Apr
07
2021
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Pathlight, a performance management tool for customer-facing teams and the individuals in them, raises $25M

The longer we continue to work with either all or part of our teams in remote, out-of-physical-office environments, the more imperative it becomes for those teams to have some tools in place to keep the channels of communication and management open, and for the individuals in those teams to have a sense of how well they are performing. Today, one of the startups that provides a team productivity app with that in mind is announcing a round of funding to fuel its growth.

Pathlight, which has built a performance management platform for customer-facing teams — sales, field service and support — to help managers and employees themselves track and analyze how they are doing, to coach them when and where it’s needed and to communicate updates and more, has picked up $25 million — money that it will be using to continue growing its customer base and the functionality across its app.

The funding is being led by Insight Partners, with previous backers Kleiner Perkins and Quiet Capital also participating, alongside Uncorrelated Ventures; Jeremy Stoppelman, CEO of Yelp; David Glazer, CFO of Palantir; and Michael Ovitz, co-founder of CAA and owner of Broad Beach Ventures. Pathlight has now raised $35 million.

Pathlight today provides users with a range of tools to visualize team and individual performance across various parameters set by managers, using data that teams integrate from other platforms like Salesforce, Zendesk and Outreach, among others.

Using that data and specific metrics for the job in question, managers can then initiate conversations with individuals to focus on specific areas where things need attention, and provide some coaching to help fix it. It can also be used to provide team-wide updates and encouragement, which sits alongside whatever other tools a person might use in their daily customer-facing work.

Since launching in March 2020, the startup has picked up good traction, with customers including Twilio, Earnin, Greenhouse and CLEAR. But perhaps even more importantly, the pandemic and resulting switch to remote work has underscored how necessary tools like Pathlight’s have become: The startup says that engagement on its platform has shot up 300% in the last 12 months.

Alexander Kvamme, the CEO of Pathlight, said that he first became aware of the challenges of communicating across customer-facing teams, and having transparency on how they are doing as individuals and as a group, when he was at Yelp. Yelp had acquired his startup, reservations service SeatMe, and used the acquisition to build and run Yelp Reservations.

He was quick to realize that there weren’t really effective tools for him to see how individuals in the sales team were doing, how they were doing compared to goals the company wanted to achieve and based on the sales data they already had in other systems, how to work more effectively with people to communicate when something needed changing, and how to tailor all that in line with new variations in the formula — in their case, how to sell new products like a reservations service alongside advertising and other Yelp services for businesses.

“Whether it’s five or 3,000 people, the problem doesn’t go away,” he said. “Everyone uses their own systems, and it hurts front-line employees when they don’t know how they are doing, or don’t get recognition when they are doing well, or don’t get coaching when they are not. Our thesis was that if software is eating the world, and you as a company are buying more software and analytics, over time managers will be more like data analysts. So we are providing a way for managers to be more data-driven.”

Five years down the line, Kvamme got the bug again to start a company and decided to return to that problem, teaming up with co-founder Trey Doig, the engineer who designed SeatMe and then turned it into Yelp Reservations and is now Pathlight’s CTO.

As they see it, the challenge has still not really been addressed. That’s not to say that there are not a number of companies — competitors to Pathlight — looking to fill that gap as well. Another people management platform called Lattice last year picked up $45 million  (I’m guessing it will be raising money again around about now); HubSpot, Zoho, SalesLoft and a number of others also are taking different approaches to the same challenge: front-line customer-facing people spend the majority of their time and attention on interacting with people, and so there need to be better tools in place to help them figure out how to make that communication more effective, figure out what is working and what is not.

And all of this, of course, is not at all new: It’s not like we all woke up one day and suddenly wanted to know how we are doing at work, or managers suddenly felt they needed to communicate with staff.

What has changed, however, is how we work: Many of us have not seen the inside of our offices for more than a year at this point, and for a large proportion of us, we may never return again, or if we do it will be under different circumstances.

All of this means that some of the more traditional metrics and indicators of our performance, praising, management relationships and learning from teammates simply is not there anymore.

In customer-facing areas like sales, support and field service, that lack of contact may be even more acute, since many of the teams working in these environments have long relied on huddles and communication throughout the day, week and month to continuously tweak work and improve it. So while tools like Pathlight’s will be useful as data analytics provision for teams regardless of how we work, it can be argued that they are even more important right now.

“I think people have started to realize that if you can empower front line to be more independent, your numbers will go up and do better,” Kvamme said.

This is part of what went into the investment decision made here.

“With the acceleration of digital transformation across the enterprise, it’s not enough to rethink the way we work — we must also rethink the way we manage,” said Jeff Lieberman, MD at Insight Partners. “Pathlight is ushering in a new age of data-driven management, an ethos that we believe every enterprise will need to embrace — quickly. We are excited to partner with the Pathlight team as they bring their powerful platform to companies across the world.”

Dec
02
2020
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Salesforce announces new Service Cloud workforce planning tool

With a pandemic raging across many parts of the world, many companies have customer service agents spread out as well, creating a workforce management nightmare. It wasn’t easy to manage and route requests when CSAs were in one place, it’s even harder with many working from home.

To help answer that problem Salesforce is developing a new product called Service Cloud Workforce Engagement. Bill Patterson, EVP and general manager for CRM Applications at Salesforce, points out that with these workforces spread out, it’s a huge challenge for management to distribute work and keep up with customer volume, especially as customers have moved online during COVID.

“With Service Cloud Workforce Engagement, Salesforce will arm the contact center with a connected solution — all on one platform so our customers can remain resilient and agile no matter what tomorrow may bring,” Patterson said in a statement.

Like many Salesforce products, this one is made up of several key components to deliver a complete solution. For starters, there is Service Forecast for Customer 360, a tool that helps predict workforce requirements and uses AI to distribute customer service requests in a way that makes sense. This can help in planning at a time with a likely predictable uptick in service requests like Black Friday or Cyber Monday, or even those times when there is an unexpected spike.

Next up is Omnichannel Capacity Planning, which helps managers distribute CSAs across channels such as phone, messaging or email wherever they are needed most based on the demand across a given channel.

Finally, there is a teaching component that helps coach customer service agents to give the correct answer in the correct way for a given situation. “To increase agent engagement and performance, companies will be able to quickly onboard and continually train agents by delivering bite-size, guided learning paths directly in the agent’s workspace during their shift,” the company explained.

The company says that Service Cloud Workforce Engagement will be available in the first half of next year.

Oct
07
2020
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YC grad DigitalBrain snags $3.4M seed to streamline customer service tasks

Most startup founders have a tough road to their first round of funding, but the founders of Digital Brain had it a bit tougher than most. The two young founders survived by entering and winning hackathons to pay their rent and put food on the table. One of the ideas they came up with at those hackathons was DigitalBrain, a layer that sits on top of customer service software like Zendesk to streamline tasks and ease the job of customer service agents.

They ended up in Y Combinator in the Summer 2020 class, and today the company announced a $3.4 million seed investment. This total includes $3 million raised this round, which closed in August, and previously unannounced investments of $250,000 in March from Unshackled Ventures and $150,000 from Y Combinator in May.

The round was led by Moxxie Ventures, with help from Caffeinated Capital, Unshackled Ventures, Shrug Capital, Weekend Fund, Underscore VC and Scribble Ventures, along with a slew of individual investors.

Company co-founder Kesava Kirupa Dinakaran says that after he and his partner Dmitry Dolgopolov met at a hackathon in May 2019, they moved into a community house in San Francisco full of startup founders. They kept hearing from their housemates about the issues their companies faced with customer service as they began scaling. Like any good entrepreneur, they decided to build something to solve that problem.

DigitalBrain is an external layer that sits on top of existing help desk software to actually help the support agents get through their tickets twice as fast, and we’re doing that by automating a lot of internal workflows, and giving them all the context and information they need to respond to each ticket, making the experience of responding to these tickets significantly faster,” Dinakaran told TechCrunch.

What this means in practice is that customer service reps work in DigitalBrain to process their tickets, and as they come upon a problem such as canceling an order or reporting a bug, instead of traversing several systems to fix it, they choose the appropriate action in DigitalBrain, enter the required information and the problem is resolved for them automatically. In the case of a bug, it would file a Jira ticket with engineering. In the case of canceling an order, it would take all of the actions and update all of the records required by this request.

As Dinakaran points out, they aren’t typical Silicon Valley startup founders. They are 20-year-old immigrants from India and Russia, respectively, who came to the U.S. with coding skills and a dream of building a company. “We are both outsiders to Silicon Valley. We didn’t go to college. We don’t come from families of means. We wanted to come here and build our initial network from the ground up,” he said.

Eventually they met some folks through their housemates, who suggested that they apply to Y Combinator. “As we started to meet people that we met through our community house here, some of them were YC founders and they kept saying I think you guys will love the YC community, not just in terms of your ethos, but also just purely from a perspective of meeting new people and where you are,” he said.

He said while he and his co-founder have trouble wrapping their arms around a number like the amount they have in the bank now, considering it wasn’t that long ago that they were struggling to meet expenses every month, they recognize this money buys them an opportunity to help start building a more substantial company.

“What we’re trying to do is really accelerate the development and building of what we’re doing. And we think if we push the gas pedal with the resources we’ve gotten, we’ll be able to accelerate bringing on the next couple of customers, and start onboarding some of the larger companies we’re interested in,” he said.

Aug
25
2020
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New Zendesk dashboard delivers customer service data in real time

Zendesk has been offering customers the ability to track customer service statistics for some time, but it has always been a look back. Today, the company announced a new product called Explore Enterprise that lets customers capture that valuable info in real time, and share it with anyone in the organization, whether they have a Zendesk license or not.

While it has had Explore in place for a couple of years now, Jon Aniano, senior VP of product at Zendesk says the new enterprise product is in response to growing customer data requirements. “We now have a way to deliver what we call Live Team Dashboards, which delivers real-time analytics directly to Zendesk users,” Aniano told TechCrunch.

In the days before COVID that meant displaying these on big monitors throughout the customer service center. Today, as we deal with the pandemic, and customer service reps are just as likely to be working from home, it means giving management the tools they need to understand what’s happening in real time, a growing requirement for Zendesk customers as they scale, regardless of the pandemic.

“What we’ve found over the last few years is that our customers’ appetite for operational analytics is insatiable, and as customers grow, as customer service needs get more complex, the demands on a contact center operator or customer service team are higher and higher, and teams really need new sets of tools and new types of capabilities to meet what they’re trying to do in delivering customer service at scale in the world,” Aniano told TechCrunch.

One of the reasons for this is the shift from phone and email as the primary ways of accessing customer service to messaging tools like WhatsApp. “With the shift to messaging, there are new demands on contact centers to be able to handle real-time interactions at scale with their customers,” he said.

In order to meet that kind of demand, it requires real-time analytics that Zendesk is providing with this announcement. This arms managers with the data they need to put their customer service resources where they are needed most in the moment in real time.

But Zendesk is also giving customers the ability to share these statistics with anyone in the company. “Users can share a dashboard or historical report with anybody in the company regardless of whether they have access to Zendesk. They can share it in Slack, or they can embed a dashboard anywhere where other people in the company would like to have access to those metrics,” Aniano explained.

The new service will be available starting on August 31 for $29 per user per month.

May
20
2020
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Directly, which taps experts to train chatbots, raises $11M, closes out Series B at $51M

Directly, a startup whose mission is to help build better customer service chatbots by using experts in specific areas to train them, has raised more funding as it opens up a new front to grow its business: APIs and a partner ecosystem that can now also tap into its expert network. Today Directly is announcing that it has added $11 million to close out its Series B at $51 million (it raised $20 million back in January of this year, and another $20 million as part of the Series B back in 2018).

The funding is coming from Triangle Peak Partners and Toba Capital, while its previous investors in the round included strategic backers Samsung NEXT and Microsoft’s M12 Ventures (who are both customers, alongside companies like Airbnb), as well as Industry Ventures, True Ventures, Costanoa Ventures and Northgate. (As we reported when covering the initial close, Directly’s valuation at that time was at $110 million post-money, and so this would likely put it at $120 million or higher, given how the business has expanded.)

While chatbots have now been around for years, a key focus in the tech world has been how to help them work better, after initial efforts saw so many disappointing results that it was fair to ask whether they were even worth the trouble.

Directly’s premise is that the most important part of getting a chatbot to work well is to make sure that it’s trained correctly, and its approach to that is very practical: find experts both to troubleshoot questions and provide answers.

As we’ve described before, its platform helps businesses identify and reach out to “experts” in the business or product in question, collect knowledge from them, and then fold that into a company’s AI to help train it and answer questions more accurately. It also looks at data input and output into those AI systems to figure out what is working, and what is not, and how to fix that, too.

The information is typically collected by way of question-and-answer sessions. Directly compensates experts both for submitting information as well as to pay out royalties when their knowledge has been put to use, “just as you would in traditional copyright licensing in music,” its co-founder Antony Brydon explained to me earlier this year.

It can take as little as 100 experts, but potentially many more, to train a system, depending on how much the information needs to be updated over time. (Directly’s work for Xbox, for example, used 1,000 experts but has to date answered millions of questions.)

Directly’s pitch to customers is that building a better chatbot can help deflect more questions from actual live agents (and subsequently cut operational costs for a business). It claims that customer contacts can be reduced by up to 80%, with customer satisfaction by up to 20%, as a result.

What’s interesting is that now Directly sees an opportunity in expanding that expert ecosystem to a wider group of partners, some of which might have previously been seen as competitors. (Not unlike Amazon’s AI powering a multitude of other businesses, some of which might also be in the market of selling the same services that Amazon does).

The partner ecosystem, as Directly calls it, use APIs to link into Directly’s platform. Meya, Percept.ai, and SmartAction — which themselves provide a range of customer service automation tools — are three of the first users.

“The team at Directly have quickly proven to be trusted and invaluable partners,” said Erik Kalviainen, CEO at Meya, in a statement. “As a result of our collaboration, Meya is now able to take advantage of a whole new set of capabilities that will enable us to deliver automated solutions both faster and with higher resolution rates, without customers needing to deploy significant internal resources. That’s a powerful advantage at a time when scale and efficiency are key to any successful customer support operation.”

The prospect of a bigger business funnel beyond even what Directly was pulling in itself is likely what attracted the most recent investment.

“Directly has established itself as a true leader in helping customers thrive during these turbulent economic times,” said Tyler Peterson, Partner at Triangle Peak Partners, in a statement. “There is little doubt that automation will play a tremendous role in the future of customer support, but Directly is realizing that potential today. Their platform enables businesses to strike just the right balance between automation and human support, helping them adopt AI-powered solutions in a way that is practical, accessible, and demonstrably effective.”

In January, Mike de la Cruz, who took over as CEO at the time of the funding announcement, said the company was gearing up for a larger Series C in 2021. It’s not clear how and if that will be impacted by the current state of the world. But in the meantime, as more organizations are looking for ways to connect with customers outside of channels that might require people to physically visit stores, or for employees to sit in call centres, it presents a huge opportunity for companies like this one.

“At its core, our business is about helping customer support leaders resolve customer issues with the right mix of automation and human support,” said de la Cruz in a statement. “It’s one thing to deliver a great product today, but we’re committed to ensuring that our customers have the solutions they need over the long term. That means constantly investing in our platform and expanding our capabilities, so that we can keep up with the rapid pace of technological change and an unpredictable economic landscape. These new partnerships and this latest expansion of our recent funding round have positioned us to do just that. We’re excited to be collaborating with our new partners, and very thankful to all of our investors for their support.”

Dec
27
2019
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Revenue train kept rolling all year long for Salesforce

Salesforce turned 20 this year, and the most successful pure enterprise SaaS company ever showed no signs of slowing down. Consider that the company finished the year on an $18 billion run rate, rushing toward its 2022 revenue goal of $20 billion. Oh, and it also spent a tidy $15.7 billion to buy Tableau this year in the most high-profile and expensive acquisition it’s ever made.

Co-founder, chairman and CEO Marc Benioff published a book called Trailblazer about running a socially responsible company, and made the rounds promoting it. In fact, he even stopped by TechCrunch Disrupt in San Francisco in September, telling the audience that capitalism as we know it is dead. Still, the company announced it was building two more towers in Sydney and Dublin.

It also promoted Bret Taylor earlier this month, who could be in line as heir apparent to Benioff and co-CEO Keith Block whenever they decide to retire. The company closed the year with a bang with a $4.5 billion quarter. Salesforce, for the most part, has somehow been able to balance Benioff’s vision of responsible capitalism while building a company makes money in bunches, one that continues to grow and flourish, and that’s showing no signs of slowing down anytime soon.

All aboard the gravy train

The company just keeps churning out good quarters. Here’s what this year looked like:

Sep
21
2019
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Facebook has acquired Servicefriend, which builds ‘hybrid’ chatbots, for Calibra customer service

As Facebook prepares to launch its new cryptocurrency Libra in 2020, it’s putting the pieces in place to help it run. In one of the latest developments, it has acquired Servicefriend, a startup that built bots — chat clients for messaging apps based on artificial intelligence — to help customer service teams, TechCrunch has confirmed.

The news was first reported in Israel, where Servicefriend is based, after one of its investors, Roberto Singler, alerted local publication The Marker about the deal. We reached out to Ido Arad, one of the co-founders of the company, who referred our questions to a team at Facebook. Facebook then confirmed the acquisition with an Apple-like non-specific statement:

“We acquire smaller tech companies from time to time. We don’t always discuss our plans,” a Facebook spokesperson said.

Several people, including Arad, his co-founder Shahar Ben Ami, and at least one other indicate that they now work at Facebook within the Calibra digital wallet group on their LinkedIn profiles. Their jobs at the social network started this month, meaning this acquisition closed in recent weeks. (Several others indicate that they are still at Servicefriend, meaning they too may have likely made the move as well.)

Although Facebook isn’t specifying what they will be working on, the most obvious area will be in building a bot — or more likely, a network of bots — for the customer service layer for the Calibra digital wallet that Facebook is developing.

Facebook’s plan is to build a range of financial services for people to use Calibra to pay out and receive Libra — for example, to send money to contacts, pay bills, top up their phones, buy things and more.

It remains to be seen just how much people will trust Facebook as a provider of all these. So that is where having “human” and accessible customer service experience will be essential.

“We are here for you,” Calibra notes on its welcome page, where it promises 24-7 support in WhatsApp and Messenger for its users.

Screenshot 2019 09 21 at 23.25.18

Servicefriend has worked on Facebook’s platform in the past: specifically it built “hybrid” bots for Messenger for companies to use to complement teams of humans, to better scale their services on messaging platforms. In one Messenger bot that Servicefriend built for Globe Telecom in the Philippines, it noted that the hybrid bot was able to bring the “agent hours” down to under 20 hours for each 1,000 customer interactions.

Bots have been a relatively problematic area for Facebook. The company launched a personal assistant called M in 2015, and then bots that let users talk to businesses in 2016 on Messenger, with quite some fanfare, although the reality was that nothing really worked as well as promised, and in some cases worked significantly worse than whatever services they aimed to replace.

While AI-based assistants such as Alexa have become synonymous with how a computer can carry on a conversation and provide information to humans, the consensus around bots these days is that the most workable way forward is to build services that complement, rather than completely replace, teams.

For Facebook, getting its customer service on Calibra right can help it build and expand its credibility (note: another area where Servicefriend has build services is in using customer service as a marketing channel). Getting it wrong could mean issues not just with customers, but with partners and possibly regulators.

Sep
05
2019
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Battlefield winner Forethought adds tool to automate support ticket routing

Last year at this time, Forethought won the TechCrunch Disrupt Battlefield competition. A $9 million Series A investment followed last December. Today at TechCrunch Sessions: Enterprise in San Francisco, the company introduced the latest addition to its platform, called Agatha Predictions.

Forethought CEO and co-founder Deon Nicholas said that after launching its original product, Agatha Answers (to provide suggested answers to customer queries), customers were asking for help with the routing part of the process, as well. “We learned that there’s a whole front end of that problem before the ticket even gets to the agent,” he said. Forethought developed Agatha Predictions to help sort the tickets and get them to the most qualified agent to solve the problem.

“It’s effectively an entire tool that helps triage and route tickets. So when a ticket is coming in, it can predict whether it’s a high-priority or low-priority ticket and which agent is best qualified to handle this question. And this all happens before the agent even touches the ticket. This really helps drive efficiencies across the organization by helping to reduce triage time,” Nicholas explained.

The original product (Agatha Answers) is designed to help agents get answers more quickly and reduce the amount of time it takes to resolve an issue. “It’s a tool that integrates into your Help Desk software, indexes your past support tickets, knowledge base articles and other [related content]. Then we give agents suggested answers to help them close questions with reduced handle time,” Nicholas said.

He says that Agatha Predictions is based on the same underlying AI engine as Agatha Answers. Both use Natural Language Understanding (NLU) developed by the company. “We’ve been building out our product, and the Natural Language Understanding engine, the engine behind the system, works in a very similar manner [across our products]. So as a ticket comes in the AI reads it, understands what the customer is asking about, and understands the semantics, the words being used,” he explained. This enables them to automate the routing and supply a likely answer for the issue involved.

Nicholas maintains that winning Battlefield gave his company a jump start and a certain legitimacy it lacked as an early-stage startup. Lots of customers came knocking after the event, as did investors. The company has grown from five employees when it launched last year at TechCrunch Disrupt to 20 today.

Apr
10
2019
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Salesforce and Google want to build a smarter customer service experience

Anyone who has dealt with bad customer service has felt frustration with the lack of basic understanding of who you are as a customer and what you need. Google and Salesforce feel your pain, and today the two companies expanded their partnership to try and create a smarter customer service experience.

The goal is to combine Salesforce’s customer knowledge with Google’s customer service-related AI products and build on the strengths of the combined solution to produce a better customer service experience, whether that’s with an agent or a chatbot..

Bill Patterson, executive vice president for Salesforce Service Cloud, gets that bad customer service is a source of vexation for many consumers, but his goal is to change that. Patterson points out that Google and Salesforce have been working together since 2017, but mostly on sales- and marketing-related projects. Today’s announcement marks the first time they are working on a customer service solution together.

For starters, the partnership is looking at the human customer service agent experience.”The combination of Google Contact Center AI, which highlights the language and the stream of intelligence that comes through that interaction, combined with the customer data and the business process information that that Salesforce has, really makes that an incredibly enriching experience for agents,” Patterson explained.

The Google software will understand voice and intent, and have access to a set of external information like weather or news events that might be having an impact on the customers, while Salesforce looks at the hard data it stores about the customer such as who they are, their buying history and previous interactions.

The companies believe that by bringing these two types of data together, they can surface relevant information in real time to help the agent give the best answer. It may be the best article or it could be just suggesting that a shipment might be late because of bad weather in the area.

Customer service agent screen showing information surfaced by intelligent layers in Google and Salesforce

The second part of the announcement involves improving the chatbot experience. We’ve all dealt with rigid chatbots, who can’t understand your request. Sure, it can sometimes channel your call to the right person, but if you have any question outside the most basic ones, it tends to get stuck, while you scream “Operator! I said OPERATOR!” (Or at least I do.)

Google and Salesforce are hoping to change that by bringing together Einstein, Salesforce’s artificial intelligence layer and Google Natural Language Understanding (NLU) in its Google Dialogflow product to better understand the request, monitor the sentiment and direct you to a human operator before you get frustrated.

Patterson’s department, which is on a $3.8 billion run rate, is poised to become the largest revenue producer in the Salesforce family by the end of the year. The company itself is on a run rate over $14 billion.

“So many organizations just struggle with primitives of great customer service and experience. We have a lot of passion for making everyday interaction better with agents,” he said. Maybe this partnership will bring some much needed improvement.

Mar
19
2019
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Salesforce update brings AI and Quip to customer service chat experience

When Salesforce introduced Einstein, its artificial intelligence platform in 2016, it was laying the ground work for artificial intelligence underpinnings across the platform. Since then the company has introduced a variety of AI enhancements to the Salesforce product family. Today, customer service got some AI updates.

The goal of any customer service interaction is to get the customer answers as quickly as possible. Many users opt to use chat over phone, and Salesforce has added some AI features to help customer service agents get answers more quickly in the chat interface. (The company hinted that phone customer service enhancements are coming.)

For starters, Salesforce is using machine learning to deliver article recommendations, response recommendations and next best actions to the agent in real time as they interact with customers.  “With Einstein article recommendations, we can use machine learning on past cases and we can look at how articles were used to successfully solve similar cases in the past, and serve up the best article right in the console to help the agent with the case,” Martha Walchuk, senior director of product marketing for Salesforce Service Cloud explained.

Salesforce Service Console. Screenshot: Salesforce

The company is also using similar technology to provide response recommendations, which the agent can copy and paste into the chat to speed up the time to response. Before the interaction ends, the company can offer the next best action (which was announced last year) based on the conversation. For example, they could offer related information, an upsell recommendation or whatever type of action the customer defines.

Salesforce is also using machine learning to help route each person to the most appropriate customer service rep. As Salesforce describes it, this feature uses machine learning to filter cases and route them to the right queue or agent automatically, based on defined criteria such as best qualified agent or past outcomes.

Finally, the company is embedding Quip, the company it acquired in 2016 for $750 million, into the customer service console to allow agents to communicate with one another to find answers to difficult problems. That not only helps solve the issues faster, the conversations themselves become part of the knowledge base, which Salesforce can draw upon to help teach the machine learning algorithms about the correct responses to commonly asked questions in the future.

As with the Oracle AI announcement this morning, this use of artificial intelligence in sales, service and marketing is part of a much broader industry trend, as these companies try to inject intelligence into workflows to make them run more efficiently.

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