You’ve onboarded the Loop Experience Platform and just started collecting customer experience data – a great first step in running a successful customer experience program.
Now, while you wait for responses to trickle in, it’s time to turn your attention to Loop’s data collection features and start strategizing how you can make the most out of the data you receive.
What is Customer Experience Data?
Customer experience data is insight collected throughout the customer’s journey; usually involving multiple channels and touchpoints.
This data can be used to better understand specific areas of your facility or provide a more generic view of the experience. Further, it’s a source of customer learning, as you can obtain insight into customer needs and preferences to shape future offerings.
How to Analyze Your Loop Customer Experience Data Effectively
How you analyze your data and the learnings you pull from it determines the action you end up taking to improve the experience. Here we touch on best practices for analyzing your data effectively.
Consistently Measure a Specific Time Period
When you begin data analysis, it’s essential that you set up a time frame that you can revisit on a regular basis. Whether that be weekly, monthly or quarterly, keeping a regular cadence provides you with the insight to consistently enhance performance.
Similarly, it allows you to compare progress over time. For example, each quarter you can look back and see how you’ve progressed and identify any trends.
Further, consistent measurement ensures you’re on top of your data and day-to-day operations. One quarter might be really busy, but the another might not, seeing this trend over and over again, can help you better schedule teams throughout the year or better manage inventory.
Establish Touch-points
Touch-points are key to distinguish, as each point you measure has different staff present, different services being offered, and overall a different environment. Therefore opportunities to improve the experience will vary greatly from one touchpoint to the next.
When analyzing your data make sure to group specific touch-points together, to ensure you’re tackling each location individually. Making changes by touchpoint improves the individual experience and helps staff get a more personalized understanding of performance.
While grouping touch-points may seem like a daunting manual task, a customer experience solution can eliminate any heavy data sorting. Loop, for instance, categorizes responses automatically, based on the pre-determined touch-points you set. If you’re a school, it’ll segment cafeteria feedback and data separately from your school store. This allows you to see individual performance and make adjustments accordingly.
Look for the Root Cause
As you continue to collect data, situations will arise where immediate action is needed. While you may address those situations and resolve them in the moment, it’s important to dissect the issue and understand how it came to fruition in the first place.
For instance, someone could write in about a messy table, while another person could write about dust on the window sill. Rather than continuously sending someone every time an issue arises, looking at the root cause allows you to understand the bigger picture, that cleanliness is not up to standard. Finding the root cause allows you to get strategic and proactively send in teams on a more regular basis to clean your establishment.
Make Note of Recurring Opportunities
Following the aforementioned point, if an issue keeps arising despite proactive efforts from management, it might be time to hold a meeting with your team. In this case, whenever analyzing data, make note of recurring issues and revisit them with relevant team members.
Compare Customer Experience Data Sets
Lastly, a key part of data analysis is comparing data sets. Different data sets like “satisfaction” “ticket open” and “cleanliness” from a single customer experience can all correlate with each other. This is a great opportunity to get critical. For example, how did the time to complete the ticket impact the low satisfaction, and, how can you improve the experience next time?
5 Key Customer Experience Data Metrics to Watch
While you want to be familiar with all of the data you’re collecting, there are key insights to keep top of mind – here we cover 5.
Customer Satisfaction Score
Satisfaction over time is a tell-all of whether your customers are happy with the experience or not. While it can be pretty generic on its own, if you compare it with other telling metrics like time of day visited, employees, working that day and touch-points, you can have a better idea of how that specific sentiment ties into the overall experience.
Tickets Received/Resolved
This metric is key for overall efficiency and employee performance. If you’re looking for ways to improve your operations, this is the metric for you.
Firstly, the time received can help you identify busy times of the day. Is there a lunch rush hour? Maybe nighttime is the best time to shop for your customer. Understanding this metric can help you better schedule staff and prepare your establishment.
Resolved tickets in comparison to tickets obtained are also insightful in sharing how many issues are tackled. Ideally, all tickets should be resolved, but if not, it’s vital to explore why and how you can resolve them in the future.
Busy Times of Day
With this metric, you can optimize operations and set your team up for success. Referencing Loop’s Heat Maps panel specifically, you can identify busy times of day and the average sentiment. For instance, on Loop’s heat map, the visual of a big red bubble can signify many responses with a low sentiment. This could indicate a busy time of day with poor service – an opportunity for you to evaluate staff on the floor and potentially bring on new members for peak-hour service.
Sentiment Tied to Customer Service
Customer service is arguably one of the most important aspects to measure as it informs satisfaction, is telling of employee performance, and can dictate future reputation.
It’s also a great metric to motivate employees. You can use it to celebrate overperforming employees during team meetings or bring up improvements for underperforming employees during 1:1’s.
Sentiment Tied to Facility Cleanliness
Last but not least, cleanliness plays a monumental role in whether a customer returns. Something as simple as an unswept bathroom can leave customers feeling unwelcome. Use this piece of data to take action immediately, but also set team standards, so experiences are consistent and clean.
