Getting It Right the First Time: Better First Call Resolution With Analytics
Mom always said you learn from your mistakes, and analyzing repeat calls using algorithms can help call center managers learn ways to get it right the first time. Better first call resolution rates mean fewer frustrated customers and potentially more sales opportunities.
In recent years, First Call Resolution (FCR) has emerged as a critical element for contact centers looking to become more customer centric.
The FCR metric focuses on how well a contact center handles customer requests the first time, and most organizations agree that it is the only true metric that delivers a clear measurement of their effectiveness and customer satisfaction (CSAT) level.
For contact center managers, improving FCR presents the unique opportunity to lower costs, increase revenue and improve customer retention -- a triple play of benefits not found with any other metric. Industry analysts who research the contact center space recognize these benefits and the intrinsic value of FCR.
"[FCR] is the only single metric that provides a balanced view of the call center's overall operating performance," according to Donna Fluss, principal of DMG Consulting.
At the most practical level, improvement of FCR lowers the number of repeat calls handled by a call center and ensures that calls are productive. Additionally, fewer repeat calls mean agents can focus more attention and deliver greater personalized service to each caller.
This is great news for customer service organizations that recognize when a customer is being well serviced there is a substantially greater opportunity for up-sell and cross-sell activity.
Given the importance of FCR, one would expect that all contact centers would have an operational metric FCR where every single call is being measured. In fact, less than 20 percent of contact centers are measuring FCR for all of their calls with only 40 percent measuring FCR at all, according to a recent study by the International Customer Management Institute.
The current disconnect between FCR's importance and its measurement is due to a simple fact -- the analytics capabilities to create an operational FCR metric have only been available to companies the last couple of years. The forward-thinking companies that have applied operational FCR measurements, however, are gaining a strategic competitive advantage in their customer operations.
Let's take Chase Card Services, for example. In 2006, Chase chose to purchase an FCR analytics and performance management system after struggling to address FCR with internal reporting projects for years. Within one year, Chase Card Services reduced its repeat calls by 20 percent, experienced a decline in its calls per card member associated with the reduced repeat calls and is achieving higher CSAT scores than ever before.
So what new analytics capabilities are now available that can not only measure FCR for every call but, more importantly, help improve it? We know that calls are defined as repeat calls if they occur from the same customer, regarding the same call reason, and within an allotted period of time, and therefore an accurate determination of these criteria is essential.
The key is the use of analytical algorithms that replace manual call dispositioning and repeat call determination performed by quality monitoring (QM) managers. These algorithms use three main conditions to assess FCR -- if it was the same caller, if it was for the same call reason, did it occur within preset callback time window. Analyzing according to these three categories, the algorithms provide the following capabilities:
- Caller: Automatically detects if a customer called recently.
- Call reason: Automatically determines a call reason(s) by categorizing patterns of customer, agent, system activities. Used to validate if a callback was about a prior issue or a new issue.
- Callback time: Automatically assesses if a callback occurred within an allotted time window for a repeat call.
Although no analytics algorithm is 100 percent accurate, they have proven to be as accurate as, or better than, a person's judgment and they are fully predictable. The main benefit of an algorithm for FCR is that it categorizes 100 percent of calls, not just a mere sample of calls as more traditional manual analysis currently delivers. Rather than delivering just another report, the data analytics can provide are used to deliver the real payoff from FCR analytics -- action.
Acting on the Information
Below is a listing of the top five actions that call centers take as a result of leveraging actionable data from an FCR analytics and performance management system:
- Targeted coaching to agents with low FCR
- Targeted training on products/call types with low FCR
- Changing of policies causing customer callbacks
- Targeted coaching/changing of policies causing unwanted transfers
- On-boarding training on top 10 callback mistakes to avoid
FCR has become a cornerstone metric for today's new customer-centric contact centers. In order to close the existing gap in operational FCR measurement, organizations need to replace their traditional, subjective reporting methods with automated, objective and accurate analytic capabilities.
Leveraging analytics enables organizations not only to deliver more meaningful insight into the specific performance of each agent, team and supervisor in the contact center, but also provide them with intelligence they need to improve performance and FCR.
The promise of these new systematic capabilities is to arm contact centers with trusted, accurate FCR data that can deliver action to improve the handling of every single call -- translating into happier customers, lower costs and higher sales.
Ron Hildebrandt is cofounder of Enkata, which provides workflow management and analytics.