big data opens the door for prescriptive analytics

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Prescriptive analytics enables organizations to:

Improve business results with more precise, timely decisions

Achieve the optimal mix of risk mitigation, profitability and positive customer experience

Strengthen customer engagement with more relevant offers

Speed up the test/learn/deploy cycle

Turn decisions into action more quickly through automation

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Driving Decisions with Greater Precision

As any line of business (LOB) leader knows, making customer-level decisions that balance risk and profit just keeps getting harder. And even when you think you have the right decisions, turning them into actions can be even trickier. You also need to consider the factors that make smart decisions difficult. Big data. Regulations. Customers who want an offer, fast, or else you’re going to lose them.

Even if you’ve tuned your business decisions with predictive analytics (what will happen next) and diagnostic analytics (why did it happen), let’s be real. Whether you’re focused on boosting performance in marketing offers, car rentals, airline pricing, credit lines or just about anything else, there’s often the looming feeling that someone else is doing it better than you are.

Consider, also, the potential for disconnects within your business. All those subprime auto loans may be a sudden shot of adrenaline for your portfolio, but another LOB is going to take a hit if you’re making too many loans or not pricing them correctly. Have you warned your collections department that they’re about to get hit with a barrage of new delinquencies within the next year?

Finally, think about the tools you’re using to make decisions. Are they really helping you add precision to your decisions? Are they taking up valuable analytic or research cycles that could be allocated to more critical areas? And when you need to make policy, process or strategy modifications, are those requests getting stuck in IT queues, meaning you can’t implement changes with the speed you need?

No doubt some of these challenges sound familiar. And this is where prescriptive analytics represents the next step in the analytic journey. For organizations that are wrestling with how to make business decisions with speed, precision and alignment,

WHITE PAPERBig Data Opens the Door for Prescriptive Analytics


When BI ceased to

be the panacea for

better decisions

Spreadsheets and other basic tools can illustrate where and how customers reacted to or purchased something last quarter. They don’t provide the sophistication or capability to capture and communicate insights on why customers behaved the way they did, or attempt with any mathematical certainty to predict what customers will do in the future.

Identifying the

optimal course

of action

prescriptive analytics can ultimately uncover new ways to drive profit and customer-centricity, and continue to provide new insights that can amplify the results, even after the initial blast of ROI.

Prescriptive analytics goes beyond predicting what will happen next. It tells you the actions you should take to achieve the right mix of risk mitigation, profitability and positive customer outcomes—all within the time frames that drive the greatest value. What are the three best auto loan deals I can offer, given this person’s ability to repay the loan and our own portfolio objectives? How can we optimize our snack food supply chain to account for seasonality, customer demand, location and machine cleaning schedules? How do we effectively onboard new wireless customers, ensuring they’re happy with their handset and plan choices while we don’t take on unacceptable risk?

In addition to a long list of practical benefits, there’s also a historical precedent for organizations to deploy prescriptive analytics. Let’s turn back the clock to explore what’s changed. Not so long ago, and indeed still today, spreadsheets helped businesses predict the future based on finding out what did or didn’t work in the past, and then applying the next set of decisions on whatever the tools predicted.

Of course, events would occur that exploded the boundaries of what those legacy tools and economic scientists could accomplish. Economies crash and burn, and recover at different speeds. New portfolios and customers are acquired, struggling LOBs sold off, and other businesses enter and leave the competitive fray, including disruptors who are built on changing the game on customer engagement. That factor alone ups the ante on other businesses in the industry to raise their game, or watch profits bleed out of the organization.



Driven by the demand for better, faster decisions, key movers and shakers in the analytics and technology world responded with new capabilities that changed the game, providing more sophisticated predictive and business intelligence (BI) tools to help organizations move from reactive to proactive actions. Still, the tendency was to look toward the past and use that information to predict the future, even if the tools (and the infrastructure) were evolving to allow consideration of more factors.

And then—you knew this was coming—big data shook the world. Despite the fact that there was nothing to capture it other than store visits, phone call logs and mail orders, big data has always been around—it just wasn’t as interesting, as critical in predicting what people would do next, or as fast and ubiquitous as it is now. Before big data, social media wasn’t around to capture someone complaining about poor service or touting the virtues of a great new product.

Today, all that information—and more—IS out there, much of it being collected, but very little of it being used in a way that can help businesses make more timely future decisions (even if the future is nanoseconds away, say, in the case of a car insurance quote or ticket price). Big data as we know it—structured, unstructured, text, video, audio, machine created, opted in, confidential, streaming, at rest, batch—defies any attempts of traditional BI and other tools to capture and leverage it for any value. “Once it hits the (data) warehouse, forget it” might be the best way to describe big data these days when it has aged beyond the point where it is useful. If I’ve just purchased two dozen eggs and subsequently get a 50% egg discount that expires in three days, someone is likely not paying close attention (unless I happen to be going through two dozen eggs every couple of days). That offer might be based on a regular proclivity to purchase eggs; however, unarmed with the recent purchase data, that offer could be ignored or perceived as annoying (why didn’t that coupon show up when I needed it?).



Identifying three

obstacles to realizing

the value of big data

Turning big data volume, variety and velocity into value is specifically why traditional BI can’t possibly keep up with the need for business decision speed and precision. Sure, you can continue to use BI to track how you’re doing and help align strategic goals – and indeed, powerful visualization tools such as Tableau are being combined effectively with predictive and prescriptive systems to see and analyze data and trends. But visualization is just one critical ingredient of the mix. Consider these “must haves” to evolve from reactive to predictive/prescriptive in the next epoch of big data decisions:

1a. You need to make sure your business decisions align with your objectives holistically—not just for your department. Even if you manage to apply analytics to your big data, what comes out may be too broad for applying to a business problem. Those 30 potential offers could be too much for your deal desk agent to make useful. And the offer chosen may be a bad long-term decision for the business as a whole—such as the portfolio-building low interest loan campaign that leaves too many dollars on the table and could create unnecessary risk due to extending cheap loans to dicey borrowers.

2a. You need to perfect the art of getting the right timing, offer mix and channel. Even if your offer mix balances all your business constraints, it also needs to appeal to customers. If you spend too long running the analytics, that customer will have already chosen another offer (or left the dealership). If the offers aren’t appealing, they’ll leave, as well, or perhaps counter-offer with something that isn’t business-beneficial. And if they apply over the internet and expect auto-approval within seconds, but get a call back instead to obtain additional information, this could also set off alarms.

3a. Your big data infrastructure needs to ease, not exacerbate, your IT burden. Today’s overtaxed IT organizations are fighting a losing battle keeping myriad legacy systems and data alive, while trying to prioritize new IT projects as well as big data infrastructures. Changes to existing analytic systems often require coding, which slows down changes to policies, processes, strategies and timely customer-level decisions. An enterprise-wide approach to big data decision management requires a long-term vision with well-defined short-term goals and the ability to achieve quick wins. There needs to be a way to fast-track application development, streamline management and reduce the impact of updates and changes on IT.



Happy days are

already here

The good news is that the obstacles above are, indeed, addressable—with prescriptive analytics helping lead the way.

1b. You need to make sure your business decisions align with your objectives holistically—not just for your department. The limitation of many BI and predictive strategies is that they are either too narrow in focus (for instance, solve a debt collection strategy problem) or too broad (adjust overall business goals without aligning to department objectives and challenges). Prescriptive analytics can solve for both ends of the spectrum. If you’re trying to optimize your auto finance collection strategy, prescriptive analytics can include as a constraint macro business level challenges (for example, grow portfolio and minimize attrition) as well as departmental needs (reduce roll rates while not adding headcount). Prescriptive analytics also solves the volume problem (reduce 30 possible offers to the best three). And you can “test and learn” various combinations to ultimately arrive at the best scenarios for your portfolio.

2b. You need to perfect the art of getting the right timing, offer mix and channel. The flexibility of prescriptive analytics allows it to consider all the above factors, with the weighting dependent on factors such as customer segmentation, macro-level business objectives, seasonality, recent purchases, competitive motions and other factors. Prescriptive analytics helps you know what to do and when to do it—as we shall see in the next section, there are indeed new, faster ways to take action on your decisions.

3b. Your big data infrastructure needs to ease, not exacerbate your IT burden. The rise of prescriptive analytics necessitates a new view on how IT and department LOBs join forces to build and manage applications. Open cloud-based technologies, spurred by the growth of SaaS, IaaS and PaaS, provide flexible deployment options and reduce barriers to entry for organizations that, due to cost and effort, were forced to delay the adoption of big data decision capabilities such as prescriptive analytics. The rise of virtually automated application development systems—powered by microservice architectures (breaking applications into services) and model-driven approaches—is reducing IT burdens by simplifying and abstracting the development process to lines of businesses. Citizen developers1 have emerged as a new resource weapon in the move to democratize business applications and help facilitate faster, smarter decisions by freeing IT from gratuitous (and slow) recoding efforts, while consequently redirecting scarce analytic resources to the most critical work.

1 Advances in analytic and decision system architectures now allow the separation of decision logic from code, allowing a new breed of user— the “Citizen Scientist”—to modify rule systems, wrangle data, do champion-challenger testing, build decision trees and scorecards, and even work on complex optimization business cases. These business analyst-level resources help overcome a key decision management infrastructure concern from a resource standpoint, since data scientists, developers and other skilled staff will perpetually be in short supply and high demand, particularly as more and more businesses start to move rightward on the analytic spectrum.



Prescriptive Analytics Use Case ExamplesThe following use cases illustrate how prescriptive analytics have helped solve business challenges that defied other approaches and tools:

• Schedule crews for 3,400 daily flights in 40 countries

• Buy ads in 10–15 local publications across 40,000 zip codes

• Pick one of 742 trillion choices in creating a professional sports schedule

• Select 5 offers out of 1,000 for each of 25 milllion customers

• Place 1,000s of SKUs on dozens of shelves in ~2,000 stores

• Decide among 200 million maintenance routing options

• Plan weekly production levels for several years ahead, accounting for conflicting business objectives and restrictions

In the preceding points (1b) and (2b), prescriptive analytics is key to solving very complex business problems. But to really turn better decisions into action, an integrated analytics platform is needed, as suggested in point (3b). This platform should enable organizations to:

• Allow citizen developers to create and manage analytically-powered decision applications, with reuse and sharing capabilities to aid deployment of new solutions.

• Quickly integrate virtually any combination of analytic tools, legacy data, ERP and other systems, as well as any combination of streaming and at-rest data.

• Easily and rapidly glean insights from data, and develop analytic models and decision services that operationalize those insights.

Within this platform, prescriptive analytics is critical to activating the right decisions given multiple constraints within the business. The good news is that this capability set is not only available now, it’s also accessible to virtually any business trying to make faster, smarter decisions.

There’s one more step to realizing the promise of prescriptive analytics, and that’s turning decisions into actions. Most businesses that have evolved down the analytic maturity spectrum have adopted various levels of automation to execute the action, greatly reducing or even eliminating human intervention where it would slow down or even negate the value of the decision.

Take this example: I apply for a car loan online and get three options via SMS within five minutes. Analytics takes care of everything—it accounts for my credit score as well as data I provide, such as loan amount, down payment, payback time frame and other information. This information is then fed into a complex optimized analytic framework that considers business and other constraints, and automatically delivers the three best offers to me via text message. I can either choose one of the offers or, alternately contact a human agent to discuss other potential offers. The agent doesn’t have to manually calculate new offers—she can input my additional preferences into the analytic framework, and the system will automatically generate new optimized offers.

Turbo-charging your

prescriptive analytics

platform



From its origins as a local telephone company in 1899, Sprint Corporation has grown into the third-largest wireless company in the United States, with 55 million subscribers. In a highly competitive and dynamic industry, Sprint’s success depends on balancing new applications with profitability, customer satisfaction and keeping good customers from going to the competition. Ultimately, they needed a prescriptive analytics platform that could help them drive profitability without increasing acquisition expense.

They chose a platform that offered best-in-class tools for solving large, complex optimization problems, along with the ability to rapidly deploy highly customizable optimization models as powerful applications without the need for supplemental development efforts or heavy IT engagement. The solution “is doing all the things we hoped it would,” says Mike McCabe, Credit Analytics Manager for Sprint. “We can now generate multiple customer scenarios very quickly, so we’re able to limit our exposure to high credit risk customers while simultaneously boosting our activation rates with low credit risk customers. This increases our profitability without increasing our acquisition expense, which is a big priority for us.”

Another key benefit is that the solution empowers subject matter experts such as Mike to deliver even more precise strategies in less time. Previously, they had been using sketches to understand trade-offs between credit policy, profit, gross adds and other factors. The solution enables them to rapidly construct charts with real numbers using both optimized and non-optimized strategies, allowing them to visualize the benefits of an optimized approach. The solution’s flexibility allows them to incorporate business or market changes observed during an afternoon into production the following day – the right mix of art and science combining business experience, analytics and technology to drive profitable and customer-centric outcomes.

Case Study: Sprint

calls on prescriptive

analytics to streamline

customer acquisitions

Millions of Possibilities

Hundreds of thousands of applications per month

• New customers (new service or port-in from other carriers)

• Renewals (i.e., upgrade phone, start new 2-year contract)

• Numerous third-party channels

• Hundreds of MSAs

• Dozens of credit and fraud score bands

Decision:

• Deposit amount

• Number of lines

• Monthly spending limit

Sprint Credit Offer Decision



For the comparatively few organizations that have integrated advanced analytics across the majority of their LOBs, only a scant subset will let automation handle a significant part of the “action” component. Others will “scale” analytics to address certain needs but not others, not operationalizing across the enterprise or leveraging prescriptive analytics in only a few select cases. There are a number of possible reasons for these hiccups on the road to analytic maturity:

1) Organizations don’t yet have enough faith in the analytics to make all the “no intervention required” decisions or take action.

2) There isn’t enough flexibility in their analytic, decision rule and other business systems to adapt to the results automation and analytic sophistication can deliver. Here’s an example: If a wireless provider’s ability to onboard new customers is reduced from hours to minutes thanks to smarter analytics and automation, will they be able to rapidly (even automatically) modify decision models and rules as the results of their efforts are measured and new business objectives applied?

3) Their “people culture” allows certain resources to hold onto some human decisioning or override responsibilities, even though these could adversely affect the bottom line, reduce regulatory transparency and create additional costs to invest in scarce productive resources.

4) The DNA specific to each organization can artificially limit what some analytics are allowed to do (this is also referred to as operational negation). If a bank decides that it periodically wants to alert a customer about a possible fraudulent transaction, even if the analytics say it probably isn’t fraud, the action may be inherent to the bank’s ultra-conservative loss management strategy, or even its customer engagement philosophy (“let’s check on Angela to let her know we’re looking out for her—and maybe we’ll also luck into finding out that it wasn’t Angela buying the dress”). On the flip side, too many “false positives” could lead unhappy customers to abandon the card due to unnecessary declines or the annoyance of constantly having to respond to fraud alerts. The bank’s strategy may be informed by analytics, but ultimately, the codification of the business—based on history, culture, risk profile, even perceived brand and reputation—may inhibit or even nullify analytic decisioning in certain situations.

Getting over the fear

factor and operational

negation

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Summary As a result of reduced IT and business application spend due to post-recessionary conditions, cloud platform investments and general business belt-tightening, there’s a relative slowdown in analytic application and tool purchases compared to several years ago. But this doesn’t mean that businesses aren’t adopting new capabilities, with prescriptive analytics leading the charge.

In fact, as point analytic and big data applications become more accessible to business users and don’t require costly IT investments or management burdens, a tsunami of analytic prowess is making waves in established businesses as well as disruptors, such as internet-only banks creating truly personalized, cost-saving, virtual agent transactions. Consider prescriptive analytics that examine the shape of data to determine if fraud is being committed; optimization and modeling algorithms that can automatically self-adjust depending on the time and the channel that a consumer responds; and new capabilities that allow analytics, calculations and decision tables to be combined in decision models, helping transform limitations of traditional “business rule” platforms that struggle to keep up with the speed and power of today’s new breed of decision analytics.

We’ve spoken about some of the perceived barriers to entry for prescriptive analytics: infrastructure, cost, resource mix, culture, and IT vs. LOB vs. overarching business needs. A necessary component of the discussion, as introduced in the previous section, is this relatively overlooked concept of operational negation—essentially, when a business won’t activate its analytic advantage to the full extent possible. In many organizations, game-changing analytic applications are being developed in some LOBs, while others operate much as they did 10, 15 or even 20 years ago. It’s not just a matter of how the business is “wired,” but also how “hard-wired” it is—how easy it is to change dated policies, cultures and practices—that will most certainly impact the long-term march to analytic maturity.

Organizations that can accumulate enough quick wins with decision analytics will likely have the best chance of moving the needle, but time will tell which types of organizations, in which industries, are most effective in evolving. Although it is hard to quantify, those businesses that allow their institutional DNA to keep pace with the speed of business will most likely find themselves in the driver’s seat when the profits are counted at the end of each fiscal period.

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big data opens the door for prescriptive analytics

Data & Analytics