With supply chain challenges and an ongoing driver shortage, there’s no time to waste when it comes to finding talent. As a driver recruitment agency, your goal is to get as many drivers hired as possible. But how do you make sure that your efforts are efficient and effective? In order to measure the success of your campaign and determine which channels are working best for you, you should consider implementing lead attribution.

 If you’re looking for ways to increase conversions in your next driver recruitment campaign, consider using one of these three models to identify exactly where leads are coming from: first-touch attribution, last-touch attribution or multi-touch attribution. Keep reading to discover which lead attribution model is right for you. 

Lead Attribution: Knowledge is power

Lead attribution is the process of determining which touches and channels contributed to the conversion event (or the desired action). If you’re wondering why that’s important, it’s because it helps us understand what works and what doesn’t work in our driver recruitment campaigns. We can then adjust our strategy accordingly to improve performance and drive better results over time.

Lead attribution can be measured using any one of three approaches: customer journey, engagement metrics, or touchpoint analysis. Customer journey mapping takes all of your interactions with customers across multiple touchpoints and shows how those interactions led up to them making a purchase decision (or taking another desired action). Engagement metrics look at different types of customer behavior over time; for example time spent on site or page views per order etc., while touchpoint analysis also considers specific actions taken by prospects as well as other activities such as phone calls or emails sent/received from sales representatives or ad clicks etc., but excludes things like website visits since those may not result directly in an acquisition activity such as signing up for email lists or filling out a contact form. 

First-touch lead generation model 

While the concept of attribution is relatively straightforward, there are three main models for measuring lead attribution: first-touch, last-touch, and multi-touch. The first model is called “first-touch” because it gives credit to where a conversion originated from—regardless of any other touches that may happen throughout a campaign. For example, if someone clicks on an ad but doesn’t convert until they see your display banner later on in the day, you would give all the credit for this conversion to the initial click. The benefits of this type of model is that it’s a simple way to track leads, however, if you are running multi-touch campaigns — it may not paint the most accurate picture of what’s driving the conversion.  

Last-touch lead generation model 

The second model is called “last-touch” because it gives credit to the touch closest to a conversion event based on when it occurred—regardless of where that touch originated from. For example, if someone clicks on an ad one day and converts two weeks later after seeing your display banner multiple times since then (but never clicked again), you would give all the credit for this conversion back to their initial click since it happened after those two weeks elapsed without any additional interaction between them and your brand. 

Similar to first-touch lead generation, this is a simple way to track leads. However, if you run an extensive social and digital ad campaign and a driver is exposed to these ads while they’re a passive candidate (still employed, or not looking for a job), they may not convert. Then, months later if they become an active candidate (unemployed or job-seeking), and start searching for a job organically, then the Google search will get all the credit. However, the previous exposure to campaigns might’ve boosted their brand awareness or affinity for your company. 

Multi-touch lead generation model 

The third model is called “multi-touch” because it takes all touches into account and determines the most appropriate or weighted credit for each touch in order to assign a fraction of credit to each touch leading up to the conversion point.

We believe this is the best way to track leads because the more touch points you can make with your prospects, the better! Touch points can be inbound, outbound, digital or non-digital (think banner ads versus billboards). There are several ways to measure credit for each touchpoint: linear, time decay, and position based models. 

  • Linear model: In this model, all touches are weighted equally and credit proportionally assigned to each touch along every stage of your campaign. Theoretically, this would result in an equal number of leads being attributed to each touch—which may or may not be the right attribution for your business.
  • Time decay model: In this model, the more recent activity is weighted higher than less recent activity. This means that if you’re using this model, your most recent driver recruitment campaign will have a greater impact on your campaign results than previous drives.
  • Position-based model: The position based model assigns a fixed percentage of credit to top and bottom performing channels based on performance volume and performance rate, then distributes remaining credit proportionately among medium performing channels within your campaign. With this method, you can identify which channel or combination of channels delivers the most new drivers, while also allocating your budget effectively. 

Which one is right for you? 

In the end, there are no right or wrong answers when it comes to attribution modeling. However, multi-touch models offer you the most data and information about each of the channels involved within your campaign. Like we said above, knowledge is power, and using a more holistic lens when it comes to lead attribution will help you best understand which campaign strategies and channels are driving results. If you’re looking for help building campaigns with transparent data and lead attribution results, KJ media can help. Reach out today.