2016 Protiviti Predictive
Analytics Survey
Introduction
Tectonic shifts in the way business is conducted have raised market stakes dramatically over the past decade. The C-suite agenda is rapidly changing: Industries are intensely competitive, margins are thin, interest rates are low and products are becoming more complex. Capital markets are volatile. Shelf space with distributors is limited and ongoing regulatory changes make the environment all the more challenging. All of this, and more, is disrupting traditional business models.
To succeed in such a demanding environment, executives require deeper and more intricate insights into their business. What is working for us? What isn’t? In which customer segments do we have the most success? Which of our products or services are underperforming? Which candidates are likely to be successful? How do we make smart decisions about appointing third parties?
Fortunately, business leaders now have better capabilities and tools to exploit the power of their internal data to help answer these and other critical questions. They are leveraging, with great success, predictive modeling for customer analytics, people analytics, operational analytics, and digital/social analytics applications. They are also emphasizing better alignment of their analytical capabilities with the agenda of the C-suite (CEO, CMO, CFO, COO and other officers).
Protiviti conducted its Predictive Analytics Survey to learn more about the predictive analytics capabilities of organizations. We engaged top executives with leading companies in the Americas, Europe and Asia across numerous industries. The survey assessed existing analytical capabilities and measured the strength of alignment of analytics teams with organizational strategy and the C-suite agenda. It is designed to help executives develop practical knowledge about issues of common importance at the regional and country level as they align to support the strategic business agenda of their boards and CEOs.
Our study focuses on: (1) understanding the priorities of companies and the approaches taken to meet “big data” business needs, and (2) providing a means for our study participants to benchmark certain aspects of advanced analytical functions. It consists of both quantitative and qualitative questions framed to collect insights from executives into current trends and the factors driving change in analytical functions.
Our results suggest organizations are trying to leverage big data, predictive modeling and advanced analytics to improve their businesses and, in fact, are making significant investments in these capabilities. However, there is substantial room for improvement in many areas of predictive analytics as companies strive to bring into alignment their analytical functions with the organization’s strategy.
Survey Methodology, Participating Companies
and Executives
Protiviti conducted its Predictive Analytics Survey from June through September 2015. There were three parts to this benchmarking study:
• Management priorities, enablers, inhibitors, critical success factors, current and future spending on advanced analytics and predictive modeling
• Typical predictive analytics applications and their use • Predictive analytics infrastructure, tools and resources
We want to thank the participants in the survey. We distributed 5,000 invitations and received more than 1,100 responses. Of this group, more than 200 executives were qualified to complete our questionnaire. Our respondents represent organizations that have demonstrated a commitment to, and interest in, moving their advanced analytical functions to the next level.
In analyzing the survey responses for certain questions, we also found it helpful to segment the responses for several sub-groups of participants:
• Location – Companies whose corporate parents are domiciled in the Americas, Europe or Asia-Pacific. • Size – Defined as large (more than 5,000 employees), medium (1,000 to 5,000 employees) and small (less
than 1,000 employees). 37% company size breakdown 40% 23% Small Medium Large respondent breakdown 51% 9% 9% 8% 7% 6% 4% 4% 1% 1%
Chief Executive Officer Chief Operating Officer Chief Financial Officer Chief Information Officer Chief Risk Officer Head of Sales / Chief Marketing Officer
Other
Chief Data Officer LOB Executive Chief Audit Executive
Energy
Hospitality, Construction & Food Distribution, Transportation & Logistics Pharmaceuticals & Life Sciences Professional Services, Legal & Real Estate
Consumer & Retail Healthcare
Technology, Communications & Entertainment Government, Education & Not-for-profit Manufacturing
Financial Services & Insurance
industry breakdown 22% 16% 13% 12% 9% 8% 6% 4% 4% 3% 3%
Approach
Respondents were asked to complete a survey instrument, which we made available via a website link or via a hard copy. In certain instances, we held a meeting or a conference call with a participant to discuss the survey and answer any questions about the study. We also fielded questions from individual participants as they completed the survey. After receiving each submission, we reviewed it for completeness and, if necessary, conducted follow-up interviews to seek clarification.
In some instances, participating companies did not answer every question. We used professional judgment in analyzing and tabulating the responses. We edited the responses to qualitative questions, in some cases to preserve confidentiality and to correct minor grammatical or spelling errors.
Limitations and Reliance
Our work was based on participant responses to the survey instrument, as well as related inquiries and discussions with management. We have not sought to confirm the accuracy of the data or the information and explanations provided by management. In addition, since completion of the survey was voluntary, there is some potential for bias if those choosing to respond have significantly different views on matters covered by the survey from those who did not respond. Therefore, our results may be limited to the extent that such possibilities exist. Despite these limitations, we believe the results herein provide valuable insights regarding how business leaders are leveraging their predictive analytics capabilities.
Notable Findings
Following are key takeaways from our survey and analysis of the results.
It Is All About the Customer
Achieving sustainable business growth is at the top of the corporate agenda and will continue to be over the next few years. Our results show that one of the most important drivers of this growth is customer centricity. Therefore, the customer agenda will retain its lead among the most influential drivers of predictive modeling and advanced analytics.
Most organizations also plan to increase their investment significantly in customer analytics and integrate them with digital and social analytics. In fact, nearly a quarter of organizations are not satisfied with the current level of investment in predictive analytics, in general. Interestingly, however, this is not the case within financial services firms, which are prioritizing operational and risk analytics – likely because of regulatory compliance requirements.
Please rank each of the following drivers of your predictive modeling and analytics
efforts – from high importance to low importance:
Customer relationship focus Management emphasis on profitable growth Earnings demands Regulatory pressures Product development/ pricing pressures Technology innovation 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% High Importance/Medium-High Importance Medium Importance Medium-Low Importance/Low Importance
79% 72% 67% 63% 63% 61% 12% 20% 19% 21% 21% 27% 9% 8% 14% 16% 16% 12%
view customer relationship focus to be highly important to their predictive modeling and analytics efforts.
79
%
48
%
view the use of customer analytics applications to be a strategic priority, with “customer lifetime value” and “loyalty/attrition” ranking as the most significant areas of focus.believe the use of customer analytics in their company will increase over the next two to three years.
Predictive Analytics Is More in Use in the Back Office
Despite a drive to become more customer-centric, it is “back-office” executives – e.g., chief data officers, CFOs, and COOs – who appear to be the key users of predictive analytics. On the other hand, only about a third of “front-office” executives (CEOs, CMOs, sales, business development, etc.) presumed to be closest to customers and their behaviors are considered regular and strategic users of advanced analytics.
Furthermore, there are executives who have never used predictive analytics. We believe one of the major reasons for the lack of analytics is that to achieve sufficient levels of precision in modeling customer behavior, an analytics team requires access to unstructured data sources, as well as data from digital and social networks. This is where customers live, breathe, shop, buy, sell and exchange their opinions. Reaching this level of sophistication demands an advanced analytics capability. Analytics teams are tempted to offer solutions to back-office functions only because a vast majority of these models can be based solely on data that is internally available from the company’s own structured data stores.
What business functions/executives are the key consumers of your organization’s
predictive modeling and analytics function, and how often do they use it?
CAO/CDO CFO COO CIO CRO Head of Sales/ Distribution CEO CMO 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Regular, planned Regular, ad-hoc Need-based One-off Never use
47% 43% 38% 33% 32% 32% 31% 29% 18% 26% 31% 29% 26% 26% 24% 32% 19% 18% 16% 25% 23% 24% 26% 23% 4% 6% 9% 7% 6% 11% 5% 12% 7% 13% 4% 12% 12% 8% 11% 2%
• Chief analytics officer/chief data officer
• Chief financial officer • Chief operating officer
• Chief marketing officer • Chief executive officer • Head of sales/distribution
Executives who plan to and regularly use
Data Is King
Data governance (availability, quality, governance, security and privacy) ranks as the most influential and critical factor and enabler of a successful predictive analytics initiative. At the same time, it is viewed as the most important inhibitor of these initiatives. Such views should not come as a surprise. You certainly do need high integrity data to deploy in a predictive analytics model – both internal company data and external data, as well as structured and unstructured data. Most organizations are working toward superior data integrity, attributing anywhere from 60 to 85 percent of overall project time to data management activities. A strong alignment between business, analytical and information technology groups is required to be successful in delivering insights with predictive analytics.
With regard to data infrastructure, data availability, timeliness and quality represent the top areas of focus for companies. Although many continue to exploit their existing internal data assets, more organizations are shifting to more external and unstructured data, which will be needed to support the quest for digital and social analytics. However, companies have moved slower in adopting “big data” infrastructures that are required to support these endeavors.
Finally, a majority of organizations are leveraging data visualization tools, which may impede their ability to analyze, visualize and interpret results of the predictive models and, therefore, to “link back” with the clients of predictive analytics.
of analytics functions currently use data visualization tools.
54
%
42
%
of analytics functions currently use machine learning as a statistical method.view the availability of data to be a key issue in their predictive analytics efforts.
71
%
68
%
view the timeliness of data to be a key issue in their predictive analytics efforts.Please rank each of the following key success factors that enable your predictive
modeling and analytics capabilities – from high importance to low importance:
0% 20% 40% 60% 80% 100%
High Importance/Medium-High Importance Medium Importance Medium-Low Importance/Low Importance
79% 13% 8% 79% 11% 10% 73% 19% 8% 73% 18% 9% 71% 20% 9% 70% 21% 9% 69% 23% 8% 68% 21% 11%
Data (availability, quality, governance, privacy laws) Accuracy/fit of predictive models Executive buy-in/approval/acceptance Business case content/formulation Internal people (skills, expertise) Budget availability/size Technologies/tools/infrastructure Interpretation/visualization/ communication of results
Please rank each of the following key success factors that inhibit your predictive
modeling and analytics capabilities – from high importance to low importance:
Data (availability, quality, governance, privacy laws) Internal people (skills, expertise) Budget availability size Accuracy/fit of predictive models Executive buy-in/approval/acceptance Technology/tools/infrastructure Interpretation/visualization/ communication of results Business case content/formulation
65% 23% 12% 60% 27% 13% 60% 23% 17% 59% 26% 15% 58% 21% 21% 56% 26% 18% 56% 26% 18% 54% 27% 19%
Achieve Better Precision and Accuracy
A predictive model can only be relied upon if it is accurate. Although we have observed a definite improvement over the past few years, model precision generally averages 80 percent, depending on the type of application, data availability and quality, and modeling technique used, among other factors. For a majority of organizations, this shortfall represents a major point of frustration.
Some have become accustomed to unsystematic ways of making decisions, typically based on a “gut feel,” which is less predictive than a coin flip. However, the picture is much clearer for market leaders and executives who use predictive analytics to gain and sustain a competitive advantage. They are able to rely on models that are at least 92 to 95 percent accurate. To reach that level of success, you need more and better quality data, as well as better models – and not simply from a quantitative engine perspective. The model definition and design team needs to be comprised of cross-disciplined subject-matter experts skilled in model design and capable of thinking outside of the box. In addition, the entire model development cycle must be supported by efficient processes and governed properly. Alignment between people, processes and technology is critical to achieving the best performance and accuracy from your predictive models.
Another interesting trend is the move to machine learning algorithms and artificial intelligence, which are a driving force for unstructured data and text analytics. From a tool perspective, “freeware” (like Python and R languages) are steadily gaining ground against the most commonly used commercial statistical engines and platforms.
rank the accuracy and fit of predictive models to be highly important for their predictive modeling and analytics capabilities.
79
%
believe the accuracy and fit of predictive models is a significant inhibitor to their predictive modeling and analytics capabilities.
There Is a “People” Disconnect
Ask any executive, “What is the most important asset in your organization?” and they usually respond, “Our people.” Yet people analytics appear to be the least mature type of predictive analytics companies are using. In fact, many have not even considered using people analytics.
This represents a significant shortcoming – one that likely will become a major area of investment for companies in the coming years (employee success propensity, employee churn/loyalty models). Organizations need to be prepared for this, including not only technical capabilities, but also in terms of compliance. Other significant opportunities exist around operational analytics (specifically, around process capacity modeling and staff optimization) and fraud prevention – areas of analytics currently at a low level of maturity, but where companies plan to invest more. Of note, both types of initiatives create excellent opportunities for experimenting with internal, unstructured data (such as emails and voicemails).
Please rank each of the following strategic priorities in people analytics –
from high importance to low importance:
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% High Importance/Medium-High Importance Medium Importance Medium-Low Importance/Low Importance
Candidate identification Loyalty/retention Pre-employment screening Recruitment propensity Promotion propensity 43% 34% 23% 49% 29% 22% 54% 26% 20% 56% 26% 18% 60% 22% 18%
of organizations are not leveraging people analytics as part of their predictive modeling and analytics activities.
37
%
of companies consider people analytics to be a strategic priority.
Aligning for Success Starts with Executive-Level Leadership
Successful organizations and market leaders know the difference between short-term gains and sustainable success. Sustainability and excellence in delivering advanced analytical solutions depend on organizational commitment to carefully selecting and deploying the “Analytical Target Operating Model” (ATOM), which aligns successful organizational strategy, processes, people and technology/data.
Our results show that the salience of a predictive analytics team is tied directly to its leadership. Top-performing teams are led by a senior executive and report to the top of the organization.
In terms of organizational design, the most common for predictive analytics functions is a centralized model. The question is whether this is “by design” or “because it’s always been this way.” That said, these functions appear to be growing – in many cases, at a rate faster than the rest of the business.
Which of the following best describes the rank/position of the
highest ranking analytics executive in your company?
0% 5% 10% 15% 20% 25% 30% 35% 40%
High Importance/Medium High Importance Medium Importance Medium Low Importance/Low Importance
Vice President Officer Director Head Manager Lead Evangelist Other 37% 29% 19% 6% 4% 2% 2% 1%
of analytics functions are led by a VP-level executive.
37
%
of analytics leaders report to the CEO.
About Protiviti Inc.
Protiviti (www.protiviti.com) is a global consulting firm that helps companies solve problems in finance, technology, operations, governance, risk and internal audit, and has served more than 60 percent of Fortune 1000® and 35 percent of Fortune Global 500® companies. Protiviti and our independently owned Member Firms serve clients through a network of more than 70 locations in over 20 countries. We also work with smaller, growing companies, including those looking to go public, as well as with government agencies. Ranked 57 on the 2016 Fortune 100 Best Companies to Work For® list, Protiviti is a wholly owned subsidiary of Robert Half (NYSE: RHI). Founded in 1948, Robert Half is a member of the S&P 500 index.
About Our Predictive Analytics Practice
Protiviti’s Predictive Analytics practice delivers business-relevant services connected to clients’ operational improvement opportunities, empowered by modern science and technology.
Our specialists are focused on concrete and tangible solutions that enable sustainable, sector-specific and business-relevant operational changes. These solutions include:
• Customer analytics • People analytics
• Operational/risk analytics • Social and digital analytics
Contacts
Shaheen Dil
+1.212.603.8378
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