Optimization is the key to delivering ever-increasing ROI, with flat or declining marketing budgets. In this whitepaper, learn best practices for optimizing your marketing activities, and do more with less.
This white paper describes the architecture of SAS Marketing Operations Management and various aspects of its deployment and security. The following areas are covered:
• High-level architecture overview
• Architectural components
• Deployment options
• Deployment best practices
This paper is intended for those involved in purchasing, selling and implementing SAS Marketing Operations Management, including system administrators and anyone seeking an
understanding of the solution’s architecture and security.
Financial institutions (FIs) must support the channels and services that consumers demand in order to remain competitive with each other and with disruptive competitors. To that end, supporting account opening, delivering new transactional features, and facilitating payments through digital channels have become table stakes. Unfortunately, the speed and convenience that these capabilities afford is a benefit to consumers and fraudsters alike. To successfully prevent fraud while retaining the benefits of offering digital financial services, FIs must understand how fraudsters are exploiting these capabilities and fight fraud with customer experience in mind.
The financial collapse of 2008 had the greatest impact on the financial services industry since World War II, resulting in consolidation and extensive regulation. The crisis coincided with increased competition from emerging economic powers, nonbanks and fintech organizations. Consumer behavior, from the adoption of mobile banking to P2P payments, forced banks to retool and respond with innovative products and investments in new delivery channels. Technology changed rapidly as well. In the capital markets, trading became fully automated, with pricing, risk decisions and settlement across exchanges made in milliseconds
When was the last time you had an outstanding customer experience? Perhaps you hesitated before answering. Now, think for a minute about your customers – would they hesitate before answering the same question about your business? If you think the answer might be yes, it’s time to consider the customer journey.
Imagine that you are in a meeting with several department heads discussing issues around enrollment. As people begin to share their thoughts, you quickly realize that each person in the room is working from conflicting information – and everyone thinks their information is correct. The group spends most of the meeting arguing over whose data and reports are correct rather than understanding the problem and making decisions. Sound familiar? When organizations have their data fragmented across many systems and departments, these situations are all too common. Without a single, trusted source of truth and easyto- use tools to interpret and understand the data, conversations are limited by everyone’s departmental perspectives and resources.
New analytics tools and services are helping organizations extract exceptional business value from the massive volumes of available data provided by various internal and external sources. Many companies are harnessing these insights to improve operational and business processes, troubleshoot problems, identify business opportunities, and generally compete and innovate better. Now the benefits of analytics in those areas are prompting companies to look to analytics to improve information security. Enterprise security organizations are under tremendous pressure to change. Traditional perimeter-focused security controls and strategies have proved inadequate against modern, highly targeted attack campaigns.
Identifying the best technology to improve marketing performance is a complex decision, especially for a growing marketing organization. Deciding where to spend valuable capital should be based on the greatest opportunity for gain. In the current marketing environment, the greatest opportunity is in analytically enabled marketing.
On an average day, 78 Americans die from opioid overdose. Last year’s total was almost 30,000 deaths, roughly two-thirds involving prescription opioids (including Percocet, Vicodin, Hydrocodone, Oxycodone, Oxycontin), the rest involving heroin. The United States, with about 5 percent of the global population, consumes 80 percent of the prescription opioids. The problem affects people of all backgrounds and across the socioeconomic spectrum; the Center for Disease Control (CDC) has officially declared it an epidemic.
Today’s customer experience requires a combination of individualized insights, connected interactions and an agile approach to meet customers in the channel of their choosing. This means more than simply doing the same things over in the new channels. It requires new ways of exploring customer trends and preferences, and being smarter about responding to these factors.
Data visualization is the visual and interactive exploration and graphic representation of data of any size, type (structured and unstructured) or origin. Visualizations help people see things that were not obvious to them before. Even when data volumes are very large, patterns can be spotted quickly and easily. Visualizations convey information in a universal manner and make it simple to share ideas with others.
No matter the vintage or sophistication of your organization’s data warehouse (DW) and the environment around it, it probably needs to be modernized in one or more ways. That’s because DWs and requirements for them continue to evolve. Many users need to get caught up by realigning the DW environment with new business requirements and technology challenges. Once caught up, they need a strategy for continuous modernization.
Banks and financial institutions have faced a spate of regulations centered on capital adequacy since the financial crisis started in 2008. The Basel Committee on Banking Supervision (BCBS) initiated a series of reforms to strengthen risk, capital and liquidity rules across banks. Among the important changes recommended are new rules for calculating Tier I and Tier II capital and the inclusion of additional risk measurement components for market risk, liquidity risk and counterparty risk. Despite these changes, a key drawback of the Basel framework is its focus on historical capital adequacy. While being useful, it does not help assess the impact of stress events on banks from an ex-ante basis. Hence regulatory agencies in several jurisdictions have mandated banks to define a forward-looking capital plan that incorporates stress scenarios.
The concept and practice of stress testing has been around for many years. While traditional stress testing methodologies are still valid for firmwide scenario analysis and stress testing, special techniques and attentions are needed to successfully achieve the goal of firmwide capital adequacy in forwardlooking stress scenarios. During the 2007 financial crisis, many financial institutions were not sufficiently prepared for the ensuing liquidity crunch and capital drains. Perhaps if banks had worked through different economic scenarios prior to the crisis, they would have been in a better position to weather the storm. Inadequate preparation for crisis can lead to systemic risk and severe economic and political turmoil.
Whether you call them customers, clients, patrons, guests or patients, customers are your organization’s most important asset. And that means customer loyalty should be among your top priorities. No matter when or where the customer journey begins – from websites and online chat to physical locations and call centers – customers expect you to provide a unique and personal experience. How can you use data and analytics to recognize your best customers across channels and know exactly where they are in their customer journey? Keep reading to find out.
Sixteen years ago, Seth Godin wrote one of my favorite industry books, Permission Marketing, which would prove to be eerily prophetic. Godin advocated a revolutionary approach to “turning strangers into friends and friends into customers” by eschewing traditional unsolicited marketing communications. Instead, he suggested that companies “date” their customers – first offering incentives to engage (essentially asking for permission to interact, possibly with a freebie or discount), then using the knowledge gleaned from those interactions to speak to customers as friends.
As the pace of business continues to accelerate, forward-looking organizations are beginning to realize that it is not enough to analyze their data; they must also take action on it. To do this, more businesses are beginning to systematically operationalize their analytics as part of a business process. Operationalizing and embedding analytics is about integrating actionable insights into systems and business processes used to make decisions. These systems might be automated or provide manual, actionable insights. Analytics are currently being embedded into dashboards, applications, devices, systems, and databases. Examples run from simple to complex and organizations are at different stages of operational deployment.
With this increased attention on building a brand as a strategic differentiator, how are marketers approaching it? A recent eMarketer global survey revealed that nearly 56 percent of marketers would increase spending on brand-building activities over the next year compared to 42 percent who said they would be focusing on demand generation. Brand awareness was a higher priority for marketers than demand generation, global business expansion efforts or event spending.
Insurance fraud has existed wherever insurance policies are written, taking different forms to suit the economic times. Today the magnitude of insurance fraud is not only startling but increasing. Recent studies by the US National Insurance Crime Bureau (NICB) reported a 24 percent rise in questionable claims for the period 2011 to 2013. The full scale of insurance fraud is not known. And if fraudulent behavior is not discovered at the time the claim is submitted, the insurer may never know it occurred. Consequently, an uninvestigated claim can’t be labeled as fraudulent to investigate.
A paradigm shift is underway in the cybersecurity industry. Cybersecurity professionals are moving from a focus on attacker prevention to attacker detection. Preventing the “bad guys” from getting in is still important, but cyber adversaries are increasingly able to bypass even the most sophisticated network defenses. Once inside, it is more important than ever to find these attackers fast, before their activities get buried in the daily volume and pulse of network communications. This is where security analytics holds promise. Security analytics provides the necessary and timely visibility into normal and abnormal network behavior. This visibility enables devices and entities acting suspiciously to be quickly identified and investigated.
Machine learning and the Internet of Things (IoT) are two of the hottest terms out there today for utilities. Both have the power to create an increasingly autonomous grid that can eventually handle billions of endpoints on utility networks, but the industry may not be maximizing the benefit of these disruptive innovations, nor adequately leveraging the connection between the two of them.
Banks have been using credit scoring models for over five decades, so managing the life cycle of models is nothing new. Most have had some kind of process in place to ensure the models they develop are robust, validated and monitored from a performance perspective and that decision makers have confidence in them. In recent times, however - partly in response to the credit crisis in 2008 - the discipline of model risk management (MRM) has become more formalized and rigorous, driving the need for enterprise-level model information management systems. The regulatory scrutiny being applied to them is intensifying and spreading globally, with US and European regulators leading the charge. For example, whereas regulators were previously more interested in the numbers they were provided, now more regulators want to have a core understanding of the models banks used to generate these numbers.
The Internet of Things (IoT) – devices and sensors connected to computing systems and networks – has received enormous attention in the last few years. The attention is due, in part, to the proliferation of connected devices, from about a million in the early 1990s to more than five billion today. In addition, the technology for connecting the devices has become more affordable and easier to integrate. The result is that IoT is helping to digitize more and more business processes, from the factory floor to tracking shipments across oceans. Digitized processes are providing a continuous stream of digital data. By analyzing the data stream, businesses can refine their processes by better understanding how those processes are performing, identifying possible issues sooner and uncovering areas for improvement.
A persistent skills gap plagues employers in all major industries, spurring SAS to provide additional resources that support the next generation of analytical talent. But we know the skills gap can mean different things to different people. This e-book features interviews with those who employ, possess, and educate analytics talent. Keep reading to learn how employers, educators and students are working to fill the analytics skills gap.