Performance Measures for Call Centers

Performance Measures for Call Centers
 

The contact center is the face of the credit union. However, measuring its performance and efficiency is an art as well as a science. Talk time and handle time are two factors that have a consistent impact on customer satisfaction scores and indicate the overall efficiency and productivity of the center. Credit unions need strategic metrics and KPIs to track to improve customer loyalty and increase revenue.

 To better understand Call Center Analytics programs, a workshop was conducted by CULytics in which Bob Little, Advisor at CULytics, and Naveen Jain,  Founder, and President at CULytics, discussed measures that must align with the Call Center Analytics of an organization. The agenda of the session comprised-

  • The role of data analytics in contact centers
  • Practical examples of contact center performance indicators and how they can be used
  • Best practices for implementing contact center metrics programs

 Read out the summary of the session for more information-

Evolving Modern Data-Driven Contact Center

Today's contact center encompasses various communication channels: web chat, email, phone, video chat, text message, and a greater dependence upon data. It delivers an experience that's more likely to satisfy immediate member wishes, leading to better word of mouth and associated growth.

Quantitative vs. Qualitative Data: What's the Difference?

Data analysis is broad, exploratory, and downright complex; it boils down to qualitative and quantitative data. Let's dive into each type of data using real-world examples.

Quantitative (hard) data

  • It is objective and can be accurately measured in basic numerical terms.
  • Examples are call-resolution rate, percentage of dropped calls, and the number of closed sales by an individual agent.

Qualitative(soft) data

  • It comprises elements like a customer's opinion, behavior, or even tone of voice.
  • It is more open to interpretation and requires a certain level of planning to measure accurately.

Common Areas for Contact Center Metrics

Contact center successes and failures are defined by KPIs and metrics measuring the extent of customer service performance and positive customer outcomes. In addition, these can pinpoint the areas for improvement that will deliver the biggest ROI and impact profitability.

Following are the areas for Contact Center Metrics--

  1. Drive Efficiency
  2. Engage Members
  3. Drive Growth
  4. Control Costs

Drive Efficiency

  • Average Handle Time (AHT): By measuring the average length of a member call, teams can assess how efficiently calls are handled and establish a benchmark against which performance can be improved.
  • Abandonment Rate: The percentage of calls dropped or ended by members without resolution.
  • Percentage of Calls Blocked: This metric calculates the number of inbound callers that receive a busy tone.
  • Average After Call Work Time: Measures the average time it takes agents to do the work associated with a call after it's finished.

Engage Members

  • First Contact Resolution (FCR): Tracks the percentage of calls where the agent can address a caller's issues without transferring, escalating, or returning the call.
  • Repeat Call Rate: Measures how often issues were not resolved satisfactorily on first contact.
  • Customer Effort Score: An aggregate score (based on a survey question) measures how much effort your members feel they must put in to resolve an issue.
  • Contact Quality: Analysis of random call recordings for accuracy, courtesy, completeness.
  • Net Promoter Score: Gauging a member's loyalty and the strength of their relationship with the credit union as an indicator of satisfaction.

Drive Growth

  • Conversion Rate: How many calls are needed before a sale is made. Enables the analysis of scripts and offers to determine the most effective ones.
  • Cost per Acquisition: By revealing how much converting each lead costs your team, this metric shows where you can save money and helps you to optimize your ROI.
  • Revenue: By measuring the total revenue per campaign and revenue generated by each agent, managers can see where additional training is needed or where offers and targets need to be revised.

Control Costs

  • Calls Handled: Inquiries handled by channel. For example, inquiries handled by live agents, IVR systems, live chat, email, SMS, or social media.
  • Peak Hour Traffic: Measures dayparts with higher call volumes. This KPI is helpful in forecasting staffing needs.
  • Occupancy Rate: How much time agents spend tending to customer-related issues. Indicates whether staffing is at an appropriate level.
  • Cost per Contact (CPC): this refers to the average cost of each call handled and indicates how much it costs to run current operations.
  • Agent Retention Rate: The percentage of agents you keep from leaving your contact center annually.

 Practical examples of contact center KPIs and how they can be used

  1. Reduced Average AHT by 40%

One organization analyzed unstructured call description logs for a specific call type to find variabilities in the resolution process. This identified various AHT improvements, including redesigning questions to understand member problems better and optimizing processes by eliminating unnecessary steps. They began proactively identifying and mitigating issues for other call types to improve member service from that foundation.

  1. Reduced Repeat Calls by 15%

A financial services firm found that for every 100 customer issues, they received more than 160 calls. It analyzed customers, agents, and processes.

  • The firm identified people who would frequently call for minor things such as status updates on a resolved issue or those who would repeatedly call if they were not happy with the initial outcome.
  • They segmented call center agents who had a low-resolution rate or spent too much time on minor issues.
  • Finally, they analyzed processes to identify gaps or systemic issues (such as a replacement card that is not delivered on time).
  1. Reduced Recovery Time from Service Outage from 17 to 8.5 Hours

A two-day outage for a typical financial-services firm can require up to a week to return to business as usual. Data modeling can predict the implications of a call center completely shutting down, partial staff availability, a downed server, or other disruptions. By modeling different interventions—such as adding capacity, rerouting customer calls, or making an IVR announcement, among others—organizations can best mitigate the outage.

  1. Improved Conversion Rates by 45%

Credit unions combined real-time data such as demographic and behavioral profiles and purchase history from service calls and ran analytics to determine which variables had the biggest effect on a member's willingness to buy, broken down by specific product offerings. They could then predict the following product the member was most likely to buy and develop specific sales scripts for each product.

 Characteristics of an Analytics-Driven Contact Center

  • A coherent vision for analytics linked to the overall business strategy, along with a road map for implementing specific use cases.
  • An organization with internal analytics capabilities and agile mechanisms to capitalize on analytics-driven insights.
  • A comprehensive data ecosystem that can support the broader analytics strategy.
  • A culture of objective decision-making based on data rather than gut instincts.

Before beginning to track these, it is crucial to understand these two. It is imperative to know which one is strategic and which one is important.

  • A metric is any standard of measurement. E.g.:

             1. Number of requests logged

             2. Number of data owners identified

             3. Percentage of requests resolved within SLA

  • A Key Performance Indicator is a quantifiable metric that drives improvement and links to strategic business outcomes.
  • A KPI is a metric, but a metric is not necessarily a KPI

 Strategies Are About Change

Strategies are about doing something different and increasing something. These depend upon the objective. Strategic Objectives as measured by (KPIs) can be achieved by:

  • Strategic objectives are qualitative and memorable descriptions of what is required to achieve. Therefore, they should be short and engaging.
  • KPIs quantify the outcomes that are expected to achieve. They are measurable on a sliding scale (e.g., increase from x metrics from 10 to 25 or reduce y metrics from 70 to 55) over a period of time.
  • Activities, on the other hand, are the programs, initiatives, tasks, and projects associated with achieving Objectives. They are usually binary (done or not done).

Balance, Quality, and Efficiency

  • "For every metric, there should be a paired metric that addresses adverse consequences of the first metric"- Andy Grove.
  • So, while trying to change some specific behavior, it might be backed by cost.
  • While working on achieving goals, measuring progress towards that outcome is essential to know that plans are performing as expected. So, remember that a good strategic goal focuses on an outcome. Also, Key Performance Indicators (KPIs) can be organization-wide or focus on departmental goals.

 Remember, it's all about the outcome and not the actions. So make smaller changes for analyzing and enjoying growth.

 

Watch the full workshop here - https://culytics.com/articles/performance-measure-for-call-center

E-mail me when people leave their comments –

You need to be a member of CULytics Community to add comments!

Join CULytics Community

 

advantedge
altair
ibi
arka
trellance
coopfs
dfa
wherescape
alkami
prismacampaigns
marquis
aiq
totex
cnet
datava
aun
cinch
know

Related Post

 

Ad Unit Settings






Ad Url Settings

 

api-lead-approach
the-amazon-lending-experience
executing-advanced-analytics-do-s-and-don-t
lending-transformation-old-vs-new
data-journey-building-strong-analytical-practices
4-step-iterative-process-building-a-relevant-analytics-practice
significant-measures-towards-new-normal
building-a-strong-analytics-practice-recipe-for-success
data-warehouse-evaluation-and-implementation
explainable-ai-trust-and-transparency
forecasting
top-50-members-using-transactional-website-jun-2020
top-50-cus-with-highest-and-lowest-efficiency-june-2020
importance-of-financial-risk-management
secret-sauce-for-long-term-sustainable-business-intelligence-succ
top-pfm-technologies
secret-sauce-for-long-term-sustainable-business-intelligence-succ
top-pfm-technologies
data-warehouse-and-bi-technologies-opportunities-challenges
top-chatbot-technologies
keys-to-building-an-effective-branch-or-atm-network
top-50-credit-unions-with-highest-and-lowest-accounts-per-member
lowest-and-highest-net-income-per-branch
marketing-holy-grail
top-50-most-and-least-delinquent-credit-unions
modern-marketing-technologies
incremental-low-cost-data-driven-wins
power-of-storytelling
the-cost-of-not-investing-in-data-governance
questions-you-should-ask-before-investing-in-data-warehouse
learnings-from-new-data-based-on-auto-loan-pricing
5-questions-you-need-to-ask-before-investing-in-data-governance
digital-marketing-maturity-models-for-credit-unions
marketing-expense-per-member
top-2-reasons-that-are-holding-credit-unions-back-when-they-are-i
using-data-analytics-to-manage-lending-complexity-while-driving-h
5-reasons-your-credit-union-should-invest-in-data-and-digital-now
top-50-most-and-least-efficient-credit-unions
retail-financial-services-outlook-during-covid-19
use-of-operational-analytics-to-mitigate-the-impact-of-covid-19
top-50-credit-unions-based-on-asset-size
cu-peer-comparison-dashboard
cu-peer-benchmark
all-about-machine-learning-engineering
top-web-design-trends
most-important-social-media-marketing-trends
state-of-digital-marketing-maturing-in-credit-unions
top-kpis-for-email-marketing
data-cloud-and-the-digital-transformation-imperative
digital-trinity-and-you
phases-of-financial-industry
analytics-roundtable-workshop
invitation-to-join-digital-transformation-hub
analytics-in-the-credit-union-business
value-of-member-centricity-and-analytics-in-the-growth-of-cus
all-about-membership-analytics
top-fraud-management-technologies
getting-started-with-your-data-analytics-journey
explore-vizualization-for-credit-unions
investment-in-website-personalization-technologies
data-analytics-supporting-cu-s-first-member-philosophy
loyalty-rewards-and-retention-technologies
member-experience-analytics
channel-analytics-and-its-importance
project-portfolio-management-technologies
investment-in-self-service-data-preparation-technologies
self-service-data-preparation-technologies
new-frontier-in-customer-experience-management
role-of-marketing-analytics-in-credit-unions
important-aspects-of-consumer-lending-analytics
kpis-on-website-analytics
journey-towards-bank-less-banking
investment-in-crm-technologies
top-omni-channel-vendors
conversational-banking-solutions
/top-kpis-for-chief-information-officer
mistakes-to-avoid-when-implementing-a-omnichannel-member
top-things-to-consider-when-building-dashboards
making-digital-marketing-more-agile-through-tag-managers
cecl-solution-providers
mistakes-to-avoid-while-implementing-marketing-automation
p2p-payment-integrated-solutions
kpis-for-social-media-tracking
kpis-for-human-resources-management
investment-in-fintechs-should-or-should-not
top-kpis-for-online-banking
investment-in-marketing-automation-technologies
investment-in-e-signature-technologies-should-or-should-not
tips-and-tricks-to-a-successful-bi-program
kpis-for-credit-card-business
kpis-for-digital-marketing
kpis-for-consumer-lending
hot-topics-for-credit-union-data-leaders
kpis-for-debt-collections
kpis-for-finance
website-personalization-tools
data-integration-technologies
robotic-process-automation-tools
why-data-analytics-initiatives-fail
electronic-signature-softwares
data-governance-tools-for-credit-unions
digital-and-mobile-banking-technologies
report-inconsistencies-are-frustrating
is-your-culture-ready-for-data-analytics
three-big-data-myths
turning-transaction-data-into-a-goldmine-a-becu-case-study
call-for-presentation-for-2019-credit-union-analytics-summit-is-n
top-10-keys-to-successful-data-analytics-practice
credit-union-chooses-accountscore-for-open-banking-transaction-da
how-much-do-you-spend-to-serve-a-customer
marketing-automation-technologies-for-credit-union
alexa-ask-first-abilene-fcu-for-my-balance
dataweb-content-management-technologies-for-credit-unions
efficiency-ratio
web-analytics-technologies
data-warehousing-software-for-banks
customer-experience-software
the-best-kept-secret-for-credit-union-data-analytics
mark-sievewright-on-technology-trends
naveen-jain-on-credit-union-analytics-summit-2018
why-analytics-doesn-t-make-a-difference-by-gary-angel
cuas2018-harnessing-the-right-data
build-a-financial-phone-assistant-for-your-credit-union-in-3-step
2018-culytics-analytics-challenge-winner
update-from-naveen
error-resolution
benefits-of-conversational-apps
who-are-your-most-valuable-members-part-1
how-alexa-can-help-your-credit-union
top-10-kpis-for-measuring-retail-channel-performance
how-much-is-too-much-personalization
top-10-kpis-for-measuring-contact-center-efficiency
pressure-on-margins-for-auto-loans-indirect-auto-loans-declining
best-business-intelligence-technologies-for-credit-unions
establishing-a-thriving-data-analytics-practice-is-a-journey
educational-presentations-from-the-2017-axfi-conference
modelling-alternatives-for-cecl-a-deep-future-analytics-study
data-analytics-use-cases-for-credit-unions-infographic
data-analytics-opportunities-in-credit-union-business
loan-application-analytics-with-cufx
machine-learning-delivers-great-consumer-experiences
deep-insights-of-credit-union-members-data-with-machine-learning
web-analytics-reporting-tips-for-credit-unions
big-data-strategy-roadmap-our-data-journey
webinar-framework-for-member-focused-decision-making
too-many-regulations-hurt-credit-union-members
digital-marketing-automation-solutions
online-banking-boom
transformation-transactions-to-relationships
top-dispute-management-technologies
2020-retail-trends
future-of-artificial-intelligence
2020-culytics-summit-attendee-dashboard
repositioning-the-role-of-marketing
marketing-automation-a-step-towards-marketing-transformation
strategic-agility
using-data-to-navigate-through-the-new-normal
digital-transformation-bcu
highest-and-lowest-new-loan-balances-per-branch-as-of-jun-2020
-new-members-ratio-as-of-june-2020
cus-with-highest-and-lowest-loan-grants-per-member-june-2020
self-service-data-preparation-technologies
highest-and-lowest-marketing-expense-per-member-june-2020
the-amazon-lending-experience
api-lead-approach
4-step-iterative-process-building-a-relevant-analytics-practice
data-journey-building-strong-analytical-practices
post-election-the-cu-outlook
most-and-least-delinquent-credit-unions-sept-2020
leveraging-ach-data-to-produce-real-outcomes
member-engagement-scores-benefits
member-engagement-key-to-serve-the-best
story-of-james-an-intelligence-transformation
executive-kpis-the-pulse-of-the-organization
untangling-member-journey
onboarding-strategy-to-deliver-success
the-importance-of-digital-technologies
top-interactive-financial-calculators
using-artificial-intelligence-to-improve-your-productivity
organizational-transformation-to-drive-growth
multi-year-journey-through-data-transformation
top-50-cus-with-the-highest-and-lowest-member-per-branch
digital-transformation-lessons-through-the-eyes-of-a-ceo
organizational-readiness-for-digital-transformation
ruthless-prioritization-to-do-more-to-learn-more-and-to-earn-more
performance-measures-for-digital-services
analytical-maturity-journey-towards-growth
less-is-more-the-necessity-of-focus-for-strategic-success
solving-the-crm-mrm-puzzle
insights-driven-messaging-member-and-product-onboarding
performance-measures-for-marketing
data-insights-that-drive-member-product-innovation
solving-the-crm-mrm-puzzle
the-agility-flywheel-a-strategy-that-never-goes-out-of-the-way
artificial-intelligence-as-a-playing-field-for-credit-unions
performance-measures-for-call-centers
top-automl-technologies
performance-measures-for-lending
building-business-case-for-data-analytics
driving-innovation-and-change
data-analyze-decide-and-create
digital-readiness-important-steps-to-achieve
digital-readiness-important-steps-to-achieve
enabling-credit-unions-with-ai
culytics-virtual-summit-2022-a-resounding-success
culytics-virtual-summit-2022-day-1
digital-banking-roundtable
digital-marketing-roundtable
transformative-lessons-from-a-chief-digital-officer
data-analytics-roundtable-mar-11
rewind-2022-culytics-day-key-highlights
data-analytics-team-roles
data-warehouse-development
data-analytics-team-size
is-your-data-analytics-program-not-delivering-results
active-deposit-management-for-profitable-growth
data-modeling
maximize-your-success-with-2023-CULytics-summit
biggest-opportunities-for-credit-unions
should-ceos-attend-the-culytics-summit
the-cost-of-a-wrong-decision
biggest-roadblocks-in-becoming-data-driven
a-journey-for-all-organizational-maturity-levels
maximize-your-data-analytics-checkup
navigating-the-data-analytics-landscape
improving-data-literacy
why-credit-union-leaders-should-invest-in-their-teams
why-credit-unions-should-not-invest-in-building-predictive-models
why-should-measure-the-success-of-data-analytics-program
cost-of-choosing-the-wrong-data-analytics-technology-stack
why-data-analytics-strategy-focus-on-supply-and-demand-side
kpis-to-measure-the-success-of-data-analytics-program
data-analytics-for-credit-union-branch-heads
data-organizing-principles
top-data-warehouse-storage-technologies
discover-the-hidden-truth-behind-watermelon-kpis
unveiling-the-hidden-dangers-of-cobra-effect-on-kpis
are-you-accurately-interpreting-your-kpi
unmasking-biases-a-guide-to-data-analysis-and-kpi-definition
uncover-the-power-of-proxy-kpis
unraveling-the-hidden-impact-of-sampling-bias-in-credit-unions
bi-department-structure
hidden-impact-of-confirmation-bias-in-credit-unions
getting-executive-attention-for-your-data-analytics-program
uncovering-biases-in-data-preprocessing
navigating-missing-data-in-credit-unions
navigating-sampling-bias-in-cu
unleash-the-power-of-real-time-data-use-cases
how-confirmation-bias-impacts-cus
breaking-down-selection-bias-in-credit-unions
unmasking-reporting-bias
elevate-your-cu-with-data-analytics-expertise
understanding-and-tackling-volunteer-bias-in-credit-unions
time-period-bias-in-credit-union
overcoming-biases-in-credit-unions
embracing-the-future-fast-future-fundamentals-program-equips-cred
unlock-growth-and-efficiency-credit-unions-guide-to-generative-ai
how-better-data-and-behavioral-biometrics-can-help-credit-unions-
harnessing-the-power-of-data-in-credit-unions
leveraging-third-party-data-a-strategic-guide-for-credit-unions
unlocking-member-insights-how-cus-can-leverage-third-party-data
enhancing-customer-experience-through-third-party-data
third-party-data-integration-techniques-and-technologies
the-future-of-lending-third-party-data-role-in-credit-decisioning
how-third-party-information-shapes-cu-strategies
using-data-to-improve-access-to-credit-for-low-income-members
designing-financial-products-for-low-income-members-using-data
measuring-and-enhancing-the-impact-of-support-programs
data-governance-why-selling-internally-is-important
selling-data-governance-in-your-credit-union
building-a-business-case-and-engaging-stakeholders
creating-a-data-governance-roadmap-and-executing-it
measuring-and-demonstrating-the-impact-of-data-governance
sustaining-momentum-keeping-data-governance-a-priority
overcoming-challenges-in-transaction-data-analysis-credit-unions
empowering-members-through-transaction-data
how-credit-unions-leverage-transaction-data-best-practices
unlocking-financial-independence-the-power-of-transaction-data
the-power-of-transaction-data-enrichment
avoid-financial-reputation-and-member-trust-issues
introduction-to-model-risk-management
week-1-mrm-a-practitioner-s-approach
week-2-guide-to-identifying-and-maintaining-models
survey-insights-navigating-mrm-in-credit-unions
week-3-application-of-mrm-insights-to-sound-model-development-eff
unlocking-the-secrets-to-attracting-gen-y-and-z
creating-a-seamless-member-experience-for-gen-y-and-gen-z
data-analytics-maturity-assessment-report
marketing-to-gen-y-and-z-strategies-that-work-for-credit-unions
the-imperative-of-engaging-millennials-and-gen-z
cu-build-lasting-relationships-with-gen-z-financial-literacy
how-social-responsibility-drives-gen-z-membership
loyalty-programs-that-work-keeping-gen-y-and-z-members-engaged
insights-on-engaging-millennials-and-gen-z-at-credit-union
ai-driven-member-experience
streamlining-operations-with-ai
innovation-and-member-inclusion-in-ai-credit-risk-models
ai-risk-management-enhancing-fraud-detection-and-cybersecurity
how-ai-is-transforming-data-analytics-for-credit-union
overcoming-ai-adoption-challenges-in-credit-unions
the-state-of-ai-in-credit-unions-survey-insights
creating-a-culture-of-innovation
building-the-foundation
closing-the-talent-gap
ensuring-data-readiness
navigating-the-roadblocks-ai-and-data-analytics-in-credit-unions