Six Key Strategies for Corporate Analytics Teams

Data and analytics are perhaps the quintessential tools for the fast-paced global environment. Big data is more than a catch phrase. Businesses generate billions of data bits by the minute, revealing vital information such as identification of trends, an objective view of how the company fares competitively or whether the customer base has been retained and/or expanded. Strategic decisions hang in the balance.

And yet, data analysts and other department leaders may be at odds over the importance of data and the cost to generate and analyze it, despite a hyper-competitive global economy in which vital information is viewed as a lifeblood. The reality is companies will not get far without an accurate picture of their current and targeted customer base including age, sex, background, income and other demographic attributes, and how to adapt business operations to maximize all of that information. Additionally, analytics can graphically and accurately detail success or shortcomings of outreach efforts for engaging and attracting new customers, as well as retaining current ones. That includes the sensitive question about how to assess customer behaviors that are a key factor in strategic planning.

Businesses learned long ago that data stored and collated is wasted. Investment in any level of big data can only be justified through leveraging information to create meaningful business decisions and profitable competitive advantage. Yet here is where the obstacle known as territorial imperative manifests itself. It’s the common scenario that occurs when analysts try to present their findings in a vacuum separate from corporate culture and business operations. Ignoring the latter two could derail growth decisions predicated on big data and data analytics.

Key Strategies

It’s one thing to talk about leveraging vital information from big data but another to bring it to fruition. What follows are six key strategies necessary for corporate and analytics teams to follow to maximize their effectiveness.

1. Create a strong data culture. According to software developer Tableau, “A data culture is the collective behaviors and beliefs of people who value, practice and encourage the use of data to improve decision-making. As a result, data is woven into the operations, mindset and identity of an organization. A data culture equips everyone in your organization with the insights they need to be truly data-driven, tackling your most complex business challenges.” Unfortunately, some companies do not have this collaborative mindset and tend to operate at odds. A better alternative is for corporate and data executives to create a working environment that prioritizes data collection, management and data analytics using artificial intelligence (AI) and machine learning (ML) to help companies understand the contributing factors that impacted the past, define the present and predict the future. Once you place data quality at the heart of your organization, data culture becomes the foundation for growth in cooperation with business operations. The former cannot be ignored if the latter is to be successful.
2. Improve customer experience. In some cases, customers interact with call center agents through one channel — the phone. Increasingly, however, contact centers operate through multiple channels including phones with text messaging, apps, email, social media and web portals. The global pandemic greatly increased the strategic value of contact centers since many customers were either unable or unwilling to frequent retail establishments, including restaurants, shops or financial institutions. Some on the corporate side may argue that analytics are unnecessary for ferreting out why customers patronize a particular business, but analysts point out that effective strategy for improving customer experience and satisfaction is best implemented through deeper analysis of data generated from these interactions. Listen to your customers.
3. Make sure business intelligence comes before artificial intelligence. At its core, business intelligence (BI) analyzes historical data or past experiences to improve decision-making. AI, on the other hand, can be most useful in predicting what is likely to occur in the future. With only BI and its history of success and failures of the past available, accurate decision-making is at risk and results in less than successful efforts to increase efficiency, reduce costs and grow the customer base. That’s why operational, information technology and analytics teams need to incorporate AI and leverage the information when understood in a historical business context. According to an article in CIO magazine in 2016, the Symphony Care Network, which operates 28 post-acute healthcare facilities in Illinois, Indiana and Wisconsin, focused on its BI first when it came to improving care for patients by trying to predict potential risks for falls. Using the commercial vendor DataRobot, the company leveraged a program that allowed them to send predictions and recommendations for patients directly to 240 doctors and nurses, accessible via their tablets and smartphones. Symphony then used DataRobot’s AI to see if those predictions were useful by creating a study that tracked readmission rates of patients to its facilities in an effort to reduce costs. The six-month study revealed readmission rates dropped from 21% to approximately 18.8%.
4. Tactfully acknowledge problems and challenges on the business side. With a common gap between business-minded and analytical executives, it’s easy for feathers to get ruffled. Analytics people may focus on the newest and best technologies that can benefit the company, but the reality is business issues must come first. For example, what happens when the IT team is proposing an expensive, long-term investment in a major technology upgrade? Without bringing the financial staff into the discussion from the beginning, they are likely to just shake their heads because they cannot justify the expense. The best approach here is a joint one. If technology analysts can emphasize how the investment will improve business decision-making by involving the business side in early discussions, they will avoid the finger-pointing scenario that creates interdepartmental rifts. The reality is that analytics tools won’t solve issues without buy-in from the corporate side, particularly those in finance.
5. Think big, start small and learn fast. For a huge company like Amazon, having a big, bold AI vision — like creating Alexa — makes sense. Similarly, when customers call Delta Airlines, their call center IVR system already knows that those customers are likely to be calling for only a few simple reasons: either to cancel or reschedule a flight or perhaps check their tickets status. The airline already has all the customer’s relevant information in their database. As a result, the airline knows what their customers want and is more likely to deliver a good customer experience. The axiom about thinking big, starting small and learning fast applies more to small- and medium-scale companies searching for innovative ways to solve business issues. A local bank, for example, needs to think small to create a positive customer experience. A bank call center is likely to have customers with myriad questions about online banking issues, mortgage loans, credit card questions, disputes or replacement checks. In this context, figuring out how customers experience satisfaction in call center interactions will pay off long-term. The key should always be to solve the business problem/question first, and then develop products and services to meet those needs.
6. Implement efficient, cross-functional operating models. For data and analytics to work effectively, it’s important for diverse teams to work together. That’s not an easy task when expertise can come from any part of the organization — from data scientists and analysts to architectural, operations and technology personnel — all under the same roof and simultaneously working on multiple priorities. Executives need to prioritize tasks and initiatives by bringing together everyone and creating a platform that allows every team to cross collaborate in a timely fashion. Understanding the needs of each group is a must if there is to be successful and timely cross-collaboration that will develop a workable and reality-based growth strategy.

Misconceptions and Common Pitfalls

Implementing these six strategies will be daunting unless companies avoid three misconceptions and common pitfalls. One is that data collection, management and analysis are too expensive for small- and medium-sized businesses. The reality is there are always reasonably priced solutions, and when properly implemented, can deliver the ROI companies expect.

A second misconception comes from the belief that data collection, management and analysis are unacceptably time intensive. With well-developed plans and the appropriate tools, data and analytics initiatives can be implemented relatively quickly. Of course, this requires corporate data, finance and other teams to do their homework and work together.

Third, it is important to recognize that data collection, management and analysis are not a one-time event. By their very nature, business needs and situations are continually evolving. Whether there are changes in staffing, products and services, pricing or emerging trends or events in the local or global economy, data needs will continually evolve. Data must be kept up to date to be accurate, relevant and actionable.

Corporate executives, even those with smaller companies, understand that a competitive edge depends upon a data- and technology-driven model. An increased customer base, clearly defined customer retention program, and marketing and growth strategy can be best achieved through a teamwork-based data collection and interpretation strategy — a team where executives, departments and analysts work together strategically, tactically and tactfully.

This article originally appeared at Analytics on June 29, 2021.

Nareshkumar Jayavelu is assistant vice president and a senior data scientist at Regions Bank in Birmingham, Ala. He works primarily on building machine-learning and deep-learning solutions. He has a degree in analytics from the University of Alabama in Huntsville and has spent more than nine years in data-related roles.