In part one of this two-part series, Greenhouse Business Analytics Specialist, Mike Linshi, wrote about the advantages of using Net Promoter Score and candidate surveys for your customer success and talent acquisition efforts. He highlighted the parallels between the two surveys, showing how recruiting teams benefit from adopting strategies from other functional departments.
To continue on the topic, we’ll discuss 3 different ways our Customer Success and Recruiting Teams at Greenhouse utilize data from respective surveys their teams use to take action on the results. Below, you’ll hear insights from the Customer Success first, followed by the Recruiting Team.
Survey Data Use 1: Measuring
The Greenhouse Customer Success Team uses NPS surveys as a KPI to track two very simple goals:
- Create more promoters than detractors
- Continuously elevate our average score quarter over quarter
Although the NPS metric is essential to understanding directionally whether or not a customer will renew, the added value comes from being able to aggregate the responses, segment the responders into different categories, then take immediate action on their sentiment.
After the segmentation is complete, we create reports and send to all Account Managers and their leadership teams for analysis and next steps.
The Greenhouse Recruiting Team tracks five quarterly KPIs to measure the health and success of our team. One of those KPIs is candidate satisfaction, as measured by the first question in the Greenhouse Candidate Survey.
We aim for greater than 90% positive response to, “Overall, my interviewing experience was a positive one.” The overarching goal of this metric is to show that we are creating a great candidate experience for all who go through our process.
Survey Data Use 2: Segmenting
The primary method of segmentation for Customer Success NPS is: Promoters (score of 9-10), Passives (score of 7-8), and Detractors (score of 6 or less). From there, we arrange the responders more methodically by adding them to our SMB (small to medium), Mid-Market, or Enterprise buckets as defined by the number of employees at the client’s company.
This tier hierarchy governs the type of service package the customer receives and determines the kind of action that is initiated when individuals in these different segments respond to our survey.
Finally, we review the direction of the feedback, meaning, whether the response was aimed at product, services, or other. The confluence of these factors all plays into the type of response and level of urgency the data commands.
Conceptually, how the Recruiting Team at Greenhouse measures and analyzes candidate survey data by segment is very similar to what Customer Success does. The Greenhouse Candidate Survey allows you to segment by date, location, and department, which is useful in identifying team-specific practices that are either contributing to or detracting from our candidate experience.
Survey Data Use 3: Taking Action, Internally and Externally
On the Customer Success Team at Greenhouse, once we get to a place where the data is collated and sorted by the attributes mentioned above, appropriate action is taken. For example, if a SMB customer is a Promoter and provides positive feedback about the product, we add them to a list of customers with similar feedback and send personalized thank you messages.
At the other end of the spectrum, if an Enterprise customer is a detractor, we assess the situation collectively as a team, formulate a strategy for appropriate next steps, and execute. In most cases, this would involve internal escalation to our leadership team and the customer’s executive sponsor.
Customer sentiment can also work to inform internal decisions and process upgrades. Because we strategically segment customers to inform their service level, we have to take a periodic gut check to make sure the accounts in those buckets are satisfied with their offerings.
Now that we have this rich data through the NPS survey, we also have a much clearer picture of the interactions that make our customers successful. For example, if enough customers are hoping for certain feature sets and this surfaces via the survey, we can take this data to the Product Team and discuss adding these features to our roadmap.
Within the Recruiting Team at Greenhouse, one distinct difference between our Candidate Surveys and NPS scores in Customer Success is that the Candidate Survey is anonymous. We can only see the location and team for which that candidate interviewed, as well as specific written comments. While we can’t slice the data quite as thinly as the Customer Success NPS survey, the data is still revealing enough to allow us to find patterns.
Like Customer Success, Recruiting looks at segments. We can often find patterns in the responses based on the location or the team. For example, if there is an overwhelmingly positive response for candidates who interviewed with the SF-based Marketing Team, we look into their hiring practice and replicate it across other hiring teams. Conversely, we may find that a specific location has a significantly lower score, which may be an indicator that it’s time for more in-depth interview training for that office.
Additionally, the Candidate Survey provides written comments, which is where the most detailed insight can be found. But again, it’s about looking for patterns and trends in the data.
One example of a pattern we found by digging into survey data was interview fatigue. Candidates felt burnt out by the end of lengthy onsite interviews and didn’t feel they were displaying their best performance by the end of the day. We added a ‘coffee break’ to onsite interview sessions that last over three hours as a mid-interview reset. It was a very simple solution to a problem we wouldn’t have known about without reviewing candidate survey data.
Whether working to understand how customers feel about your product and services or how prospective candidates feel about your hiring process, it’s imperative to collect and analyze survey data.
As outlined in the examples above, there are different ways to capture sentiment, but the most important step is to take specific action on the feedback and optimize workflows with the data from those responses in mind.