Categories
AI

Can AI help Oracle take on Salesforce to boost B2B sales?

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.


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. 

Event

MetaBeat 2022

MetaBeat will bring together thought leaders to give guidance on how metaverse technology will transform the way all industries communicate and do business on October 4 in San Francisco, CA.

Register Here

“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.” 

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.

Repost: Original Source and Author Link

Categories
AI

Salesforce boosts customer data platform strategy as rivals circle

Elevate your enterprise data technology and strategy at Transform 2021.


Salesforce today announced AI enhancements enabled by the Einstein platform into its customer data platform (CDP), just as rivals large and small are making similar investments. Salesforce is also tightening integration between the ecommerce cloud platform and the CDP, as well as making it possible to segment audiences in real time based on factors like membership status, loyalty tier, and points balance.

Announced during Salesforce Connections 2021, these offerings headline a wider series of updates Salesforce is making across its Digital 360 portfolio of applications and services as part of its overarching Customer 360 strategy. Other new offerings range from reports that analyze customer journey by channel to features that make it simpler to engage customers via Snapchat and WhatsApp platforms.

There’s also now a Progressive Web Application (PWA) Kit and Managed Runtime. Enabled by headless services provided by Salesforce, they enable developers to more easily decouple front-end and back-end technologies to create customize application experiences. This capability should allow organizations to accelerate digital business transformation initiatives, allowing them to make use of Salesforce application programming interfaces (APIs) to drive faster development of applications while retaining control over the front-end application experience.

Building a ‘single source of truth’

In terms of strategic initiatives, the Salesforce CDP is a crucial battle for Salesforce. Rather than housing their customer data in a traditional customer relationship management (CRM) application, which can be more challenging to access, organizations have started to employ CDPs as a way to make that data more accessible to a range of omnichannel applications that drive multiple digital business transformation initiatives. The CDP, in effect, becomes the hub around which customer engagements occurring in real time over email, phone, social media platforms, and mobile applications are all tracked. The result is data that’s more accessible to a range of omnichannel applications. In effect, the CDP becomes the hub around which customer engagements — whether occurring in real time over email, phone, social media platforms, and mobile applications — are all tracked.

“It’s a powerful single source of truth,” said Lidiane Jones, executive vice president and general manager for Commerce Cloud at Salesforce.

The source of truth in many cases is now at the core of digital business transformation strategies that require companies to finally unify customer data in a way that makes business insights actionable in near real time, rather than generating yet another business intelligence report long after it’s too late to have any meaningful impact on the outcome.

Taking on disparate rivals

The challenge for Salesforce is the fact that rivals large and small are all making similar CDP investments. While a CDP doesn’t replace the need for a CRM application for a sales team, it does play a more strategic role by enabling organizations to engage customers in a much more consistent fashion. Engagements that occur across social media networks and mobile applications can be more easily personalized, monitored, and analyzed. IT vendors, spanning from makers of marketing automation platforms to providers of enterprise resource planning (ERP) applications, are all vying to become providers of the CDP any organization standardizes on.

Salesforce is clearly betting on the fact that much of the data that organizations are looking to shift into a CDP already resides in its CRM and marketing applications. In its most recent quarter, the company reported revenue of $5.96 billion, a 23% increase over the same quarter a year ago. Salesforce also revealed it expects revenue for the second quarter to exceed $6.22 billion. Overall, the company is expecting revenue for the full 2022 fiscal year to range between $25.9 billion to $26.0 billion, representing a 22% growth rate.

Competing against rivals in the CRM space is one thing. Battling for market share against everyone from Adobe and Microsoft to Oracle and SAP for dominance of an emerging CDP market may be quite another undertaking, especially for a company that still reported a slight loss for its most recent quarter. Regardless of the outcome, the contest for CDP dominance will most certainly be nothing less than brutal in the months ahead.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Despite challenges, Salesforce says chatbot adoption is accelerating

Elevate your enterprise data technology and strategy at Transform 2021.


Chatbot usage has exploded during the pandemic as organizations look to bridge emerging gaps in customer service and onboarding. In 2020, the chatbot market was valued at $17.17 billion, and it is projected to reach $102.29 billion by 2026, according to Mordor Intelligence. There was also a 67% increase in chatbot usage between 2018 and 2020.

This uptick correlates with chatbots’ expanding capabilities, as they enable brands to tailor offers and recommendations without humans in the loop. Chatbots leverage customer, product, and interaction data to improve experiences in real time, leading to reduced wait times, service costs, and customer churn.

To discuss trends in chatbots and conversational AI more broadly, VentureBeat talked with Greg Bennett, conversational design principal at Salesforce. Bennett believes the technology presents an opportunity for businesses to express their brands through words and languages, creating a greater degree of intimacy with customers.

Accelerated adoption

According to recent estimates, Gartner predicts that by 2022, 70% of customer interactions will involve emerging technologies such as chatbots — an increase of 15% from 2018. That’s not surprising, considering a significant portion of consumers say they prefer chatbots to other virtual agents.

“At Salesforce, we’re seeing more than a 700% increase in sessions with our Einstein bot products. I think a lot of that is due to the fact that we’ve experienced isolation as a result of a pandemic, but it also points to the need to scale up business,” Bennett said. “It may not necessarily be that businesses got the idea because of the pandemic, but rather the pandemic accelerated their timeline.”

One example is Lee’s Famous Recipe Chicken Restaurant in Englewood, Ohio, which partnered with startup Hi Auto to build a conversational AI experience for its drive-thru customers. As a result of the pandemic, drive-thru orders in the U.S. saw an uptick of 22% in 2020. Consequently, drive-thru wait times increased by an average of 30 seconds, putting additional strain on employees.

Hi Auto worked with Lee’s on a solution to the challenge. At the restaurant, the company’s chatbot greets guests, answers questions, suggests menu items, and enters orders into the point-of-sale system. If a customer asks an unrelated question — or requests something that’s not on the menu — the chatbot automatically hands them off to a human. It also integrates with Lee’s employee headsets, allowing employees to provide real-time updates to inventory, as needed.

Lee’s plans to implement the chatbot at more of its drive-thrus, and Hi Auto says pilots with other restaurants are underway.

“The automated AI drive-thru has impacted my business in a simple way. We don’t have customers waiting anymore — we greet them as soon as they get to the board, and the order is taken correctly,” Lee’s owner Chuck Doran said. “We see improvements in our average check, service time, and improvements in consistency and customer service. And because the cashier is now less stressed, [they] can focus on customer service as well.”

Internal use cases

Chatbots can have value beyond customer service. For example, they can assist in the employee onboarding process, fielding screening questions, recording answers, and guiding new employees through company policies and protocols. Chatbots can also address common problems, which gives IT service desk agents the opportunity to fix more complicated issues.

Salesforce took a step toward addressing these use cases last year, according to Bennett, with the introduction of the Einstein bot intro template. Available in beta, the intro template lets developers create chatbots for onboarding, with popular Salesforce actions like creating a case or a lead, looking up an order, and adding a comment to an existing case.

“Companies can take this baseline conversation design and customize it to fit their needs. That’s really what we’re seeing — we’re seeing a shortened time between developing and deploying chatbots,” Bennett said.

The data bears this out. According to a McKinsey survey, at least a third of activities could be automated in about 60% of occupations. And in its recent Trends in Workflow Automation report, Salesforce found that 95% of IT leaders are prioritizing workflow automation technologies like chatbots, with 70% seeing the equivalent of more than four hours of savings per employee each week.

Challenges in design

Asked about trends in the chatbot industry, Bennett pointed to growing awareness of inclusive approaches to design. He’s worked with teams at Salesforce to ensure chatbots don’t discriminate against certain vernaculars, like African-American English or Chicano English.

“We ask ourselves, how can we make sure that, for example, a Black woman from the South in the U.S. doesn’t have to change their language in order to get the chatbot to react in the way that they don’t expect? We as research scientists, designers, product managers, and engineers have a responsibility to not only think about the bottom line, but also think about a total addressable market and consider the users that are being left behind.”

Natural language models are the building blocks of apps, including chatbots. But growing evidence shows that these models risk reinforcing undesirable stereotypes, mostly because a portion of the training data is commonly sourced from communities with prejudices around gender, race, and religion. Detoxification has been proposed as a fix for this problem, but the coauthors of newer research suggest even this technique can amplify rather than mitigate biases.

The increasing attention on language biases comes as some within the AI community call for greater consideration of the effects of social hierarchies like racism. In a paper published last June, Microsoft researchers advocated for a closer examination and exploration of the relationships between language, power, and prejudice in their work. The paper also concluded that the research field generally lacks clear descriptions of bias and fails to explain how, why, and to whom specific bias is harmful.

“As a linguist, I look at conversation as really the fabric or the currency with which we negotiate relationships in society. Technology has now reached a point where this sort of traditionally human behavior — conversation — is something machines can partake in,” Bennett said. “The challenge now is to design a chatbot in such a way that that it adheres to human expectations about what was once an exclusively human behavior.”

Potential solutions

Bennett suggests one solution to models’ shortcomings might be developing tools for customers to evaluate quality. He points to Robustness Gym, a framework developed by Salesforce’s natural language processing group, which aims to unify the patchwork of existing robustness libraries to accelerate the development of novel natural language model testing strategies. CheckList — from Amazon, Google, and Microsoft — takes a task-agnostic approach to model benchmarking, allowing people to create tests that fill cells in a spreadsheet-like matrix with capabilities and test types, along with visualizations and other resources.

In a recent paper submitted to the Association for Computational Linguistics (ACL) 2021 conference (“Reliability Testing for Natural Language Processing Systems”), Bennett and Kathy Baxter, Salesforce’s principal architect of ethical AI, argue for reliability testing and contextualization to improve accountability. They explain that reliability testing, with an emphasis on interdisciplinary collaboration, will enable rigorous and targeted testing, aiding in the enactment and enforcement of industry standards.

Bennett also advocates including key stakeholders throughout the chatbot design process so biases can be accounted for and mitigated — at least to the extent possible. A recent attempt at this is the Masakhane project, a grassroots organization of 400 researchers from 30 African countries (and three countries outside Africa) whose mission is to strengthen natural language research in African languages. As of February 2020, the group has published on GitHub more than 49 translation results for over 38 African languages, many of which had never been translated at scale.

“Any institution has the opportunity to use a chatbot to essentially extend itself in a relationship with a customer — with prospective students, with job applicants, the list goes on. These are opportunities to create relationships and have a meaningful exchange,” Bennett said. “There’s a linguistic reason why someone uses a period versus an exclamation mark or emojis versus not. These things convey additional meaning about the state of the relationship at hand, which is the kind of thing that will continue to be really important on the product and engineering side. With chatbots, we need to think through the conversational aspects of the conversation besides everything about the text, whether the bot or the user takes the first turn.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Salesforce introduces new Sales Cloud features to boost automation and remote collaboration

Join Transform 2021 this July 12-16. Register for the AI event of the year.


Salesforce today rolled out the next generation of Service Cloud, including enhancements to Cloud Voice and Einstein Bots. The company says that the features and products are intended to address the new reality brought about by the coronavirus pandemic.

Over the past year, service agents moved quickly to work from home but were forced to rely on legacy technology that wasn’t designed to manage distributed workforces. Today, parts of the world are beginning to reopen, but these reopenings are raising questions around updated policies, protocols, and safety measures. This adds a new level of challenge for agents, who are already contending with increased workloads.

Service Cloud Voice, Salesforce’s service that brings together phone, digital channels, and customer relationship management data, can now connect with existing phone systems via a new product called Service Cloud Voice for Partner Telephony. With Service Cloud Voice for Partner Telephony, enterprises with landlines can benefit from features including real-time call transcription and AI-powered guidance on recommended next steps.

Beyond Service Cloud Voice for Partner Telephony, Salesforce is introducing Service Cloud Workforce Engagement, a workforce planning product that uses AI to predict how many requests will come into a contact center and on which channels (for example, phone, email, web chat, text, and social). First announced in December, Service Cloud Workforce Engagement provides agents with a workspace that integrates data, as well as real-time coaching and on-demand training with Salesforce’s MyTrailhead online learning portal.

Chatbots and remote service

Salesforce also today previewed Pre-built Einstein Bots, a collection of chatbots powered by Einstein Bots, the company’s platform for conversational bots that resolve issues like processing a return or checking a flight. And the company furthered detailed Visual Remote Assistant, a service launched last year that helps companies deliver service without coming on site by walking customers through solutions remotely.

Visual Remote Assistant, an offshoot of Salesforce Field Service, provides tools like annotations, a live pointer, and more. Leveraging AI-powered character recognition and scalable video, the service launches sessions in a browser, integrating customer service data from Service Cloud, Field Service, and third-party systems.

Service Cloud Workforce Engagement and Service Cloud Voice for Partner Telephony are expected to be generally available in June 2021. Pricing information will be made available at general availability. Visual Remote Assistant is generally available today, while Pre-Built Einstein Bots are currently in beta and are expected to be generally available in October 2021.

According to Kate Leggett, VP and principal analyst at Forrester, the new Service Cloud features align with trends accelerated by the pandemic. For example, according to Salesforce, 61% of salespeople believe their roles have changed since the pandemic began. Even when salespeople are able to return to the road and in-person workplaces, 51% expect to travel less than they did before the pandemic — and fewer than half expect to go back to an office.

“Customer service leaders must stay abreast of three megatrends in 2021 as they weather the storm,” Leggett wrote in a recently published report. “They are: AI-fueled digital experiences underpin great customer service, modern agent desktops empower agents to best serve customers, and customer service technology enables resilience and sustainability.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
Tech News

Work towards a Salesforce Administrator certification with this $50 course

Credit:
William Fortunato/Pexels

TLDR: The training in the Prepare for Your 2021 Salesforce Administrator Credential Course is how young Salesforce learners acquire the skills to become true Salesforce master admins.

Being a certified Salesforce administrator is more than just knowing how to use Salesforce’s extensive web of customer relationship apps. The real skill is in implementing that system.

A certified Salesforce admin is someone equipped to stand back and see how all aspects of a business are working together in concert. These individuals are perhaps in the best position to fix what ails a company. They also optimize operations for sales and marketing to production and customer satisfaction.

The Prepare for Your 2021 Salesforce Administrator Credential Course ($49.99, 75 percent off, from TNW Deals) is for those who have already worked through the opening Salesforce Admin training and are ready to take the next step toward full Salesforce mastery.

Your guide in this journey across 85 lectures and 16 hours of content is noted Salesforce certified application architect, developer, and admin Jimmy Tanzil. With over 20 years of experience in the IT field, Tanzil is an expert in all phases of app development, including analysis, design, coding, testing and implementation, and who it all intersects in Salesforce.

As students head down their Salesforce training trailhead, Tenzil leads through earning four separate Superbadges, credentials earned by applying the Salesforce training to actual hands-on real-world challenges.

The training helps learners understand everything. That ranges from user authentication and management and how to oversee products, quotes, and contracts, to organizing projects and customizing an organization to take on new business teams.

This training is direct prep for taking and passing the Salesforce Certified Administrator exam, a key credential for showing someone understands how to customize Salesforce, configure the platform to service-specific business needs, manage users and seek out innovative paths toward using Salesforce to its utmost potential.

This $200 Prepare for Your 2021 Salesforce Administrator Credential Course is now on sale at 75 percent off its regular price, down to just $49.99.

Prices are subject to change.

Repost: Original Source and Author Link

Categories
AI

Salesforce introduces AI-powered features to improve meetings and identify sales

Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more.


Salesforce today announced new features designed to help companies transform their organizations for digital sales during the pandemic and in a post-pandemic world. Beginning this week, following new AI-powered account-based marketing tools, Sales Cloud 360 is gaining Einstein Conversation Insights, an AI-powered technology that analyzes video call transcripts, as well as global models for opportunity scoring and precall summaries with customer histories.

According to Salesforce, 61% of salespeople believe their roles have changed permanently since the COVID-19 pandemic began. Even when salespeople are able to return to the road and in-person workplaces, 51% expect to travel less than they did before the pandemic — and fewer than half expect to go back to an office.

To address this new normal, Salesforce is introducing Einstein Conversation Insights, which gathers insights on the frequency of certain keywords or types of interactions during video calls. The goal is to create customized training and one-on-one coaching that aligns with teams’ needs, according to Sales Cloud chief revenue officer Warren Wick.

Salesforce Meetings Digest

Above: The new Meetings Digest screen in Sales Cloud 360.

Image Credit: Salesforce

“Salesforce created the playbook for sales 22 years ago, and today we’re rewriting it for an all-digital world,” Wick said in a press release. “Over the past year, we held more than 6 million calls with customers to understand what they needed to be successful as they worked to transform their business with more urgency than ever before. We’ve reimagined Sales Cloud to guide every company as they rethink the digital sales experience, from leads to coaching to processing revenue.”

Alongside Einstein Conversation Insights are new features in Salesforce Meetings, including post-call action items that Einstein automatically surfaces to keep deals moving. Meanwhile, global models for opportunity scoring use aggregated, anonymized trends across Salesforce customers to empower teams with AI before they have enough of their own data. Einstein shows the factors that have contributed most to the score, both positively and negatively. For example, when extra steps are added to an enterprise deal, it indicates that the deal is progressing.

Forrester predicts that spend for marketing automation tools will grow “vigorously” over the next few years, from $11.4 billion annually in 2017 to $25.1 billion by 2023. It’s estimated that 55% of marketing decision-makers plan to increase their spending on marketing technology, including AI and machine learning, with one-fifth of the respondents expecting to increase by 10% or more.

Einstein Conversation Insights filtered

Above: Salesforce’s AI-powered Einstein Conversation Insights.

Image Credit: Salesforce

Another new capability in Sales Cloud — pipeline inspection — helps track changes week by week, using AI to focus on the deals that ostensibly matter most. Other additions include in-app learning for MyTrailhead, which surfaces relevant education materials like competitor analysis in sellers’ workspaces; Tableau Business Science, a set of AI-generated predictions, insights, and automated explanations from Tableau; and Mulesoft Composer, which allows sales operations teams to connect apps and systems to Salesforce, automate sales processes, and get end-to-end sales data visibility without have to write code or wait on development resources.

Salesforce says that Einstein Call Insights, Mulesoft Composer, and the new Salesforce Meeting features will be generally available as of March 24. Tableau Business Science in the 2021.1 release will be available later this month. And in-app learning for MyTrailhead, the global models for opportunity scoring, and pipeline inspection will arrive in summer 2021.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Salesforce intros new AI-powered account-based marketing tools

Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more.


Salesforce today unveiled new AI-powered account-based marketing (ABM) capabilities within Salesforce Digital 360 designed to help sales teams scale their campaigns. Einstein Key Accounts Identification analyzes engagement-based buying signals and customer characteristics to identify the accounts most likely to make a purchase. Meanwhile, Accounts as Campaign Members lets companies target an entire account directly even if there aren’t any contacts for the account.

ABM is increasingly seen as critical for marketing teams looking to deliver personalized experiences. In fact, in a 2021 Salesforce survey, 92% of business-to-business marketers now cite ABM as “extremely important” to their overall marketing efforts. SiriusDecisions reports that 33% of companies allocated at least 30% of their marketing budgets to ABM in 2016, while in 2017, that percentage increased to over 50%. ITSMA and the ABM Leadership Alliance recently found that 80% of marketers adopting ABM achieve significantly higher returns on investment.

“Every business needs to deliver connected digital experiences for their buyers, from anywhere. However, buyer behavior is changing — and they’re bringing business-to-consumer buying habits to business-to-business purchases,” Salesforce Pardot SVP Meredith Brown said in a blog post. “These buyers have done the research, read the reviews, and they know all about your product before the sales team reaches out.”

Salesforce Einstein Key Account Identification

Above: Salesforce’s new Einstein Key Account Identification feature.

Image Credit: Salesforce

With Einstein Key Account Identification, companies can analyze marketing engagement data across the web and customer relationship management platforms to tier accounts in a prioritized order. The goal is to help marketing and sales teams focus on priority accounts, potentially optimizing the resources to close deals faster, according to Salesforce.

Accounts as Campaign Members allows companies to leverage AI-powered insights to create personalized ABM campaigns for buyers within top-tier accounts. The feature automatically syncs new account contacts into marketing campaigns the moment they’re identified. Previously, marketers could only target an account if they had an individual contact in their roster.

Salesforce Einstein Account as Campaign Members

Above: The Salesforce Einstein Account as Campaign Members feature in Salesforce Digital 360.

Image Credit: Salesforce

“The COVID-19 pandemic set off a seismic shift in the marketing industry, as every business was forced to transition to a digital-first world,” Brown continued. “Previously, business-to-business reps would meet customers where they were looking to do business — dinner meetings, live entertainment events, and industry conferences. Now, these customer interactions are primarily happening digitally, as Zooms are the new conference rooms and sales deals are closed in home offices. As customer demands for digital experiences grow, business-to-business companies need a platform to build a single view of their customer, identify key accounts, and quickly turn new leads into deals.”

Both Einstein Key Account Identification and Accounts as Campaign Members are available now for Salesforce Digital 360 customers.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform 2021: Learn More
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Salesforce launches tool to help businesses drive loyalty during the pandemic

Join Transform 2021 for the most important themes in enterprise AI & Data. Learn more.


Salesforce today unveiled a product designed to help companies that are selling through distribution channels automate, scale, and leverage AI-driven insights for their rebate programs. Called Rebate Management, it taps a customizable data model to promote visibility and collaboration for sales teams and partners.

The pandemic and corresponding rise in online shopping threaten to push supply chains to the breaking point. Early in the COVID-19 crisis, Amazon was forced to restrict the amount of inventory suppliers could send to its warehouses. Ecommerce order volume has increased by 50% compared with 2019, and shipment times for products like furniture more than doubled in March. Overall U.S. digital sales have jumped by 30%, expediting the online shopping transition by as much as two years.

With the launch of Rebate Management, Salesforce is making the well-informed bet that some companies currently lack a way to see and optimize their customers’ incentive attainment. To this end, Rebate Management identifies cross-sell opportunities and exposes a view of attainment progress alongside account information within Salesforce’s customer relationship management platform. It also provides channel partner visibility into incentive programs, allowing sales teams to share threshold attainment with partners. Moreover, Rebate Management optimizes and automates incentive programs with analytics, enabling companies to model and analyze rebate programs while leveraging recommendations to drive upsells with offers.

“Unlike other rebate solutions, Rebate Management gives you a single source of truth into your rebate program that is easily accessible, updated in real time, and can be viewed by your entire company and channel partners,” a Salesforce spokesperson told VentureBeat via email. “With your rebate program in the [customer relationship management platform], companies also have a holistic view into their overall business performance, including closed opportunities, run-rate business, sales agreements, [and] channel incentives.”

The spokesperson said the rebate solution is built with the theme of driving intelligent insights to launch the right incentive programs for revenue growth and is made possible by leveraging the AI capabilities of Salesforce’s platform.

According to a recent Boston Consulting Group report, only 20% of businesses take part in value-based discounting, like rebate marketing. Logistical issues are one barrier to entry, but the loyalty benefits far outweigh the costs. Sixty-one percent of small and medium-sized businesses report that more than half of their revenue comes from repeat customers. On average, loyal customers are worth up to 10 times as much as they spend on their first purchase.

Rebate Management is the latest of several products Salesforce has introduced over the past year to address pandemic-related enterprise challenges. In May, the company released four quick-start pandemic business packs for Commerce Cloud, its platform for brands to create buying experiences across multiple channels. More recently, Salesforce rolled out a new Service Cloud workforce planning tool aimed at helping manage contact centers with remote workers. Then there’s Einstein Automate, a set of AI-fueled workflow solutions the company announced during its virtual Dreamforce conference in early December, as well as a platform called Salesforce Anywhere that’s designed to let teams collaborate and share data wherever they are.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform
  • networking features, and more

Become a member



Repost: Original Source and Author Link

Categories
AI

Salesforce Research wields AI to study medicine, economics, and speech

In 2015, Salesforce researchers working out of a basement under a Palo Alto West Elm furniture store developed the prototype of what would become Einstein, Salesforce’s AI platform that powers predictions across its products. As of November, Einstein is serving over 80 billion predictions per day for tens of thousands of businesses and millions of users. But while the technology remains core to Salesforce’s business, it’s but one of many areas of research under the purview of Salesforce Research, Salesforce’s AI R&D division.

Salesforce Research, whose mission is to advance AI techniques that pave the path for new products, applications, and research directions, is an outgrowth of Salesforce CEO Mark Benioff’s commitment to AI as a revenue driver. In 2016, when Salesforce first announced Einstein, Benioff characterized AI as “the next platform” on which he predicted companies’ future applications and capabilities will be built. The next year, Salesforce released research suggesting that AI’s impact through customer relationship management software alone will add over $1 trillion to gross domestic products around the globe and create 800,000 new jobs.

Today, Salesforce Research’s work spans a number of domains including computer vision, deep learning, speech, natural language processing, and reinforcement learning. Far from exclusively commercial in nature, the division’s projects run the gamut from drones that use AI to spot great white sharks to a system that’s able to identify signs of breast cancer from images of tissue. Work continues even as the pandemic forces Salesforce’s scientists out of the office for the foreseeable future. Just this past year, Salesforce Research released an environment — the AI Economist —  for understanding how AI could improve economic design, a tool for testing natural language model robustness, and a framework spelling out the uses, risks, and biases of AI models.

According to Einstein GM Marco Casalaina, the bulk of Salesforce Research’s work falls into one of two categories: pure research or applied research. Pure research includes things like the AI Economist, which isn’t immediately relevant to tasks that Salesforce or its customers do today. Applied research, on the other hand, has a clear business motivation and use case.

One particularly active subfield of applied research at Salesforce Research is speech. Last spring, as customer service representatives were increasingly ordered to work from home in Manila, the U.S., and elsewhere, some companies began to turn to AI to bridge the resulting gaps in service. Casalaina says that this spurred work on the call center side of Salesforce’s business.

“We’re doing a lot of work for our customers … with regard to real-time voice cues. We offer this whole coaching process for customer service representatives that takes place after the call,” Casalaina told VentureBeat in a recent interview. “The technology identifies moments that were good or bad but that were coachable in some fashion. We’re also working on a number of capabilities like auto escalations and wrap-up, as well as using the contents of calls to prefill fields for you and make your life a little bit easier.”

Medicine

AI with health care applications is another research pillar at Salesforce, Richard Socher, former chief scientist at Salesforce, told VentureBeat during a phone interview. Socher, who came to Salesforce following the acquisition of MetaMind in 2016, left Salesforce Research in July 2020 to found search engine startup You.com but remains a scientist emeritus at Salesforce.

“Medical computer vision in particular can be highly impactful,” Socher said. “What’s interesting is that the human visual system hasn’t necessarily developed to be very good at reading x-rays, CT scans, MRI scans in three dimensions, or more importantly images of cells that might indicate a cancer … The challenge is predicting diagnoses and treatment.”

To develop, train, and benchmark predictive health care models, Salesforce Research draws from a proprietary database comprising tens of terabytes of data collected from clinics, hospitals, and other points of care in the U.S. It’s anonymized and deidentified, and Andre Esteva, head of medical AI at Salesforce Research, says that Salesforce is committed to adopting privacy-preserving techniques like federated learning that ensure patients a level of anonymity.

“The next frontier is around precision medicine and personalizing therapies,” Esteva told VentureBeat. “It’s not just what’s present in an image or what’s present on a patient, but what the patient’s future look like, especially if we decide to put them on a therapy. We use AI to take all of the patient’s data — their medical images records, their lifestyle. Decisions are made, and the algorithm predicts if they’ll live or die, whether they’ll live in a healthy state or unhealthy, and so forth.”

Toward this end, in December, Salesforce Research open-sourced ReceptorNet, a machine learning system researchers at the division developed in partnership with clinicians at the University of Southern California’s Lawrence J. Ellison Institute for Transformative Medicine of USC. The system, which can determine a critical biomarker for oncologists when deciding on the appropriate treatment for breast cancer patients, achieved 92% accuracy in a study published in the journal Nature Communications.

Typically, breast cancer cells extracted during a biopsy or surgery are tested to see if they contain proteins that act as estrogen or progesterone receptors. When the hormones estrogen and progesterone attach to these receptors, they fuel the cancer growth. But these types of biopsy images are less widely available and require a pathologist to review.

In contrast, ReceptorNet determines hormone receptor status via hematoxylin and eosin (H&E) staining, which takes into account the shape, size, and structure of cells. Salesforce researchers trained the system on several thousand H&E image slides from cancer patients in “dozens” of hospitals around the world.

Research has shown that much of the data used to train algorithms for diagnosing diseases may perpetuate inequalities. Recently, a team of U.K. scientists found that almost all eye disease datasets come from patients in North America, Europe, and China, meaning eye disease-diagnosing algorithms are less certain to work well for racial groups from underrepresented countries. In another study, Stanford University researchers identified most of the U.S. data for studies involving medical uses of AI as coming from California, New York, and Massachusetts.

But Salesforce claims that when it analyzed ReceptorNet for signs of age-, race-, and geography-related bias, it found that there was statically no difference in its performance. The company also says that the algorithm delivered accurate predictions regardless of differences in the preparation of tissue samples.

“On breast cancer classification, we were able to classify some images without a costly and time-intensive staining process,” Socher said. “Long story short, this is one of the areas where AI can solve a problem such that it could be helpful in end applications.”

In a related project detailed in a paper published last March, scientists at Salesforce Research developed an AI system called ProGen that can generate proteins in a “controllable fashion.” Given the desired properties of a protein, like a molecular function or a cellular component, ProGen creates proteins by treating the amino acids making up the protein like words in a paragraph.

The Salesforce Research team behind ProGen trained the model on a dataset of over 280 million protein sequences and associated metadata — the largest publicly available. The model took each training sample and formulated a guessing game per amino acid. For over a million rounds of training, ProGen attempted to predict the next amino acids from the previous amino acids, and over time, the model learned to generate proteins with sequences it hadn’t seen before.

In the future, Salesforce researchers intend to refine ProGen’s ability to synthesize novel proteins, whether undiscovered or nonexistent, by honing in on specific protein properties.

Ethics

Salesforce Research’s ethical AI work straddles applied and pure research. There’s been increased interest in it from customers, according to Casalaina, who says he’s had a number of conversations with clients about the ethics of AI over the past six months.

In January, Salesforce researchers released Robustness Gym, which aims to unify a patchwork of libraries to bolster natural language model testing strategies. Robustness Gym provides guidance on how certain variables can help prioritize what evaluations to run. Specifically, it describes the influence of a task via a structure and known prior evaluations, as well as needs such as testing generalization, fairness, or security; and constraints like expertise, compute access, and human resources.

In the study of natural language, robustness testing tends to be the exception rather than the norm. One report found that 60% to 70% of answers given by natural language processing models were embedded somewhere in the benchmark training sets, indicating that the models were usually simply memorizing answers. Another study found that metrics used to benchmark AI and machine learning models tended to be inconsistent, irregularly tracked, and not particularly informative.

In a case study, Salesforce Research had a sentiment modeling team at a “major technology company” measure the bias of their model using Robustness Gym. After testing the system, the modeling team found a performance degradation of up to 18%.

In a more recent study published in July, Salesforce researchers proposed a new way to mitigate gender bias in word embeddings, the word representations used to train AI models to summarize, translate languages, and perform other prediction tasks. Word embeddings capture semantic and syntactic meanings of words and relationships with other words, which is why they’re commonly employed in natural language processing. But they have a tendency to inherit gender bias.

Salesforce’s proposed solution, Double-Hard Debias, transforms the embedding space into an ostensibly genderless one. It transforms word embeddings into a “subspace” that can be used to find the dimension that encodes frequency information distracting from the encoded genders. Then, it “projects away” the gender component along this dimension to obtain revised embeddings before executing another debiasing action.

To evaluate Double-Hard Debias, the researchers tested it against the WinoBias data set, which consists of pro-gender-stereotype and anti-gender-stereotype sentences. Double-Hard Debias reduced the bias score of embeddings obtained using the GloVe algorithm from 15 (on two types of sentences) to 7.7 while preserving the semantic information.

Future work

Looking ahead, as the pandemic makes clear the benefits of automation, Casalaina expects that this will remain a core area of focus for Salesforce Research. He expects that chatbots built to answer customer questions will become more capable than they currently are, for example, as well as robotic process automation technologies that handle repetitive backroom tasks.

There are numbers to back up Casalaina’s assertions. In November, Salesforce reported a 300% increase in Einstein Bot sessions since February of this year, a 680% year-over-year increase compared to 2019. That’s in addition to a 700% increase in predictions for agent assistance and service automation and a 300% increase in daily predictions for Einstein for Commerce in Q3 2020. As for Einstein for Marketing Cloud and Einstein for Sales, email and mobile personalization predictions were up 67% in Q3, and there was a 32% increase in converting prospects to buyers using Einstein Lead Scoring.

“The goal is here — and at Salesforce Research broadly — is to remove the groundwork for people. A lot of focus is put on the model, the goodness of the model, and all that stuff,” Casalaina said. “But that’s only 20% of the equation. The 80% part of it is how humans use it.”

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform
  • networking features, and more

Become a member

Repost: Original Source and Author Link

Categories
AI

Salesforce taps AWS to bring ‘intelligent document automation’ to Health Cloud

Salesforce today announced a new product to help health care and life sciences companies digitize their document management processes.

Intelligent document automation (IDA), as it’s called, is designed for use with another new Salesforce tool called intelligent form reader.

With IDA, Salesforce is promising its customers reduced manual data entry while enabling them to manage all patients or members from a single place. So any incoming documents, including typed or handwritten forms such as patient referrals that may have arrived as a digital or hard copy (e.g. by fax or post), can now be automatically analyzed and routed to the right queue for review and processing in Salesforce’s Health Cloud.

Above: Using Salesforce’s intelligent document automation in Health Cloud

The intelligent form reader, which leans on optical character recognition (OCR) technology, is powered by Amazon Web Services’ (AWS) Textract. AWS launched Textract in 2019, leveraging machine learning smarts to enable any business to automatically extract content from tables, forms, pages, and more. A few months back, AWS introduced added support for handwriting recognition and a host of new languages.

Any business wishing to use Salesforce’s intelligent form reader must also have a separate Textract license, which is available through Salesforce.

*Article updated to clarify that the Textract license is available through Salesforce.

VentureBeat

VentureBeat’s mission is to be a digital town square for technical decision-makers to gain knowledge about transformative technology and transact.

Our site delivers essential information on data technologies and strategies to guide you as you lead your organizations. We invite you to become a member of our community, to access:

  • up-to-date information on the subjects of interest to you
  • our newsletters
  • gated thought-leader content and discounted access to our prized events, such as Transform
  • networking features, and more

Become a member

Repost: Original Source and Author Link