B2B DaaS vendor Live Data Technologies closed on a $5 million Series A led by Entrada Ventures with “further participation from firms and individuals with deep backgrounds in data tech.” Previously, the firm was funded from revenue, but “we raised this round when we saw the opportunities made possible by overwhelming investor interest and support.”
“We have been behind Live Data Technologies since the company’s inception,” said Entrada Ventures Managing Partner and Live Data Technologies Board Member Jason Spievak. “The core technology has multiple compelling applications across industries and verticals. The accuracy and quality of job change event detection are mission critical to anyone making informed decisions.”
Live Data focuses on contact changes and ensuring accurate contacts in the CRM. It maintains data on 70 million contacts and 2 million companies, capturing 30,000 daily job changes from the open web. Live Data argues that mining this intelligence from the web ensures more timely contacts than traditional data collection methods.
Detecting promotions that create opportunities for success stories, upsell opportunities, and case studies.
“Each go-to-market strategy requires essential ingredients working together like a great recipe. But, even the most perfect strategy will fall short if the people you put your message in front of are no longer relevant, or you lack up-to-date contact records, to begin with,” blogged Director of Growth Jason Saltzman. “The bottom line is that business decision-making is done at the human level, and up-to-date contact-level data is essential for successful go-to-market programs.”
The firm will continue refining its DaaS offering and improving its data set and delivery. “Our Series A represents a confirmation that we are building something valuable,” said CEO J. Scott Hamilton. “We see it, our investors see it, and our customers see it. With all this recognition comes a pressure to take Live Data to the next level – and, in our eyes, pressure is a privilege.”
Attention, an AI-powered sales assistant, exited stealth mode and closed a $3.1 million seed round led by Eniac Ventures. Other participants include Frst, Liquid2 Ventures, Maschmeyer Group Ventures, Ride Ventures, and the founders of Ramp, Level AI, Truework, CBInsights, and Zoi. The new funds will be deployed towards advanced AI capabilities and market growth for the New York City-based startup.
Attention helps sales teams “overcome inefficiencies at every stage of the sales cycle,” including CRM hygiene, sales acceleration, and revenue growth. Attention accelerates sales rep ramp-up, drafts follow-up emails based on customer statements, improves forecasting, and automatically enriches Salesforce and HubSpot with post-call deal intelligence.
Attention maps discussions to custom CRM fields specific to standard sales methodologies such as MEDDIC and BANT.
Attention also provides real-time suggestions and question responses, “resulting in all sales representatives having higher rates of success and closed business.” Recommendations include product responses to technical questions and objections handling. The Attention AI determines common unsupported questions and builds battlecards based on sales responses. Battlecards can also be developed or edited by the sales enablement team.
One novel feature is the ability to query the meeting transcript with a question, allowing reps to summarize or revisit the discussion around key topics quickly.
Sales reps can share call snippets with managers or SMEs via Slack, allowing them to forward open questions in the voice of the customer.
“Attention is a game-changer. We’ve rarely seen any product like this in terms of efficiency gains and ramp-up acceleration. We’re also blown away by how fast they’ve been releasing new capabilities.” said Peter Santis, head of sales at RocketChat. Santis both licensed the service for RocketChat and participated in the seed round.
Founders Anis Bennaceur and Matthias Wickenburg founded competing AI software startups before joining forces in September 2021 to build Attention.
“We’re thrilled to partner with Anis and Matthias as they leverage the latest developments in AI generation and natural language understanding to superpower sales organizations,” remarked Hadley Harris from Eniac Ventures. “We love working with repeat founders and couldn’t be happier with the strong pull they’re already getting from the market.”
Attention supports communications platforms, including Gmail, Outlook, Slack, Teams, Meets, Zoom, and Zapier.
Attention’s initial customers are in the technology sales space, most commonly with 50 to 100 licensed sales reps.
According to Atlanta Inno, ABX Platform Terminus has completed all but $1.5 million of a $24 million raise. The round was smaller than its $90 million March 2021 round. Twenty-eight investors participated.
The round follows a May restructuring. At the time, the staffing cuts were estimated at 15% of its workforce.
In total, the firm has raised $140 million. It also closed on a $50 million debt facility with CIBC in Q4 2021 around the time it acquired CDP Zylotech.
The funding round has not been press released, and Terminus did not provide any comments to Atlanta Inno or GZ Consulting on the round.
Gartner recently named Terminus a Leader in its Q4 2022 Magic Quadrant for Account-Based Marketing Platforms. Terminus offers a “full range of ABM capabilities, including “broad support for channels including display advertising, retargeting, social advertising, website personalization, website chat, and tracking for email with personalized dynamic signatures.”
Gartner called out Terminus’ “strong and broad native channel support that includes advertising (display, retargeting, video, connected TV, and audio), site personalization, chat, and email tracking.
Gartner also praised Terminus’ “industry-specific playbooks” that “help customers implement use cases such as brand awareness, pipeline building, and acceleration, retention, and expansion.”
“The acquisition extends Bigtincan’s lead in AI-driven revenue intelligence by improving B2B sales organization’s ability to scale by delivering insights and recommendations directly to sales reps for better decision-making to increase revenue,” the firm told investors. “The SalesDirector.ai technology links people, activity, and engagement across the buyer’s journey to derive insights, including opportunity risk and relationship strength, and then makes intelligent recommendations. By capturing all sales, marketing, and customer success activity the technology drives actionable revenue insights required to make the right business decisions.”
Bigtincan stated that SalesDirector’s technology would improve its AI-powered insights capabilities and forecasting accuracy.
SalesDirector ingests data from GSuite/Gmail, Slack, Microsoft Office, and Microsoft Exchange and feeds contact data (e.g., Title, Department, Level, Phone) to Salesforce and Microsoft Dynamics. SalesDirector also captures Engagement Signals and writes them back to CRMs as custom fields:
Executive Engaged on Opportunity / Account
Single or Multi-Threaded Opportunity Relationship
Last QBR Complete / Next QBR Scheduled
Partner / Sales Engineer Engaged on Opportunity
Next Step / Meeting Scheduled
AI tools include stakeholder identification, individuals who are supporters or detractors, disengaged stakeholders, account risk scoring, sentiment analysis, and deal health.
“Every organization has to be more productive,” said Bigtincan CEO David Keane. “With the Sales Enablement market shifting towards a more holistic approach encompassing Revenue Enablement, we can deliver more value to our customers by providing full-cycle sellers with AI-driven recommendations on the next best actions based on intelligent sales analytics from SalesDirector.ai.”
SalesDirector pricing begins at $29 per user per month for activity capture. Revenue Insights pricing is not published.
Bigtincan will incorporate SalesDirector’s functionality into its product suite and phase out the SalesDirector brand.
“Traditionally, when we do small tech-focused acquisitions, they become part of the Bigtincan product suite, so that capability gets added to the existing platform we have,” said Keane. “We don’t tend to run these things as separate businesses, mostly because we miss out on some of the benefits for our shareholders of taking it all together and taking that technology and really getting in the platform. This is no different than our existing strategy – embed, connect it all together and make it something that our customers can choose to add. We do believe that it is all about choice, and we want people to be able to buy these technologies from us as additional value-added options. I think that’s going to be the case here as well.”
Bigtincan paid $1.2 million in cash and equity for SalesDirector.
The entire SalesDirector team is joining Bigtincan.
Bigtincan, listed on the Australian Stock Exchange, has acquired a series of RevTech companies, including ClearSlide, Brainshark, and VoiceVibes.
Dale earned a BA and MA from Valparaiso University with a concentration in ethics. He also received an MBA from the Kelley School of Business at Indiana University.
What experience have you had developing AI tools?
As the SVP of Product Management at Salesloft, I am working with our team to bring Rhythm, Salesloft’s AI-powered signal-to-action engine platform, to life. Rhythm ingests every signal from the Salesloft platform as well as signals from partner solutions via APIs, ranks and prioritizes those signals, and then produces a prioritized list of actions. The action list gives sellers a clear, prioritized list of actions that will be the most impactful each day, along with an expected outcome prediction. In addition to simplifying a seller’s day-to-day, it helps them build their skills by providing the context about why each action matters.
AI is becoming increasingly important in RevTech, with many of our interactions being mediated by AI. Where do you see AI having the biggest impact on Sales reps between now and 2025?
AI will enable significant improvements in both seller efficiency and effectiveness. The most obvious impact will continue to be automating away low-value, repetitive work. What will surprise people will be the rapid advance and adoption of AI to suggest next best actions to take and content to use in those interactions with buyers. A typical workday for a seller will see them greeted by a recommended list of actions to take each day. Each action will be prioritized based on where the seller sits in relation to their targets, with each action accompanied by suggested content where appropriate. For instance, I might see a suggestion to respond to an email from a champion in an in-flight deal. The recommendation will include suggested text for the response as well as a resource to attach to the email. That’s a future we are actively investing in at Salesloft, which is at the heart of our soon-to-be-released Rhythm product.
Same question, but looking further out to 2030…
As AI becomes more commonly deployed across the sales profession, buyers will experience a more consistent sales experience in each buyer-seller interaction. As this becomes more common, it’s going to raise the bar on what buyers expect from a sales experience today. That will put more pressure on sales teams to deliver consistently in ways that today may seem unreasonable but will be possible with AI assistance.
One of the key ways to raise the seller performance bar will be high-impact, tailored coaching. Manager time is a constrained resource, and seller coaching augmented by AI provides a path to realizing performance improvement without manager time constraints. We should fully expect AI to help coach sellers to hit their goals based on each seller’s unique profile. We can expect AI to evaluate the seller’s entire game (activities, conversations, and deal management) to identify the highest leverage areas each individual seller should focus on to improve. Some of the coaching will be provided by AI at the point of execution, like on a call or when writing an email, with the rest provided throughout the workday as recommendations.
What are the most significant risks of deploying AI broadly across the Sales Function?
Two areas come to mind. First, AI used without clear boundaries in a sales process can lead to problems. If you employ AI and automation capabilities, it should be to allow the user to be better armed to make a decision, not make it for them. AI tools should not replace the human touch but rather augment it. There’s a lot of pseudo-science tossed up around the topic of AI, but ultimately, humans understand the nuance of relationships better than machines. One of the ways to address that concern is to deliver models that not only provide a recommendation but can provide the insights that led to it; humans will better trust the model when making decisions based on those recommendations as well as know when to ignore the recommendation.
Second, there’s a privacy component as well. Companies may create AI models that share data about a particular buyer with other companies’ sales teams without said buyer’s knowledge. The buyer may know they shared their data with one company but have no idea that multiple other customers at this company are using that same data. Creating models with this type of function puts companies and sales teams in a high-risk zone that can tread on the unethical. It isn’t clear that building models in that way may be considered legal in the future. If you plan to deploy AI in a sales org, it’s important to understand how data is collected and used.
AI Models are only as good as the underlying training data. How concerned are you about biased models recapitulating discrimination? For example, emphasizing sales skills that are gender or racially biased when evaluating sales rep performance?
It is a legitimate concern. AI products are based on probabilities, not certainties. The recommendations you receive or workflow automations that fire happen based on the probability that the given recommendation or action is right. Not the certainty that it is right. In a good product, the model is correct more often than a human would be when faced with the same decisions. At times, this is because the model can evaluate a larger set of factors, and in some cases, it is simply that machines can apply rulesets at a higher level of consistency than humans.
One of the key determinants of the AI model’s value is the dataset upon which it was trained. If the dataset does not properly represent the real world, the model will produce results that are either biased or provide poor recommendations. We’ve already seen several examples of that with image editing software that didn’t include black-skinned people in the training dataset. This led to either poor outcomes or worse dehumanizing results when the AI product was used in the real world. If you plan to deploy AI in your business, you should ask the provider what precautions they take to prevent bias in their models. We are very intentional about removing factors that could lead to bias in our training datasets. Still, it isn’t something I see most technology companies paying attention to in the revenue tech space.
How do you curb racial and gender bias when performing sentiment analysis?
We take great care at Salesloft to remove things that would lead to discriminatory factors. For example, for our Email Sentiment model, one of the ways we prevent bias is by removing all mentions of people’s names within the email because that could provide clues to their gender, race, or ethnicity. We do that kind of preprocessing with any data we use in an AI model before we build our models.
One of our assets is our scale. We’re fortunate that we operate globally and are the only provider in our space with offices in the Americas, Europe, and APAC. As a result, we work with organizations of all sizes globally, including many of the world’s largest companies. That means when we build models, we have one of the largest datasets in the world for sales execution. This enables us to train models based on datasets with both breadth and depth. When we build a model, it is easier to train it in a way that fairly represents reality and includes safeguards to avoid racial or gender bias.
AI will increasingly be deployed for recommending coaching and mediating the coaching. What concerns do you have about replicating bias when coaching?
As with any AI product making a recommendation, the potential to make a recommendation with bias is a concern that needs to be addressed when building models.
We take our responsibility to avoid bias in any product we release very seriously. The revenue technology industry as a whole hasn’t demonstrated a similar commitment to avoid harmful bias as of yet. I don’t hear other companies talking about proactive steps to avoid it, but I think that will change. We’re monitoring potential governmental action in both the US and EU that will require companies to raise their standard in this area. It is only a matter of time before laws are passed that require companies to prevent unlawful bias in their AI products.
Sales activities are becoming increasingly digitized, a boon for revenue intelligence, training, and next best actions. What guardrails do we need to put in place to ensure that employee monitoring does not become overly intrusive and invade privacy?
Let’s start by recognizing it is reasonable for an employer to have insight into what work is getting done and how it’s getting done. On the other hand, getting a minute-by-minute record of how each seller spends their day is unreasonable, as is dictating every action the seller takes from morning until nightfall.
We have to start with the right first principles. I think we can all agree that humans have inherent worth and dignity. They don’t lose that when they go to work. The challenge is that we have some companies in the technology industry that forget that fact when developing solutions. When you forget that fact, I believe that you actually harm the customer that you’re trying to serve. That harm happens in two ways.
First, you lose the opportunity to realize the true potential of AI, which is to serve as a partner that enables humans to do what they do best…which is to engage with and relate to other humans. AI should not be used to make final decisions for humans or to dictate how they spend every minute of their day. Good AI solutions should be thought partners and assistants to humans. It’s Jarvis to Tony Stark’s Iron Man.
The second way overly intrusive technology harms companies that employ it is via employee turnover. It’s no secret that industries that offer low autonomy to employees suffer from high turnover. Most humans fundamentally desire a base level of autonomy; if that’s threatened, they leave whenever a good option opens up.
In short, if the seller is working for the technology instead of the inverse relationship, we’re on the wrong path.
In 2018, Salesforce CEO Marc Benioff argued that the best idea is no longer the most important value in technology. Instead, trust must be the top value at tech companies. How does trust play into ethical applications and AI?
We get to build the future we want to realize. We can either build a future that perpetuates the things we don’t like about today’s world, or we can build a future that elevates human potential. AI can be used to take us in either direction. That means what we choose to build with AI and how we build it should be a very value-driven decision.
We can absolutely build highly effective AI-powered solutions that elevate the people who use them and deliver tremendous business value. The people that believe otherwise simply lack the imagination and skill to do it.
What I love about our team at Salesloft is that we exist to elevate the ability of the people we serve and to enable them to be more honestly respected by the buyers they serve. In sales and life, the way you win matters. It matters to the people you serve on your revenue team, and it matters to your customers.
An emerging category of AI called Generative AI constructs content (e.g., images, presentations, emails, videos). It was just named a disruptive sales technology by Gartner. They stated that “By 2025, 30% of outbound messages from large organizations will be synthetically generated.” What risks do you see from this technology?
There are two immediate risks that come to mind. First, the messages need to be reviewed by a human before they are sent. The technology has made extraordinary leaps forward. I’ve spent a fair amount of time playing around with some of the tools released by OpenAI and others. The output is impressive and also, at times, very wrong. This goes back to the fact that the output is based on a probability that the answer provided is correct. You can get a very professional, persuasive email, or you can get something that approximates a professional email but won’t land well with your intended customer.
Second, it has the potential to make every outbound message sound the same. Generative AI doesn’t replace the need for human skill. It changes the areas of focus for that skill. Specifically, the opportunity for humans is to use Generative AI to help generate a higher volume and variety of ideas and then to edit and refine the output. The returns available to creativity are always high, but they become even higher when everyone is doing the exact same thing in the same way.
Having said that, I see tremendous potential in the technology and think if used properly it will be very valuable to revenue professionals.
Kyle is absolutely right. At the end of the day, a sale happens when a seller connects with a buyer to help them solve a problem. You can’t do that without authentic connection and trust. Generative AI should not replace that human connection, and I don’t think buyers want it to replace human connection. A close friend of mine was a sales leader at a now-public PLG-driven SaaS company. They added sales reluctantly. When they did, the company learned that buyers both bought more from them and were happier customers. That company now wishes it had added sales much earlier. How we interact with one another can evolve as technology evolves, but it doesn’t change the fact that humans are wired to connect with each other. I think emerging tools like Generative AI will help us be more productive, but they won’t replace the need for authentic human connection and trust.