Good customer service comes from knowing what the customer wants; knowing what the customer wants comes from data.
According to a 2019 Forbes article, “50 Stats That Prove The Value Of Customer Experience,” 77 percent of customers have a better opinion of brands that seek out and apply their feedback. But to get feedback, companies must first decide what to measure. Just ask customer success professionals from the following Colorado companies.
AIR Communities, AMP and Flatfile all use data analysis to improve customer success. Built In Colorado sat down with leaders from these companies to learn about what they measure, why they measure it and how they leverage that data to help their clients.
AIR Communities is a publicly traded real estate investment trust. For Senior Vice President of Data and Analytics Matthew Holmes, the critical element of data analysis is frequency — measuring customer satisfaction at every stage of the process.
What are the key metrics you track when it comes to your customers? Are there any “outside-the-box” metrics you track?
Most critically, we track our renewal percentage and customer satisfaction.
Renewal percentage is the ultimate measure of the purchasing decision; once customers have experienced our communities for a year or more, do they opt to continue making their home with AIR?
While important, renewal percentage is a relatively infrequent measure; our customer satisfaction metric tracks feedback much more frequently. We survey customers after their initial tour, at move in, after service requests, prior to the renewal process, after the renewal is completed and after move out. All of our surveys offer the opportunity to provide open-ended feedback. Our data teams mine these comments to discern trends at each community and highlight customers who might benefit from outreach from our leadership teams.
Which metrics do you prioritize when identifying accounts that might be in danger of churning?
We believe churn actually starts long before a customer becomes one of our residents. We’ve identified a number of metrics that are predictors of churn during the sales process, and we’ve customized our marketing and sales process to increase the percentage of the lowest-churn customers that eventually move in. During the customer’s residency, we track payment patterns and customer satisfaction to determine churn likelihood.
We survey customers after their initial tour, at move in, after service requests, prior to the renewal process, after the renewal is completed and after move out.”
Give an example of how you used this data to tailor your reengagement strategy or otherwise improve and nurture customer relationships.
In Miami last week, our data analytics identified a resident who would be a good candidate for contact based on survey scores and the comments made. Our east area vice president made a personal phone call to this resident to discuss their concerns and our approach to solving them. This resident was not expecting their comments to be read, let alone lead to a response from an officer within our company. They were blown away by the level of attentiveness and care we provided, and are now planning to renew in large part because of that interaction.
AMP uses AI-driven robots to efficiently sort recyclable materials. Being able to remotely monitor robot uptime and health allows AMP to determine how their robots are meeting customer needs.
What are the key metrics you track when it comes to your customers? Are there any “outside-the-box” metrics you track?
The key metrics we track are uptime, which we define as the time that the robot is actively picking material; and picks-per-minute, or the amount of material the robot is attempting to capture in a minute. These two metrics allow us to paint a picture of robot health without having to account for differences between facilities.
Some customers are more concerned with having a robot capture everything that comes through, which can mean a slower belt speed and fewer recoverable objects, whereas others want to recover as much as possible even with a high throughput. Distinguishing between the two informs what recommendations we make for calibrating the robot’s performance. With either situation, the important thing is to have the robot running in the first place, which is where uptime comes in handy.
An outside-the-box metric we track is the difference between the quantity of material the robot is currently capturing and what it could potentially capture if we adjust some of its settings. This is useful to show our customers what materials they could potentially be missing, while also helping to put into context why the robot might not pick certain material.
Which metrics do you prioritize when identifying accounts that might be in danger of churning?
Once again, uptime is a key metric we use to identify possible churn. If we see limited usage of the robot or are not able to remotely connect to their system, it implies there might be something going on with the robot. Most of the time, it’s humming along, and our connection might just need a reset — but it’s a good place to start.
Alongside that, there is a strong correlation between proper maintenance and high performance. Turnover is commonplace in a typical materials recovery facility; often, when maintenance personnel leaves, knowledge leaves with them. The loss of a trusted maintenance manager can mean that the equipment in the facility may be neglected, and our focus at this point is ensuring they have the support and knowledge to maintain the equipment.
One of our goals is to set expectations properly, and sometimes that involves a reset. Once the customer sees what the robot is capable of with proper maintenance, we can then bring in our data platform to help them better understand how much material the robot is able to recover and what impact the robot can have in their facility.
We can bring in our data platform to help customers better understand how much material the robot is able to recover and what impact the robot can have in their facility.”
Give an example of how you used this data to tailor your reengagement strategy or otherwise improve and nurture customer relationships.
I’ve used data to improve a customer relationship by analyzing their material composition. In one case, we had a customer whose robot was targeting natural High Density Polyethylene milk jugs and polypropylene containers.
With our material composition data, I was able to identify a portion of material that we were not previously targeting because the system was not 100 percent sure it was polypropylene. However, with the continuous development of our AI, our system’s confidence in positively identifying materials has increased over time, meaning that we can be more aggressive in what we positively identify — especially so for PP. We shifted the robot to an aggressive PP capture strategy while keeping its approach toward the more-valuable natural HDPE unchanged.
The result of this adjustment was about 50,000 more polypropylene containers captured per month while maintaining the number of HDPE containers captured. Critically, the amount of contamination in the PP bales from the more aggressive material capture settings was negligible, which helped to further develop the customer relationship. Turns out they like making more money!
The importance of data is built into Flatfile’s business model, since its platform makes data easy to view, organize and analyze. Customer Success Manager Eric Rutledge tracks metrics to identify trends and see what improvements can be made to the product and customer experience.
What are the key metrics you track when it comes to your customers? Are there any “outside-the-box” metrics you track?
We’re obsessed with ensuring we meet the needs of our customers, so it’s essential to track a multitude of metrics. We constantly monitor adoption rates based on aggregated usage metrics, abandoned files and workflow, completed files and number of users. Monitoring the number of support tickets based on category is also important; by identifying trends, we can see where there may be an opportunity for enablement or a feature enhancement that could benefit the customer base. One additional item I track is any changes in roles and responsibilities for current administrators of our application so nobody gets left behind.
Which metrics do you prioritize when identifying accounts that might be in danger of churning?
We prioritize usage metrics and monitor the number of active users and application admins, which gives us some insight into the possibility of a downturn in their business or if there is a change in stakeholders managing the product. This is an interesting data point because we can identify change without explicitly asking an often uncomfortable question.
I also monitor support tickets to see if these new stakeholders understand the product and if there is a relationship between tickets and usage changes. It sheds light on whether there is an aspect of the product causing friction with the users and prepares me to share possible solutions to give them success.
We’re obsessed with ensuring we meet the needs of our customers, so it’s essential to track a multitude of metrics.”
Give an example of how you used this data to tailor your reengagement strategy or otherwise improve and nurture customer relationships.
We take a three-pronged approach using metrics to create a holistic picture. I often populate a message to the sponsors that starts with a quick review of the partnership goals between our two companies. I’ll remind them that my personal goal is to monitor the product’s success and their business objectives.
Secondly, I share evidence in the most consumable format: graphs, charts, tables, etc. I want to present them with the hard facts I am seeing. And finally, I pass along my observations of the data and what possible solutions I recommend, which may include recently released features or a solution using an upcoming feature on the roadmap.
Using that approach, I encourage a conversation; it’s a great way to schedule a QBR or executive business review. I tailor the conversation to highlight the goals centered around increasing ROI for the customer. The goal is to discover additional opportunities to improve those metrics and find other use cases to help them meet their business goals.
