How can you know which campaigns, or specific channels within a driver recruiting campaign are working? Monitoring where leads and conversions are coming from allows you to see which campaigns or channels are performing and which ones aren’t. Accurate lead attribution leads to more efficient spend allocations.
In the big picture, lead attribution tracks and shows which campaigns are responsible for your leads or conversions. These lead attribution models can also be applied within a specific campaign to pinpoint exactly which channel within the campaign mix is performing. The information may be presented or look slightly different depending on whether you are using marketing automation, a CRM, or an ATS, but the end goal is the same. Find out what’s working, and what isn’t.
Lead attribution tracks and assigns value to the various touch points that a prospect has come in contact with. The value assigned comes down to the attribution model used.
Let’s take a look at several of the most popular and widely used attribution models. In talking through these models, I will be referring to touch points. These touch points are the points of contact that have been made with prospective drivers.
When applying an attribution model to entire campaigns, each separate campaign can be considered one such touch point. When using lead attribution on a specific campaign, each channel within that campaign (Facebook, Search Ads, content marketing) is considered a separate touch point. With that being said, let’s get started.
First touch lead attribution is exactly what it sounds like. When using this model, the first point of contact receives all the credit for the lead or conversion. This credit is given regardless of how many other touch points the prospect engaged with along the way.
This also applies to the time frame of the touchpoints. All credit goes to the first channel (within a specific campaign) or campaign (when comparing multiple campaigns) the prospect made contact with. Most models do, however, use set time limits. For instance, if your ATS uses a 90 day lookback window like Google Analytics does, the credit would be assigned to the first point of contact within that 90 day window.
The general idea behind this model is that no matter what, the first contact made with the prospect is most important.
On the flip side of the same coin is the last touch lead attribution model. With this model it’s the last touch that receives 100% of the credit for the conversion or lead and not the first.
The reasoning behind this is similar to that behind the first touch model. It doesn’t matter how many campaigns or channels within a campaign the prospective driver saw or interacted with, there’s one point of contact that is more important than the rest.
At first glance these “one touch” attribution models seem to make sense. First impressions do matter. That first interaction can set you up for success or failure right from the start. On the other hand, you’re only as good as your last. A prospect could be on the fence and that last Facebook ad or email nudge is what pushed them to commit to your fleet.
However, we all know that very few leads or conversions are truly gained from a single interaction. That’s the whole reason behind tracking traffic and nurturing perspective drivers all the way up to conversion. Realizing this, led to the advent of multi-touch models.
When using a multi-touch attribution model, the credit isn’t assigned to a single campaign or channel, but rather spread out among all the campaigns or channels that are being measured. Under the umbrella of multi-touch attribution there are several variants, each divvying up credit in a different way.
The linear attribution model takes every touch point along the way to a conversion into account. Instead of assigning all credit to a single source, the linear attribution model distributes the credit among all touch points equally. For instance, if your digital campaign had a mix of Facebook, Twitter, paid search, and email – each channel would receive 25% of the credit for the conversion.
This equal distribution holds true when comparing multiple campaigns as well. If a prospective driver engaged with four separate recruiting campaigns, each campaign would receive 25% of the total credit for that driver being hired by the fleet.
The credit is divided and distributed equally no matter how many channels or campaigns are considered. For example, if five campaigns or channels within a specific campaign were touch points, each would receive 20% of the credit.
Another popular multi-touch attribution model is called the time decay model. Where the linear model assigns equal credit to each touch point, the time decay assigns more credit to those touch points closer to the conversion and less credit to those that are further away.
The idea being that the last point of contact pushes a prospect to complete the desired action, and is more important than previous touches. Earlier touches are still considered and receive credit, but are deemed less critical than the last touch.
The final form of multi-touch attribution we will touch on is called position based lead attribution. The position based model values the first and last touches more than the rest. These two touches receive the bulk of the credit and are weighted equally, with the remainder of the credit divided equally among all other touch points.
No matter what method you may prefer, to be truly successful and avoid wasting time, money, and effort it’s important that you use some form of lead attribution to monitor where your leads or driver conversions are coming from.
The single touch models of first and last touch are more basic and provide the least amount of opportunity to gain new information to help modify and optimize campaigns. Single touch attribution models typically work best when dealing with fewer campaigns or channels. With limited options it’s not as critical to measure how effective each specific campaign or channel is. The models are useful in indicating where and when prospects become aware of you and what drove them to conversion.
Multi-touch models such as linear, time decay, and position based all offer more detail and information than the single touch counterparts. The advantage being, this extra information gives you the ability to tweak recruiting campaigns and put more weight and spend into higher performing campaigns or channels within specific campaigns. This added information allows you to see exactly which methods are contributing and if they are performing consistently over time.
When it comes to choosing which model is right for you, it really comes down to your goals and preferences. How you build your campaigns and what information you find most important will ultimately point you to the model that is right for you.
