Artesian CEO Andrew Yates recently discussed Artesian Solutions with Sudipto Ghosh as part of the MarTech Interview Series. Artesian was founded to help resolve the disparity between B2B buyer and seller tools. “We saw that businesses had transformed the way they buy, but that sellers had not adapted. This mismatch led us to create a vision of better B2B sales engagement that is customer-centric at its heart, and to develop the world’s most powerful customer intelligence application to support it.”
Yates described technology as “the biggest disruptive force in the world” and his entrepreneurship as “a desire to disrupt the status quo, solve problems, remove complexity and make a difference.” He sees Artesian Solutions as a “disruptive force for good in our sector, providing engagement smarts for companies and markets in the same way that LinkedIn has done for people insights.”
Artesian is incorporating new AI technologies into its platform including the Arti chatbot based upon IBM Watson. As they are doing so, they are repositioning from Social Selling to “A.I.-powered sales intelligence.”
Yates warns that businesses look for CRM platforms to help customer facing departments build customer-centric businesses and a full customer view. Often, though, they become frustrated when CRMs do not provide the desired customer experience and engagement. But CRMs are only as good as the data entered into them and are subject to ongoing data decay. Further compounding this issue is
“the sheer volume of data businesses need to grapple with. Often unstructured, this data is increasingly hard to find, rationalize and interpret. Inaccurate or out-of-date data has several inevitable consequences. Take-up and enthusiasm for CRM input wanes as the volume of data increases, and time spent just keeping up-to-date with existing customer data impacts negatively on time spent researching and acquiring new ones. Opportunities to respond to real-time customer news and market insight are missed, and customers looking for instant action and results are left disappointed. Likewise, deals are lost through mistakes, and errors in messaging and targeting become more frequent. Forecasting accuracy diminishes as emerging trends go unnoticed.”
Yates recommends working with a data partner that provides a full view of customers and contacts, including contextualized customer insight; news, market trends and social media monitoring; real-time intelligence; and single sourced company and contact profiles with “social profiles, opinions, and expectations.”
Predictive Analytics and Audience Management vendor Leadspace completed its Series C. The funding round was led by Arrowroot Capital and joined by JVP. The $21 million round will be used “to grow our customer team in San Francisco and Denver, and our AI and data management product teams in Israel.”
The firm is assessing additional locations, including possible offices on the East Coast and Europe, “perhaps” London.
Arrowroot has taken a seat on Leadspace’s Board. The firm wanted growth equity advisors instead of traditional VCs for Round C. “At this point the investment is not just in the idea and the team, but also the underlying metrics and performance of the business,” said CEO Doug Bewsher. “Once you have “Product/Market fit”, the kinds of questions investors ask are whether you are ready to scale; what are the opportunities for further growth; and apart from additional investment can we be an investment partner that can help you address these opportunities?”
Bewsher noted that marketing has been transformed over the past seven years since Leadspace was founded. Firms are switching from tactical demand generation programs to targeted Account Based Marketing (ABM) communications. “No longer is it OK to just send out blanket “nurture” emails to everyone and hope that will generate positive customer engagements. No longer can you rely on a single data source as the basis to know your customer. No longer is it enough for marketers to just think of leads — they need to market to accounts, and teams of people. Neither can marketers afford to ignore intelligence and information from external parties, and simply rely on the limited info they gather internally.”
Not only has the nature of B2B marketing been transformed, but “world class B2B sales and marketing organizations” need to become more like consumer companies with a deep understanding of the account at multiple levels. Echoing Sirius Decisions, Bewsher said that B2B marketers need to “really know your customer at the account, demand unit and individual level, and then target and personalize your messaging to cut through the noise. And think customer-first.”
As an analytics company, Bewsher talks up the value of AI for sales and marketing as it begins to address specific problems and workflows:
AI is everywhere. While there is no doubt that it is going to change every corner of our life, both as private users and business people, I think we will start to move from the promise to the reality in 2018. In business-to-business sales and marketing in 2017, it was enough to say: “We have a ton of great data scientists who are working on new ways to better engage your customers.”
But in 2018 customers will look to see actual results — like the 90 percent increase in email connection rates we have seen from the deployment of AI to recommend the right way to engage a specific user. This will require a maniacal focus on specific use cases from the emerging area of AI.
One area where AI will improve revenue generation effectiveness is in ABM programs which has been limited by the human ability to consume information and the historical lack of data availability. However, “AI is changing all this, with the ability to consume and understand unprecedented amounts of information and turn this into action at scale and in real time. So sales and marketing teams now have the opportunity to drive much more relevant and effective engagement programs for their entire potential target audience.”
According to Leadspace, they are trusted by over 130 B2B brands and seven of the top ten enterprise software companies. Clients include Microsoft, Marketo, Oracle, and RingCentral.
British sales intelligence vendor Artesian Solutions announced that it received £3.5M in expansion capital from Columbia Lake partners. The funds will be used to refinance current debt obligations and provide working capital for “further growth and expansion.” The refinancing also provides better terms and business flexibility after the firm reached a profitability milestone in July.
Artesian has begun integrating artificial intelligence into its tools. Earlier this year, it launched Insight Agent, “the first step in a series of intelligent chat bots aimed at automating many of the tasks carried out by B2B professionals” along with Arti, the firm’s interactive digital assistant.
“This is an exciting milestone in our company’s history and positively reinforces the leadership position we have attained. We are constantly looking at the ‘what next’ scenario, pushing boundaries to establish our business as one of the leading innovators in B2B software for commercial teams, this has paved the way for our new risk mitigation capabilities which will be released in the New Year.”
Artesian Solutions CEO Andrew Yates
The firm has recently shifted its positioning from social selling to “A.I. powered sales intelligence.”
“Social Selling sits at the heart of Artesian’s founding principles,” explained Yates. “But as sales best practice has evolved, so has Artesian. Our goal is to be at the forefront of technology evolution for enterprise B2B, delivering a suite of A.I.-powered tools to make prospecting, engaging and closing deals easier. As our forthcoming risk mitigation capabilities demonstrate, we will continue to evolve to ensure we remain a trusted partner of our enterprise customers and to maximise the impact of their business relationships”
Artesian offers products for both the British and American markets.
Recently, I had the opportunity to sit down with Artesian Solutions CEO Andrew Yates and discuss topics including artificial intelligence and risk tools they are integrating into their social selling service. This is the second in a series of interview excerpts I am publishing this week. On Monday, Andrew discussed Artesian’s 2016 entry to the US market.
Michael: You have recently begun to introduce AI capabilities into your platform.
Andrew: What we’ve done in our first incarnation of bot-driven AI is we’ve created something that we call an “insight agent” that, through an API into Salesforce, can build you a view of threats and opportunities within your pipeline. Which, in itself, is pretty damn useful; much more useful than a forecast report or a dashboard which is the way you see it in Salesforce today. Then we’ll lay out all of those deals by stage and value and overlay today’s new social and demographic context on top. That’s pretty useful.
With the latest release, we’ve created a bot which literally reads and interprets the news in relation to the stage of the sales process that you’re at. And, where it sees a particular trigger that has meaning in relationship to a particular stage, it flags that. Most organizations have implemented the concepts of sale stages when they’ve implemented CRM.
Typically, when I ask somebody, “how many stages do you have?” They’ll say, “between five and seven.” The system automatically builds you a view depending on how you’re implementing Salesforce, however many stages you’ve implemented and what you call them. Then what the bot does, is it crawls all over the news looking for things that could impact those opportunities at the stage they are at.
Let’s say, I’ve got a six-stage process where stage six is closed and stage five is a negotiation. Artesian’s insight agent finds out about a CIO who has left the business. The insight agent will notify the user that there’s a potential problem with the deal in their pipeline. The agent will tell them why there is a problem and how it’s been categorized. There’s half a dozen next-best actions that we bundle up with the insight as we deliver it. That’s our first attempt at taking the concept of machine-based learning and natural language processing, combining it with an AI bot, and trying to make that useful for customers.
We’ve introduced the ability for the user to customize their own topics, keywords, and trigger events. We offer a bunch out of the box, and we also wrap a managed service around it and easy implementation to every customer.
We’re also seeing a lot of activity in the “RegTech/RiskTech” arena with the growth of cybercrime and terrorism, and the sensitivity around regulation of any financial, FCA [UK Financial Control Authority] regulated [business]. There are regulations that organizations need to comply with. We’re increasingly being asked by our financial services customers, particularly the banks, to get deeper into being able to provide those capabilities inside of Artesian.
Organizations want to mitigate risks. They want to fall within the arena of whatever the regulation is and comply with the law, but they also want to exploit the technology as best they can to make sure they write the best business that they can. We’re doing some work at the moment in conjunction with one of our demographic data suppliers. What we’re looking to do is extend the capabilities in Artesian to provide some of the capabilities that our customers are asking for in the RegTech / RiskTech environment. We’re going to introduce risk agents. Risk agents look at the real-time present and it looks at the past. It specifically looks at things that are in-line with the regulations and also in-line with the stated risks that the customer has mapped out.
What that translates into is a service that is not only compelling in terms of customer acquisition, customer retention, and yield, but also compelling from a kind of, you don’t go to jail if you’re using Artesian because it’s doing the regulation and risk job for you as well.
Michael: When you say risk app, are you talking more about supplier risk, compliance risk, credit, reputational?
Andrew: There are 40 or 50 pretty big companies doing this thing already. What we’re talking about is company-centric intelligence, but also the people associated with that company and the intelligence that we’ll need to derive around whether something is risky or not. It could be the performance of a business. It could be some adverse news in relation to that performance. Or it could be that an individual who has a beneficial ownership, more than a 5% stake in a business, happens to be on a naughty list in terms of the PEP [Politically Exposed Persons] or sanctions.
At the moment, we have risk triggers in the opportunity view. They’re not compliance risk triggers. If you’re going to a client, they need to know about key beneficial ownership.
Michael: Is that part of the opportunity view or is that a new type of view?
Andrew: A new type of view. We have risk triggers in the opportunity view, but they’re not compliance risk triggers. If you go into a bank, they need to know about beneficial ownership, adverse news going back three years, PEP, sanctions, real-time alerts from stock exchanges. None of that is feasible within a generic instance of Salesforce.com in an opportunity view.
Michael: It sounds you’re looking to move beyond the sales and marketing teams to start to get to into things like onboarding, KYC [Know Your Customer], AML [Anti-money Laundering], PEP, and other compliance aspects that really go into monitoring of clients as well as the initial onboarding.
Andrew: Yes, if you go back to the whole customer curious mantra and deep relationship management, we like to say that we put the R back into CRM. We are all about that relationship.
The conversations we are having with our large customers would indicate we are on the right track with that.
The interview will be continuing over the next few days with discussions of what it means to be a “customer curious” business and how Artesian maintains a very high engagement rate amongst its users. Monday’s blog discussed Artesian’s 2016 entry into the US market.
US and UK social selling vendor Artesian Solutions claimed that their 16.1 software release was their “biggest ever.” The release included functional upgrades, expanded content, user experience improvements, and new artificial intelligence (AI) capabilities.
The new Insight Agents are their first step towards delivering “intelligent chat bots aimed at automating many of the tasks carried out by B2B professionals daily.” The new AI tool combines advanced natural language processing and behavioural analytics. The goal is to “deliver commercially valuable and immediately actionable insights, telling the user what they need to know and what action they need to take.”
The evolution of Natural Language Processing, Machine Based Learning and Artificial Intelligence technology is set to have a profound impact on the activities of those in commercial teams. This product release is a significant stepping stone towards an AI future, the culmination of months of behind-the-scenes R&D based on an incredibly rich understanding of the enterprise B2B landscape through the eyes of some of the biggest and most influential companies in the world. We have been working closely with them to understand what their future looks like and where the biggest gains can be made.
Artesian CTO Steve Borthwick
Continued Borthwick, “Artesian has been evolving and improving its relevance and analytic algorithms over the last 10 years, benefiting from the feedback and success that our trusted customers have provided, and that has always kept them one step ahead of the sales intelligence race.”
Insight Agents are initially available within the AppExchange as Salesforce Opportunities. This feature surfaces trigger events based upon the opportunity stage and preferences of each client. “It provides a step change in productivity and customer engagement, enabling you to focus on the most important aspects of your most critical deals,” said VP of Product Management Rich Clark.
For example, Insights for Sales Opportunities will place higher emphasis upon opportunity alerts at the front of the pipeline and risk-based alerts during later stages.
“This latest release without doubt places Artesian at the leading edge of innovation for enterprise B2B,” said CEO Andrew Yates. It delivers the first milestone on our road-map to predicting customer needs, automating and directing pipeline activities, and the delivery of hyper-personalised communications and custom marketing.”
Administrators can now tailor the trigger topics available to their users including adding custom topics and constructing a topic taxonomy focused on industry-specific triggers.
User experience improvements include easier access to topic filters, color-coded triggers in email alerts, and adaptive HTML design for improved mobile navigation and display.
Other design upgrades include the social media viewer which now surfaces social media links alongside inline blogs and tweets and a social selling leaderboard at the team or corporate level which benchmarks Artesian usage versus peers.
“Hotness” flags were added to the US service. They have long been available in the UK edition.
In the UK edition, Artesian partnered with Blue Sheep to add three million contacts and double the number of emails to 875,000. The additional executives were focused on non-directorial positions, helping expand coverage beyond filed corporate directors. The new set of contacts raised the UK contact count to 9.7 million. Furthermore, the new execs were matched against the Full Contact file, providing additional social media links and biographic relevance to contact profiles.
The expanded contacts and social media links are also available to users of the Artesian Ready mobile app which combines mobile calendars with Artesian insights. Ready helps reps prepare for meetings by supporting on demand company and executive research, sharing meeting notes with colleagues, and reviewing the latest attendee triggers and social media posts.
The firm also added Auditor Fees as a new UK select. Auditor Name was already supported.
For the US edition, Artesian now supports a “Hotness” indicator highlighting firms with recent key trigger events and improved event prospecting (see image above on right). The Hotness indicator was already available in the UK edition.
Artesian has formalized its training and certification program with an integrated leaning management system. The older training was more PowerPoint oriented but the new system is more blog-like with videos and quizzes. Along with tool certification, Artesian includes annual refresher courses.
A November study by Demandbase and Wakefield Research of 500 B2B marketers (250+ employees) found that while marketers are confident that Artificial Intelligence (AI) will reshape marketing by 2020, they lack confidence in how to implement the new technology. According to Demandbase, “80 percent of all marketing executives believe AI will revolutionize marketing over the next 5 years, but only 26 percent are very confident they understand how AI is used in marketing and only 10 percent of marketers are currently using AI today.”
Marketers had numerous concerns about implementing AI, including
Integrating AI into their existing technology (60%)
Training employees (54%)
Difficulty interpreting the results (46%)
Implementation costs (42%)
On the benefits side, marketers listed
Better insights into accounts (60%)
More detailed analysis of campaigns (56%)
Identifying prospective customers (53%)
Expediting daily tasks (53%)
“As someone who has been studying AI for many years, I’ve recognized the promise of AI and B2B marketing for some time, which makes it really rewarding to see this vision is now shared by marketing executives,” said Aman Naimat, SVP of Technology at Demandbase. “This data reveals that in order to be successful, marketing leaders need to lead the charge and present opportunities for AI instruction and experience for their teams, to ensure implementing it into their B2B technology stacks is effective.”
In a November Harvard Business Review article titled “How Artificial Intelligence Will Redefine Management,” (Vegard Kolbjørnsrud, Richard Amico, and Robert J. Thomas), the authors offered a set of best practices for managers. Noting that managers spend 54% of their time on administrative tasks such as scheduling, monitoring, and reporting, they suggest that managers transition administrative tasks to AI. Instead managers should focus more on judgment work which combines rules with “their knowledge of organizational history and culture, as well as empathy and ethical reflection.” Thus, there will be a greater emphasis upon “judgment-oriented skills” such as “creative thinking and experimentation, data analysis and interpretation, and strategy development.”
The authors also suggested viewing AI as a trusted colleague instead of a “race against the machine.” Thus, managers can merge judgment with AI-based decision support, simulations, and search and discovery activities. A full 78% of managers believe they will trust the advice of intelligent systems. Furthermore, because AI will be approachable through voice and other intuitive interfaces, AI will be their “always-available assistant and adviser.”
Another recommendation was harnessing the creativity and ideas of co-workers and team members. With time freed from administrative tasks, there is more time for synthesizing multiple ideas and formulating new products and processes. “Manager-designers bring together diverse ideas into integrated, workable, and appealing solutions. They embed design thinking into the practices of their teams and organizations.”
Finally, managers will need to hone their social skills with an emphasis on networking, coaching, and collaborating.
The authors concluded that “writing earnings reports is one thing, but developing messages that can engage a workforce and provide a sense of purpose is human through and through. Tracking schedules and resources may soon fall within the jurisdiction of machines, but drafting strategy remains unmistakably human. Simply put, our recommendation is to adopt AI in order to automate administration and to augment but not replace human judgment.”
In the late 1980’s, when my career was first beginning, I worked on a technology helpdesk for an insurance agency automation system (Aetna’s Gemini platform). Many of the calls were routine with an easily road mapped set of resolution steps. So the firm decided to invest in artificial intelligence (AI) and began interviewing its most seasoned experts to identify the problem resolution path.
After several months of development, an AI module would be unveiled that walked the user through problem resolution. It was basically a set of if-then-else and case statements providing pre-coded branching logic. Support reps started with a category and were walked through a set of questions to ask and resolution steps to convey over the phone.
The solution was expensive and lacked the ability to learn. Thus, if new problems arose or the problem resolution changed due to new hardware or software being introduced, the rules no longer applied.
It was far from intelligent. Heck, I’d coded a twenty-questions game in a first semester programming class that was more intelligent than the service. At least my Q&A game had the ability to learn new questions to ask without requiring an expensive consultant.
Finally, it was only used by new hires as much of the routine steps were just that — routine.
Solutions like this quickly proved that Artificial Intelligence wasn’t intelligent and after a few years, the term AI fell from favor and returned to the realm of sci-fi killer robots.
Nearly three decades later, the term AI is once again being rolled out. But now it does convey an impressive level of intelligence which makes our devices feel smart. It’s why we call them smartphones. They are able to leverage vast amounts of data and make decisions in the blink of an eye. Whether it is asking Siri a question or having Google map the best route to a location subject to current traffic patterns and transportation mode, we expect our devices to be intelligent.
AI represents a massive change in technology. You might call it a “paradigm shift” or “disruption” or we could just stick with “massive change.” What we’re trying to say is, AI is kind of a big deal. And just like the arrival of the personal computer, cloud computing, and the mobile smartphone, AI is going to fundamentally change the way things work, forever.
So it was with a smile that I saw the term AI being used by Salesforce in positioning their new Einstein service. Each year at Dreamforce, CEO Marc Benioff discusses a new underlying technology or cloud. Most recently it has been Lighting (UI and workflows), Wave (analytics), and the Internet of Things Cloud. At Dreamforce 2016, it is Einstein, their artificial intelligence platform to assist with sales, marketing, and service.
Salesforce presents AI simply as
Lots of data + cloud computing + good data models = smarter machines
So while much of this technology has been provided as consumer applications for over a decade, businesses have been lagging behind when the scope goes beyond a mobile app or e-commerce portal.
Shouldn’t the full transactional and service history be available to help understand past purchases, preferences, and potential cross-sell and upsell opportunities?
Wouldn’t we want it delivered no matter the touch point?
That is the type of intelligence that Einstein is looking to bring to Salesforce customers. Einstein is “the world’s first comprehensive artificial intelligence platform for CRM. I’ve never been more excited about the innovation happening at Salesforce,” said Benioff.
Einstein is available both programmatically (for developers) and “declaratively for non-coders,” said Benioff. It is integrated directly into the SFDC platform and available across all of the clouds. For example, an Einstein widget displays a set of insights identifying competitor news, recommended actions, and account intelligence.
Einstein builds models with no coding or initial training by users. For example, the system is able to determine which trigger events are important to sales reps and surface news about competitors without asking “who are your competitors?” The system also can make recommendations concerning high-scoring leads based upon both fit (firmographics, biographics) and behavior (e.g. recent viewing of a demo).
Not only does the system recommend activity, but it then offers recommended email copy including a proposed call time.
The platform is built on a series of recent acquisitions including RelateIQ (rebranded SaleforceIQ), MetaMind, Implisit, PreductionIO, and TempoAI. The firm now has a team of 175 data scientists “stitching together this amazing platform,” said Benioff.
“The new platform will “democratize artificial intelligence” and “make every company and every employee smarter, faster and more productive,” continued Benioff. “This is going to be a huge differentiator and growth driver going forward as it puts us well ahead of our CRM competition once again.”
The new platform infuses their sales, cloud, and marketing platforms with AI capabilities for “anyone” regardless of their role or industry. According to Salesforce, Einstein lets employees “use clicks or code to build AI-powered apps that get smarter with every interaction.”
Einstein is positioned as having your own data scientist focused on applying AI to customer relationships. Einstein has access to a broad set of intelligence including CRM data, email, calendar, social, ERP, and IoT to “deliver predictions and recommendations in context of what you’re trying to do. In some cases, it even automates tasks for you. So you can make smarter decisions with confidence and focus more attention on your customers at every touch point.”
Several predictive analytics companies used the launch to shout, “hey wait, we’ve already mastered AI for sales and marketing.” LeadSpace CEO and former Salesforce CMO Doug Bewsher stated, “B2B marketers need a complete solution that works across multiple channels, in their existing marketing stack.”
“Bad data is the Achilles heel of AI,” continued Bewsher. “AI is only as good as the data available to it. Marketers who want to get the full benefit of AI need to address their data problems first, or they’ll see the same diminishing returns as with traditional marketing automation.”
Shashi Upadhyay, CEO at Lattice Engines was a bit more diplomatic in welcoming Einstein. “After having led the market for several years, we are really excited to see the mainstream attention shifting towards AI-based solutions for marketing and sales. The Einstein announcement from Salesforce is a great step forward, as it will serve to educate the market and signal that predictive solutions are here to stay.”