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When Oracle announced its next-generation Fusion Sales in late July, as part of its Oracle Fusion Cloud Customer Experience (CX) powered by artificial intelligence (AI), a PR representative wrote in an email to VentureBeat that the product “raises the bar for the entire industry and stomps all over the Salesforce area.”
While Salesforce declined to comment on Oracle’s claim, it’s clear that Oracle wants to use AI and machine learning (ML) to compete with the customer relationship management (CRM) giant as well as take down related startups like Gong and Salesloft. keep out. The company says it believes its Fusion Sales is the next generation of CRM focused on helping sellers in an era of business-to-business (B2B) sales transformation.
“We increasingly realized that the way we built Fusion as a more modern cloud stack allows you not only to orchestrate processes all the way from front to back, but to use machine learning to help people do their jobs better. . with CRM tools,” said Rob Tarkoff, executive vice president and general manager of Oracle’s Fusion Cloud Customer Experience.
The first generation of big-tech digital sales tools (including Salesforce and Microsoft Dynamics) has traditionally been about sales forecasting and included a variety of third-party integrations, he explained. Now Fusion Sales can help sales professionals plan campaigns, target key accounts in both advertising and marketing, and implement a unified sales effort that includes content management, advertising, and sales orchestration.
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“We know we’re not the largest supplier of CRM tools — that’s Salesforce,” Tarkoff told VentureBeat. “…but we think if we drive these innovations, we can raise the bar for the rest of the industry to capitalize on them.”
Oracle aims to transform B2B sales post-pandemic
Historically, B2B selling has been what Tarkoff calls the “last bastion of relationship-based selling.”
“Sellers and customers had long-lasting relationships that were primarily formed physically,” he said, adding that this model has changed dramatically: “It’s clear that today it’s a lot more about digital engagement — people have confidence in buying a product without ever a seller even for large ticket purchases.”
As a result, B2B selling has become more about using data to orchestrate processes that are more personalized to the buyer, knowing that they have probably already done 70-80% of their research. Reference stories from other customers help companies validate the quality of their offerings.
“It’s really about how effectively you use references to sell, because nobody wants to be the risk taker, so we’ve made reference sales the most important part of the B2B flow,” he said. “It’s about refining a personalized set of assignments and references that are much more relevant.”
Ultimately, he explained, the seller’s role is no longer to educate the B2B buyer about products, but to have a conversation about what like-minded customers have been doing successfully and why they should join the ranks. . In addition, it is important to unify the previously separate sets of activities for sales and marketing.
“You start to unite around the one thing that matters in B2B, which is having enough mature, qualified opportunities and knowing enough about those prospects or customers’ journeys to most effectively convert them into buyers,” Tarkoff said. “It turns that into a series of data points that help you determine, through artificial intelligence and machine learning, which is a really conversation-ready opportunity.”
While that may sound mechanical, he points out that B2B selling has become much more prescriptive and orchestrated.
“It’s less about having an outgoing personality and winning over your client with your charm,” he said.
Using AI to support data-driven decision-making
According to Robert Blaisdell, senior director and analyst at Gartner, by 2026, 65% of B2B sales organizations will move from an intuition-based strategy to data-driven decision-making, using technology like Oracle’s that unifies workflows, data, and analytics.
“Most of the major trends we see with AI are aimed at supporting B2B sellers in their day-to-day sales tasks by saving time and effort while providing insights,” he told VentureBeat via email.
These insights can include recommending which leads to prioritize or provide insights about a sales lead or customer, as well as enable a greater sense of empathy from salespeople to improve customer engagement with hyper-personalization.
“If you look at the impact AI has had on other business areas such as supply chain management, customer service and marketing outreach, we’re just beginning to see the impact AI could have on sales effectiveness and efficiency – the potential is great,” he said
Today, Blaisdell says he sees AI being implemented in many facets of broader sales technology.
“CSOs are working to free up time for salespeople, sales leaders, marketing and customer success teams to address delicate customer cases that require acute problem-solving skills, empathy and creativity,” Blaisdell said, adding that the use is often seen in enhanced revenue intelligence, increased sales engagement and better conversational intelligence technologies.
“These are powered by capabilities that prioritize opportunities based on certain criteria, determine a salesperson’s best action to push through or close a deal, or highlight trends to help sales managers determine what to coach salespeople. ” he said.
Oracle Focuses on Data Quality for Machine Learning
Tarkoff said Oracle uses the power of the company’s customer data platform (CDP) to “build comprehensive profiles for each of our prospects that can then be activated more effectively through the machine learning models we bring in, so we’re constantly testing new models.” .”
That depends on the quality of the data set delivered to those models, he explained.
“That’s where we’ve seen the most progress because one of the problems with machine learning and AI is that you have to constantly refine your data set to make sure you’re training the models well,” he said.
Blaisdell pointed out that Oracle allows customers to bring their own models.
“It’s hard for us to say we can build all models better than any company if they know their industry,” Tarkoff said. “They want to be able to take their CDP and build changes and additional attributes on the fly and tweak the attributes.”
Oracle’s core approach to its Fusion applications, built on Oracle Cloud, has always been to build as many advanced machine learning models into flows as possible, from the database layer all the way to the application layer.
“The best and biggest advancement here is that we’re bringing all those insights to the surface in the form of guided flows for a vendor to follow, rather than having to hire teams of data scientists to interpret what comes out,” he said. “We’ve built all that into a guided user interface that I think will reach the next level of machine learning-influenced outcomes because we’ve done the work to make it easier for the seller.”
What should sales organizations think about?
While AI has great potential in B2B sales, Gartner’s Blaisdell says organizations should consider the most pressing set of priorities AI can solve when choosing AI tools.
“Implementing and delivering results beyond the hype can be challenging when trying to achieve everything at once,” he said, advising sales organizations to focus on one to three positive outcomes of adopting AI to ensure that process and organizational change can be used with AI.
One of the main reasons for this is that insights from AI are only as good as the data it uses, he explains.
“Many sales organizations miss the mark when it comes to consistent, high-quality data because of salespeople’s low data knowledge and poor input,” Blaisdell said. “If the ultimate goal of investing in AI is to gain insights that drive better decision-making, sales organizations need to ensure their current data set is clean, along with implementing governance policies that help [ensure] consistent correct data is used regardless of the source.”
The future of AI and B2B sales
While the use of AI for sales organizations has been trending for years, the pandemic has been a catalyst for increased use, Blaisdell added. The need for sales organizations to become efficient and effective in a rapidly evolving unfamiliar environment drove a rapid evolution in technology and a greater need for use, he said.
“We see that trend continuing, but at a steady pace,” he said. “The future is where AI can contribute more, aligning sales organizations with a greater buyer preference for seller-free engagement and multithreaded selling experiences across both the seller and digital channels.”
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