Case Study
A Credit Union’s Data-Driven Business Transformation
Background
The Challenge
Market Challenge
The Credit Union faced the challenge of identifying untapped opportunities for improvement across multiple lines of business, as well as capturing the Voice of the Member (VoM) in a timely manner. Traditional approaches to data-driven business transformation were not yielding the desired results, and a survey-based approach to VoM measurement meant that it was difficult to close feedback loops in a satisfactory timeframe. The Credit Union sought a fresh solution to uncover potential areas for improvement.
Organization Specific Challenge
Leveraging better and smarter data for better decisions and business impact
With the credit union managing over 60,000 phone conversations per day with its members and between 3,000 to 5,000 non-voice interactions spread across 6 different contact centers in different regions, the credit union was finding it hugely challenging to homogenize, augment, and understand the volume of disparate interactions they were having.
Traditional approaches to members, operations, and employee (agent) intelligence were frustrating them in that they felt like the data wasn’t accurate enough, supplied them enough context, and certainly wasn’t proving them, the overall level of intelligence that they desperately needed.
The Member Experience VPs were frustrated at the lack of insightful data and the absence of a qualitative and quantitative view of where the actual challenges were.
We knew we were making bad decisions with bad data before we were introduced to Call Journey. Our initial assesment showed us that a staggering 63% of agent summaries / disposition codes were incorrect, and as a bank we were making some pretty bad decisions and bets based on this data / intelligence
The Contact Center VP and Operators were frustrated similarly in that the key initiatives that they were driving lacked effective intelligence, hampering the success of those initiatives.
We’ve been on a big drive to self service and automation, but the initiatives that we had in play weren’t shifting the dial as we’d hoped – we figured there must be something wrong and we soon found out it was the data that we were getting to help drive the areas of focus
There were a number of operational challenges for the credit union, but they decided to focus on four areas of statistical performance concern:
- Cost per Contact was unfavorable by 17% ( -17% unfav). The credit union derived cost per contact by dividing the total operational costs, including agent salaries, infrastructure, and overheads, by the total number of calls handled within a specific period.
- Their digitization/automation/self-service program was lagging by 33% to their cost-out target and other measures below were also lagging (-33% to cost-out target). The credit union set itself internal goals to shift call volumes from voice to self-service and part of that program included finding interactions that could be digitized, automated, or provided to their members as FAQ. The initiative also had cost-out benefit goals attached.
- Speed to resolution was unfavorable 15% ( -15% unfav). Speed to resolution was a measure the credit union raised as quite important, as the goal was to reduce repeat callers and create better coaching activities to improve agent knowledge based on the types of calls that were causing lengthy contact (higher AHT), repeat contact, and dissatisfied members.
Agent productivity was lagging by 38% ( -38% unfav). The credit union like most contact centers, had an agent productivity measure. In this case, the credit union looked at AHT, speed to answer, logged in and available and total FTE count aligned to workforce planning. In short, if their call volume or call duration went over the forecast/budgeted goal, they had to either: reduce the call handling time or hire more agents. This was a key measure because even a small error in capacity planning meant a significant cost overrun in agent / FTE costs.
There were some pretty hairy challenges we were trying to stare down as a credit union, and looking at result pre adopting Call Journey Ci, we were clearly flying blind because we were behind in four of key metrics
The Selection
What was really confusing to us a credit union member experience leadership team, was that so many suppliers in the market were telling us that their technology would help us solve some of our key member experience problems. Many vendors told us they did things like topic analysis/keyword spotting to uncover important data, others told us that they uncover member needs at scale.
Additionally, as a credit union, our internal resources were limited and stretched and whilst a key decision point for us was to find a vendor solution that provided the level and sophistication of data and intelligence required, we were looking for a solution that took us as close to action as possible – i.e we were looking for the solution to tell us the story behind the data, not just present us the data for us to wrangle.
Lastly, we were looking for speed to value and high ROI. We didn’t want to engage a vendor solution that then saw us having to do the heavy lifting around our key uses cases as once again, we didn’t have capacity.
When we had the chance to do a discovery session with Call Journey Ci, see the platform in action and understand its delivery we were blown away. The key points for us in deciding to partner with Call Journey Ci were:
Vendor Comparison
Call Journey unlike all other vendors utilized far more details relevant and context aware data mining techniques to surface things like root cause of member contact, root cause of dissatisfaction, and cause of high AHT and low FCR. All other vendors fundamentally were utilizing basic word-spotting and topic analysis which statistically showed to be surfacing inaccurate and misleading data and insights.
Call Journey Ci has extensive well-researched and constructed modules, reports, and insights based on common but key use cases, which meant we were able to hit the ground running with accurate and effective data and intelligence from the get-go.
Call Journey Ci seems to philosophically employ the approach of almost “business intelligence in a box” whereby we didn’t need to lean into our internal data science or BA capabilities because CJ had already done the heavy lifting for us thru effective and efficient data storytelling.
When we assessed potential ROI from the solutions available in the market, the combination of lack of accuracy and lack of relevant use cases (many vendors were still heavily agent AQM or coaching focus) showed a significant difference in business benefit, particularly because we could solve for high cost, high-impact business challenges vs. low-impact and piecemeal agent insights.
The Solution
How The Credit Union Uses Call Journey Ci
What was really confusing to us a credit union member experience leadership team, was that so many suppliers in the market were telling us that their technology would help us understand why our member were actually calling – nirvana for us as you could imagine! However, it wasn’t until we really dug into the different vendor solutions, that we saw a massive disparity of what we were actually getting in way of output and intelligence. Call Journey’s detail, levels of data mining, sophisticated modelling and multiple layers of accurate categorization, really stood out from the market. In the end, I’m relieved we decided to work with Call Journey’s platform, as the tests and proof of concepts we did with the other suppliers, was serving up poor data and incorrect interaction assessments.
We’d struggled to really shift the needle in a meaningful way in our automations and self service/digitization efforts. Call Volumes weren’t decreasing anywhere near where we expected and the members experience with our digital and self service assets was actually pushing volume back into our contact center channels. Call journey’s platform, using Ai and it’s models, actually showed us were the focus should be based on the source we should have been mining properly in the first place – the lived experience of our members from the member mouth (and fingers!)
Understanding the member journey is a huge challenge for anyone, and in the credit union with multiple products, platforms and systems, tracking and understanding the member journey, and most importantly the member effort in engaging with us, is a massive priority. Previous to Call journey Ci we’d relied on surveys and systems intelligence to give as member effort guidance, but again, this approach ignored the most important element of our member interaction – the member themselves. Being able to track and understand EXACTLY where the member friction points are in interacting with us, and where member effort is high, will be a game changer for us. Tapping into the actual lived experience of every member across every interaction and being able to score, assess and measure that is the game changer we were looking for!
The Results
The conversation analysis yielded a total of well over 100 distinct opportunities for business improvement within the Credit Union, which they subsequently grouped into 4 different categories and focus areas. . They included process and service optimization, agent training, resource allocation, and technology enhancements. The potential savings/business benefit associated with these opportunities were estimated at $7.5 million in potential savings – just over $4.6m of that in tangible and directly measurable cost/business benefits.
Key Findings
Implementations
Conclusions
Once we had implemented the Call Journey Ci platform, which was super easy, we were staggered with the new and unique insights we could uncover. Additionally, the data and intelligence were far more detailed and context-aware. It showed us how flawed the data we were previously viewing was. We could quickly tie tangible business benefits to those insights – of the roughly $7m of business benefit we could identify, over $4.5m of that benefit we could confidently tie to tangible and directly measurable and correlatable cost and business benefits. And we’re barely scratching the surface
