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When Oracle announced its next generation of 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 Salesforce’s territory.”
While Salesforce declined to comment on Oracle’s claim, it is clear that Oracle is looking to use AI and machine learning (ML) to compete with the customer relationship management (CRM) giant as well as fend off related startups like Gong and Salesloft. The company says it believes its Fusion Sales is the next generation of CRM, focusing on helping sellers in an era of business-to-business (B2B) sales transformation.
“Increasingly, we’ve realized that the way we built Fusion as a more modern cloud stack not only allows you to orchestrate processes all the way through from the front to the back, but to use machine learning to help people get their jobs done 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 (which include Salesforce and Microsoft Dynamics) were traditionally 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 across both advertising and marketing, and move through a unified selling effort that includes content management, advertising and sales orchestration.
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“We know that we’re not the largest provider of CRM tools – that is Salesforce,” Tarkoff told VentureBeat. “…but we think that if we drive these innovations, we can raise the bar for the rest of the industry to respond to that.”
Oracle seeks to transform B2B sales post-pandemic
Historically, B2B sales were what Tarkoff refers to as the “last bastion of relationship-based selling.”
“Salespeople and customers had long-term relationships primarily formed physically in person,” he said, adding that this model has changed dramatically: “Obviously today, it’s a lot more about digital engagement – people have confidence in buying a product without ever meeting a sales rep even for large ticket purchases.”
As a result, B2B sales has become more about using data to orchestrate processes that are more personalized for the buyer, knowing that they have already done probably 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 turned reference selling into the key part of the B2B flow,” he said. “It’s about finely tuning a personalized set of engagements and references that are much more relevant.”
Ultimately, he explained, the sales rep’s role is no longer to educate the B2B buyer on products but to have a conversation about what like-minded customers did successfully and why they should join the ranks. In addition, it is important to unify what used to be separate sets of activities for sales and marketing.
“You start to unify around really the only thing that matters in B2B, which is having enough mature, qualified opportunities and knowing enough about the journey of those prospects or customers to most effectively convert them to buyers,” Tarkoff said. “It’s turning that into a set of data points that help you determine, through artificial intelligence and machine learning, what is a truly conversation-ready opportunity.”
While that may sound mechanical, he points out that B2B sales have become much more prescriptive and orchestrated.
“It’s less about having an outgoing personality and winning over your customer 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 transition from intuition-based strategy to data-driven decision-making, using technology like Oracle’s that unites workflows, data and analytics.
“Most of the big trends we see with AI focus on supporting B2B sales reps in their daily sales tasks by saving time and effort while also providing insights,” he told VentureBeat via email.
These insights can include recommending which leads to prioritize or providing insights about a sales lead or customer, and also enabling a greater sense of empathy from sellers to improve customer engagement with hyper-personalization.
“When you look at the impact AI has had on other areas of business, such as supply chain management, customer service engagement, and marketing outreach, we are 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 throughout many facets of broader sales technology.
“CSOs are working to free up time for sellers, sales leaders, marketing and customer success teams to deal with delicate customer cases that require acute problem-solving skills, empathy and creativity,” Blaisdell said, adding that the use is often seen in improved revenue intelligence, increased sales engagement and better conversation intelligence technologies.
“These are driven by capabilities that prioritize opportunities based on certain criteria, determine a seller’s next best action to advance or close a deal, or highlight trends to help sales managers zero in on what to coach sellers,” he said.
Oracle focuses on data quality for machine learning
Tarkoff said Oracle is using the power of the company’s customer data platform (CDP) to “build extensive profiles on 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 hinges on the quality of the dataset provided to those models, he explained.
“That’s where we’ve seen the most advancement because one of the problems with machine learning and AI is you have to constantly be refining your dataset to make sure you’re training the models properly,” he said.
Blaisdell pointed out that Oracle allows customers to bring in their own models.
“It’s hard for us to say we can build all the models better than every company if they know their industry,” Tarkoff said. “They want to be able to take their CDP and build on the fly changes and additional attributes and modify 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, from the database layer all the way into the applications layer.
“The best and the greatest advancement here is that we are surfacing all those insights in the form of guided flows for a sales rep to follow rather than having to hire teams of data scientists to interpret what’s coming out,” he said. “We built that all into a guided UI that, I think, will get to the next level of machine learning-influenced outcomes because we’ve done the work to make it easier for the salesperson.”
What sales organizations should consider
While AI has great potential in B2B sales, Gartner’s Blaisdell says that when it comes to choosing AI tools, organizations need to consider the most pressing set of priorities that AI can solve.
“Implementing and gaining results beyond the hype can be a challenge if everything is tried to be achieved at once,” he said, and recommended that sales organizations focus on one to three positive outcomes from instituting AI to ensure that process and organizational change can be leveraged with AI.
One of the main reasons for this is because insights from AI are only as good as the data it uses, he explained.
“Many sales organizations miss the mark when it comes to consistent high-quality data due to low seller data literacy and lackadaisical input,” Blaisdell said. “If the goal of investment into AI is ultimately to yield insights that shape better decision-making, sales organizations need to ensure their current dataset is clean along with instituting governance policies that helps [ensure] consistent correct data is utilized 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 was a catalyst for increased use, Blaisdell added. The need for sales organizations to become efficient and effective in a quickly evolving unknown environment drove a rapid evolution in the technology and increased need for usage, he said.
“We see that trend continuing, but at a steadier pace,” he said. “The future holds where AI can contribute more, helping align sales organizations toward an increased buyer preference for seller-free engagement and multithreaded sales experiences between both seller and digital channels.”
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