Healthcare analytics
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Customer Analytics: The Role of Integrated Systems

According to International Data Corporation (IDC)

The basic processes of acquiring, retaining, and growing customers are as old as commerce. Yet the quote by the Greek philosopher Heraclitus that “you cannot step twice into the same river” rings truer today than at any time in history. While these core tasks of sales, marketing, and customer service professionals have not changed, everything around them has:

  • The channels of interaction have multiplied and include in-person and online interactions across a range of media and communication methods.
  • Customer expectations have changed in line with best practices by new, innovative consumer-driven companies.
  • Digitization of customer interactions is generating huge volumes of data.
  • New research continues to provide evidence of the value of analytics and data-driven decision making for producing positive outcomes across industries.
  • Progress in information technology (IT) has resulted in vastly improved price/performance parameters for acquiring, managing, and analyzing large volumes of multi structured data.

These are just a few examples of the new factors facing sales, marketing, and customer service managers as they seek to make sense of the deluge of data, uncover insights that can drive better decisions, and empower analysts, customer-facing employees, and customers with the right information at the right time — a variable that can mean anything from a specific time of day and day with a specific weather outlook to the right location and the right mood of the customer.

Among the latest solutions available to organizations to address these opportunities and challenges are workload-optimized systems that support a wide range of analytics workloads on big data.

 

Situation Overview

The convergence of intelligent devices, social networking, pervasive broadband networking, and analytics is ushering in a new economic reality that is redefining relationships among producers, distributors, and consumers of goods and services.

In the enterprise, these trends have resulted in a significant decrease in the ability of managers to rely effectively only on experience or intuition to make decisions. The old cause-and-effect models are becoming less relevant, while the demand to respond faster and with greater insight to ongoing internal and external events based on facts is increasing.

In this environment, not only access to information but also the ability to analyze and act upon information creates competitive advantage in commercial transactions, enables sustainable management of communities, and promotes appropriate distribution of social, healthcare, and educational services.

The newly available information opens unprecedented opportunities and challenges for organizations to unlock its value. In 2012, $95 billion was spent worldwide on a range of business analytics software, hardware, and services. New use cases, case studies, and market research have confirmed the value of analytics. For example, a recent IDC study shows that 88% of organizations that have widely deployed business analytics have recognized tangible benefits from these projects. In addition:

  • For 90% of these organizations, the benefits met or exceeded expectations.
  • For 82% of these organizations, the time to achieve quantified benefits met expectations or was shorter than expected.

Addressing All Customer-Centric-Processes

The opportunities to apply customer analytics can be broadly segmented into those related to the processes of acquire, grow, and retain.

  • The acquire process highlights the use of analytics at the intersection of marketing and sales to qualify and prioritize leads and to personalize interactions. This could include personalized and optimized pricing based on historical transactions, price sensitivity, available inventory, and risk factors. Personalized pricing is common in the hospitality, travel, and insurance industries, but it is expanding to other sectors such as retail. It also includes targeted recommendations derived from behavioral segmentation based on what customers do rather than who they are (demographics) or what they say (attitudinal studies).
  • The grow process highlights upselling and cross-selling strategies directed at a specific customer based on his or her individual buying pattern rather than a group of customers. Customer analytics enable call center staff to stop asking clients generic questions — such as “Anything else I can help you with?” — And instead probe for new opportunities based on questions specific to the individual customer and probabilistic likelihood of the next purchase to be made and the current “needs state.” Online businesses are able to promote growth by utilizing social network analytics, historical transaction trends, and click-stream analytics to drive recommendations engines, thus presenting customers with next best action options — ideally “best” for both seller and customer. Supermarkets are using customer analytics to provide recommendations in the form of coupons based on longitudinal and real-time analysis of a customer’s purchases and market basket analyses.
  • The retain process highlights the use of analytics to keep customers from leaving, or churning as the process is known in some industries. Importantly, the retention process includes keeping not only customers who are at risk of defecting but also customers who are profitable, loyal, satisfied customers — and growing that loyalty. The retain process includes assessment of historical trends, real-time interactions, and predictive modeling to identify factors leading to churn. Mobile communications companies with pay-as-you-go contracts use analytics to determine the risk of customer churn on a regular basis, with the goal of predicting and minimizing such adverse events. Analytical models based on total minutes consumed by a customer can help predict customers likely to churn and then trigger a special offer, such as a discount on the next package of minutes. Newer churn models are now taking account of data on each call to better understand an individual’s calling circle and seek to provide better insight into the customer’s calling behavior. Some call centers parse in real time through dozens of attributes, including source, type of phone, address location, and credit rating, with the goal of determining churn risk. Depending on the score, the call is then routed to the customer service representative best trained to handle the call. The retain process also includes identification of customers with a significant sphere of influence via social network analysis or social analytics and empowering them to be brand advocates to solidify or increase loyalty.

These are just a few examples, but they highlight the need to tap into larger and more diverse data sets — creating the rationale for an integrated system optimized for such customer analytics use cases. Given the ever-growing amounts of data, these customer analytics processes need to be performed more quickly, using more data types and data sources, utilizing new analytic techniques, and, unfortunately, with fewer dedicated IT resources to ensure the right information gets to the right employees, the right channels, and, ultimately, the right customers.

 

Analytics in an Era of Healthcare Reform

A significant contributor, in fact the linchpin, to success under healthcare reform is the ability to leverage the volumes of data now available to support decision making at the point of care as well as mitigate adverse health events. As healthcare providers’ reimbursement moves from pay for volume to pay for value, the industry must define value as a balance of cost and quality. The industry, for example, has limited means to evaluate the “value” of paying for a procedure at one hospital or another. We can compare costs (though not easily), and in some cases, we can evaluate outcome to get at value, but the data and tools to quantify value have been limited to date.

Traditionally, healthcare organizations have relied on structured retrospective transaction data for analysis, but as access to unstructured data becomes available, we are learning that nonclinical attributes often drive clinical outcomes. For example, a large integrated delivery system in Texas used unstructured clinical data from electronic health records to identify that drug and substance abuse and living situation (living alone or with friend and family) were driving re-admissions for congestive heart failure. In another example, utility bills were used to identify seniors without air conditioning during an extended heat wave. These at-risk individuals were notified of the availability of “cooling stations.” With new data sources becoming available all the time, the industry faces the challenges of digesting the information and identifying insights for point-of-care decision making as well as understanding patient and consumer attributes that mitigate adverse events or that change unhealthy behavior.

 

Healthcare Applications for Integrated Systems

 

Conclusion

Increasingly, the decision to acquire and utilize the appropriate technology will be influenced by LOB managers, including those leading their organizations’ sales, marketing, and customer service groups.

In today’s highly demanding environment for self-service customer analytics, IT needs to be able to react quickly and be ready to address evolving requirements.

To do so, IT leaders need to:

  • Assess and evaluate the typical customer analytics use cases. There’s a difference among tactical, operational, and strategic decision-making patterns.
  • Understand the unique requirements of the various decision-maker groups: executives, managers, analysts, data scientists, customer-facing employees, and customers. Each has different preferences for technology tools, patterns of interaction with the data, and scope of decision freedom.
  • Recommend the most appropriate BDA technology. This should be done after workloads and use cases are defined based on variables such as data types and sources, concurrent users, query complexity, expected scalability requirements, and security needs
  • Consider workload-optimized integrated systems as a viable option in the broader technology portfolio to support customer analytics needs.
  • Consider factors such as power consumption, capacity, scan rates, availability, performance (both query and load speeds), and maintenance and administration costs when evaluating integrated systems.

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