Managing a successful call center in today’s customer-first environment is challenging.
Call center leaders are tasked with running an operation that is efficient, customer-centric, and effective, yet they’re constantly getting asked to move fast and do more with less. Being forced to run lean means analyzing data in spreadsheets and focusing on the problems at hand, rather than building systematic solutions to address the deep challenges of the call center.
Failure to run lean puts the entire operations team at serious risk of spiking overtime costs (due to poor planning), seeing CSAT scores plummet, and worse, watching customer attrition spike.
With all that pressure and so much at stake, how can you truly optimize call center operations while maintaining (or even improving) service performance and quality?
Having spent many hands-on hours working with call center managers/leaders/VPs across the nation, our experts have consistently figured out a proven way to leverage data and analytics to:
If any of the statements above interest you, this whitepaper is going to come in handy. Our goal is simple, help you realistically do more with less at your call center.
In this whitepaper, we’ll cover the critical role data plays in improving the customer experience in call center operations and how the trio of Snowflake, Tableau, and phData can help organizations foster more data-driven decision-making.
Consumer habits are reflecting a higher sensitivity to service issues. They want to reach support immediately and expect an excellent experience and full resolution from that contact.
When those experiences are not met, consumers are increasingly likely to move to another provider–even if that means fragmented customer experiences. In a world where nearly 74 percent of consumers will switch providers after a single poor customer experience, enterprises must provide exceptional customer service or else risk high churn rates.
At the frontlines of the customer experience lies the call center, where almost every call, text, or chat is a make-or-break moment. According to a forecast by Insider Intelligence, approximately 30 percent of customers will need to interact with a call center representative each year through 2024.
This means going beyond hiring more agents to service customers. For most organizations, it’s about using the power of data to improve the customer experience at the call center.
The not-so-secret way to noticeably improve the customer call center experience is for agents and leaders to have quick access to relevant customer data. The problem is that so few companies can do this effectively.
Historically, the vast majority of call center data typically lives in separated silos throughout the business. It’s not uncommon to find contact routing, scheduling, planning, and finance data scattered across multiple platforms.
These silos have made it increasingly challenging for agents to gain permission and access to critical information at the moment with a customer. Hindered by data silos, legacy data and analytics infrastructure, call center agents and leaders are not able to get the full picture of a customer’s experience (depositing checks, monitoring transactions, budgeting, managing loans, and mortgages, etc.).
We’ve all been in the customer seat before,
“Please wait on hold while I look up your information”
In order truly improve call center KPIs, contact center leaders need to leverage all of this siloed data in various combinations in real-time to gain proper insight. Not addressing this can result in several negative downstream results including higher customer churn, poor agent attrition, and higher costs.
The Cost of Customer Churn
Unplanned customer churn costs banks around $15.6 billion. Like other sectors, more bank customers switch providers (35.9%) than plan to (20.6%). This means that 74% more bank customers end up leaving banks than want to.
While call centers are aided with the support of interactive voice response systems (IVR) and other data collection technology, these solutions produce large volumes of data that can be hard to process. Organizations are often left with large stores of data without the ability to leverage the information to gain insights.
To remedy this problem and drastically improve the customer (and agent) experience, call center operations need to invest in three critical components:
With a single source of truth, call center operations improve the customer experience through a consistent call experience and streamlined operations. To do so, call centers must transform their data platforms to meet the requirements of the business by selecting a platform that scales as the business grows.
The other piece is data interoperability–data that can be created, exchanged, and consumed with clear and shared expectations for the contents, context, and meaning of that data.
Finally, combined with a data visualization platform, call centers analyze customer or agent information from various sources and personalize their customer experience. Over the next few sections, we’ll cover all three of these components in more detail.
phData’s Root Cause Analysis dashboard identifies the root cause of variability in staffing – so you can optimize staffing with your Customer Satisfaction Scores.
Call centers have a lot of data, but it is often spread out across different systems and departments. This can make it difficult to get a clear picture of what is happening and even murkier for making decisions based on that data.
To remedy this, we recommend choosing a centralized, cloud-based data platform that serves as the single source of truth for your data. We’ve seen customers have the best success with the Snowflake Data Cloud.
Snowflake easily ingests and stores call center data and can handle massive amounts of data quickly and efficiently, ultimately providing users with the ability to run complex queries and analyses. This makes Snowflake an ideal platform for centralizing call center data.
Data interoperability is the ability to exchange data between two or more systems in a way that is consistent and accurate.
In other words, data can flow freely between different systems, platforms, and applications. There are a few key things to keep in mind when creating data interoperability for call centers:
A call center dashboard is a visual display of the most important information that call center managers need to know at a glance. This information includes key performance indicators (KPIs) such as average handle time, first call resolution rate, and abandoned call rate.
Call center dashboards help managers improve customer experience by allowing them to see how their center is performing against key metrics. They can also spot trends and problems quickly, and take action to improve performance.
There are many different KPIs that can be important for call centers, but some of the most important ones are:
Average Handle Time: Measures the average amount of time it takes for a call center agent to handle a call from start to finish. A shorter average handle time is generally better, as it indicates that agents can resolve calls quickly.
First Call Resolution Rate: Captures the percentage of calls that are resolved on the first call. A higher first call resolution rate is generally better, as it indicates that agents can resolve calls quickly and without the need for escalation.
Abandoned Call Rate: Measures the percentage of calls that are abandoned by customers before they are answered by an agent. A lower abandoned call rate is generally better, as it indicates that customers are less likely to hang up before they reach an agent.
phData’s Average Handle Time dashboard monitors handle time, trends within the metric, variance against forecast, and downstream effects in agent ramp time due to performance.
Tableau is the ideal choice for creating call center dashboards simply because it is a powerful data visualization platform that makes it easy to create beautiful, informative dashboards.
Now that we have a general idea of how to improve the call center experience with data, let’s explore four real use cases where data can play a vital role in improving the customer experience in call centers.
Staffing, hiring, and attrition data are critical to understanding both financial risk and service risk as capacity and demand requirements interact. Analytics in this space should flow into iterative planning processes that regularly inform strategic responses to anticipate and address risk before it can impact results.
Historical data can help to understand the root causes of service excesses and deficits, and in turn, shape the accuracy and effectiveness of planning for future weeks and months. This allows staffing to hit targeted occupancy levels that balance cost with results.
phData’s Call Center Capacity Summary dashboard showcases service KPIs and variability so operational leadership can monitor the drivers senior leadership are constantly monitoring.
Phone and digital channel service performance require that staff is available the moment contacts are routed to them. Views of staffing availability against forecasted requirements at a weekly, daily, and intra-day level are necessary and must be frequently evaluated and actively managed.
An organization may have internal or external service targets to meet, requiring that call center operations meet standards for how frequently and how long callers wait in a queue before their call is answered. Service Level, Average Speed of Answer, or Abandonment Rate are all key targets. Performance guarantees in specific industries may mean financial penalties if these goals are missed.
In all cases, near real-time views of these metrics are critical to ensuring they are met and managed.
To a customer, a contact center employee they interact with is their best representation of the company itself. Those call or chat interactions are the front lines for driving customer retention and influencing NPS (Net Promoter Score).
Contact center operations need a clear picture of how well customers are served once the contact is handled. Metrics like first call resolution, post-call survey results, post-contact account actions, and general call quality metrics help draw a line to NPS results, which loop back to contact rate drivers.
Poor call quality will in turn drive additional call volume and increase staffing requirements as well as customers making more calls to get the outcomes they are seeking.
The shift of consumer preference from phone to digital channels presents both opportunities for creative solutions in workforce optimization, as well as challenges in maintaining high quality and favorable outcomes from those digital channel interactions.
Chat interactions remove the need for digital transcription, making sentiment analysis and other Natural Language Processing (NLP) models more accessible for managing quality and outcomes. However, the nature of chat makes the perception of wait time more complex to understand and manage effectively. On a phone call, customers have high sensitivity to wait times, and responsiveness is considered a “must-have.”
With chat, customers feel “on hold” after a question they type, and each reply now has its own response time to measure. There may also be an observed shift in overall handle time and resolution rate across channels, as customers with more difficult or complex issues may opt to call rather than chat.
This leaves the phone lines as a reliably critical channel to support and increases the challenge of converting those calls into positive interactions as the “easy-wins” stay in digital channels.
The combination of the Snowflake Data Cloud and Tableau helps call centers turn massive volumes of siloed customer and call center data into actionable insights. The secure and flexible platforms help improve operational efficiencies across the call center–ranging from predictive analytics for capacity planning and fraud to actionable business science supporting call quality and positive customer outcomes.
Organizations obtain strong outcomes by centralizing data in a single, secure location. With a single source of truth built for Tableau users, data sources can be combined for a holistic view of both the customer and the agent. With Tableau, speed to insights is decreased from weeks to minutes.
phData is a data and analytics consulting company with a long history of helping some of the largest organizations in the world with their data needs. We’ve implemented Snowflake within organizations to securely store, share, and integrate data with multiple sources. Our industry leadership using Tableau as a visual analytics platform has allowed us to democratize data across call center operations.
Our advisory services have propelled firms to derive data-informed decisions to reduce customer churn through call center insights, develop new channels of revenue through personalization, or systematically decrease risk variances within lines of business.
phData also offers platform accelerators that decrease the speed of implementation (migrations to Snowflake are decreased from months to weeks). And with quick data transformations, our dashboard accelerators for Tableau allow for deep call center insights (comparing staffed vs planned, call volume variance, root cause analysis, etc.) with a swap of a data source.
Growth-forward businesses choose phData because of our deep understanding of data platforms, ability to drive change through data modernization, and expertise within multiple verticals, customer experience, and call centers.
With Snowflake, Tableau, and phData, your call center ensures a strong customer experience by building a single source of truth to power visual analytics across your organizations.
Challenge: Listening to customer feedback and seeing a slight, but consistent uptick in customer churn–and looking to stay ahead of the competition, one of the leading regional banks in the Southeast chose to modernize its data-driven decision-making processes. Limited by slow legacy systems and processes, and without any centralized data platform, they looked to validate the value of Snowflake for analyzing data from a variety of sources and for a range of business use cases.
Solution: phData helped the regional bank prove out the value of Snowflake as a viable data platform for their organization. Through a five-week pilot program, the phData team created automated, scalable pipelines for two Snowflake use cases. The work included reviewing data sources, designing ingestion architecture using Fivetran and dbt Cloud, and building call center insights with Tableau.
Results: After building a data mart in Snowflake, the speed to insight moved from two weeks–where analysts were coming in on the weekend to make reporting look “automated” for call center leadership meetings on Monday mornings–to actually automating call center insights–giving the weekends back to the analytics team while allowing them to also focus on bigger, deeper insights within the business, resulting in annual savings of $425,000.
Businesses have so much to gain by transforming their call center data into actionable insights. From a noticeable improvement to the customer experience to better long and short-range capacity planning, the time is now to better harness data to make more impactful decisions.
If you’d like to learn more about how Snowflake, Tableau, and phData can help drive actionable insights from data, don’t hesitate to reach out to the experts at phData! We’re happy to answer any questions about the concepts covered in this guide, costs for implementing Snowflake and Tableau, timelines, and much more.
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