SAS Make Customer Intelligence Engaging and Valuable

My colleague David Menninger recently wrote about the SAS Analyst Summit, concluding that “the SAS analytics juggernaut keeps on truckin’.” He observed, as I have done in the past, that SAS has a vast array of products that it regularly updates to keep up with market demand, ensuring it remains one of the premier vendors of data management and analytics systems. Dave’s perspectives provide in-depth insights into what these products do, while I focus on how they help with business outcomes around customer experience. I was therefore intrigued to hear at SAS’s European analyst event that its products support four types of user – data scientist, business analyst, intelligence analyst and IT analyst. The presenter used simple quotes to illustrate the differing priorities of these groups: For the data scientist, the one that caught my eye was “I need the latest algorithms to solve the latest problems”; for the business analyst I picked “I need to get my report done quickly and easily”; the information analyst is about “identifying patterns of interest that can prompt active decision-making”; and the IT analyst is about “issue resolution and redemption” (mainly operational analysis). In short each type of user needs different products and capabilities, hence the array of products. Nearest to my research practice is the business analyst, who wants easy access to reports and analysis to resolve business issues, and this is where the company’s Customer Intelligence product plays a part.

As I previously wrote this system has evolved into what SAS calls its Customer Decision Hub. It brings together a number of products so organizations can capture and synchronize all forms of customer data to “deliver the best customer experience.” The Decision Hub can gather customer data from a variety of sources and synchronize it for a customer. It provides rules to govern what happens to the data and how it is used, and reports, analysis and prompts so that employees can deliver information to users and customers in the most appropriate manner. It also includes an array of other capabilities such as information to drive marketing campaigns, data to support event-based interactions, customer journey maps and a 360-degree view of the customer. The latest version of the Decision Hub improves support and capabilities to better manage Web-based interactions, email, mobile and social media channels of interaction.

The next step in its development is SAS Customer Intelligence 360. It is an enhanced version that has a single HTML5 user interface, additional public and RESTful APIs so that data can be collected from third parties, a single data and decision hub for all things related to customer experience and support for both inbound and outbound interactions across all channels. It is available as a multitenant cloud-based service, but data can reside on the user’s premises. Customer Intelligence 360 includes four components. Master Audience Profile supports collection and synchronization of all sources of customer data, both internal and third-party, to build customer profiles. Workflow and Collaboration support creation and development of marketing content across multiple groups. Intelligent Orchestration manages engagement across channels to ensure that customers receive consistent information and to harmonize marketing programs. Unified Measurement and Optimization helps analyze the outcomes of engagement and marketing programs to optimize them in the future. Together these components enable organizations to build complete pictures of their customers, ensure that business groups coordinate how they engage with customers regardless of purpose or channel, and analyze the outcomes to improve them.

Some of these messages obscure what for mevr_Customer_Analytics_08_time_spent_in_customer_analytics is an important feature – the single customer data hub. Our benchmark research consistently shows that organizations have a diverse set of customer-related data source: business applications such as billing, CRM, and ERP; communications systems such as voice, email, text, Web and chat scripts and social media; and operational systems such as network control that provide event-based data such as calls made, films downloaded or energy used. Managing all this data creates issues for organizations. Indeed, nearly two-thirds of organizations participating in our research into next-generation customer analytics said that the data they need as input into customer-related analytics is not readily available. The research also finds that users spend more of their time preparing and reviewing data than they do analyzing the outputs, which undermines productivity and impedes getting actionable information to decision-makers.

SAS offers a combination of data management and analytics to overcome these issues. Buried inside the data management tools is another key capability – identity management. Our research into next-generation customer engagement shows that companies support an average of nearly seven communication channels, and each of these is likely supported by different systems. In such cases, each interaction record has its own unique customer identifier or combination of identifiers, which are difficult to standardize and use as one. To get close to producing a 360-degree view of a customer or a journey map of channels used, organizations need systems that link all these identifiers so the data can be associated with a single customer. The tools in SAS Customer Intelligence are among the few I have come across that do this; I recommend that companies looking for such analysis should carefully evaluate this product.

I support Dave’s view that the SAS juggernaut is rolling on, and systems such as Customer Intelligence can help companies improve customer engagement. However, as organizations evaluate such products, I caution them not to get bogged down in all the components but to look at the overall system and how it can ingest and manage all the organization’s data and any from third-party sources; scrutinize how easy it is to use for all the different potential users. It is also worth remembering that the early focus for Customer Intelligence was to support marketing, and many of its messages are still colored by such thinking. Everything I have seen and heard in recent briefings shows it is applicable across all customer-facing business groups, including the contact center, so I recommend organizations looking to improve enterprise-wide customer engagement evaluate how SAS can help.


Richard J. Snow

VP & Research Director, Customer

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Modeling Revenue Recognition for Contracts to Meet New Regulations

I recently wrote about the challenge some companies will face in planning and budgeting when new revenue recognition rules go into effect in most countries in 2018. It’s important for companies that will be affected to be sure they have the appropriate systems, processes and training to handle the more difficult demands imposed by the new rules. With the change in accounting, the time lag between when a contract is signed and when a company recognizes revenue from it may be more variable and less predictable than in the past. In extreme cases, performance measured by financial accounting will diverge materially from the “real” economic performance of the organization. Consequently, executives – especially those leading publicly listed companies – will need the ability to look at their plans from both perspectives and be able to distinguish between the two in assessing their company’s performance. In companies where the timing of revenue recognition can diverge substantially from current methods, financial planning and analysis (FP&A) groups will need to be able plan using models that incorporate financial and managerial accounting methods in parallel. They will need to be able to identify actual-to-plan variances caused by differences in contract values booked in a period and differences between the expected and actual timing of revenue recognized from contracts signed in a period.

I don’t want to overemphasize the impact the new revenue recognition rules will have on companies’ planning. While some companies need to understand that they will have to alter their planning and review processes, I expect that most will be unaffected by the new accounting for contracts, for at least one of three reasons:

  • A majority of their revenue does not come from contracts. (Retailing is one industry in which many companies do not transact business using contracts.)
  • No single contract or type of contract is large enough to have a material impact on reported revenue.
  • The time lag between signing a contract and fulfilling the contract is short (a month or less) or the time lag between booking a contract and fulfilling it is reliably consistent from month to month or quarter to quarter.

For many companies, tracking individual contracts will be unnecessary, impractical or both. It may be unnecessary because the relative size of contracts matters. Even if an organization’s individual contracts differ significantly in terms of the interval between signing it and recognizing revenue from the transaction, if there are enough of and even the largest represents an insignificant percentage of total revenue, the difference won’t matter. That is, in most cases the difference between expected and actual timing of revenue recognition of individual contracts is likely to be cancelled out. Moreover, tracking individual contracts will be impractical for many organizations because their volume will make it is too expensive and time-consuming to capture the relevant terms and conditions for each contract, which is necessary to be able to isolate the factors driving actual to forecast or budget variances. For FP&A groups the challenge will be in creating models that accurately forecast the average lag between contract signing and when revenue is recognized. Analysts also should confirm that the standard deviation of this lag under the new rules will be small enough to avoid the need to segment contracts into major types. (I’ll return to this point shortly.)

Nonetheless, a significant number of organizations – either entire corporations or business units with revenue responsibility – will need to change their approaches to creating and using planning models in order to accurately measure variances between their plans or budgets and their actual results. This means developing models that enable them to separate variances that are the result of differences in when business was booked and those in which the timing of the revenue recognition process turned out to be longer or shorter than expected. Certain types of businesses that have large, complex contracts with their customers, such as aerospace, construction and engineering, are likely to find that they need to plan and track results by contract – at the very least the 20 percent of their contracts that account for 80 percent of their revenues.

Another type of company or business unit that will need to adopt a more granular approach to tracking contracts  under the new rules is one in which there are significant differences between the timing of revenue recognition for different types of contracts. Even though the value of individual contracts booked in a period is an insignificant percentage of the total, it may be necessary for organizations to segment contract bookings and revenue recognized for each major type of contract. This would be the case if there are significant differences in the timing of revenue between types of contracts and the mix of contract types varies from one month to the next. For example, imagine that Company X has contracts that have three distinct revenue recognition profiles. In one of them, which accounts for one-quarter of annual bookings, there is a consistent one-month interval between when the contract is signed and when revenue is recognized. For a second type of contract (representing 40 percent of annual bookings) it can take up to several months before revenue can be recognized, and then it happens all at once. The remaining contracts are recognized over a year after a contract is signed. Any significant differences in the mix of contract types signed from month to month will make it difficult to reconcile variances and accurately identify differences caused by better than expected or inadequate contract bookings and those caused by timing differences.  So it’s necessary to create and use models that segment revenue by mix of contract types.

It is time for companies to get serious about adapting their business to the new revenue recognition rules. They will have to cut over to new processes and systems in 2017 to comply with the new standards and be able to make year-on-year comparisons when the new methods go into effect in 2018. Financial planning and analysis groups should be considering whether their forecasting, planning, budgeting and reporting models and processes will need to change under the new accounting standards. Those that will have to change should look into acquiring a dedicated planning and budgeting application if they (or affected business units) are currently using spreadsheets for planning. That will include many organizations: Our next-generation business planning research vr_NGBP_09_spreadsheets_dominant_in_planning_softwarefinds that two-thirds (65%) of companies use spreadsheets to manage their budget process. A dedicated planning application will help them prepare better to understand whether a difference was due to the new accounting rules or poor performance using actual data rather than opinions.

FP&A groups should be aware of their company’s exposure to new revenue recognition rules. If the rules will have a material impact on how the company accounts for contracts, they should determine whether it will be necessary to plan and budget for “real” and accounting data in parallel. If so, and if their company currently plans and budgets using desktop spreadsheets, I strongly recommend that they look into acquiring a dedicated planning application. In addition to dealing with increased complexity, this type of software can improve the budgeting and planning processes, making them more efficient.


Robert Kugel

Senior Vice President Research

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