January Putting predictive analytics to work Good business sense but where do you begin?

Transcription

1 January 2012 Putting predictive analytics to work Good business sense but where do you begin?

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3 Table of contents The heart of the matter 2 Predictive analytics: Businesses want to do it, but few know where to begin An in-depth discussion 4 Asking the right questions up front is a keystone of success What this means for your business 14 Companies that do their homework can reap multiple benefits January 2012

4 The heart of the matter Predictive analytics: Businesses want to do it, but few know where to begin

5 Today s companies have vast amounts of existing data on hand. And, as if this were not complicated enough, they continue adding to the pile by collecting extraordinary amounts of data. Designing an effective predictive analytics initiative one that stems from a careful review of the business, the problem to be resolved, and the desired outcome is a complex undertaking that requires a significant investment of time and effort up front. As we see it, before setting out to design and implement an analytics initiative, companies should step back and find answers to some key questions. If your company is ready to leverage predictive analytics for a specific problem or decision, first ask yourselves: What will the cost be to our company if management makes a wrong business decision? What types of business decisions are we looking to inform with predictive data? Is there a correct tool we should plan on using? The answers to these and other key questions will help you identify and successfully engineer a solution to your company s specific analytics goals. The information garnered during this process will contribute, in large part, to a how-to guide that will serve as a strong foundation on which to build your company-specific predictive analytics initiative. The bottom line: Those companies that invest the time and effort up front to inform decision making and carefully tailor their analytics strategy can move forward confidently to implement a well planned initiative one that is designed to meet identified analytic goals, and reap multiple benefits stemming from better use of their data. When thoughtfully designed and properly implemented, predictive analytics can deliver improved operations, and significant revenue growth. The heart of the matter 3

6 An in-depth discussion Asking the right questions up front is a keystone of success

7 To illustrate this point: Let s assume that the proactive manager of a hotel is looking for ways to attract new customers during the holiday season, including those of the local competition. To that end, she makes it her personal goal to do all that she can to create a quality customer experience. So, you have identified a few problems you d like to tackle with predictive analytics and you re about to invest in this initiative. But first, there are some things you need to consider. Finding answers to the right questions early on is a critical success factor. Ask yourselves: 1) What would the cost to our company be if management were to make a wrong decision to a specific problem? Before embarking on a costly initiative, you will want to determine the extent to which you should run extensive analyses to determine the return or cost savings of a proposed strategy or new action. Often, the best way to answer that question is to understand the cost of a wrong decision wrong being when an undesirable outcome of the strategy or event occurs. For example, let s say a company decided to send a piece of direct mail to a particular customer, but the mailing failed to elicit the desired response from the recipient. The company made a wrong decision by sending the mail to that particular recipient. But other factors should be considered: What was the margin on that effort? What was the overall response rate for that mailing? And how does that compare to the return? In keeping with this goal, the manager spends as much time as possible in the lobby area, making herself available to resolve any issues, or simply acting as a warm and welcoming presence. She soon observes that families with small children form a large percentage of the hotel s holiday clientele. Realizing that happy children produce happy parents and that family groups generate significant revenue the manager decides to make families a priority. Since toys are often the way to a child s heart, she decides to purchase and wrap a variety of toys, and to keep the toy sleigh on hand for visiting children. Whenever the manager spots a family with small children, out comes the sleigh. To their delight, each child is invited to choose a toy even if they were not guests and had only eaten at the restaurant. Imagine the value that can stem from each of these small interactions. Specially, these families are now more likely to return to this hotel whenever they are in the area because of this positive interaction. If, for example, a family were to return and book a suite for a week, this savvy manager will have converted a few nice toys into a stay that would generate over $2,000 in room charges, meals and parking. And even if families that made their home in the neighborhood returned to dine in the hotel s restaurant, the value would also exceed the investment. Now suppose that one of the families in the above example did not return to that hotel after all. What would have been the cost of the manager s wrong decision? The hotel would only be out the cost of a few toys a negligible cost compared to the potential upside. In this case, does it make sense for the hotel to build a predictive model to determine which lobby guests should be offered toys in the hope that they will come back? Probably not! But when the cost of being wrong is high, we believe a predictive solution does make sense. For example, an $8 billion beverage distributor will lose sales revenue when products are out of stock on a retailer s shelf. If out of stocks for the distributor are more than twice the industry average, does it make sense to invest in a predictive solution? Of course! This simply serves to underscore our belief that determining the cost of making a wrong decision early on can save a company from making low value investments in predictive analytics. An in-depth discussion 5

8 To illustrate this point: Let s suppose that a healthcare firm was facing two problems 1) predicting a fraudulent insurance claim, and 2) predicting the impact of lowering reimbursement rates for patient re-admissions in hospitals. 2) What types of decisions do we want to make? Once a company has determined a need for an investment, it is important to categorize the type of decision to be addressed. This will inform and guide the modeling approach and related resource allocations toward specific skill sets and technologies. Decisions can be broadly categorized as either operational or strategic. Operational decisions are those that have a specific and unambiguous correct answer, whereas with strategic decisions an unambiguous correct answer is not always apparent. This is because strategic decisions often have a cascading effect on adjacent and related system dynamics that can sometimes be both counterintuitive and counterproductive to the original strategic goal. We see many firms focusing their analytics efforts on operational decisions, while very few have tried to integrate analytics into their strategic decision-making process. Regarding the first problem, an insurance claim is either fraudulent or it is not. The problem is specific and there is an unambiguous correct answer for each claim. Most transaction-level decisions fall into this category. But the second problem is more complicated. The firm is hypothesizing that lower rates would incent physicians and hospitals to focus on good patient education, quality follow-up outpatient care, and complete episodes of care for patients during their time in the hospital. This would presumably lower costs for the health plan. However, it could also lead to delayed discharge of patients during their first admission, or to physicians treating patients in an outpatient facility when they should be in an immediate-care setting. Think about it: This cascading effect may actually increase the cost of care and undermine the intent of the original policy, in that strategic decisions have myriad causal loops that are not apparent and make an unambiguous right answer hard to nail down. How should companies attack each of these problems? From where we stand, operational decisions require both established statistical techniques such as regressions, decision-tree analysis, and artificial intelligence techniques such as neural networks and genetic algorithms. The key focus of our example is to predict, based on historical data, whether or not a claim is fraudulent. For the strategic problem, however, predictive modeling approaches that are more explanatory in nature are needed, since it is critical to understand systemic relationships and feedback loops. A model should accurately capture the nature and extent of relationships between various entities in the system and facilitate testing of multiple scenarios. In the readmission policy example above, a model will help to determine the cost impact based on the various scenarios of provider adoption and behavior change the percentage of providers and hospitals that will improve care versus those that will not adapt to the new policy. Simulation techniques such as systems dynamics, agent-based models, Monte Carlo and scenario modeling approaches are appropriate for such problems. In fact, one of the benefits of using simulation techniques is that historical data isn t necessary in a traditional sense. One could actually develop useful assumptions about the nature and extent of relationships by doing primary or secondary research, developing proxies, leveraging expert opinions, etc. These techniques are quite effective when there is a lack of historical data. Bottom line: It is important to remember that strategic and operational decisions need different predictive modeling approaches. There are two questions to ask: First, is the decision you want to drive operational or strategic in nature? And, second, are you using the appropriate modeling approach and tools? 6 Putting predictive analytics to work

9 3) So, how do we get going? Is there a correct tool we should plan on using? Actually, choosing the right tool is the easy part! The popular statistical techniques frequently used in business analytics, such as linear regression and logistic regression, are more than half a century old. System dynamics was developed in the 1950s, and even neural networks have been around for more than 40 years. SAS was founded in 1976, while the open-source statistical tool R was developed in So the tools and their benefits and their limitations are already well understood. Instead of focusing on choosing the best tool, companies seeking to invest in a predictive analytics initiative should be focused on these important aspects of a predictive analytics solution: Unambiguous definition of the business problem Clear analysis pathway toward desired outcomes Thorough understanding of various internal datasets However, while choosing the right tool is not a great mystery, the real challenge lies in finding an experienced user who understands the pros and cons of each tool and adapts the techniques to solve the identified problem. Companies will be well served by investing in the right analytical team one that can actually be a source of competitive advantage. This notion is understood within the analytics-practitioner community, but the same cannot be said of business users and executives. In our experience, many senior executives still believe that cutting-edge techniques such as neural networks should be used to solve their business problems, and that predictive analytics tools are a key differentiator when selecting analytics vendors. That s why we believe that the analytics community needs to do a better job of educating business users and senior executives helping them to understand that the decision around vendor selection should depend upon the specific business problem and the latest and greatest tool may not be the best answer to their business challenge. Clear idea of the business value of the proposed investment, along with a sense of the potential return An in-depth discussion 7

10 4) Have we considered thirdparty data? In predictive analytics initiatives, companies place a large focus on the entirety of their internal data and rightly so. However, it is also important to consider that incorporating relevant third-party data into analysis and the decision-making process can offer valuable supporting information that few companies leverage. Investments in targeted, relevant datasets can generate far greater returns than does time spent developing sophisticated models that are data poor. Some degree of creativity must be employed here to think through not only what data could possibly be relevant but also how to measure or procure this data. It s not as easy as it would seem! To illustrate this point: Now suppose that a wedding-gown retailer wants to pursue a geographical expansion strategy. How would the company determine where to open new stores? How should it evaluate the performance of existing stores? Should a store in the Chicago suburbs produce the same volume of business as a store in Austin, Texas? To answer these and similar questions, an organization would need a great deal of data that is not within its firewalls specifically: How many competitors stores sell wedding gowns in the same area? (competitive market); how far are potential brides willing to travel to buy a wedding gown? (travel consumption preferences); and what are the income and spend profiles of people in the market? (customers budget set). Data about existing store sales and customer base are important, but they tell only part of the story and do not provide the entire context on which to base smart decisions. Other potentially useful inputs to the puzzle may include: Marriage registration data (from the National Center for Health Statistics) Socio-demographic data (from a company such as Claritas or Experian or the US Census Bureau) Business data (from Dun & Bradstreet, Hoovers or InfoUSA) Real-estate prices and custom survey data of potential brides All of these variables should be woven into the retailer s store-location analysis. 8 Putting predictive analytics to work

11 5) How can we make analytical insights available to the decision makers at the point of the decision? In our experience, successful decision delivery is challenging, as it requires cross-organizational coordination between the analytics, business, and IT groups to ensure that information lands in the hands of the decision maker at the point of decision. The job of the analytics group is to make their analysis results relevant to the decision maker s workflow. This could take the form of a mobile handheld device for a distributed sales force, Customer Relationship Management (CRM) systems integration for call centers, or an executive dashboard for reporting system integration. All of these examples require close collaboration with the IT group, whose job it is to take the results of a predictive model and integrate it with the relevant front end or reporting infrastructure. There is also a need for training to ensure that end users know what to do with the information. The program management effort required to execute such a cross-organization initiative is significant and, all too often, unanticipated by the project sponsors. Underlying all of this is the fact that a good program manager is critical to most complex predictive analytics projects. He or she is tasked with coordinating the various stakeholders and achieving alignment on problem definition, outcome format, technology integration, and training to drive user adoption of predictive analytics solutions. These individuals also play a critical role in the final steps of a project measuring and assessing the efficacy of the models. We have seen predictive analytics projects derail due to lack of coordination among the various groups within the organization and/or under investment in program management resources. To illustrate this point: A physician was struggling with the fact that some of her patients were not taking the medications she prescribed for them because they believed they could not afford to purchase them. As a result, before going to work, she took the time to log on to the website of a large pharmacy chain and print out a list of popular generic drugs that were covered by that chain s low cost prescription plan. She subsequently would check this separate list upon creating prescriptions to inform her patients they could, in fact, afford their medications after all. Why were patients and their doctors not able to access this information automatically? The chain s covered drug list was not integrated with the mobile medical and drug decision support software for doctors used to verify drug dosage and interactions prior to writing prescriptions. Therefore, when it came time to write prescriptions, physicians who had not made a personal effort to inform themselves (as our physician did) had no way of knowing whether or not a specific drug was covered within the chain s plan. Since the right information was not made available at the point of decision, patients likely paid too much for their medication or simply did not fill the prescriptions neither of which were optimal outcomes. The situation has since improved since the chain s low cost prescription drug list has been integrated into the drug decision software application. Resolving delivery problems or last-decision-mile issues such as this is critical to a successful analytics initiative. An in-depth discussion 9

12 6) How can we begin to leverage data visualization? Good data visualization can communicate the information that drives smart decisions. Above we just touched on the importance surrounding the lastdecision mile, where analysts should ensure timely and appropriate information for decision makers. That said, it is never easy to convince decision makers to deviate from their modus operandi. Specifically, convincing them about the efficacy of a solution based on predictive analytics and getting them to approve change typically takes more than an R-squared or a mean absolute percentage. Recently, an online services client was using multiple mass-market media channels to execute their marketing strategy. The strategy generated a lot of inbound sales calls, but the client lacked a clear understanding of which media channels were contributing in what amounts to the sales activity. PwC was able to accurately model the relationship of spend by media channel to response activity. With the model, we could flex spending inputs by level and channel to predict the effect on sales Relationship of client market spend to cost per call and number of calls Cost per call Marginal cost per call increases from $200 to $800, or by 400%! 150 $120 $150K spend ,000 1,100 Number of inbound sales calls Model I Model II activity, cost per unit of activity, and cost per closed sale. Rather than using equations and tabular data to demonstrate these findings, we used a simple but effective visualization. Specifically, we were able to show that spending an incremental $100,000 beyond the base level media spend of $150,000 only generated an additional 125 sales $250K spend $350K spend opportunities. Consequently, the marginal cost of the sales opportunities rose from about $200 per opportunity to almost $800! Factoring in conversion rates and customer value, it became clear the incremental spend was not viable. The simple picture shocked the audience into rethinking their media strategy! 10 Putting predictive analytics to work

13 To understand the importance of visualization, consider this: In 1854, a visualization map by Dr. John Snow saved many lives. When a cholera outbreak struck the SoHo district in London, Dr. Snow began working to uncover how the deadly disease was spreading so quickly through that area. At the time, little was known about how diseases were transmitted. After interviewing residents in the area, Snow hypothesized that a water pump was the source of the outbreak. He created a map to illustrate that cases were centered around the pump. Thanks to this visualization, he succeeded in his effort to have the authorities shut down the pump, thereby helping to prevent further spread of the disease. This says it all! In 1879, Edison obtained an improved Sprengel vacuum pump, and it proved to be the catalyst for a breakthrough. Edison discovered that a carbon filament in an oxygen-free bulb glowed for 40 hours. Soon, by changing the shape of the filament to a horseshoe it burned for over 100 hours and later, by additional improvements, it lasted for 1500 hours. Julian Trobin [ We believe every predictive analytics project needs a single money-visual that ties together the analyses and an unequivocal demand for change or a call to action. 7) How important to ongoing success is prototyping, piloting and scaling? Truth be told, Thomas Edison did not invent the light bulb. Rather, he commercialized it! Edison took a working concept and developed hundreds of prototypes, rapidly tested them, and then figured out improvements that were required to scale the invention for commercial use. This took into account both economics and manufacturing. PwC took a page from Edison s book when we developed our own prototype, pilot and scale approach to deploy analytics solutions for our clients. Rapid prototyping is essential to showcasing the value of the initiative to senior executives who are needed to drive such projects and the viability of the initiative, by proving that the predictive model can work. Piloting helps to refine the prototype and plan for potential adoption pitfalls among end users. Finally, scaling effectively implements the sum of the necessary changes or solutions across the identified scope to achieve the identified value. An in-depth discussion 11

14 The following example illustrates the importance of the prototype, pilot and scale approach. To illustrate the complexity of these projects a real-world example: One of our healthcare clients wanted to institutionalize a data-driven culture within its sales organization. Specifically, they requested our help in identifying and focusing sales efforts on high potential customers. However, they also faced two problems that, if unresolved, would get in the way of a successful national rollout: First, there were skeptics among the sales personnel who did not trust the model a situation that would make it difficult for the company to enact any suggested changes. And 2) change management blind spots existed in the company s current decision making process which coupled with de facto processes that govern the day-to-day activities would be difficult to incorporate into the strategic initiative, given that they are not always perceptible or documented. We recognized that an understanding of the reality of how things are getting done helps to anticipate roadblocks and align all processes to achieve a more successful strategy that everyone in the company could believe in. So, we set out to identify and focus sales efforts on high potential customers and resolve these two stumbling block issues. First, we developed a prototype of a predictive scoring model that identified high potential customers. Then we mapped the results of that model to existing efforts, which showed that greater than 50 percent of the sales force s time was being used ineffectively, and that there was money left on the table. We designed a pilot with the following objectives: Prove the validity of the predictive model Create evangelists from the sales team of the pilot regions Identify the big-data gaps and establish a process of continually refining (CRM) data Establish and refine the key performance metrics to report to senior management Understand the key questions and concerns of the sales team in adopting the system. During the pilot phase, we collected a great deal of rich quantitative and qualitative data. That data not only conclusively proved the value of the predictive model s impact, but it also provided us with insights to incorporate into the rollout process. To cite two examples: some customer addresses were not updating in the data warehouse, and sales managers wanted to understand the factors behind the predictive customer score before they felt comfortable using it. Scaling the pilot required cross-organization coordination and strong program management to ensure that the pilot s lessons learned were incorporated in the rollout. Positive word-of-mouth regarding the solution, and minimal impact on day-to-day business were also priorities. Finally, we designed the compensation rules and reporting metrics around feedback garnered during the pilot, which helped us to build a system that achieved buy-in from sales force leadership. Our client saw a significant uplift in revenue after the first three months of rollout. By that time, the sales organization had come to appreciate the value of a data-driven approach, to the extent that they hired a team to support other sales analytics initiatives. 12 Putting predictive analytics to work

15 Long story short: Whether you opt to use PwC s approach or to develop your own, adopting predictive analytics based solutions is essential for organizations seeking improvements to their bottom line and a place at the forefront of the competitive arena. These are some of the lessons learned and pitfalls we have encountered as we have helped our clients adopt predictive analytics solutions. Predictive analytics should be on every company s radar and once ready to invest, this guide will help firms get started on their journey. An in-depth discussion 13

16 What this means for your business Companies that do their homework can reap multiple benefits

17 The sooner you start, the sooner you ll reap the rewards Clearly, designing a company specific strategy and implementing a carefully planned predictive analytics initiative are complex undertakings that require significant investment of up-front time and effort. Recognizing this, some leading companies have turned to outside advisors with a depth of experience to help them begin this journey. Whether you opt to leverage the experience of an external predictive analytics team or to tackle it on your own, it is imperative that you do the necessary homework before taking any action. Dealing with these complexities takes a holistic approach We believe that there are several extremely important facets of a successful analytics project data, modeling, skills, business context, technology, and program management all of which play key roles. All too often, though, we see companies focusing on just some of these components while neglecting the others, and ending up paying a steep price down the road. To spur organizations like yours down a holistic analytics path making it possible to consistently repeat and recreate success and reach a true information advantage we compiled the aforementioned questions. What this isn t and, more importantly, what it IS Asking, and answering, the right questions will not provide you with a complete roadmap for a successful predictive analytics initiative, nor will it magically deliver a formal framework for determining where your company should focus its analytics investments. But what it will do is provide you with a basic guide to get you started and help you figure out the scope of both the problem to be resolved and the solution. It can also help you get a sense of potential returns on your investment. In short, with these answers in mind, your firm can more effectively engineer a solution to its sought after analytics goals. Is it worth the time and effort? You bet! Organizations that go through the recommended fact finding and planning process up front and base their next steps on the input garnered during that phase can position themselves to enjoy multiple benefits. Well-planned and properly implemented predictive analytics initiatives lead to improved operations and bottom-line growth. The time to get started is now. What this means for your business 15

18 To have a deeper conversation about how this subject may affect your business, please contact: William Abbott william.abbott@us.pwc.com Amaresh Tripathy amaresh.tripathy@us.pwc.com 2012 PricewaterhouseCoopers LLP, a Delaware limited liability partnership. All rights reserved. PwC refers to the US member firm, and may sometimes refer to the PwC network. Each member firm is a separate legal entity. Please see for further details. This content is for general information purposes only, and should not be used as a substitute for consultation with professional advisors. NY