Prescriptive Analytics Drive Tech Refresh Decisions

Transcription

1 Prescriptive Analytics Drive Tech Refresh Decisions Sales Analytics, Inc. (408)

2 Table of Contents Descriptive to Prescriptive Analytics affect business results Descriptive Analytics What happened? Predictive Analytics What will happen? Prescriptive Analytics How can we make it happen? Tech Refresh Bridging the gap between insight and action The path to achieving Prescriptive Analytics Identifying an optimal course of action When BI ceased to be the panacea for business decisions Prescriptive Analytics use case examples Turbocharging your Prescriptive Analytics platform Powerful end-to-end decision management Summary 2

3 Introduction From business insight to business action, analytics can add clarity and timeliness to business decision processes. This review describes a key step in adopting an analytics approach to business decisions with a focus on tech refresh: understanding the different types of analytics descriptive, predictive and prescriptive the management role each can play and the value each can deliver. Giving data meaning despite its growing volume, variety and velocity As facilities and assets become more IT-like instrumented, intelligent and interconnected the convergence of physical and digital infrastructures makes their management increasingly complex. And in a physical world outfitted with millions of servers and networked appliances, vast amounts of facilities- and asset-generated data make extracting meaning increasingly difficult. Business leaders recognize the potential these changes in technology hold. In fact, a recent IBM survey of 1,700 CEOs and public-sector leaders identified technology change as the most critical external factor impacting organizations. To take advantage of the opportunity to transform facilities and asset management, however, organizations need analytics capabilities that can identify operating anomalies in real time, predict outcomes and deliver optimization models. Organizations need analytics solutions that can extract meaning from huge volumes of data to help improve decision making, handle wide varieties of data and data sources from within and outside the enterprise, and keep up with the rapid velocity of data in motion. They need capabilities for analyzing historical and real-time data, as well as forecasting the future, to distill what s valuable, detect patterns and reveal insights they may not even have thought to ask about. With such solutions, they can achieve benefits ranging from increased revenue to lowered operating expenses, enhanced service availability and reduced risk. Analytics platforms can be customized by enterprise-level organizations or offered on a SaaS basis with emphasis on vertical markets like manufacturing, healthcare, or technology. 3

4 Descriptive to Prescriptive Analytics affect business results Predictive Analytics and Prescriptive Analytics are complementary technologies that build on one another to answer critical questions and give organizations an informational advantage that can drive successful business outcomes. Predictive modeling and analytics provide a forward-looking view by answering the question: What is likely to happen next? Prescriptive Analytics builds on the predictive model by applying business rules and optimization modeling to answer the question: What should we do about it? Descriptive Analytics, What happened? Descriptive Analytics chronicle what happened, how it happened, where it happened, and how often it happened. Diagnostic in nature, Descriptive Analytics force characterizations so data can be aggregated and results summarized. Hindsight then begs the question, Why did it happen? which can lead to Insight, and eventually, Foresight. Predictive Analytics What will happen? Predictive Analytics helps managers anticipate likely scenarios for a proactive, rather than reactive, approach to decision making. Predictive Analytics, rooted in statistical techniques and mathematical models, applies advanced algorithms that process historical data, learn what has happened in the past and create models that can be applied to make decisions about current or future cases. By uncovering patterns, interactions and relationships hidden within data, Predictive Analytics delivers potentially actionable insights on likely scenarios and future outcomes. According to TDWI Research, the top five reasons companies should use Predictive Analytics are to understand customers, improve business performance, drive strategic decision-making, and predict behavior. 4

5 Prescriptive Analytics How can we make it happen? Prescriptive Analytics leverages mathematical optimization methods to rapidly explore every possible scenario and recommend the best action that meets a given business objective. Prescriptive Analytics is the critical next step on the path to insight-based actions. It creates value through synergy with Predictive Analytics by taking the forward-looking insight to the next level and suggesting the optimal way to handle the present or future situation. Prescriptive Analytics utilizes mathematical algorithms to handle situations with many alternatives, requirements and trade-offs, thereby helping organizations act fast in dynamic conditions and make superior decisions in uncertain environments to gain a strong competitive advantage. Tech Refresh - Bridging the gap between insight and action To thrive in today s complex and ever-changing digital environment, companies/institutions need to gain rapid insights into their business and translate those insights into actions. For a hospital, Predictive insights might indicate that demand for a particular procedure will likely rise as the local population ages projecting a need for faster equipment, more equipment, or an increase in staff. For a commercial enterprise, demands on IT might foretell a 5

6 need to replace limited computing or storage capabilities with the latest hardware/cloud/software technology. Prescriptive Analytics platforms from suppliers like IBM or Sales Analytics not only analyze historic data, they also assemble likely solutions and score them based on their viability. Sales Analytics looks at every historic/factual aspect of an end customers installed computer equipment specs, energy consumption, leasing basis, footprint, maintenance history and compiles a summary analysis that is then compared with current specs to produce Prescriptive solutions for customers. Decision optimization for customers offers them a recommendation from a set of possible actions. The challenge is how to make business decisions when insights are translated into actions. Whether the analysis takes place on the vendor or customer side, the integration between Predictive Analytics and Prescriptive Analytics helps organizations to take data, extract insights from it, and determine what to do based on those insights. Sales Analytics provides a predictive engine for analyzing patterns that can help anticipate likely scenarios for data-driven and proactive planning and a decision support toolkit for development and deployment of optimization models using various algorithms that recommend an optimal action given business rules, resource constraints and goals. SAI takes decision making to the next level by giving organizations the tools to predict likely scenarios and integrate these insights into a prescriptive engine, so that decisions are dynamically optimized with a forward-looking view. Organizations spend millions every year on predicting whether a customer or prospect will make a purchase. Predictive insights are informative, but they fall short of prescribing an optimal tech or financial solution. Companies like Sales Analytics proactively execute the first step in how to determine the optimal solution, giving sales the confidence that a tech refresh makes good business sense based on customer foresight vs predictive insight. The path to achieving Prescriptive Analytics There is no question that today, companies need to go beyond business intelligence to have an edge. They need a map that gives them a clear understanding of the impacts, trade-offs and likely outcomes of their choices and decisions. Mathematical optimization can help decision-makers anticipate best- 6

7 case, expected-case and worst-case scenarios; understand trade-offs, alternatives, bottlenecks and inconsistencies; and develop plans and schedules that can be adapted in the course of operations. Prescriptive Analytics can enable 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 Identifying an optimal course of action 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? How do we provide CIO s with indisputable data that green-lights an IT upgrade? When BI ceased to be the panacea for better decisions 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. 7

8 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. 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. Prescriptive Analytics Use Case Examples The 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 local publications across 40,000 zip codes Select 4 server upgrade offers out of 4,000,000 for each of 50,000 OEM customers worldwide 8

9 Turbo-Charging your Prescriptive Analytics platform 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. For 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 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. This is essentially what Sales Analytics SAFARI platform provides for computer OEMs and their customers, saving time and expediting business decisions. 9

10 Powerful end-to-end decision management An IBM study shows that organizations using analytics to determine why and what they need to be doing are twice as likely to outperform their industry peers. It is important for your organization to match its infrastructure, technologies and processes its level of analytics maturity to the stage of analytics it is able to perform or acquire and the business goals it wishes to accomplish. A recommended strategy is to begin with solutions that work with existing data to gain immediate insights while putting into place the technologies and processes to support more complex analytics. Summary As analytics 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. Consider Prescriptive Analytics that examine the shape of data to determine if fraud is being committed; optimization algorithms that can automatically selfadjust depending on the time and the channel that a consumer responds; and new capabilities that allow analytics and decision tables to be combined into models that replace traditional business rule platforms. Organizations that can accumulate enough wins with decision analytics will likely have the best chance of moving the needle. Sales Analytics SAFARI Refresh Platform can help tech OEMs and Channel Partners achieve end-to-end decision management for dramatic results and improved return on investment (ROI): Predict: Analyze patterns that are found in historical and current transaction data to predict potential future outcomes. Optimize: Find the optimal solution given various choices, alternatives and influences that might affect the outcome. Decide: Decide the best course of action to take given the objectives, requirements and constraints. 10

11 About Sales Analytics Established in 2002, Sales Analytics, Inc. (SAI) specializes in helping companies manage the technology-buying lifecycle of their customers in the critical areas of technology refresh and customer-information systems. Based in Charleston, NC, SAI uses prescriptive computer modeling to identify actionable and immediate selling opportunities a breakthrough in the management of a company s customer install base. Our ground-breaking technology is coupled with our commitment to earn the trust of, and build long-term relationships with, our clients. Our high-touch customizable approach empowers organizations of all sizes the framework and knowledge to grow their sales and marketing, and maximize their customer install base revenue and satisfaction. Sales Analytics, Inc. Contact: Kerry Roller, President Charleston, SC T: E: success@salesanalytics.com W: 11