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Why Data Scientists Are Important
In 300 BC, the ancient Egyptians built the library of Alexandria. Long before anyone uttered the words “data science,” their effort to hold all human knowledge marked the first time that people tried to capture data and make it useful.
Today, the amount of data individuals, businesses, and organizations generate is mind-numbing. People, businesses, and organizations generated around 79 zettabytes of data in 2021—that’s 79 million gigabytes. For decades, most collected data sat untapped in storage, because we didn’t have cost-effective ways to mine, analyze, and use it.
Actions and intentions have changed from random data collection en masse to driving business value from actionable data at scale.
Today we have digital transformation technologies that make data actionable, and huge storage facilities like data farms, data lakes, and the cloud. Innovators have created platforms to mine big data. But they still needed professionals who were adept in several challenging fields to mine the data, analyze it, and put it to good use. Addressing this need is especially important for business process outsourcing (BPO) providers like iQor as they drive continual improvements to the employee and customer experience.
Enter the Data Science Professional
While the roots of the term “data scientist” date to the 18th century, it wasn’t used to reference modern data science professionals until closer to 2010.
Data scientists have a strong background in mathematics, statistics, and programming, as well as advanced data analytics and modeling technologies like artificial intelligence (AI), machine learning, natural language processing (NLP), and deep learning. Without this extensive background and skillset, they wouldn’t be able to understand the underlying assumptions of structured and unstructured data, use sophisticated data analytics tools, or develop complex predictive models.
Data scientists work with large quantities of data. They use predictive analytics to mine big data for patterns and trends and surface the results through reporting and data visualization tools to improve processes, boost productivity, reduce costs, and even enhance the employee experience within the company. In many organizations, a data scientist sets best practices for collecting, analyzing, and interpreting data. In sum, data scientists contribute to a better customer experience and a stronger partnership between the BPO and client.
Data scientists have become increasingly vital in BPOs because of their ability to understand challenges and use data to develop solutions for BPOs and their clients.
In this blog post, we’ll look at the role of data scientists in BPOs, how they’re different from data analysts, and how they add value to a BPO’s clients in ways that help forecast future needs and consistently improve CX performance at scale.
The Difference Between a Data Scientist and a Data Analyst
Check any search engine for information about data scientists and you’ll see many entries about data analysts. But that doesn’t mean they’re the same.
Data analysts and data scientists perform two similar but very different roles. A data analyst sifts through data to find trends and insights, while a data scientist creates models to make predictions based on those trends.
Data analysts may understand the principles behind the core disciplines that a data scientist masters, but they aren’t required to have the same in-depth knowledge or the ability to practice them.
Both roles are important in business process outsourcing. The data analyst is more focused on understanding the past, while the data scientist is more concerned with predicting the future.
A BPO Data Scientist’s Most Value-Added Contributions to Improve Employee and Customer Relationships
A data scientist performs a variety of essential functions for a BPO provider that contribute to more satisfying outcomes for employees and customers. Here’s a sampling of ways a data scientist uses big data for a BPO:
- Pinpoint areas of opportunity to improve CX with previously unknown remedies.
- Identify data sources relevant to the specific need.
- Perform data mining, data engineering, and data quality practices consistent with BPO industry best practices.
- Build predictive models to inform interventions that improve metrics (AHT, FCR, NPS, and more).
- Perform risk modeling designed to minimize attrition.
- Develop new customer-centric products or services.
- Find new ways to improve operational efficiency.
- Identify opportunities for growth.
- Analyze market trends.
- Optimize existing processes.
- Find ways to improve performance specific to client scorecards.
- Evaluate the effectiveness of marketing campaigns to increase customer retention.
- Continually present research findings to stakeholders.
Data scientists add value to BPOs by discovering ways to optimize productivity and make processes more efficient. They often help BPOs save time by finding ways to automate tasks and reduce manual effort.
Plus, they can design models to help the company get a better understanding of:
- How employees feel about their workplace.
- Which employees are likely to pursue opportunities elsewhere.
How Data Scientists Help Supercharge CX
The data scientist’s role is a differentiating factor in creating the most value for the BPO’s clients, especially in the areas of employee and customer experience. Data scientists have exposure to data spanning a variety of industries within a BPO provider. They are uniquely positioned to create models and propose interventions and/or optimizations within a client’s environment. Some of those models and interventions may help the client improve efficiency and productivity in ways they wouldn’t have seen without the help of the BPO’s data scientist.
The BPO and the client implement any agreed actions in partnership.
The depth of a data scientist’s skills differentiates their functional group from that of the data analyst.
The wide variety of projects, data sources, solutioning, and outside-the-box ideation arm the data scientist with the unique breadth of skills required to successfully refine business processes and improve outcomes.
For a BPO data scientist, the most exciting position to be in is to create a solution to emerging challenges that no one yet knows how to solve.
The data tells its own story, and the data scientist works as the interpreter between the vast data and the next best step to ensure optimal solutions. To this end, when evaluating which BPO best fits your brand’s needs, it’s helpful to make sure they have in-house data scientists that offer critical contributions to their CX delivery model.
A BPO Data Scientist Blends Data With the Human Element
At iQor, data science is a part of our process to deliver the most rewarding employee and customer experiences for a diverse set of brands. To do this, it’s essential that our data scientists rely not only on the data itself but also on observations in the field.
Beyond the data layer, a data scientist proves the most value by fully understanding the fabric of CX operations. This is where site visits and ingesting the environmental data in person—eyes and ears on the production floor—can add a pragmatic and meaningful experimental design that can be deployed. The deeper the data scientist’s role is integrated with operations, the better the outcome for the client.
The power of the data is in the people behind the numbers. By listening to calls, observing interactions, conducting focus groups, and studying physical environments, data scientists can develop the background knowledge they need to better inform their data analysis and predictions to deliver stronger outcomes that make employees and customers smile. 
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.
Andrew Reilly is a data scientist on the AI & Data Science Team at iQor. Connect with Andrew on LinkedIn.
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