From Insights to Impact: How Analytics Will Enhance Your Recruitment Marketing

As Shaker’s VP of Analytics, one of the most common questions I receive from talent leaders is “How can I leverage data effectively so it impacts my hiring objectives?” They’re often under pressure to meet stringent quotas but are limited by budget and time constraints. They know they need to invest in data but have a full plate of other activities and need help putting the pieces of the puzzle together.

This is where a well-developed analytics strategy can shine.

Every strategy, though, needs a strong foundation to be successful. I had the opportunity to speak on this topic at LEAP HR Retail in Austin, TX, where I laid out what we believe at Shaker to be building blocks of an effective and high-performing analytics strategy.

Step 1: Set Goals 

First, we need to set goals based on our specific needs. Data can revolutionize strategic planning but if it’s not tied to a goal, it won’t be effective. We have a higher chance of achieving our goals if they’re SMART – specific, measurable, achievable, realistic, and timely.

The difference between a non-SMART goal and a SMART goal is like saying “I need some stuff from the grocery store” instead of “I have $15 to spend on butter, eggs, sugar, and flour to make cookies for a party on Saturday.” Although well-intentioned, the first method probably won’t get us the results we need, and we’ll likely exceed our budget (especially if we’re shopping hungry).

So, rather than saying we need “more” applications or “as many as possible,” we should look at historical data, sales forecasts, annual business plans, and other sources to determine what exactly will satisfy the needs of the organization. And let’s not stop there – qualitative data matters as much as quantitative data. Do you have emails from colleagues from a year ago telling you how their store is suffering, and they need more hires? That’s data we can leverage in understanding our future needs.

Step 2: Document Our Needs 

Once our research and goal setting are complete, we’ll have a pretty good idea of what our core needs are. It’s important to document them to have something to work against and reference (it also helps with establishing benchmarks, but more on that later.)

There are four primary aspects of documentation that we look at to inform our data strategies. 

Budget 

Everything starts here. Budget is a true arbitrator to what we can and can’t put into motion. 

Peak hiring periods 

What are your baseline needs? Is there a particular point of the year that you need to ramp up hiring?  

For retail clients, the holiday season often calls for increased hiring. To show the impact of this seasonality, I showed the following example where my team and I pulled data for all retail associate roles in 2023 for one of our clients. There were 23k jobs in Q1, and this increased to 43k in Q4 that were actively sponsored on Shaker’s network. There was almost a 100% difference in the amount of activity. In Q1, the average CPA (Cost Per Acquisition) was $7, and the average was $20 in Q4. This is a huge difference – three times as high on average. This demonstrated how seasonality impacts competition and how much it could cost. 

 

Pin Down Specific Locations 

Where a job or job set is located is a factor. Whether urban, suburban, or rural, each has issues that will impact the strategy. If we’re a retail chain and we’re hiring in a rural area where most people live 20 miles from our location, our targeting would look different than if we’re hiring in a more populous city, where there could be more candidates in a smaller radius. Changing your view can affect our insights – and, as a result, our costs. 

We see this in action in the example below. The left-hand side shows CPA by state. We can see that it’s vastly different if we narrow it down to metro areas on the right-hand side. The size of the dots represents the number of applications, and from the graph we can see that in half of the country, there’s little activity. This is something we wouldn’t be able to tell at a state view level. Location, location, location – it matters dramatically! 

Role Categories 

The roles themselves also matter. This is more than exempt/non-exempt – difficulty-to-fill varies by role and that must be considered when planning. In the chart below, the left-hand graphic shows retail associate positions distributed in metro areas. There are few in low populated areas. In contrast, driver jobs in the right-hand graphic are much more concentrated. There’s far less, so you’d expect less competition, but because they’re so focused on certain areas where candidates are limited, the competition is greater. 

Step 3: Set Our KPIs 

Finally, KPIs. It’s critical to look at the bottom of the candidate funnel and the metrics that signal success, according to our goals. The three we like to look at are applications, quality applications, and hires. These are the metrics that make the most strategic impact as we plan and budget for the future, and while they’re important to look at holistically, there are pros and cons to each. 

Applications 

Pros:

• This is as close to real-time data as we can get 
• The App:Hire ratio enables allows us make quick decisions about when to stop or ramp up spend 

Cons:

• Every app is different  
• Shifting our focus from quality apps could result in extra spend and additional work for the TA team 
• It isn’t a hire 

Quality Applications

Pros:

• Even though it isn’t real-time, candidates can be quickly dispositioned to this stage 
• QApp:Hire ratio is closer to 1:1 and more standardized across locations and roles 

Cons:

• There may be some lag in moving candidates because this is dependent on recruiters 
• The definition of a ‘quality’ app varies  
• It isn’t a hire 

Hires 

Pros:

• This is the end state that needs to be achieved 

Cons:

• Time to Fill limits our ability to make real-time, strategic decisions 

Because of the research, SMART goal setting, and documentation we did in steps one and two, we should have an idea of what’s achievable for each. These numbers will act as performance benchmarks, which we’ll continue to monitor to see how well our data strategy is working. As time goes on and our strategy matures, we‘ll continue to benchmark against our own efforts to see how much we’ve improved.  

Conclusion 

As we’ve explored and seen, having a strategic approach to analytics can have a huge impact on our ability to make informed decisions in a dynamic market environment. Regardless of how complex the landscape grows, these three steps are designed to ensure your data works for you, not against you, as your organization evolves.  

1. Set goals
2. Document our needs
3. Set our KPIs 

If you didn’t catch me at LEAP HR Retail and are interested in implementing a more robust analytics strategy, get in touch with us so we can help you reach your goals and capture the quality candidates you need.