also a single-click model, and only offers a limited view of the sales process.
Multi-Click Attribution (Linear, Time Decay, Position-Based, Data-Driven)
Linear Attribution: Tracking and giving equal credit to every source a buyer visited before coming to your dealership to make a purchase. This model assumes that, regardless of the order in which they were encountered, every touchpoint made a difference in the consumer’s path-to-purchase. As a multi-click approach, this option provides a more complete view of the entire sales journey, while remaining relatively simple to analyze and understand. Of course, not every channel always has equal influence, and more complex models may offer a clearer perspective.
Time Decay Attribution: Tracking and giving credit to sources based on recency. Like the last-click model, this approach assumes that recent channels must be the most important, as they are the sources that ultimately triggered the buyer to make the final purchase decision. Yet this option also recognizes that multiple touchpoints had a role to place in the sales process. So, the time decay model distributes some credit to all channels, with recent sources weighted more heavily than initial sources. For example, before buying a piece of machinery, a consumer may see an ad, then read a review, then view a listing on a third-party marketplace. An equipment dealer using the time-decay model may give the ad 10% of the credit, the review 30%, and the listing 60%.
Position-Based Attribution: Tracking and giving credit to sources based on strategic position. This approach adopts the perspectives of both the first-click and last-click models, assuming that the initial touchpoint which started the consumer down their path-to-purchase, AND the final touchpoint which triggered the actual sale, are BOTH more significant than the channels encountered in-between. So, the position-based model splits most of the credit between the first and last channel, with remaining credit split between any other sources. Using the previous example, an equipment dealer using this approach may give the initial ad 40% of the credit, the final listing 40%, and the mid-position review 20%.
Data-Driven Attribution: Tracking and giving credit to sources based on data. A limitation of the previous approaches we’ve discussed is that they make rigid assumptions about the influence of touchpoints. For example, the time decay model will always value recent touchpoints over initial ones, regardless of what those channels actually are (paid-search, price comparison tool, etc.). In contrast, the data-driven approach uses digital evidence and algorithms to compare sales paths with and without various channels, to determine the actual contributions of those sources, regardless of their position. The data-driven model is the most sophisticated approach, but it also requires a significant amount of digital information to provide accurate results, making it a model that is useful to large companies, but may not work for smaller dealerships.
Whether you sell wheel loaders or balers, understanding and examining attribution data can be a competitive advantage for heavy equipment dealerships. If your dealership is missing out on valuable lead intelligence, give us a call at 1-888-993-4363 to learn more about the essential lead data we can provide.
And stay tuned to the Equipment Trader blog, as our next article will break down specific strategies for using lead intelligence information. Until then, we want to know what you think: Does your dealership use attribution analysis? Which attribution model do you think would work best for your dealership? Let us know in the comments below.
About the Author
Ethan is a Content Curator for Trader Interactive, serving the commercial brands Commercial Truck Trader, Commercial Web Services, and Equipment Trader. Ethan believes in using accessible language to elevate conversations about industry topics relevant to commercial dealers and their buyers.