The 10 Steps of Analytics as a Service

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.