Stephen Hurrell's Analyst Perspectives

Oracle Sales has Priority on Engagement and AI

Posted by Stephen Hurrell on Mar 3, 2021 3:00:00 AM

The current pandemic has disrupted many of the traditional sales methods used by field-sales organizations to engage, and sell to, buyers. In an effort to provide help, many vendors have recently announced new features that focus less on the management of sales organizations and more on tools to help salespeople sell. This has been coupled with a renewed interest in using data to help with the science, alongside the art, of selling, as referenced in my AP: The Art and Science of Sales from the “Inside Out". Oracle has called this new emphasis “Responsive Selling,” with an aim to harness data and machine learning (ML) to aid sellers in this new, challenging environment.

Oracle Sales has evolved over the last 15 years and is now a substantive option for heads of sales organizations to consider. New releases in 2020 not only aim to improve the management of sales, in what we call Sales Performance Management (SPM), which we rate highly as a Value Index Leader, but also to offer tools and services that support the sales representative and front-line manager, which we refer to as Sales Engagement.

This shift in emphasis is essential, as we assertVR_2021_Sales_Assertions_1_Square (1) that by 2023, one-quarter of sales organizations will replace applications with more intelligent ones that focus on optimizing sales performance to guide sales organizations for maximum outcomes.

The focus on helping front-line salespeople, whether field or inside sales, as well as partner selling, is critical, as the market for sales technology has transitioned to improving the selling experience and the level of engagement needed to ensure desired outcomes. This transition has accelerated with the pandemic and the need to provide business continuity, an important imperative that my colleague Mark Smith pointed out in his recent perspective.

For Oracle, the most notable new features are targeted at field-sales personnel. Oracle’s own customer research has identified a number of key lessons. Salespeople do not “live” in Customer Relationship Management (CRM) or Salesforce Automation (SFA) systems of the past, but more humdrum applications such as email or where they can engage their customers and prospects. To this end, in a similar fashion to many inside-sales dialing and cadence tools, new email add-in for Microsoft Outlook/Exchange, as well as Google Gmail, allows for users to access relevant information around prospective accounts from within their favored email app.

Oracle’s Sales integration with SPM functionality links sales activity to quota, compensation and territory planning. And, although positioned as “of value” to sales management, this feature has particular interest to salespeople and front-line managers as the integration of individual sales opportunities to commission and quota-attainment plans enables individuals to accurately project their potential renumeration. This avoids distorting behavior by eliminating the use of inaccurate, back-of-the-envelope calculations or personal spreadsheet models. All of which is a competitive advantage for Oracle and should be more integral to its marketing and sales to ensure sales organizations recognize the value of this offering for salespersons and line managers.

Oracle identifies itself as a “data business”; much of its launch of “Responsive Selling” emphasizes that Oracle is utilizing data, firmographic data such as Dun & Bradstreet, and their own data derived from the acquisition of Datafox in late 2018 and partnerships with location data providers. This enriched data allows for the more targeted prioritization of potential accounts by adding contextual information about prospective leads. This supports additional information about prospective companies through the capture of news and event happenings such as acquisitions, expansion and personnel changes. In addition, for organizations using Oracle ERP, integration with the Oracle Sales ensures that sellers have accurate and up-to-date customer and product information at their fingertips.

Moving beyond opportunity and lead scoring, which has proven to be of limited value to salespeople, “next best step” recommendations have the potential to be of real use. An issue holding back a wider adoption of artificial intelligence (AI)-assisted selling technology is concern about poor-quality data about customers and prospects. This has been one of the most frequently cited impediments by customers as to why advanced analytics and AI have failed to deliver improved revenue attainment. Oracle’s Sales integration with applications such as Outlook supports the automatic capture of sales activity data, including emails and calendar entries, removing the need to rely on salespeople to enter relevant data in a timely manner.

Oracle is focused on delivering out-of-the-box AI/ML models that address key aspects of the sales process, including the aforementioned lead and opportunity scoring, as well as more advanced steps, such as recommended “best next action.” Oracle is elevating the focus on AI/ML operations that happen on data in situ (no need to extract and load into a different data store), thus reducing latency and effort as well as promising real-time model evaluation against live data. We agree and assert that by 2023, less than one-quarter of organizations will deploy AI-assisted technology to help navigate buyer organizations and improve effectiveness in the focus on sales engagement.

In addition, recognizing that one size doesVR_2021_Sales_Engagement_Assertion_6_Square (1) not fit all, Oracle is able to accommodate industry-specific features and processes, offering both an out-of-the-box model delivery as well as their ML workbench that enables appropriate customer resource to build, test and deploy custom models that address industry or customer-specific questions. For this to be effective, custom models need to be transparent to an end-user and, in addition, appear to be just part of the application.

With a richer data set than many other providers, Oracle has the potential to deliver a truly effective assisted-selling experience. But, as history has shown, there is an inherent bias against machine-assisted selling from within sales organizations. Although AI/ML promises much, recent experience has shown that unless there is a solid adoption strategy undertaken by both vendors and sales organizations, these initiatives will continue to flatter to deceive. Whether through process or via the product itself, Oracle needs to ensure that there is a mechanism by which sales and front-line managers can understand how these aids can help them sell and not just manage accounts, contacts and opportunities. If not, these advancements will not gain the traction needed for helping Oracle gain adoption of its Oracle Sales offering versus the competition. Oracle’s challenge is to continue to provide value to all professionals within the sales organization, and articulate the value for everyone and how individually and working together is better with their approach, or they will not show growth in their market position. Prospects looking for a new approach to applications for sales should take a look at Oracle, not just in the management and operations of the sales organization, but also for tools and aids to help the selling professional. Existing customers should pay attention to the newest developments that are targeted to helping people sell.


Stephen Hurrell

Topics: Sales, Analytics, Data, Product Information Management, Sales Performance Management (SPM), Digital Technology, AI and Machine Learning, sales enablement, sales engagement

Stephen Hurrell

Written by Stephen Hurrell

Stephen is responsible for the overall research direction for the Office of Revenue at Ventana Research, including the areas of digital commerce, price and revenue management, product information management, sales enablement, sales performance management and subscription management. He brings 20+ years of experience in product and CS leadership, developing data-driven applications in sales enablement, financial reporting and planning, and billing and monetization platforms, helping to scale product teams and support customers such as Workday, NCR, Thomson Reuters, Broadridge Financials, JP Morgan Chase, Unilever and AAA (NCNU), before moving into an analyst role. Prior to joining Ventana Research in 2020, Stephen was General Manager at where he was responsible for the acquisition of C9 Analytics, VP of Product and AI strategy at RecVue and held roles at Oracle, Exigen and Aviso. Stephen earned his BS in Economics from the London School of Economics.