Cloud based predictive analytics poised for rapid growth

Transcription

1 Cloud based predictive analytics poised for rapid growth James Taylor CEO, Decision Management Solutions More information at: The results of research into predictive analytics in the cloud show that early adopters are breaking away and that there is potential in many kinds of cloudbased predictive analytic solutions Decision Management Solutions

2 Predictive Analytics in the Cloud Innovation happens at the intersection of two or more different, yet similar, groups. Where one technology meets another, one discipline meets another, one department meets another. Valdis Krebs, Founder & Chief Scientist, orgnet.com Predictive Analytics Cloud Contents: Introduction 1 Survey Results and Implications 2 Recommendations 14 Vendor Profiles 18 Survey Demographics 32 Conclusion 33

3 Predictive Analytics in the Cloud An Introduction Analytics are hot. There is really no debate anymore on whether to add or not to add analytics to the information technology (IT) and business activities within an organization. Instead the debate centers on how to make the best use of the myriad of analytical opportunities that are out there. Jane Griffin, Principal, Deloitte Consulting LLP and Tom Davenport, Senior Advisor, Deloitte Analtyics Predictive analytics and cloud solutions are separately changing the way organizations do business, and bringing these two technologies together opens up new horizons. This timely research study will help organizations make investment decisions and effectively plan for the future. Robin Fray Carey, CEO of Social Media Today LLC Both predictive analytics and cloud are hot topics in business today. Predictive analytics use historical data to build models or algorithms that calculate a future probability the likelihood of a service cancellation, the risk of fraud or the potential for a cross-sell. In the era of Big Data the use of predictive analytics to improve business performance is top of mind for many. Cloud computing delivers computing resources as services rather than as a set of installed products configured by an internal IT department. With cloud, multiple organizations use the Internet to share the same hardware and software infrastructure and pay based on what they use. With constrained technology budgets and overwhelmed IT departments increasingly the norm, cloud based solutions are fast becoming established as the default option for new software purchases. But what is the potential for predictive analytics in the cloud? Based on our research and a recent survey of over 200 professionals hosted on SmartData Collective, cloud-based predictive analytics are poised for rapid growth. While industries vary in their maturity, the use of cloud-based predictive analytics to improve an organization s focus on customers is particularly powerful. As early adopters look likely to build a sustained competitive advantage, organizations should have a plan to rapidly adopt cloud-based predictive analytics to avoid being left behind. 1

4 Survey Results and Implications What s most impressive? It s the amount of money (millions of dollars) that can be returned to the company's bottom line using good predictive analytics. Survey respondent Applying advanced analytic decisions to impact my customer interactions without being limited by where my data is and incorporating data gathered to re-decision in real time. Tools must be easy for my business teams to use and understand the results, they aren't sophisticated modelers! Lower entry barrier for new application fields. Less organisational overhead. Survey responses to What other opportunities do you see from predictive analytics in the cloud? Rather than report survey results question by question the results, and their implications, have been grouped into a number of sections. Each section highlights significant results from the survey and discusses its implication. Business solutions are what organizations need Predictive analytics are showing real strength Customers are the focus for predictive analytics and cloud Cloud-based predictive analytic scenarios are gaining momentum Early adopters are gaining a competitive advantage Decision Management matters to predictive analytic success There are still some barriers and concerns with cloud-based predictive analytics Industries vary in their adoption and concerns A mix of clouds is appropriate Traditional data sources dominate predictive analytic models After the survey results and implications are discussed we will make some recommendations and identify pros and cons of the various options. Demographics and vendor profiles complete the paper. 2

5 Survey Results and Implications Business Solutions Needed Most potential buyers of predictive analytics in the cloud are not really looking for cloud solutions. Years of successful industry adoptions of predictive analytics and growing awareness are resulting in more demand for analytically-based solutions. Yet many organizations are not looking for predictive analytic solutions either. What the vast majority of organizations seek is a solution to a specific business challenge. Predictive analytics can help them address the challenges they face. A cloud based approach can make these solutions faster to deploy, more cost effective and more collaborative. Nevertheless the primary driver remains a need for a solution to a business problem. One of the reasons for this is that companies realize that technology is not enough, they also need best practices and industry or solution-specific implementation help. Few solutions are purely softwarebased; most involve configuration and specialization to work for a specific organization. This requires domain expertise as well as technical know-how. Similarly, successfully adopting solutions that embed predictive analytics often involves significant business change such as a willingness to establish control groups and experimentation or to assign marketing priorities differently across the organization. This solution focus goes back to some of the earliest cloud-based predictive analytic solutions. Nearly 30 years ago shared, hosted applications for fraud detection and credit risk management were offered by credit card processors. A key driver for the adoption of these packages was a desire on the part of banks and credit card issues to get access to advanced analytic solutions as a packaged offering. This driver remains front and center decades later. Predictive Analytics Strong This focus on business solutions cannot obscure the strength of demand for solutions based on predictive analytics. 82% of those surveyed work at companies that either have specific plans to adopt or are already using Predictive Analytics. Many, probably most, of these companies have gone through years of investment in increasingly sophisticated data infrastructure such as data warehouses and business intelligence. The value of this data infrastructure has always been in its ability to improve decision-making. Given that decisions affect the future and that the available data is all about the past, the potential value of this data can be increased if it can be used to make effective predictions that can shape decision-making. The survey clearly showed that the use of predictive analytic models has started to make a real difference in organizations (Figure 1). Few (18%) have no plans to adopt predictive analytics while far more (43%) have already seen an impact from successful predictive analytic implementations. For an approach that has only recently become a mainstream topic in organizations this is an impressive showing. Perhaps even more impressive is that more than one in 10 of those responding (11%) say that this impact has been transformative. Figure 1 The impact of predictive analytic models on responding organizations 3

6 Survey Results and Implications In terms of the number of models involved the vast majority of respondents had less than 10. Some were up in the 1,000s, however, with a core group of 20% having between 10 and 100 models in use. Customers are the Focus Survey respondents were given a number of different areas to identify their focus areas in both predictive analytics and cloud-based solutions. The top outcomes identified for predictive analytics (respondents could select more than one) were (Figure 2): Improved targeting/allocation of resources (59%) Improved targeting/development of customers (58%) Improved targeting/acquisition of prospects (48%) rounded out the top 3. Managing non-credit risks and increased ROI from data scored over 40%. Interestingly credit risk scored 22% and fraud reduction only 28%. Given the long history of using predictive analytics for risk and fraud management these were somewhat surprising. They may reflect the rapid growth of predictive analytics in customer-facing activities or the widespread use of packaged predictive analytic models in fraud may mean it is just not top of mind for respondents. Improved targeting and development of customers scoring well was not a surprise but improved targeting/allocation of resources was equally widely cited and that was frankly unexpected. Some companies reported that although their initial interest is driven by demand-side or marketing operations, there is a significant interest in supply-side optimizations as well and that may account for the strong showing of resource allocation. A similar question focused on the areas in which predictive analytic models were going to be adopted or were already in use. The top two areas were clearly marketing/customer acquisition and customer retention this was true whether Likely to adopt or just actual implementations plus specific plans were considered (Figure 3). Other areas that scored well included the broader category of customer management, sales and cross-sell/up-sell. Fraud detection scored quite highly on currently implemented but showed little growth with few plans, probably reflecting how well established predictive analytics is already in this area. Among those experienced with predictive analytics and getting positive results it was much more likely that customer management and customer retention would be implemented and much less likely that planning and scheduling or operational efficiency would be the focus. Figure 2 Outcomes achieved or desired from predictive analytics 4

7 Survey Results and Implications A similar question focused on cloud adoption showed website, campaign management, and CRM scoring highest (Figure 4). Taken with the focus of predictive analytics above and it is clear that it is the effective acquisition, management and retention of customers that is the sweet spot for predictive analytics in the cloud. Given the demand from business people for solutions to improve customer acquisition and retention and the pressure to develop existing customers more effectively this is hardly surprising. The need for large numbers of often geographically dispersed staff to work on customer-facing Figure 4 Areas where predictive analytics are being applied processes also contributes as cloudbased options are very appealing in these kinds of tasks. One other note on the focus areas for cloud. Platform as a Service or PaaS shows the biggest potential it has far fewer using today but similar numbers for likely to use or more to the top categories thanks to a large group of Likely to Use shown in dark green in Figure 4. It is to be hoped that PaaS vendors will integrate predictive analytics more quickly than the developers of traditional application development Figure 3 Areas where cloud is being applied platforms, most of which still lack this capability. 5

8 Survey Results and Implications 5 Scenarios Gaining Momentum Five distinct opportunities for using predictive analytics and cloud were identified in the research and discussed in the survey: Pre-packaged cloud based solutions. Solution offerings, delivered as cloud based or SaaS solutions, that provide decision-making based on predictive analytics as a core feature of the solution. For example cloud based applications offering next best action, offer selection, fraud detection or instant credit decisions. Predictive analytics for SaaS. The use of predictive analytics solutions that are cloud based to inject predictive analytics into other software products that are cloud based or delivered as SaaS. For example embedding customer churn predictions in SaaS CRM solutions or delivering risk predictions into cloud based dashboards. Predictive analytics for on-premise. The use of predictive analytics solutions that are cloud based to inject predictive analytics into disparate internal systems and multi-channel environments. E.g. using cloud based deployment to link an internally developed cross-sell offer service (that uses propensity to buy models) to multiple customer-facing systems. Modeling with the data cloud. The use of cloud based predictive analytic solutions to respond to the increasing amount of relevant data available in the cloud rather than on-premise. For instance building predictive analytic models in the cloud based on customer purchase and behavior data stored in a SaaS CRM system as well as third party data available from a cloud based web service. Elastic compute power for modeling. The use of cloud technology to provide predictive analytics solutions that can scale elastically to meet demand. E.g. assigning extra resources dynamically when optimization Figure 5 How important are the 5 scenarios or other demanding algorithms are being used to build sophisticated predictive analytic models against large datasets. All five of the scenarios were seen as potentially powerful solutions with over 2/3 of respondents reporting that each of them has real potential (Figure 5). None of them are that widely adopted yet but prepackaged analytic applications have the greatest penetration. Given the solution focus of many customers, and the newness of predictive analytics for many organizations, this is not a surprise. Pre-packaged solutions are easier to link to business problems and generally involve less development and consulting work, making them easier to adopt. The runner up was the use of cloud based solutions to embed predictive analytics into on premise applications. This interesting result shows the importance of deploying predictive analytics. It is not enough to build accurate predictive analytic models; organizations must also get these models into their (legacy) applications. 6

9 Survey Results and Implications While none of the solution types are widely adopted among the respondents, pre-packaged solutions and the use of the cloud to embed predictive analytics into on premise systems as well as the use of the cloud to embed predictive analytic models into SaaS systems all had some level of adoption in over 50% of respondents (Figure 6). A deployment orientation seems to dominate predictive analytics in the cloud at least for now pervasiveness through the cloud seems more important than scalability and data availability. It is also true that the number of solutions for building predictive analytic models in the cloud is smaller than those for deploying and using the models once constructed and this must have slowed adoption. Only modeling in the cloud (using data in the cloud) failed to get over 50% of respondents to say it was at least somewhat likely to be adopted (Figure 7). It got almost the same Very Likely score as the two options for embedding predictive analytics in existing systems but fell behind when those saying Somewhat likely were also included. Pre-packaged solutions that include predictive analytic models and the use of the cloud to Figure 6 How widely adopted are the 5 scenarios embed those models in existing systems did the best with elastic compute also showing well when those somewhat likely to adopt are considered. Given the lower scores for modeling in the cloud this implies that on-premise grids and private clouds are being considered to provide a more elastic compute environment for model construction. Early Adopters, Competitive Advantage When a new technology is being adopted, organizations must choose whether to be leaders or laggards. They must decide if they will adopt the new technology before it is obvious whether it will be successful or not rather than wait for more data. There are clear risks in being an early adopter of any technology and these must be balanced against the potential competitive advantage. This balance is different for each wave of technology. Figure 7 How likely are the 5 scenarios to be adopted 7

10 Predictive Survey Results Analytics and Implications for SaaS In the case of cloud-based predictive analytic solutions, this balance seems to come down firmly on the side of being an early adopter. A number of factors suggest that the gap between these early adopters and their competitors is likely to widen as early adopters see more positive results, have fewer objections and are determined to adopt the technologies more aggressively. For instance: Organizations with the most experience with predictive analytics were more likely to have plans to adopt more cloud-based predictive analytic solutions. They were much less likely to have performance or privacy concerns about the solutions. They were more likely to embed predictive analytics in operational systems, a driver of positive results discussed earlier, and to recognize and value the deployment agility and cost reduction inherent in cloud-based predictive analytics solutions. They were more likely to take advantage of big data from the cloud with 60% seeing themselves as very likely to use cloud data in models. Comparing those respondents with no predictive analytics in the cloud solution widely deployed with those that have at least one widely deployed we see a Figure 8 Percentage rating scenarios as very important dramatic increase in the degree of estimated importance every one of the scenarios (Figure 8). They were twice as likely to rate cloud-based predictive analytics as very important with pre-packaged solutions getting a 3x rating improvement. It is also clear that those already deploying at least one cloud-based predictive analytics solution are much more likely to adopt solutions going forward (Figure 9). They say they are very likely to deploy the solutions at 4x the rate of those who have not yet started. Pre-packaged solutions were rated as very likely to be adopted at more than 5x the rate. These early adopters contrast with those not using cloud-based predictive analytics yet who see themselves as much less likely to adopt any of the solutions of any kind. This was even true of packaged solutions, the most preferred option for those who have not yet adopted any solution. Figure 9 Percentage rating scenarios as very likely to deploy Taken together this implies that those already adopting cloud-based predictive analytic solutions are not only getting positive results but that these results make them more likely to accelerate and broaden their 8

11 Survey Results and Implications adoption of these solutions. Those hesitating about adopting them run the risk that they will be left behind, watching early adopters establish a lead that grows with time. Decision Management Matters Decision Management was clearly an important element for successful analytic adopters. Respondents were asked how they used predictive analytics in their decision making. Four options were presented predictive analytics provide occasional insight, predictive analytics are in regular use, predictive analytics are the primary driver for some decisions, and predictive analytics are embedded in operations. Overall respondents were most likely to say that predictive analytics were providing occasional insight or that predictive analytics were being tightly integrated in their operational systems both at 28%. This last option is the basis of Decision Management Systems that embed predictive analytics in automated decision-making systems. However the results become more interesting when considering those who have already seen significant positive results from predictive analytics. For these the percentage tightly integrating predictive analytics into operations rose while occasional use dropped (Figure 10). Among those transformed by predictive analytics a predictive analytics with day to day operations a clear illustration of the power of decision management to transform organizations with predictive analytics. As the graph below shows the initial impact often comes from occasional use of predictive analytics but as more impact is reported so the likelihood that predictive analytics are used in a more operational context grows. It should be noted that many of these operational integrations are still batch-oriented today. This is rapidly moving to more interactive systems, one analytic decision at a time in real-time, and this change will require even tighter operational integration in the future. One other point should be made about early adopters. When asked about the need to understand predictive analytic models in a solution 58% of all respondents said it was essential to understand and control how the models are developed and tuned. This rose among those with more experience (rather than declining). It rose to 80% for those transformed by predictive analytics. The result could mean that experience does not make people any more comfortable with black box models. However it is possible that experience with predictive analytics makes clear the value of really understanding what is predictive and that this drives business innovation. As a result the increasing demand for transparency may not be an issue of trust so much as a perspective on the value of understanding. Figure 10 How predictive analytic models are used whopping 2/3 (64%) said they tightly integrate their 9

12 Survey Results and Implications Still Some Barriers and Concerns While there is a clear enthusiasm for and optimism about these solutions there are still some barriers and issues as companies consider predictive analytics in the cloud. Data security and privacy was clearly the top issue with 65% listing it as very important. Regulatory issues and concerns with complexity and responsiveness were also significant. Interestingly those who had already had positive results worried a little less about data security and privacy, about complexity, and about latency and responsiveness. Only regulatory concerns remained as strong. Familiarity clearly results in at least a little less concern about these issues. From conversations with companies, these concerns are particularly strong in those industries where the core data required for predictive analytics is regulated data. It is also clear that some of the variation in cloud choices (public, private or hybrid) is driven by these concerns with private clouds, for instance, being prioritized where the data involved is sensitive or where responsiveness is critical. It should be noted that many organizations thinking about cloud solutions is still in its infancy as noted below. Industries Vary There was significant variation across industries in the results: Retail banks and other financial services much more likely (2x) to have had significant or transformative impact. Given the long history of analytics in these industries this was to be expected. These two industries were much more likely to focus on credit risk, other risks and fraud though customers are still #1. These are riskcentric industries so a stronger focus on risk and fraud is unsurprising. The financial services industry is trying to adopt a more customercentric (rather than account or product-centric) view of their business also, however, so the focus on customers remains strong. As you would expect given their results, these organizations were more likely to tightly integrate predictive analytics into operational processes. Decision Management is well established in these industries. Telcos have aggressive plans with 67% having specific plans to adopt predictive analytics. Healthcare delivery organizations meanwhile are clear laggards with over 40% with having no plans at all for predictive analytics. Among the five scenarios, Financial Services and Banking are much more likely to adopt both elastic compute and modeling with cloud data. As noted in the recommendations section, this reflects their maturity level. In addition they show a generally greater sense that predictive analytics in the cloud is going to be important and a higher likelihood of adoption across the board. Financial Services and Banking organizations are significantly more focused on deployment agility and cost reduction than is typical. Financial Services and Telco are more concerned about privacy and security. Retail banking respondents are big believers in pooled data, unsurprising given the strong showing for pooled data in helping build predictive analytic models for predicting credit risk and credit card fraud. 10

13 Survey Results and Implications A Mix of Clouds Cloud-based solutions can be public, private or hybrid solutions. Respondents were currently weighted to private clouds (55% on average) with few using hybrid public/private clouds (<10%). Over the next few years the respondents expected to see a clear drop in private clouds, down by an average of 9% while hybrid clouds rose 13% and public clouds rose 6%. This likely reflects a growing comfort in public cloud solutions as well as a broadening array of public cloud offerings as vendors move from offering only private cloud solutions to true public cloud, on-demand solutions. Of those using cloud based solutions, about 32% are still using them tactically while 34% have well defined guidelines and another 34% are developing such guidelines. This focus on standardization implies that cloud is solidly on the IT department s agenda even if impact has been limited so far. In general the solution itself is the main driver with the use of cloud, and the type of cloud, being largely a secondary issue. Once it is understood that a solution is cloud based, the value of the cloud in being able to get around IT barriers and get to value quickly is clearly appreciated, however. Conversations with organizations suggest that most private cloud users are trying to connect disparate elements of their business to improve collaboration and sharing. Public clouds are more popular once predictive analytic models are developed and when predictive scores and decisions need to be widely distributed. Many vendors report being asked about hybrid clouds such as with an on-premise appliance being used to deliver a private cloud while using the public cloud to provide some cloud-based management and results distribution. The line between public and private clouds Figure 11 Outcomes achieved or desired from cloud solutions is clearly still in flux and it is not clear where the line will ultimately be drawn. Within an organization their choice of public, private and hybrid is largely driven by the need for privacy and information security with public cloud being selected for lower risk scenarios. Overall responsiveness of the solution (private clouds can be more responsive with lower latency) and the time/cost to upload data also play a role in selection. Of note, almost half of those responding see cloud primarily as a way to reduce demand on internal IT (Figure 11). This is perhaps an indication that many organizations are not yet internalizing cloud as a source of business solutions not just IT infrastructure and that could be limiting the impact of cloud on organizations. Other results commonly discussed as benefits of cloud computing scored well also including replacing capital expenditure (on hardware and software) with operating expenditures, improving business user control of IT and reducing TCO. Interestingly transaction based pricing did poorly which was somewhat unexpected transaction pricing has a lot to offer, especially in a decisions as a service cloud-based solution but it s not apparent that it is a big driver amongst those taking the survey. 11

14 Survey Results and Implications Traditional Data Sources Dominate The analytics community is abuzz with discussions of Big Data, unstructured data or text analytics and new data sources beyond the traditional structured data managed in an organization s data infrastructure. Despite this buzz, respondents were much more focused on the effective use of structured data when building predictive analytic models in the cloud. Structured data from other cloud sources such as SaaS was the most important for building predictive analytic models in the cloud followed by pooled data (structured data from multiple companies pooled for analysis) and structured data uploaded from on-premise solutions to the cloud. Unstructured data was just not as highly rated. This became more pronounced when you consider only organizations that already have some successful results from predictive analytics those with some positive impact already. These more experienced organizations ranked structured cloud and pooled data as more important and unstructured data sources as less important (Figure 12). Success with predictive analytics seems to focus organizations on the effective use of structured data much more than on new, less structured data sources. Figure 12 Percentage rating different kinds of data as important A case could be made that companies would be better investing in consortia to manage pooled data to improve their models rather than in trying to include unstructured data in their analysis. While it is trendy to focus on unstructured sources the evidence from successful implementers of predictive analytics clearly ranked pooled data as the more useful resource. Pooling data across multiple organizations allows for predictive analytic models to be based on a larger sample and, generally, a sample that is more representative of the overall population. Besides the kinds of data being included the survey asked about different styles of data access. How important, survey respondents were asked, is real-time or near real-time data relative to batch and more static data? This too showed an interesting difference between those with more experience and those with less. Overall near real-time and real-time data slightly out performed batch and static data in terms of their usefulness for predictive analytic model development, though all four categories scored highly. However when those without positive results from predictive analytics are compared with those who have had positive results there are some clear differences (Figure 13). Near realtime data was slightly more important, static data was somewhat more important and batch data was significantly more important among those with more experience and positive results. Real-time data actually dropped when experienced users of predictive analytics were considered. Real-time data, it seems, is widely expected to produce better results by those with limited experience. Those who have successfully built and deployed models seem to show that this is not necessarily true, 12

15 Survey Results and Implications however, showing a stronger preference for batch data. Taken together this implies that organizations can get started with predictive analytics, and get positive results from using predictive analytic models, even if those models are built only from structured data in a batch environment. Organizations don t need to wait until they can also manage unstructured data sources or manage their data in real-time before they can be effective users of predictive analytics. Figure 13 Importance of data timeliness to predictive analytic models In the next section we will explore recommendations for organizations at different levels of maturity as well as the pros and cons of cloud-based predictive analytics solutions. 13

16 Recommendations Different approaches for different organizations Predictive Analytics has not hit the mainstream yet because most businesses see at as a fairground voodoo magic trick to be marveled at, and then go back home to regular business. For those that see potential the entry barrier can seem too high so that only the largest and the bravest businesses venture forth. Using a cloud-based model to address Predictive Analytics may be the solution to this vicious cycle, Gagan Saxena, CIO Apple Vacations, a survey participant The basic value proposition of predictive analytics in the cloud is clear: organizations can make predictive analytics more scalable, more pervasive and easier to deploy using cloud technologies. As more organizations seek competitive advantage through analytics, their ability to rapidly make analytics pervasive and to tightly integrate analytics into their business strategy and day to day operations is going to be a critical factor. For many of the challenges such organizations face in their journey to becoming analytic organizations, cloud-based solutions have much to offer. The research and the survey results make it clear that organizations should make cloud-based Predictive Analytics part of both their development approach and their deployment architecture. Using cloud-based predictive analytic solutions to develop predictive analytic models makes it easier to adopt new data sources, especially cloud-based Big Data, while improving the effectiveness of scarce modeling experts by making the power they need available on demand. The pervasiveness of the cloud and the ability to define simple, standard interfaces make it a compelling deployment platform for predictive analytic models, helping to put predictive analytics to work throughout an organization s operational systems and processes. The reality is that organizations are at different stages in their adoption of predictive analytics and they must pick the right approach for their level of sophistication. They 14

17 Recommendations can begin their journey with pre-packaged solutions, use cloud-based deployment to expand and adopt cloudbased development to scale their predictive analytic operations. A blend of on-premise, private and public cloud solutions can be found to match their privacy, security, regulatory needs. Organizations also need to be aware of the pros and cons of cloud-based solutions so that they can maximize their advantages while minimizing their risks. Three Stages of Adoption The different kinds of solutions available under the umbrella of predictive analytics in the cloud allow organizations at every level of sophisticated to adopt cloud-based predictive analytics. Organizations can use predictive analytics in the cloud to jump-start their adoption of predictive analytics, speed and support expansion or refine an already sophisticated approach. The different solutions also allow different parts of an organization to share a focus on the cloud, and potentially cloud resources, while still moving forward in a way that makes sense allowing more sophisticated or experienced departments to have a different adoption strategy to those with no prior predictive analytics experience. Before adopting cloud-based predictive analytic solutions organizations should understand where they fall on the maturity curve. A first group is those just getting started with predictive analytics, with little or no prior experience. A second group is those with some prior experience building and using predictive analytics that have not yet made predictive analytics widespread in their organizations but wish to do so. The final group is those widely using predictive analytics and looking for ways to make their approach more effective. New to Predictive Analytics Organizations that are new to predictive analytics can get a jump start on becoming a more analytic organization using pre-packaged cloud based solutions. These Decision as a Service solutions are focused squarely on being a complete solution to a business problem, making them easier to budget for and justify even in organizations with no prior history of success with predictive analytics. These solutions typically have very simple interfaces, minimizing or even reducing to zero the need to bring IT resources to bear on adoption. By returning a simple list of the right customers to contact, a fraud flag for a transaction or the best offer to make a customer, they make it easy to improve your day to day operations using analytics. Because the analytics are embedded in a solution, organizations don t need any analytic expertise to adopt them. Early Adopters of Predictive Analytics For organizations that have already started to see some value from predictive analytics, but are not yet widely adopting analytic decision-making, cloud solutions offer the potential for rapid expansion. The only way to get value from predictive analytics is to act on it. Therefore the analytic insight you generate must be embedded into your organization in the form of improved decision-making. The pervasiveness of the cloud makes it easy to connect any existing operational process or system to a cloud-based predictive analytic solution. Standard and simple interfaces are the norm in cloud solutions, making integration easier and reducing the need for extensive IT work. Cloud-based solutions also support the extended enterprise by allowing integration with systems managed by the organization as well as those being used by its channel and supply chain partners. Both cloud based predictive analytics for SaaS and cloud based predictive analytics for on premise systems are 15

18 Recommendations worth considering. Whether your existing systems are SaaS or on-premise it is highly likely that they are not being powered or improved by predictive analytics. Using the cloud to push your predictive analytics more widely into your systems will increase adoption and help show a greater return on your analytic investment. Established Predictive Analytic Factory Finally for those organizations with an established predictive analytic factory, with an industrialized analytic approach, the cloud can be a powerful tool for improving the efficiency and effectiveness of this factory. The use of elastic compute power, in a private or public cloud setting, allows modelers to be more efficiently supported. The more time a modeler spends waiting for a result the less efficient they are. Yet modelers don t need massive amounts of compute power all the time as only certain steps in the predictive analytic process require it. Being able to elastically provide the right amount of compute power to analysts and being able to share compute resources using grid or cloud approaches maximizes their efficiency while minimizing investment costs. The effectiveness of the models you develop can also be improved using the cloud. Predictive modeling with data in the cloud makes it easier to being new data sources to bear on difficult modeling problems. This is especially true of emerging Big Data sources and third party data. Most established predictive analytic groups have an insatiable appetite for new data sources. Bringing modeling to the data available in the cloud can make it much easier to expand the range of data used to build your models. In every case it is worth noting that private, public and hybrid approaches are possible. Organizations need to think about the right blend of on-premise, private cloud and public cloud to match their privacy, security and regulatory needs. Before adopting cloud-based predictive analytic solutions, organizations should be aware of the pros and cons of these solutions. Pros Time to value, pervasiveness, agility, scalability and data access are the key pros of cloud-based predictive analytics. Time to Value The total cost of ownership of a solution matters and is typically measured over multiple years. For many organizations the time to value also matters. They don t want to wait more than a few months before seeing a positive outcome. Using cloud-based predictive analytic solutions has a much faster time to value than alternatives and this represents a critical advantage for the approach. This rapid time to value is also generating interest in cloud-based solutions for testing, experimentation and development even where deployment will ultimately not be cloud-based. Pervasiveness The pervasiveness of the cloud, the fact that cloud based solutions can be made available anywhere there is an Internet connection, is a major factor in their value. Organizations are increasingly distributed, with teams spread across multiple locations. Organizations are using more subcontractors and outsourced service providers too. This spreading out of the work of an organization makes the pervasiveness of cloud-based solutions increasingly valuable. This is true both of deploying predictive analytics into operations and of developing predictive analytics. Deployment Agility Cloud-based solutions focus on simple, standardized interfaces. This minimizes integration effort and cost, reducing an organization s dependency on IT resources to adopt these solutions. It also allows 16

19 Recommendations tight behavioral integration without tight systems integration. Cloud-based predictive analytic solutions can be used to make existing systems and processes work more effectively without massive disruption of those systems and processes. Organizations can deploy new analytic insights rapidly and easily into their operational infrastructure. Scalability Elastic compute resources are important to both the execution and the construction of predictive analytic models. The role of scalable compute resources in making modelers more productive is clear it minimizes the infrastructure needed to provide modelers with a responsive environment. As organizations increased their consumption of predictive analytic models they will also see the value of scalability in deployment. More use of predictive analytic models leads to a wider variety of users and this creates elastic demand. A scalable environment will be required to match this demand. Improved Data Access Organizations are steadily adopting more cloudbased systems and new sources of data are increasingly available in the cloud. Many new Big Data sources are only available in the cloud. As enterprises adopt a hybrid infrastructure consisting of public, private and semi-private clouds for their operational systems they will also be less restricted by data transmission rates. All this increases the value of moving analytic modeling to the cloud where it can be near these new sources of data. Cons The cons of cloud-based predictive analytics are around privacy and security, regulatory issues, bandwidth for moving data to the cloud and increased complexity. Privacy and Security The data used in building predictive analytics is often about consumers and their behavior. As such keeping it private and secure is an ongoing challenge for organizations. While predictive analytic models generally do not require personally identifiable information like Social Security Numbers, this data is often mixed in with the data they do need. Moving this data to the cloud therefore makes organizations nervous as they feel more exposed. Mitigate by keeping personally identifiable data out of modeling and leveraging private clouds when necessary Regulatory Issues In addition to privacy and security, there are often regulatory issues. It may not be legal to access certain data in a certain way or to combine sources to, for instance, determine the identity of someone in a data source that is supposed to be anonymous. It may not be legal to move certain data to other countries. All these regulations must be accounted for when considering moving data to clouds. Mitigate by working with vendors experienced in your industry Moving Data to the Cloud Few enterprise transactional or operational systems reside in the cloud today making data relocation/transfer a necessity. Today this means pushing data through a fairly narrow pipe at the edge of the Internet. For organizations with lots of data on-premise this can be a significant problem for cloud-based predictive analytics. Mitigate by moving data directly rather than aggregating first and then moving to the cloud. Complexity Private and hybrid clouds can require an organization to adopt some new technologies and approaches and this can increase total solution complexity. Mitigate by working with experienced vendors and leveraging more public cloud infrastructure. The next seven sections are profiles of each of the sponsors of this study. 17

20 Clario Analytics Clario Analytics offers a marketing analytics platform that transforms raw customer data into actionable intelligence. Matt Redlon, CEO and co-founder, Clario Analytics Clario offers us the opportunity to quickly integrate best practices for marketing analytics and strategic planning through their Core cloud applications to support our internal marketing resources without having to commit to large investments in infrastructure projects or tying up our internal IT department. Dave Mathews, President, CEO Healthy Directions Clario Analytics develops and markets analytic marketing solutions via a cloud-powered, Software-as-a-Service deployment model. Clario Analytics has been marketing cloud-based analytic solutions since Clario Analytics offers a predictive analytic platform and industry solutions for marketing including contact optimization, geo-demographic data, campaign performance, customer inventory and list creation. Clario s solutions are designed to easily integrate with existing campaign management and other marketing solutions as well as with third party services. Facts Based in Eden Prairie, MN Home Page: Contact or clarioanalytics.com/about/contact facebook.com/clarioanalytics 18

21 Clario Analytics Offerings Clario Core is Clario s proven marketing analytics platform with a corresponding set of applications that work with Clario Core. These five add-on applications perform modeling and scoring, reporting and selection functions, on top of Clario Core. Customers can now buy a platform only license for Clario Core and do it themselves or they can purchase one or more of the applications on top of the platform. Clario Core, the underlying platform, allows marketing organizations to pull together all their raw customer data and integrate it into a single view so that both reports and predictive analytics can be developed against that data. Core Modeling The Clario Core Modeling Application provides the foundation for efficient and effective marketing model development and scoring, including workflows for creating a rich, point-in-time customer attributes data store. Reporting Three powerful reporting applications deliver meaningful analysis built around critical predictions. Campaign Reporting shows key performance metrics by customer segment for ongoing campaigns and uses predictive analytics in sophisticated match-back reports to tie revenue back to the right campaign, even where the results come in a different channel from the original campaign. Migration Reporting values a company s customer and prospect base by valuing segments and predicting future value of these customers as they move between segments from new to established customers for instance Future Value Reporting helps marketing planning by predicting the long-term value of customer segments in part by predicting the value of converting prospects to new customers. Core Selection Core Selection is an operational application for creating and measuring the effectiveness of large numbers of highly targeted customer lists using existing segmentation, customer and channel information and predictive scores from Clario Core Modeling. Clario Stream Clario Stream is a contact optimization solution that helps multi-channel merchants optimize their direct mail and marketing efforts. With Clario Stream, marketers can reduce their mailings by large numbers (creating a big saving) for a tiny reduction in results by focusing on the most likely to respond. A fitness company that sells directly to consumers through a catalog, other direct mail, the internet, TV and retail channel, this company has several brands. Clario s cloud-based analytics solution was used to build buyer and inquiry models to target catalogs for one of the brands. This brand's catalog goes out three times per year and because of the high price point of a typical fitness equipment purchase, identifying the right customers to target is critical. Attributes were created from a variety of internal and cloud-based sources such as customer, order, item, inquiry activity and demographic data. These attributes drove predictive response models for current buyers and catalog inquirers based on historical mailing and purchase information. The final predictive analytic models rank order customers and inquirers according to expected sales. Model scores are updated prior to each campaign using Clario Core. For one campaign the buyer model had a 25% higher response rate and 44% higher ROI compared to prior campaigns. The inquirer model had an ROI increase of 174%. 19

22 FICO Leading organizations worldwide rely on FICO analytics to power billions of business decisions each day that accelerate growth, control risk, and boost profits. With our network of partners, we pioneered cloud-based analytic and decision services that have supported the needs of our high-performance and security sensitive clients for many years. We continue to see the cloud as a great way to leverage our analytic innovations and unlock new value for our clients. David Lightfoot, Vice President of Product Management, FICO Everything we need to make a loan decision is right at our fingertips. It has definitely simplified operations and made life easier. Beverly Pile, Vice President of Consumer Underwriting, Prosperity Bank FICO delivers superior business rules management, predictive analytics, and optimization solutions that drive smarter decisions. FICO s innovative products include the FICO Score, along with industry-leading decision management software that their clients use to power billions of decisions each year. FICO offers a number of cloud-based decision management applications in areas such as loan and credit card approval, collections and recovery, credit and debit fraud detection and marketing. A number of these applications are provided through relationships with credit card processors such as TSYS and FDR. FICO s predictive analytic model development tool supports grid and hosted deployments while its business rules management system supports cloud-based deployment of decision-making systems. Facts Headquarters: Minneapolis, MN Home Page: Contact: info@fico.com or facebook.com/pages/fico 20