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“Your most dissatisfied customers are your greatest source of learning.”
Customers interact with your business or service every day, and the quality of the interaction experience can drive your brand forward. Brands that want to stay in the game and move forward in the marketplace know that they need to continuously listen to customers to deliver services that align with customer expectations.
Using customer data to improve the customer experience can help retain customers, which is easier than acquiring new customers. A satisfied customer will not only return to your service, but will likely promote your business through word of mouth.
So how do we achieve this level of customer satisfaction? CSAT, a metric that directly measures customer satisfaction, has become more than just a fad. Ideally, you send customer satisfaction surveys to see how your customers feel about an action your company has taken, or about certain aspects of your products/services.
Measuring customer satisfaction using feedback surveys is the starting point, but you can do more with this data to ensure an improved experience. The following case study shows how this can work:
Introducing CSAT Case Study
This case concerns TPA (name anonymized), a B2C software company. TPA is a video editing software service with a global presence that allows consumers to download the software through their website and provides various video editing features. They have a customer service portal for customer inquiries via phone, email, chat, etc. The customer service portal is both in-house and outsourced, with the in-house team also having a virtual team. The issues they address range from account-related issues to performance characteristics.
TPA’s CSAT saw a sudden drop and SLA metrics (hold time, lead time) increased significantly. The operational leadership team was very concerned and needed to determine what was going on.
Using their BADIR Data-to-Decision framework, we were able to quickly identify the causes of TPA’s declining CSAT scores and recommended actions to address 65% of the CSAT decline.
Let’s explain to you how we did it.
Step 1: Identify the business demand
TPA needed insights and actions as soon as possible due to the heavy impact on SLAs.
Our first step was to identify the real business questions behind the questions about CSAT decline and SLA rising metrics. Using a detailed question framework, we arrived at the real business question: what causes customer satisfaction to drop and how do we solve the problem?
Step 2: Create an analysis plan
After identifying the questions to answer, we used hypothesis-driven planning to narrow the scope of our analysis to the core hypotheses that were at hand. This allowed us to choose the right data and the right analysis techniques.
Based on discussions with relevant stakeholders, we first hypothesized the segments where the dip could occur and then identified the key hypotheses, such as the following.
- Channel: CSAT dip due to chat support issues.
- Region: EMEA has problems with recent privacy laws.
- Call center type: Outsourced call centers are driving a dip in CSAT due to recent changes in agent profiles.
- Type of problem: CSAT drops due to last product push issues.
We also identified the critical metrics impacted as part of the SLA as:
- First Contact Resolution (FCR)
- Customer Satisfaction (CSAT)
- Waiting time
- Processing time
Based on these hypotheses and statistics, we determined the necessary data and identified correlation analysis as the appropriate method for analyzing this data.
Step 3: Data Collection
By applying the first two steps of the BADIR method to our case study, we had a solid foundation to keep our data collection focused on the real business question and the analysis plan we had developed.
We collected the following data on the segments and success metrics and performed a data audit to ensure a clean data set.
|First Contact Resolution (FCR)
|Customer Satisfaction (CSAT)
|Call center type
Step 4: Using CSAT to Get Insights
Before we get into the reasons, we wanted to check if CSAT is indeed affected and if there is any impact on the SLAs. Any analysis must follow these three essential steps.
A. Is there a problem?
We checked the CSAT and SLA time for the past four weeks and noticed a significant difference in the CSAT score and mean wait time.
Now that we have confirmed the dip and its impact, we looked for insights using correlation analysis to understand what is driving the decline in CSAT.
B. Where is the problem?
To test the hypotheses we set up in the analysis plan, we performed bivariate analyzes of the segments over the weeks to test each hypothesis.
Our analyzes showed that the CSAT decreased in all channels and regions, and there was no significant difference between segments.
CSAT is declining in all call center types, but is more important in internal virtual call centers. The counts for in-house call centers are significant, so we narrowed down one of the problem areas.
We ran the same analysis for all types of issues and noted that the CSAT dropped for account recovery issues across all types of channels.
Next, we wanted to understand the relative influence of each channel and problem type on the CSAT dip, in order to quantify the impact before making recommendations.
C. What is the impact?
We used the CSAT delta between weeks and the weekly volume for problem type and call center type to understand which segments caused the maximum decline.
We found that “Account Recovery” issues had a 65% impact on the CSAT dip, “Upgrade” caused another 13% and “Order Tracking” caused another 12% dip. The largest share of impact revolves around the in-house call centers.
Step 5: CSAT-Based Recommendations
The purpose of this exercise was to determine the cause of the recent CSAT drop. The analysis found that “Account Recovery” issues had a significant impact (~65%) on the CSAT dip, with internal call centers having the largest impact (~34% of the total impact).
Based on the findings, Aryng advised deep diving and triage with internal call centers, particularly around concerns about recent changes.
We also looked at the Pareto to identify the critical types of issues raised by the customers. Resolving “Account Recovery”, “Upgrade” and “Order Tracking”, which account for 90% of the total customer satisfaction dip, will help improve customer satisfaction and reduce SLA-related time factors.
Analytics can be complicated with huge databases to comb through and CSAT scores are just a few numbers at first glance. Critical analysis of CSAT helps uncover the drivers and helps identify brand strengths and critical customer pain points.
The Data-to-Decision method (BADIR framework) is a valuable recipe for making impactful decisions by focusing on actions based on well-structured analytics. When applied to the TPA company, this method allowed a quick identification of the main problem. This instructed the leadership team to coordinate with the right team instead of being distracted by an overwhelming amount of data and too many plots.
If you have any questions, you can download the extensive white paper here†
Piyanka Jain is an internationally acclaimed bestselling author†
Ananth Mohan is a consultant in product analysis at Aryng†
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