Tag: Analytics & Business Intelligence

How Machine Learning Can Help You Reduce Frontline Employee Attrition

Posted on by Joe Przybylowski, Andrew Reilly, and Terri Robertson

Leveraging Machine Learning to Predict Employee Attrition Risks and Guide Retention Interventions  

How Machine Learning Can Help You Reduce Employee Turnover is a free webinar available on demand, presented by iQor’s Vice President of Operations Terri Robertson, Senior Vice President of IT Joe Przybylowski, and Data Scientist Andrew Reilly. Here’s an overview of what they discussed.  

Why Retention Strategies Are Critical to Your Business Success 

Employee attrition is a more pressing concern for organizations than ever, as evidenced by cultural dialogue like “The Great Resignation” and “quiet quitting.” Attrition is even more significant for BPOs like iQor whose business is to deliver outsourced customer service to the brands that entrust us. 

The cost of employee attrition is significant in both direct and indirect ways. For example: 

  • The average cost to hire and train a replacement employee can be up to 200% of that employee’s salary. 
  • Replacing an experienced employee with a new hire can lead to a decrease in productivity and efficiency, which can affect revenue. 
  • A high turnover rate can lead to discontinuity in customer service, which can result in loss of business. 
  • High turnover can impact morale among other employees and cause a snowball effect of attrition, incurring further costs.  

The value of investing in employees speaks for itself. At iQor, employee experience is a cornerstone of our business model. Creating a work environment that promotes job satisfaction, recognition, and growth opportunities is critical to retaining valuable tenured employees. Optimize the customer experience through human-centric interaction with agents.

Using Predictive Analytics to Help Prevent Attrition With Proactive Retention Strategies at iQor 

For over 15 years, iQor has used cutting-edge technology to provide an irresistible employee and customer experience (CX). Our team of expert data scientists has developed models for predictive analytics to guide our strategies for revenue recovery, customer satisfaction (CSAT), net promoter scores (NPS), and more. So, why not leverage our data analytics expertise to predict attrition before it has a chance to materialize?

Using the power of predictive analytics, we asked the fundamental question of attrition: How do you know when an employee is thinking about leaving?

To answer this question, our team of expert data scientists developed a machine learning model to identify variables that correlate with attrition and predict which employees might be at risk of leaving the company. Our goal was to play offense rather than defense to the problem of employee attrition by developing proactive retention strategies for at-risk employees. The model successfully:  

  • Analyzes which variables influence employee attrition. 
  • Identifies employees at risk of attrition. 
  • Guides our operations team’s retention strategies. 
  • Contributes to the retention of thousands of at-risk employees. 
  • Informs an improved employee experience through a culture of empathy and professional development opportunities. 

Using the power of predictive analytics, we asked the fundamental question of attrition: How do you know when an employee is thinking about leaving?

Customized Employee Retention Processes Guided by Predictive Analytics 

Predictive analytics placed the ball in our court to proactively shape a positive employee experience that improves retention. The foundation of machine learning and predictive analytics is data. To begin, iQor’s data scientists needed to know what data they required to build their “crystal ball” for proactive employee attrition prediction. 

Their approach to data modeling consists of three stages: 

1. What data is available?  

The existing body of structured data could include tenure, compensation and benefits, workforce management data, interaction analytics, coaching outcomes, performance reviews, bonus history, training data, and other information that iQor already gathered about our employees.  

2. What are we missing?  

After conducting an inventory of existing data, we could identify data gaps. Gaps often include unstructured data, which is more difficult to gather and too subjective to easily evaluate, such as employee feedback.  

3. What can we create?  

Identification of data gaps can then drive strategies for gathering that data. For example, annual surveys for employee feedback are an option, but that doesn’t provide enough data for accurate analytics. Filling in this gap requires a high-frequency method of employee surveying for gathering reliable and timely feedback regarding their outlook on the job.  

The answers to these questions create a broader data pool of structured and unstructured data to begin identifying which data points correlate to attrition risks. 

Transforming Raw Data Into Usable Variables  

Data is just raw material; it needs to be refined to be useful, as described by Gartner’s Four Phase Data Maturity Model. iQor’s data scientists had to decide how to categorize the data into variables that could be computed by their algorithms. They then validated each data feature using a process called variable importance testing to determine what to include and exclude from the production model. This process assesses how much influence a particular variable or feature has historically impacted our attrition. Those that rank high are selected while those that rank low are rejected. 

Using these variables and a combination of five main algorithms, our data scientists deployed a five-fold cross-validated Stacked Ensemble Meta learner with auto-tuned hyperparameters—or, in layman’s terms, they created a customized employee retention model that could assess the probability of employee attrition within the next 60 days. 

From Data Science to Pragmatic Action: The Plan for Proactive Frontline Employee Retention 

At iQor, we place a high value on providing exceptional employee experiences. As our data scientists and operations teams collaborated to develop a practical, specific, repeatable process for proactive retention, they kept the employee experience top of mind. The process needed to be simple for employees and managers in terms of time and effort expended; it needed to be standardized yet flexible; and it needed to be embeddable into our feedback culture. The result was a Measurable Skip-Level Meeting, also known as “The Touch Base.” 

“The Touch Base” Strategy for Employee Retention  

Every week, iQor runs our machine learning model for all 40,000 employees company-wide. We analyze each individual’s probability of attrition between 0% and 100%. At-risk employees are identified at a threshold of 65%. Employees who meet or exceed this threshold for attrition risks will automatically engage the company’s proactive retention strategy enacted by the operations teams. 

As a result of these analytics, our operations teams are informed of at-risk employees every week. They receive the employees’ names, the names of their direct supervisors, and the skip-level managers responsible for scheduling the Touch Base. These managers have a structured guide with clear, consistent guidelines, a system of record, and a method for feeding results back into the analytics model. 

The Touch Base is standardized enough to provide data points that can guide improvements to the machine learning model but also conversational and customized to each employee’s needs.  

It’s important to note that the Touch Base is not focused on the employee’s performance. Rather, it is about encouraging open communication to identify and support the employee’s needs. The anatomy of the Touch Base is as follows:  

  • Before the Touch Base 
    • The manager reviews the employee’s information to approach the conversation with an informed and empathetic mindset. 
    • The manager schedules the 30-minute session with the employee through our workforce management system.  
  • During the Touch Base 
    • The manager asks open-ended questions in an effort to identify what the employee may be experiencing that makes them at risk.  
    • The manager lets the employee guide the conversation to help the employee feel listened to, valued, and supported.  
    • The manager thanks the employee for their time and willingness to share. 
  • After the Touch Base 
    • We use the same module that guides our coaching to measure the effectiveness of each Touch Base. The Touch Base checklist includes a summary with the following priorities: 
      • Clarify timelines, deliverables, and expected outcomes. 
      • Agree on next steps. 
      • Schedule a follow-up meeting with the employee. 
      • Escalate to the next level of management, if needed. 
    • Lastly, the manager documents the meeting in the system with as many details as possible. 

The manager also rates the Touch Base for their impression of the employee’s overall state of being. In some cases, just having the conversation with the manager can make the employee feel valued and clear up the risk of attrition. In other cases, the situation is not something the manager can easily address, and escalation may be necessary.  

Combining Clinical Trial Methodology and Human Storytelling to Gauge the Effectiveness of the Touch Base Process and Guide Next Steps 

For iQor’s machine learning model to be effective, it has to be able to measure the effectiveness of our interventions and the results of these Touch Base meetings. We performed a controlled study, much like a clinical trial, to go beyond the manager ratings to use a scientific approach to our evaluations.  

Comparing Retention Outcomes Between Control and Experimental Groups Showed a 2.6x Increase in Retention for Touch Base Employees 

Out of 100 at-risk agents, we withheld a small sample of at-risk agents to serve as a control group. They received no Touch Base interventions. The other 95% served as the experimental group and proceeded through our Touch Base intervention process. The results were clear. After 60 days, the control group experienced 18.3% attrition while the experimental group experienced 7% attrition.  

This led to two main findings:  

  1. The model appropriately identifies employees at risk of attrition. 
  1. The intervention is meaningful.  

At-risk employees who go through the Touch Base process are 2.6 times more likely to stay with the company than employees who do not receive proactive retention strategies. We refer to this as our Attrition Mitigation Factor. The takeaway? Our Touch Base strategy worked to effectively retain at-risk employees and embody our commitment to providing an outstanding employee experience.

Attrition Mitigation Factor, How Machine Learning Can Help You Reduce Employee Turnover

Stories Shared by Agents and Managers Through a Culture of Feedback Relate Positive Outcomes Beyond the Science 

What steps actually work to retain at-risk employees? The answer to this can help us implement a repeatable process and create a culture of feedback. Feedback and storylines from our operations teams about their experiences with the Touch Base process guides continuous improvement to the model.  

Using the Touch Base process, managers now get to talk directly to frontline employees instead of just hearing from their direct managers. This makes it much easier to find resolutions and improve relationships to encourage retention. When managers listen, they can facilitate adjustments that lead to employee retention. Sometimes, preventing employee attrition is as simple as adjusting schedules. 

Putting the “Active” in “Proactive:” How the Machine Learning Model Constantly Self-Improves 

iQor’s machine learning model is dynamic. Future improvements are guided by the data science team, the operations team, and stakeholders, who have the opportunity to provide feedback and ask questions during quarterly reviews. The key is data. Each time the machine learning model processes new information, it improves its own ability to fulfill its intended purpose.  

Best Practices for a Machine Learning Approach to a Proactive Retention Strategy 

iQor’s machine learning model is successful because our data science, operations, and frontline teams collaborated to define, implement, and improve the process. Embodying our iQorian Value of open communication helped our teams develop an effective machine learning model that kept the human experience at the forefront of the process. 

Here are some best practices our teams followed: 

  • Define the problem in as much detail as possible through collaboration, data discovery, and analysis.  
  • Acquire high-quality data by asking three main questions: what data do we have, what data can we create, and what data can we enrich by joining it to another data source? 
  • Name the project to give it its own brand. Our Touch Base system makes the project memorable to stakeholders and builds value.  
  • Create a control group like a clinical trial to evaluate results for an accurate indication of effectiveness.  
  • List all KPIs to specifically define the model’s parameters.  
  • Start with a quick win to motivate involvement and maximize collaboration potential.  
  • Fail small and fast to allow the data science methodology to break the project into bite-sized pieces that are not overwhelming.  
  • Communicate between teams so they can seek support across the organization. Establishing a regular cadence for communication keeps channels open that we can use as potential input sources for continually improving the model. 

Investing in Employees to Retain Tenured, High-Performing Teams 

Employee attrition can be a significant issue for any company. Our proactive retention strategies help us stay ahead of the curve through processes that incentivize valuable employees to stay on board. iQor’s machine learning model successfully predicts attrition and guides effective intervention strategies, empowering us to transform at-risk employees into tenured career professionals that provide irresistible customer service.  

Experience the Best in CX  

iQor offers analytics as a service to enhance employee, customer, and client outcomes. Our proprietary speech analytics platform, cloud computing, machine learning, artificial intelligence, and data analysis enable us to provide effective workforce management solutions, flexible work environments, and improved coaching processes. We prioritize our employee experience and aim to cultivate a culture of success that fosters loyalty and high performance. 

As a managed services provider of customer engagement and technology-enabled business process outsourcing (BPO) solutions, iQor provides a comprehensive suite of full-service and self-service scalable offerings that are purpose-built to deliver enterprise-quality CX.  

Our award-winning CX services include:  

  • A global presence with 40+ contact centers across 10 countries.  
  • A CX private cloud that maximizes performance and scales rapidly across multiple geographies on short notice.  
  • A partnership approach where we deploy agents and C-level executives to help maximize your ROI.  
  • The perfect blend of intelligent automation for scale and performance coupled with an irresistible culture comprised of people who love to delight your customers.  
  • Virtual and hybrid customer support options to connect with customers seamlessly, when and where they want.  
  • The ability to launch a customer support program quickly, even when you need thousands of agents ready to support your customers.  
  • A best-in-class workforce management team and supporting technology to create a centralized organization that can better serve your entire business.  

iQor helps brands deliver the world’s most sought-after customer experiences. Interested in learning more about the iQor difference? If you’re ready to start a conversation with a customer experience expert, contact us to learn about how we can help you create more smiles. Heart

Joe Przybylowski is senior vice president of IT at iQor. Connect with him on LinkedIn.
Andrew Reilly is a data scientist at iQor. Connect with him on LinkedIn.
Terri Robertson is vice president of operations at iQor. Connect with her on LinkedIn.

How to Use Machine Learning to Power Conversations and Retain More Contact Center Employees

Posted on by Joe Przybylowski and Andrew Reilly

Improve the Employee Experience for Customer Service Agents and Supervisors Through Conversations Informed by Predictive Analytics   

Creating rewarding employee experiences and retaining employees is key to running any business. Using machine learning as a retention enabler is the focus of this blog post. 

While appearing on CNBC in 2019 to announce a new tool IBM had created—with AI, machine learning, and predictive analytics—to identify employee flight risk candidates, former IBM CEO Ginni Rometty said, “The best time to get to an employee is before they go.”  

By harnessing digital technology innovation, forward-thinking business process outsourcers (BPOs) can take action to reduce employee churn—especially among frontline workers.  

In this blog post, we’ll explore how BPOs can use machine learning and predictive analytics to retain more contact center employees by assessing frontline employee attrition risk and leveraging that knowledge to create an employee experience that often addresses their unique needs.  

We’ll cover: 

  • Employee retention in the 2020s. 
  • Costs of replacing contact center employees who leave. 
  • Modeling data to identify employee attrition risk patterns. 
  • Predicting and reporting employee attrition risks. 
  • Intervening to retain employees. 
  • Measuring intervention outcomes. 
  • Calculating the impact of intervention on P&L. 
  • A real-world example.

Employee Retention in the 2020s  

Since the onset of the pandemic, employee retention has become an even greater challenge than it was when Ginni Rometty appeared on CNBC in 2019.  

A global survey by PwC revealed that, across all lines of work, one in five employees expected to find a new opportunity in 2022.  

Digitally savvy BPOs use every tool at their disposal in an attempt to retain their frontline customer experience team members. 

Costs of Replacing Contact Center Employees Who Leave  

Direct and indirect costs add up quickly when replacing contact center employees who leave voluntarily. 

Direct costs include recruiting, onboarding, and training each new employee. It can take months to hire a new employee and train them to become fully versed in a new customer experience position. 

Indirect costs become factors the moment an employee departs. That’s when the employer loses the employee’s skillset, everything the employee learned about the company and the CX program they supported, as well as internal processes when working with their team and stakeholders.  

Moreover, teams have to cover the gap left by the departing employee until a new employee is onboarded and up to speed.  

When a team member departs, their loss can also lower morale. When an experienced worker leaves—especially one in a supervisory role—indirect costs rise even higher.

Watch Our Webinar

iQor SVP IT Joe Przybylowski, Data Scientist Andrew Reilly, and VP Operations Terri Robertson explain how iQor uses machine learning to help reduce employee turnover.  

Modeling Data to Identify Employee Attrition Risk Patterns

Organizations have long used annual surveys to measure employee sentiment. Surveys can provide a limited sense of how employees feel about working for an organization to help guide general improvements. Annual surveys are limited in their ability to identify individual employee sentiment and predict which specific employees are likely to leave, for several reasons: 

  • Many workers don’t complete the survey. 
  • Some workers might respond with what they think is the “right” answer instead of how they really feel. 
  • Some organizations only use anonymous surveys, so the organization doesn’t know which employees fit the profile of a flight risk. 

For a more accurate snapshot of individual employee sentiment, quick surveys conducted at regular intervals that identify employees are often more effective.  

Training the Machine Learning Model 

Data Scientists can use machine learning to analyze harvested data from former employees’ surveys and create profiles of employee sentiment that suggest when a worker is likely to voluntarily separate from the BPO. With these profiles, data scientists can create an employee attrition risk training model for a machine learning algorithm. As new data is compiled from existing workers and as more workers depart, machine learning automatically updates the algorithm to make it more accurate. 

Adding More Data Sources for Modeling Accuracy 

While using a single source of data may provide some guidance, there’s always a risk that the model is biased in some way, or that the sample size is too small. Using multiple data sources makes the model more accurate.  

Data scientists follow a formal process to identify and validate potential data sources based on elements of a worker’s environment, including how they interact with coworkers and supervisors. 

Environmental data sources might include: 

  • Schedule. 
  • Login and logout times. 
  • Frequency of breaks. 
  • Compensation. 
  • Complexity of the tasks assigned. 
  • Coaching interactions. 

Data scientists diligently explore other data sources through cross-functional analysis of processes.  

Predicting and Reporting Employee Attrition Risks 

Using multiple data sources minimizes bias built into a single data source. With enough data, predictive analytics (a subset of machine learning) can forecast which employees are the most likely flight risks and why they are at risk of leaving. 

Once data analysts have an accurate view of which employees are likely to voluntarily separate—and the probable causes for their separations—they share their findings with the employees’ managers. These reports get to the heart of who’s at risk and why, and spare managers the tedious task of having to decipher the data.  

The manager then determines the next step to take with each employee. Knowing the likely reasons an employee is an attrition risk gives managers a relevant starting point when they intervene proactively in their attempt to retain the employee. 

Intervening to Retain Employees 

Knowing why an employee is considering leaving can enable a manager to determine the best approach to resolve the employee’s concern, create a better experience for them, and help them remain on the team. 

When the manager is empowered to take an empathetic approach to intervening with the employee, they help the employee understand their commitment to resolving the concern. 

For example, if predictive analytics identifies an employee encountering scheduling concerns that make their job logistically challenging, the manager can work with the employee to design a more flexible schedule that better meets the employee’s needs. 

If, for instance, predictive analytics identifies an employee seeking additional career growth opportunities, the manager and employee can develop a plan to support the employee’s aspirations to learn and grow within the organization. 

With these and many other examples, when a manager knows the likely reasons an employee might voluntarily separate, they can tailor their approach to address the employee’s individual needs and create a better employee experience that recognizes their value to the organization and keeps them on the team.

More Real-World Examples

Learn How Machine Learning Can Help You Reduce Employee Turnover
Check out our webinar featuring iQor SVP IT Joe Przybylowski, iQor Data Scientist Andrew Reilly, and iQor VP Operations Terri Roberts to discover how to harness technology to engage employees and reduce turnover. Get the details directly from the experts responsible for this program. 
Watch Now

Measuring Intervention Effectiveness 

To determine the efficacy of interventions, companies run tests. Among a group of employee churn candidates, a portion is placed in a control group to measure the differences in outcomes between those who receive interventions and those who don’t.   

Calculations over time have proven how much more effective intervening is than not intervening to retain employees predicted to be at risk of voluntarily separating through the intelligent model explained in this blog post. This can result in significant boosts in contact center employee retention as well as improved overall employee experiences, including career advancement for retained employees.  

Bottom Line: Retaining Customer Experience Agents Improves CX 

Experienced customer service agents build relationships with their teammates, serve as mentors to newer agents, treat their customers with the care and respect they deserve, and champion the brand they represent. 

With machine learning and predictive analytics, BPOs can positively affect frontline employee retention and keep their employees and customers smiling. Heart

Experience the Best in Data Analytics   

iQor’s analytics as a service offering uses a combination of iQor’s proprietary speech analytics platform, cloud computing, machine learning, artificial intelligence, and data analysis to develop custom interventions for identified areas in need of improvement along the customer journey. The results produce targeted improvements for the employee, customer, and client. 

iQor is a business process outsourcing company ideally suited to help brands create amazing customer experiences. iQor provides a comprehensive suite of full-service and self-service scalable offerings that are purpose-built to deliver enterprise-quality CX. 

Our award-winning CX services include:   

  • A global presence with 40+ contact centers across 10 countries.   
  • A CX private cloud that maximizes performance and scales rapidly across multiple geographies on short notice.   
  • A partnership approach where we deploy agents and C-level executives to help maximize your ROI.   
  • The perfect blend of intelligent automation for scale and performance coupled with an irresistible culture comprised of people who love to delight your customers.   
  • Virtual and hybrid customer support options to connect with customers seamlessly, when and where they want.   
  • The ability to launch a customer support program quickly, even when you need thousands of agents ready to support your customers.   
  • A best-in-class workforce management team and supporting technology to create a centralized organization that can better serve your entire business. 

iQor helps brands deliver the world’s most sought-after customer experiences. Interested in learning more about the iQor difference? If you’re ready to start a conversation with a customer experience expert, contact us to learn about how we can help you create more smiles.   

Joe Przybylowski is SVP of IT at iQor. Connect with Joe on LinkedIn.
Andrew Reilly is a data scientist on the AI & Data Science Team at iQor. Connect with Andrew on LinkedIn.

The Power of Analytics as a Service to Predict and Prevent Attrition

Posted on by Andrew Reilly

How Analytics as a Service Unlocks Insights to Reduce Attrition, Boost Retention, and Elevate CX

Ongoing advances in big data and artificial intelligence (AI) provide countless insights and opportunities to improve the customer experience. One such capability is the use of machine learning to predict employee attrition. By collecting and analyzing data to identify employees at risk of attrition, data scientists can identify opportunities to intervene and reduce employee churn. These insights provided through analytics as a service can empower operations and human resources teams to develop strategies that raise employee engagement and retain key talent, ultimately improving business outcomes and creating a more rewarding employee and customer experience. Optimize the customer experience through human-centric interaction with agents.

In this blog post, we’ll dive into the data sources and processes that make this possible along with the insights and outcomes they produce. But before we do so, let’s revisit what analytics as a service is and why it’s so valuable.

What Is Analytics as a Service?

Data-driven innovation happens when data scientists are integrated into the business culture and processes. Through analytics as a service, data scientists offer guidance and expertise to improve business outcomes that can measurably support our client’s customer support programs. A digital transformation strategy that includes analytics as a service provides access to massive amounts of data which the data scientists and operations teams dissect in order to harness its potential.

As a managed services provider of customer engagement and technology-enabled business process outsourcing (BPO) solutions, iQor offers analytics as a service as part of our comprehensive CX solutions to some of the world’s top brands. Through iQor’s vast digital ecosystem, we harness advances in AI-powered technology along with the expertise of our data scientists and operations teams to perform data analytics that yields measurable outcomes to improve employee and customer experiences at scale.

Data-based digital technology solutions have the power to drive meaningful results specific to the operating environment. iQor’s data scientists interpret the data housed in our private CX cloud to translate technical research and statistics into actionable insights that inform operational decisions and improve business outcomes. The 10-steps of analytics as a service typically start small and gradually expand to incorporate the entire customer service program. One of the many areas in which they provide insight is employee attrition and retention.

The Process of Predicting Attrition

The ability to predict and prevent employee attrition presents opportunities for success in customer service. Retaining qualified, experienced, and high-performing customer-care agents and supervisors can make a world of difference in the quality and consistency of service provided to the end customer.

Accessing data through analytics as a service to provide insight into the causes of attrition can provide invaluable information to inform interventions that can increase retention and ultimately minimize employee churn.

Data scientists begin by introducing new data sources to the machine learning model to identify agents at risk of leaving the company. In order to do so, they follow a formal process for identifying and validating potential data sources, gathering the data, and cleansing it. This enables them to generate new variables out of the data sources and then run variable-importance testing that compares the predictive power of every potential variable. If the predictive power exceeds the threshold, it’s included in the machine learning model to yield insights into attrition.

This is an ongoing process by which data scientists continue to explore additional data sources to add to the model in order to make it as accurate as possible for the entire company population.

Expanding the Data to Power Predictions

In order for predictive analytics to accurately forecast attrition, the projections must originate from more than one data source. This helps ensure a balanced view of each employee’s experience and accounts for a lack of data points for certain employees.

For example, iQor gathers data from weekly Mood-o-Meter surveys that employees have the option of completing. The survey gauges their job satisfaction and generates a net happiness score that provides helpful insights into their experiences. However, if certain agents never respond to the optional survey it cannot serve as a data source for deciphering whether those agents are at risk of attrition.

To offset this type of feedback-driven source, data scientists cast a wide net and include additional data sources based on each agent’s environment to help assess how they interact with iQor. These environmental data sources include how much time the agent spends logged in to work, participating in trainings, and taking a break. They also include the agent’s total pay, bonus history, and the complexity of the customer support program on which they work. Coaching interactions serve as another source for deriving environmental data. When an agent participates in the iQor Coaching ecosystem, data scientists are able to harvest this critical data from internal systems versus needing to seek a direct response.

Together, these data sources provide a more comprehensive view of the agent experience to determine if they are at risk of churning.

Analytics as a Service Yields Up to 2X Retention Rate for At-Risk Agents

Analytics as a service yields powerful results in reducing employee attrition.

Over the past year, the insights provided by iQor’s data scientists led to employee retention strategies that have helped retain more than 1,000 customer service agents.

iQor’s Machine Learning algorithm correctly identifies agents at the highest risk of attrition on a weekly basis. This is evidenced by the difference in attrition rates between the control group (a 5% sample of at-risk agents who do not receive an intervention) and the experimental group (those receiving an intervention). The experimental group retention rate is consistently up to 2 times higher than the control group retention rate.

By improving retention, these data-based interventions have increased the overall tenure makeup of the enterprise, ultimately leading to enhanced performance. Indeed, when deployed enterprise-wide, these attrition prevention methods can have a positive impact on employee engagement and improve business outcomes on a large scale. This benefits the client’s customer support program while also instilling confidence in agents and supervisors, which can lead to improved recruiting efforts. Furthermore, the iterative nature of the process allows for ongoing opportunities to add data sources and improve the model to produce increasingly valuable results.

Experience the Best in Data Analytics

iQor’s analytics as a service offering uses a combination of iQor’s proprietary speech analytics platform, cloud computing, machine learning, artificial intelligence, and data analysis to develop custom interventions for identified areas in need of improvement along the customer journey. The results produce targeted improvements for the employee, customer, and client.

iQor is ideally suited to help brands create amazing customer experiences. iQor provides a comprehensive suite of full-service and self-service scalable offerings that are purpose-built to deliver enterprise-quality CX.

Our award-winning CX services include:

  • A global presence with 40+ contact centers across 10 countries.
  • A CX private cloud that maximizes performance and scales rapidly across multiple geographies on short notice.
  • A partnership approach where we deploy agents and C-level executives to help maximize your ROI.
  • The perfect blend of intelligent automation for scale and performance coupled with an irresistible culture comprised of people who love to delight your customers.
  • Virtual and hybrid customer support options to connect with customers seamlessly, when and where they want.
  • The ability to launch a customer support program quickly, even when you need thousands of agents ready to support your customers.
  • A best-in-class workforce management team and supporting technology to create a centralized organization that can better serve your entire business.

iQor helps brands deliver the world’s most sought-after customer experiences. Interested in learning more about the iQor difference? If you’re ready to start a conversation with a customer experience expert, contact us to learn about how we can help you create more smiles.

Andrew Reilly is a data scientist on the AI & Data Science Team at iQor.

The 10 Steps of Analytics as a Service

Posted on by Andrew Reilly

Analytics as a Service Creates Custom Problem-Solving Solutions to Empower Employees and Improve CX

Seemingly endless quantities of big data offer today’s brands many opportunities for data-driven innovation. However, to make such large quantities of data relevant and accessible to inform decisions, it is essential to integrate data scientists into the business culture and processes. Indeed, through analytics as a service, data scientists can offer guidance and expertise in choosing which metrics to use to improve outcomes. But for many businesses, accessing this data is elusive and even more challenging to sort through and analyze to inform decisions, strategy, and operations to improve experiences for employees and customers.

That’s where analytics as a service comes in as part of a digital ecosystem designed to create rewarding employee and customer experiences that build brand loyalty. With analytics as a service, customer support programs have access to massive amounts of data along with the data scientists and operations teams to make sense of it.

Digital transformation strategies that include advanced data analytics can provide insight into all steps of the customer journey and provide information about challenges along the way to inform custom solutions that improve outcomes.

Culling, storing, sorting, analyzing, and using the data to develop and test interventions is a lengthy and detailed process. Business process outsourcing (BPO) companies like iQor with a vast digital ecosystem can harness advances in artificial intelligence (AI)-powered solutions along with the expertise of our data scientists and operations teams to cover all aspects of data analytics, from start to finish, to create measurable outcomes that improve employee and customer experiences at scale. 

What Is Analytics as a Service?

Data-based digital technology solutions drive meaningful results. But in order to do so, that data must be analyzed effectively. In any customer service environment, when operations encounters an issue they are unable to resolve efficiently, data scientists can provide assistance. Through powerful analytics, data scientists can assess a problem and recommend how to best fix it. The solution is data-driven according to the particular operating environment.

As a managed services provider of customer engagement and technology-enabled BPO solutions, iQor offers analytics as a service as part of our comprehensive solutions to create great customer experiences that build brand loyalty.

iQor’s 12 terabytes of data stored on our private CX cloud is accessible to our data scientists for analytics. But regardless of how much data exists, in order to make data analytics useful, it must yield meaningful and pragmatic results.

Analytics as a service is a multi-step process involving strong collaboration between data scientists and operations teams to identify and solve problems that reduces costs and improves performance.

It is important for data scientists to build relationships with operations teams as part of a broader data-driven culture to best understand customer service program requirements. This enables the data scientists to structure their data analyses specific to each problem to develop purposeful and manageable interventions for each distinct group. The process typically starts small to test the analytics solution on one subgroup, and then gradually expands to incorporate the entire customer service program.

Because each project uses different technologies, algorithms, and tools, there are many opportunities for different technical connections. These enable data scientists to effectively identify risk and look for data outliers in the program by analyzing related statistics and scores specific to each program element.

Data journalism is an essential part of this process. Because of the technical nature of AI algorithm outputs, the information is not typically consumable by operations teams or anyone without data science expertise. As part of an ongoing effort to ensure strong and productive working relationships between data science and operations, iQor’s data scientists focus on the explicability of the outputs to create savvy translations from technical research and statistics into actionable insights that can inform operational decisions and improve outcomes.

The 10 Steps of Deploying Data Analytics

To understand the process and the power of harnessing data science analytics to improve the customer experience, it’s helpful to look at it through a real-world example. Over the span of one and a half years, iQor implemented a data analytics as a service solution designed to improve dialer optimization strategies to reach higher-risk customers for a prominent credit card issuer in the United States.

The analytics as a service solution yielded amazing results. The client saw a 180% increase in inbound right party contacts, a 15% reduction in dialer spend, and a 2% improvement in roll rate, savings thousands of dollars each month while simultaneously increasing revenue by thousands each month. Moreover, the analytics solution helped customers get back on track and deepened brand loyalty.

To yield such powerful results, iQor’s data scientists deployed a 10-step analytics as a service process as part of iQor’s digital technology ecosystem that creates irresistible CX solutions. By following our tried-and-true set of best practices for analytics success, we delivered strong outcomes for the customer, employee, and client.

Step 1: Pinpoint the Problem

The first step of analytics as a service is to identify the core problem that needs fixing. Using the credit card issuer case study as an example, the core problem was the challenge of reaching a certain subset of customers.

The advent of cell phones altered the effectiveness of outbound customer service calls. Caller ID empowered customers to decline calls and number blocking enabled them to prevent many calls from even going through to the customer. This made it increasingly challenging for customer service representatives to reach customers by phone. The goal was to reduce the number of outbound calls per customer while reaching the same number of customers.

Step 2: Data Discovery

Once the problem is identified, the next phase of analytics as a service involves accessing the data warehouse to cull all relevant data to support the problem-solving process. This data mining includes gathering records of customer interactions and disposition codes, customer surveys, account details, and all other relevant data points from a massive pool of data.

Step 3: Analyze Outcomes Within Data

Once the relevant data are identified, the next step in the analytics process is to analyze the data according to outcomes. In the outbound calling example, this included a variety of outcomes. Did customer service agents reach the wrong number when dialing the number associated with the customer account? Did they reach the wrong party? Did the call lead to voicemail? Did the customer service agent reach the customer? Were they able to help the customer get back on track with (or through) a payment schedule? What is the customer’s payment history?

By analyzing the data according to various relevant outcomes, data scientists are able to more clearly see patterns and trends.

Step 4: Identify Trends in the Data

By joining data tables from the different outcomes together, data scientists can make relevant associations and find valuable insights. In identifying trends and associations among all the results, they can make sense of large quantities of data to inform decision-making related to the specific goal or problem.

Step 5: Exploratory Data Analysis (EDA)

With the problem named, data sorted, and trends identified, the fifth step in the analytics process involves looking closely at how the business is actually operating. Data scientists communicate openly with operations to share data and potential findings with them. Through this open and ongoing dialogue, data scientists delve into an exploratory phase and are able to gather more information to better understand the meaning of each data point, what it represents, and how to best utilize it to create solutions for the problem.

Step 6: Experimental Design for Solutions

Next, the data scientists take their analysis and use it to inform their data-backed decisions and solutions. This is a critically important phase for ensuring data scientists make a positive impact on the operating environment. The goal is to start small, implementing solutions on a micro-scale to test their results before rolling them out on a broader level. This stage can be the most challenging aspect of analytics as a service if data scientists are perceived as outsiders wanting to change business operations. But, in a data-based culture with strong relationships and open communication in place, this stage can yield powerful results.

When the data scientists and the operations team partner together to implement solutions with the goal of boosting outcomes, the collaboration can produce great improvements.

Step 7: Understand the Parameters and Group for Similarities

By harnessing the power of artificial intelligence, data scientists can group the data into more specified clusters for more meaningful results. Data scientists can develop an AI algorithm to identify specified data according to certain similarities between customers, separating it into any number of clusters. The goal is to further group the data into distinct clusters that are still large enough to be meaningful. In this example for optimizing outbound calls, they separated the data into three clusters to generate the most meaningful results. The goal is then to apply the most appropriate intervention to each of these separate clusters.

Step 8: Apply the Intervention

When applying the intervention, our data scientists always start small with one of the cluster groups. This makes the interventions more manageable for operations because the solution is tested on one small group within a cluster instead of the entire program at once. This also facilitates a more focused measurement of the various impacts of the intervention.

Step 9: Measure the Impact

Once applied to the cluster group, it is essential to measure the impact of the intervention. Through careful experimental design with the test group and control group, data scientists measure and analyze results to identify differences and solve the specific problem.

Step 10: Refine Processes and Scale to the Entire Program

While testing the intervention on a small group and a control group, data scientists work with operations to optimize existing processes (adjust the intensity, timing, etc. of intervention) to make them more efficient for the entire program. It is essential that the goal for the program solution remain clear throughout this process to drive relevant results. After tweaking the intervention, data scientists and operations collaborate to scale the solution to the entire program, generating improvements and outstanding results.

Analytics as a Service Improves CX and Increases Revenue

Analytics as a service powered by AI and clearly defined processes by data scientists can yield tremendous results in the customer experience. In this example, to improve outbound processes for a major credit card issuer, the team of data scientists identified a way to dial less while still reaching the same number of customers. In fact, they reduced outbound dialing by almost two-thirds but still reached the same number of customers.

Moreover, using data and analytics they reduced dialing attempts from eight to four daily calls. Through analytics as a service, they determined the efficacy of leaving a voicemail message after four call attempts. Many customers that received a message called back. Inbound call volume increased from customers who then worked with customer service representatives to develop payment plans that worked for them, helping them get back on track and building brand loyalty. With analytics as a service, the program saw increased revenue through inbound channels and greater outcomes from accounts receivable management, two very difficult outcomes to achieve.

Experience the Best in Data Analytics

iQor’s analytics as a service offering uses a combination of iQor’s proprietary speech analytics platform, cloud computing, machine learning, artificial intelligence, and data analysis to develop custom interventions for identified areas in need of improvement along the customer journey. The results produce targeted improvements for the employee, customer, and client.

iQor is ideally suited to help brands create amazing customer experiences. iQor provides a comprehensive suite of full-service and self-service scalable offerings that are purpose-built to deliver enterprise-quality CX.

Our award-winning CX services include:

  • A global presence with 40+ contact centers across 10 countries.
  • A CX private cloud that maximizes performance and scales rapidly across multiple geographies on short notice.
  • A partnership approach where we deploy agents and C-level executives to help maximize your ROI.
  • The perfect blend of intelligent automation for scale and performance coupled with an irresistible culture comprised of people who love to delight your customers.
  • Virtual and hybrid customer support options to connect with customers seamlessly, when and where they want.
  • The ability to launch a customer support program quickly, even when you need thousands of agents ready to support your customers.
  • A best-in-class workforce management team and supporting technology to create a centralized organization that can better serve your entire business.

iQor helps brands deliver the world’s most sought-after customer experiences. Interested in learning more about the iQor difference? If you’re ready to start a conversation with a customer experience expert, contact us to learn about how we can help you create more smiles.

Andrew Reilly is a data scientist on the AI & Data Science Team at iQor.

How to Optimize Your Digital Customer Experience Through Digital Transformation

Posted on by Nicole Gobbo

Designing the Digital Customer Experience Within Your Digital Transformation Strategy

Digital transformation is the purposeful integration of technology into all areas of your business. The digital customer experience (DCX) is one element of this transformation that generates a big impact on your business. Indeed, with clear leadership and consistent vision, digital transformation can improve efficiencies and experiences with your brand in today’s digital economy. The digital customer experience is foundational to digital transformation strategies with these common elements: 

  1. Customer experience.
  2. Operational agility.
  3. Culture and leadership. 
  4. Workforce enablement.
  5. Digital technology integration.

In this post, we focus on the digital customer experience component of a comprehensive digital transformation strategy. Digital CX is the sum total of all online interactions a customer has with your brand and how those interactions make them feel about your brand. It could be exclusive to your company website, but often the experience also includes mobile apps, chatbots, social media, email, and any other digital customer touchpoints you employ. All of these elements work in unison to create the overall experience customers have with your brand.  

That experience is valuable to you. It defines how likely customers are to turn to you when they need a product or service like yours, if they will tell others about you, and if they will leave a positive online review. Your business is made up of a multitude of elements that together influence your customers’ experiences one way or the other: website, product and service offerings, pricing model, marketing campaigns, customer support, billing, collection and more are all vital parts of the customer experience. 

As your business continues to evolve and incorporate new technologies, implementing the right digital customer experience strategy for your brand will grow more important. In fact, Gartner predicts that 70% of customer interactions will involve emerging technologies this year. Digital technology can improve your brand’s ability to understand and serve its customers, but the customer experience is a human experience and the essence of human connectivity should remain at the heart of every customer interaction. 

Why Is the Digital Customer Experience So Important?

With CX driving over two-thirds of customer loyalty (more than brand and price combined), it has an enormous impact on the growth potential of your business. In the digital age, more and more customers expect to access your customer support services across a variety of digital platforms. The digital customer experience is about creating smiles through frictionless virtual interactions at each step of the journey. 

Digital CX is the greatest opportunity for engagement, advocacy, acquisition, and retention. It is increasingly important in the midst of changing preferences and expectations due to COVD-19. In fact, the top 5% of brands that scored the highest across industries on Forrester’s U.S. 2021 Customer Experience Index adjusted their DCX strategy during the pandemic to meet core customer needs. They outperformed other brands in effectiveness and ease to create enjoyable customer experiences that generated more goodwill towards their brands.   

With so many touchpoints through which you can offer your product or service, it’s crucial that you strategize early on about how best to execute a CX strategy that integrates them all effectively. But what exactly does it mean for brands today to have an effective digital CX strategy? 

In practice, digital customer experience strategies are both data- and people-driven. The whole point of digital CX is to provide customers with more choice, greater convenience, and speed while maintaining the human touch so customers feel appreciated. Data helps you do that by providing insight into how customers are interacting with your brand as well as why they aren’t choosing to do so at certain times or in certain ways. 

When the data shows unwelcome trends cropping up in the customer journey, you can use it to develop strategies for optimizing the digital touchpoints that contribute to the gap. Data helps you prioritize mission-critical tasks and leave less important optimizations for later. It can also help you identify and modify or eliminate digital optimizations that don’t produce positive outcomes for the customer. Ensuring these positive outcomes is essential to prevent customer churn. According to customer experience research by PwC, even when customers in the U.S. have positive impressions of a company or product, 59% will walk away after several bad experiences and 17% will leave after one bad experience. 

Digital Customer Experiences Improve Your Relationships With Customers

Digital transformation means modernizing every aspect of your business, starting with customer interactions. You can accomplish this by providing new digital customer service options that better connect you with customers and improve their overall experience while maintaining a consistent and clear brand voice across channels. 

Chatbots and other artificial intelligence-powered technologies (AI) can play a key role in the digital customer experience. The point is not to remove the human customer service agent from the customer journey entirely. Rather, AI offers an opportunity to empower agents to focus their attention on higher-value tasks—not frequently asked questions that can be adequately handled by digital customer service solutions that extend beyond your company’s knowledge base. Hybrid human and AI-enabled contact centers, for example, can greatly improve the speed, efficiency, and satisfaction of customer interactions. To this end, Juniper research forecasts that this year chatbots will provide an annual savings of more than $8 billion across industries. 

In the retail industry alone, brands are harnessing the competitive advantages of AI to automate time-consuming, routine tasks and increase operational efficiencies. The adaptive and time-saving features of chatbots have led about 80% of brands globally to use chatbots or plan to use them in the future. Their uses extend beyond answering customer questions to engage consumers and provide personalized service as well. Retailers are innovating the use of AI to improve customer experiences in a multitude of ways, from customized recommendations and personalized digital product displays to auto-generated shopping lists and digital support to answer customer questions while in the store. 

At iQor, we create personalized experiences like these through a combination of digital technology solutions and contact center agents. We foster amazing human-centered digital customer experiences around the globe to provide a faster and more efficient service across multiple digital channels on behalf of our clients spanning industries such as healthcare, telecom, retail, insurance, energy, banking, and others. Our contact center agents are efficient and happy because our digital transformation model integrates internal or third-party data sources along with an AI infrastructure to rapidly feed information to our human agents on the frontline of customer support. With this model, easy, frequent tasks are resolved faster, while more complicated problems are handled by human agents. The result is a more cost-effective customer experience model with happy agents and most importantly, satisfied customers.  

Digital Customer Experiences Improve Efficiencies 

Today’s consumer wants to access your customer service options through the digital channel of their choice, and they will do so at all hours of the day. Many companies have a global customer base, which makes it necessary to serve customers across all time zones at scale. 

Digital transformation is your commitment to revolutionize the digital customer experience allowing you to fulfill customer expectations at every stage of the customer journey, resulting in enhanced brand loyalty and immense productivity benefits. At a high level, your digital transformation strategy needs these four basic elements for an optimized DCX:  

  • Understand the evolving needs of your customers. 
  • Enable customers to engage with you easily on digital channels of their choice. 
  • Respond to and resolve customer inquiries quickly. 
  • Integrate human-centric support with digital support. 

It’s essential that your digital customer experience is as frictionless as possible. Done right, your digital transformation strategy empowers you to deliver greater speed and convenience to your customers. You can also scale it as you grow, which is your reward for providing an outstanding customer journey. For example, a retail client is able to scale their customer service during peak seasons by 750%. 

Digital transformation will make your organization more productive and help drive customer loyalty. Many customer inquiries can be resolved through digital technologies such as interactive voice response (IVR) that can provide quick and convenient answers to simple questions like “what is the status of my order?” A digital customer experience lets customers self-serve issues on their own with ease. Once they receive an answer to their question or complete their transaction, they’re more likely to be a satisfied customer, increasing their loyalty and your ability to sell more or earn a referral.  

The Benefits of Integrating Data Into the Digital Customer Experience 

The two cornerstones of data integration in the digital customer experience are analytics and insights. While analytics lets you identify patterns in need of optimization, insights point to actual customer behaviors and trends in real-time. As a result, you’ll be better prepared to make decisions about how to optimize your customer experience.  

Data integration, analytics, and insights help you quickly identify trends and obstacles in your customer experience journey. Once you’ve identified them, you can decide how best to prioritize areas of improvement to maximize their impact on customer satisfaction. iQor, for example, utilizes a unified workforce management (WFM) solution to reduce labor waste through intelligent forecasting and scheduling while eliminating mundane tasks and enabling agents to focus on value-added tasks for meaningful customer engagements with more timely results.  

Data integration into the digital customer experience allows you to:  

  • Deliver targeted and relevant content to customers at just the right moment. 
  • Create a continuous customer experience across all media channels and touchpoints. 
  • Keep an up-to-date profile for each customer, so that you know their history and can predict their future needs. 
  • Resolve questions and complaints faster while enhancing customer satisfaction.  

Why a Digital Customer Experience Is Imperative

From requesting a quote or paying a bill to signing up for a service, we live in an increasingly digital world; offering your customers speed and convenience through multiple channels will advance your brand’s reputation. Now more than ever, companies must optimize the digital customer experience to meet and exceed customer expectations.  

The Digital Customer Experience Improves Outcomes 

The pillars of digital customer experience management are intelligent automation, digitization of CX infrastructure, and improved customer service agent interactions. An effective digital transformation strategy simplifies the customer experience at every turn. For example, call deflection using intelligent chatbots can save time and money while providing a great customer experience at virtually any time of day, for any time zone.  

The digital customer experience is all about streamlining processes to create a smooth and convenient journey for customers. They must be able to find what they need, when they need it, without having to jump through unnecessary hoops. Thanks to business intelligence powered by data, you’re able to predict customer needs and wants better than ever before and use that data to optimize results.  

This streamlined approach is not just a matter of convenience. By providing omnichannel marketing and digital interaction, brands can attract new customers while keeping current ones. If consumers engage with a brand’s marketing through their preferred channel, they should also be able to interact with the brand through their chosen channel for customer service. Ultimately, the path to satisfied customers (and more business) centers on making the journey as efficient as possible—digital customer experience management provides the means to do just that.  

Digital Solutions to Streamline Customer Experiences

The digital customer experience paradigm requires businesses to step up their game to deliver an empowered customer journey across all digital channels relevant to their customer base. One of the best ways to impact the customer experience is by closely monitoring and proactively responding to what customers say about your brand or products across social media platforms like Facebook, Twitter, Instagram, YouTube, and TikTok. 

How exactly do you do this?  

It’s no secret that consumers are active on social media. Customer care agents can monitor a diverse range of digital channels to derive insight into customer needs and expectations. Your agents can answer questions, respond to complaints, and redirect customer comments to the relevant support channels.  

Social media monitoring can improve customer satisfaction and elevate the reputation of your brand. For example, a telecom provider ramped up their social media support by 400%, jumping from 10 to 40 specialized agents to support new promotional programs. iQor trained experienced agents to maximize their impact on Facebook, Twitter, and Instagram while utilizing digital social media monitoring solutions to streamline their responses at scale. Agents outperformed their 6-minute response time SLA, averaging 5.3 minutes per response, while also nearly doubling net promoter scores (NPS). Instead of focusing on concurrence (measuring how many conversations an agent can handle at once), overall customer experience and satisfaction served as the primary metric because of the increased exposure social media affords.

But social media monitoring is just one possibility. You can also use automation to send out personalized thank you messages to your customers or in some cases use face-to-face teleconferencing to interact with a customer directly. Contemporary customer communications technology enables you to engage your customers and enhance their experiences in as many ways as you can imagine.  

Choosing among various digital solutions that could improve the customer experience depends largely on your industry. The key is to use data to determine the best ways of connecting with customers on their terms.  

How to Optimize Digital Solutions for the Best Customer Experience 

Optimizing the digital customer experience is an ongoing and ever-evolving endeavor. There will always be opportunities to ensure your omnichannel customer experience options are available in an intuitive, hassle-free interface that’s fast and easy to use. Just because your customer services are accessible during normal operating hours doesn’t mean they’ll work the same way outside of business hours. In fact, sudden surges of customer interest have the potential to overwhelm any of your digital channels if their infrastructure isn’t optimized.  

In particular, data can help you identify areas for improvement in your digital customer experience offerings. Every digital marketing experience you create can provoke a customer response, be it impressions, clicks, or conversions. As customers interact with your brand, their encounters will be logged in your system as customer data, allowing you to build up a repository of information and insights about customer support questions and resolutions. This provides an excellent source of data that you can mine for long-term business advantages. Assessing value-added service offerings to determine where else you can provide efficiencies, increase customer satisfaction, or improve employee retention and engagement facilitates the continuous improvement of customer experiences. 

Some of the key standards to use when measuring the effectiveness of digital solutions for customer experience management are:  

  • Convenience
  • Customizability
  • Performance
  • Omnichannel availability

At iQor, we deploy digitally-enabled intelligent technology that meets these standards and empowers agents to elevate customer experiences. Our unified omnichannel CRM provides a 360-degree view of the customer and their interactions, allowing agents to deliver highly personalized, seamless, and efficient resolutions to create a consistent brand experience.

iQor’s Unified Desktop technology bundles multiple applications, tools, and screens into one simple user interface regardless of the media type. Seeing only the necessary scripts and customer interaction history, agents can more easily provide fast, efficient, and exceptional service.

We also use real-time AI that mimics human perception to detect speech patterns, tone, dead air, and word choices. It provides agents with immediate digital alerts or recommendations so they can respond to customers with a higher degree of empathy. Without engaging a live supervisor, AI coaching provides emotional intelligence to take CX to the next level.

In sum, proper DCX needs to meet customers where they are, at any time of day. You’ll need to analyze which digital channels are most effective for your business, such as your website, social media platforms, mobile apps, etc., and adapt your customer experience options accordingly. Maintaining up-to-date CX technology infrastructure that operates at high availability in response to customer requests is necessary for an optimized digital customer experience.  

How to Keep Data Secure Off-Site and in the Office 

The shift to digital channels has made customer experiences more convenient while simultaneously leading companies to take a more comprehensive approach to cybersecurity. With global malware attacks on the rise, remote workers logging in from more endpoints, and customers buying and interacting in new ways, businesses must extend their endpoint protections to reduce risk. John O’Malley, senior vice president of platforms and desktops at iQor, recently wrote about the importance of a comprehensive work-at-home endpoint security ecosystem for globally distributed access points. This is one of the multiple layers we employ to provide world-class security for our clients.

Investing in and maintaining cybersecurity infrastructure promotes better customer experiences, improved trust and loyalty, and protects your brand reputation. One way of accomplishing this in today’s landscape is through software specialized for remote work environments that enables BPOs to secure agents’ company-issued thin clients, PCs, or personal machines, at scale. iQor uses Secure Remote Worker (SRW) software to temporarily lock access to personal programs on work-at-home agents’ personal devices while providing access to the same secure cloud environment they use when onsite in support of the client’s customer experience program.

The Bottom Line

The digital customer experience is here to stay. Even if your business has a significant offline component, it is evident that digital customer interactions are rapidly outpacing offline ones in many industries. A total digital transformation of your customer experience is essential to help you keep pace as new technologies continue to evolve. 

A successful digital CX strategy is one that accounts for all the customer experience variables relevant to your brand. These are not the same for every business, and you don’t have to do everything at once. Rather, examine your current customer experience options and look for touchpoints that can be optimized for digital, provided they improve the customer experience. With customer data and predictive analytics, you can refine your customer service options and provide a better experience for each customer while also creating a supportive environment for employees on the frontline serving your customers. 

If you’re ready to take your digital customer experience to the next level, partner with iQor. We’re purpose-built to deliver the world’s most sought-after customer service. Learn more by listening to our podcasts. Schedule a call with a customer experience expert today to explore how we can help you create more smiles. Contact us here.