One of the most important SalesTech trends, besides the emergence of ChatGPT, is the rapid incorporation of engagement datasets alongside intent datasets for prioritization and messaging.
A few years ago, we saw the emergence of intent data sets such as first-party web visitor tracking, second-party product review site research, and third-party B2B media research. Initially, this content was integrated into MAPs, ABX platforms, and CDPs, but it was not well integrated into SalesTech. We are now seeing intent data being integrated into SalesTech platforms in a simplified fashion (e.g., High Intent Topics in CRM profiles and Slack alerts) that is digestible for sales reps.
However, intent data only indicates whether a company is in-market, not whether the buying committee is considering your offering or seriously engaged with your sales team. This intelligence comes from a new category of engagement data captured from digital interactions between the revenue team (sales, marketing, and customer success) and the buying committee. Engagement intelligence consists of both traditional digital interactions (e.g., clickthroughs, downloads) and Natural Language Processing (NLP) analytics derived from sales and buying team activities.
NLP helps RevTech platforms determine who is interacting with your firm. It also analyzes buyer sentiment, buyer concerns, deal health, and risk flags. The primary sources of engagement data are emails, recorded phone calls, and recorded meetings. However, any digital interaction between buyers and sellers can be captured such as activity in digital sales rooms, webinar attendance, chat messaging, and scheduled meetings. I anticipate that customer support platforms will also be tapped for engagement data to help gauge churn risk and friction during product trials.
Engagement data indicates whether a deal is on track and what issues could result in lost deals or pushed out pipeline. For example, engagement data assesses whether:
Discussions are single or multi-threaded
Key decisionmakers are involved (e.g., has a security review been performed or has legal been included?)
Competitors have been mentioned
Pricing concerns were raised
Follow on meetings have been scheduled
Meetings had a positive flow or were dominated by the sales rep
In short, engagement data provides sales reps and managers deal health and risk analytics that improve forecasting and ensure that deal risks are quickly mitigated. And as interactions are digital, managers can discuss these issues during one-on-ones or offer quick tips on next steps. They can even review the discussion associated with the risk and identify skills and knowledge gaps for coaching.
Nektar’s Insights Hub details buyer-seller interactions, leading indicators, buying committee engagement, MEDDIC adherence, etc.
The interesting thing about intent and engagement data is they are highly complementary with each other. Operations teams should be looking at integrating intent data alongside engagement data. Intent data is valuable for identifying who and when to reach out to ideal customers. However, once a relationship is established, the focus shifts to engagement data for monitoring deal health. After a deal is signed, both engagement and intent data are in play. Intent data identifies cross-sell opportunities and churn risk through second and third-party intent topic monitoring while Engagement and Product Usage data evaluate adoption rates and potential implementation issues.
Engagement data and deal health analytics can be found in Revenue Intelligence services (e.g., Clari, Revenue Grid), Sales Engagement (e.g., Salesloft, Outreach, Groove), Conversational Sales (e.g., Gong, Chorus), Revenue Operations (Nektar), and Sales Enablement (e.g., Seismic, Bigtincan) platforms.
TruVoice can also be used for customer experience, competitive analysis, and churn analysis.
Primary Intelligence has collected win/loss intelligence through manual post-mortem deal interviews for twenty years. Automating the process both lowers the cost of intelligence collection and widens the scope of intelligence collected. Survey response rates are between 25% and 35% due to the request coming from the sales rep. Online surveys run ten to fifteen minutes and are dynamically adjusted based on the responses. TruVoice collects both qualitative and quantitative responses.
Primary Intelligence Buyer Insights provide a roll-up of win/loss insights at an account. Users can drill down to “direct evidence” from the interviews.
“We’ve taken the live interview model that we have honed over our entire existence and turned it into an online experience, where we call it an online interview. But it really is something that a respondent can do in ten minutes on a mobile device. There are some quantitative questions for them to record voice responses, which we then transcribe and…publish that data. And then we still do the live interview.”
“The value of win-loss-no decision analysis at scale is that you have continuous, near real-time feedback on a higher percentage of accounts for improved insights and confident strategy adjustments across all of your revenue teams,” said Primary Intelligence CEO Ken Allred. “But the biggest breakthrough is having the ongoing rep-by-rep, deal-by-deal intelligence to drive situational training and enablement. This eliminates the bias of reps providing their own feedback or requiring managers to review hundreds of hours of call recordings.”
Gong and Crayon Partnerships
Primary Intelligence recently partnered with conversational sales platform Gong to integrate in-cycle deal intelligence. Primary Intelligence delivers a daily log of competitive mentions to sales reps, providing them with battlecard links and links to call transcripts that help them write follow-on messaging that parries competitive statements. The Gong integration ensures that conversational intelligence from deals is available alongside post-deal analysis, providing a clearer view of why deals are won or lost, comparative strengths and weaknesses versus competitors, and improved messaging.
“Our approach in the last five years or so has been [asking] ‘How can we take the operational lift and automate as much as possible?” explained Primary Intelligence President Nick Siddoway to GZ Consulting. “Now it’s a process that begins in Salesforce or whatever CRM you’re using.”
The Performance Gaps view displays competitive strengths and weaknesses as seen by buyers and displayed by priority.
Primary Intelligence also recently partnered with Competitive Intelligence Platform Crayon, marrying competitive and win/loss intelligence in a common platform. The joint solution “helps teams better understand their competitors.”
“In today’s competitive climate, competitive intelligence and win-loss analysis are essential for B2B companies looking to increase win rates,” stated the firms. “While win-loss interviews and surveys with buyers are filled with critical competitive insights, getting these insights updated into competitive intelligence deliverables, such as battlecards, has traditionally been a slow, manual process — until now.”
Crayon and Primary Intelligence highlight competitor offerings’ relative strengths and weaknesses, providing improved positioning for sales and marketing teams. A separate pricing module helps debunk sales rep claims that deals are regularly lost on price, providing a more accurate view of deal loss. By helping differentiate offerings and identify why deals are won or lost, vendor offerings become less price sensitive.
Primary Intelligence also provides views at the rep level, providing insights into the strengths and weaknesses of reps and where they would benefit from coaching.
“The future of sales enablement is providing custom, rep-specific coaching in the flow of work. Ideally, those recommendations are based on actual performance feedback from real customers,” said Erik Peterson, Chief Executive Officer of Corporate Visions. “The acquisition of Primary Intelligence will enable us to make invisible problems visible and then provide personalized coaching to individual reps and revenue teams based on how buyers and customers respond.”
“What better evidence that your strategies and spend are actually working as intended than actual customer feedback connected to wins, losses, and no decisions, as well as renewals and expansion cycles?” Peterson added.
B2B DecisionLabs
The acquisition provides Corporate Visions with 100,000 buying decisions spanning twenty years, which will be incorporated into its B2B DecisionLabsresearch and advisory business. Corporate Visions, which positions itself as a Decision Science company, calls Primary Intelligence its “fourth lab.” B2B DecisionLabs incorporates behavioral research, brain studies, and field trials, alongside customer feedback.
TruVoice customer feedback is Corporate Vision’s latest B2B DecisionLabs laboratory.
“We will engage our B2B DecisionLabs research director, Dr. Leff Bonney, co-founder of the Florida State University Sales Institute, to effectively leverage the incoming data points into insights using all applicable and appropriate academic research-based approaches, tools, and techniques,” explained Tim Riesterer, Chief Strategy Officer at Corporate Visions and Chief Visionary at B2B DecisionLabs, to GZ Consulting.
“Our expectation is that this steady flow of buyer-driven deal insights will completely distinguish B2B DecisionLabs among other research and advisory firms who rely on their subscriber clients to provide data snapshots and self-reported survey responses to formulate their industry insights,” continued Riesterer. “This will be in addition to our completely unique brain study lab and ongoing field trials with actual clients.”
The Primary Intelligence dataset provides a deep set of historical and cross-industry data that captures deals both in progress and after closing. This research complements its other three research laboratories.
“This will give our advisory clients even more confidence in the B2B DecisionLabs recommendations compared to opinion surveys and moment-in-time snapshots of data,” said Riesterer. “It will also mean we can provide more reliable tools than you otherwise get from peer communities that only curate unexamined personal experiences and unsubstantiated claims of expertise.”
“This ongoing flow of customer-sourced data will also be used to continually expand and enhance our revenue growth services to ensure Corporate Visions’ clients always have access to the industry’s best and most updated intellectual property,” Riesterer added.
Corporate Visions offers science-backed revenue growth services for sales, marketing, and customer success. Along with hosting conferences and training, Corporate Visions helps firms “articulate value and promote growth” in three ways:
Make Value Situational by distinguishing your commercial programs between customer acquisition, retention, and expansion.
Make Value Specific by creating and delivering customer conversations that communicate concrete value, change behavior, and motivate buying decisions.
Make Value Systematic by equipping your commercial engine to deliver consistent and persistent touches across the entire Customer Deciding Journey.
By April, Corporate Visions plans to combine automated win-loss-no decision customer feedback with automated skills coaching and customer messaging content from Corporate Visions. Riesterer intends to launch the “first fully automated, situational enablement solution that identifies rep-by-rep weaknesses based on actual customer feedback, to direct specific, custom coaching videos to help address these challenges – in the rep’s flow of work.”
This vision shifts sales rep training from “just-in-case” event-based generic classroom training to “just-in-time” situational coaching and enablement that is customized to each rep and deal. This training will be “always on, deficit-based situational coaching and enablement” that does not require managers to “listen to a bunch of calls or read a lot of feedback and then formulate a custom coaching plan.”
Data Anonymization
Corporate Visions has already considered which data can be employed for aggregate analytics. Research protocols are subject to Institutional Review Board (IRB) review and approval. Data will only be available for aggregate analysis with the consent of customers. Unique identifiers are stripped from the data and replaced with arbitrary data identifiers, and no individual customer’s data will be published.
B2B DecisionLabs has “partnered with Florida State University as the primary means of data analysis and have taken the steps as outlined in GDPR protocols regarding ‘Information Processors’ to ensure that data is passed to FSU without any unique respondent identifiers,” explained Bonney. “To decrease any risk of inadvertent identification of a customer in the data, Primary Intelligence will assign the ‘ID number’ to customer data and then pass [it] to the B2B DecisionLabs and FSU research team, who will have no input or insight into how data ID numbers are assigned. Additionally, Primary Intelligence will remove any data fields that may be used to ascertain the identity of any one customer.”
The FSU IRB will review Primary Intelligence’s anonymization protocols.
Real-time Coaching
Managerial deal coaching “just doesn’t happen and won’t happen at scale,” remarked Riesterer. Furthermore, “because the system continues to run and generate customer deal feedback, you will be able to monitor, measure, and modify enablement interventions on the fly to see the impact and make continuous appropriate adjustments.”
Thus, the merged company will combine neural research concerning purchasing behavior and buyer studies, with in-the-moment situational coaching tailored to each rep and deal.
Both services provide keyword intent, with Standalone Intent delivered to third-party platforms. (Source: Demandbase)
ABX PlatformDemandbase, which also offers a data cloud and sales intelligence solution, rolled out its keyword intent dataset for third-party platforms. Demandbase Intent is available both inside the Demandbase One platform (embedded) and delivered to other platforms (standalone). Demandbase Intent joins Demandbase’s other Data Cloud assets, including firmographics, technographics, and contacts.
Standalone Intent, which supports 375,000 keywords and ingests 18 billion daily signals, provides buying signals for predictive models, data stores, and analytics. Data is delivered via API, cloud delivery, or CSV flat files.
New keywords are added weekly, with historical intent maintained for twelve months.
Demandbase Intent helps marketing teams target in-market accounts and refine their messaging. Furthermore, Demandbase Intent can trigger campaigns, avoid churn, and expand accounts.
“Our intent takes multiple sources into account, providing a much stronger and more accurate signals than others in the space,” said Demandbase VP of Product and Industry Marketing Jackie Palmer. “By using Demandbase Intent, data scientists, corporate strategists, and sales and marketing analytics professionals can build and improve their predictive models, helping them to better understand buyers’ goals and navigate the anonymous buying journey. As they identify patterns, trends, and opportunities, they can be more precise in prioritizing accounts and gaining deeper insight into their revenue potential.”
Demandbase claims that its keyword library provides superior targeting compared to taxonomically-based intent datasets, allowing vendors to target niche industries and segments, track competitor offerings, and dovetail on partners’ intent. Furthermore, customers can add new keywords “to fit their needs, whereas other intent providers limit customers to a finite, predefined list of topics.” They can then feed keyword intent to their data lakes, data warehouses, or business intelligence platforms, making the intent data available to data scientists for propensity-to-buy models.
Palmer told data scientists, “What you can do is build your…propensity-to-buy models, all the different things you need for predictive analytics around your account intent activities. You can stream that directly into your CRM systems, marketing automation systems, or any go-to-market systems that you need to.”
Demandbase Intent can be used alongside other intent datasets. Demandbase One also supports Bombora’s third-party intent, G2 second-party technology research intent, website visitor intelligence, and other datasets licensed by its Demandbase customers.
“Our mantra is the more intent data, the better,” Palmer explained to GZ Consulting. “So, that’s why within the platform, we always integrate with Bombora, G2, etc. But this is now for standalone people that may not want the Demandbase platform but also want to add additional concepts of intent into their data lakes [and] data warehouses.”
Furthermore, keyword intent is “totally complementary” to taxonomic intent data sources.
The Demandbase AI assesses keyword usage and the age of the article (older articles provide higher relevance), related articles the user has read, and “rare and hyper-qualifying keywords and themes to identify personas and buying committee roles.”
“We track the relevant articles,” explained Demandbase Data Cloud Product Marketer Imran Ahmed to GZ Consulting. “If you want to look at how the market does it, they do it through co-ops. They do it through metatags. We’re doing it through articles that help us identify not only the keywords but help us gather all the users looking at those keywords across the web. That brings up signal relevance and helps us get more granular and more exposure.”
Demandbase Intent does not look at Google search terms but looks a layer deeper at which articles are being viewed.
“Demandbase intent data is based on years of AI research and delivers more breadth and relevance than any you’ll find anywhere else. Why? Because we own the technology to identify anonymous accounts and pair that with our direct access to the bidstream — the source for the most intent signals. Then we beef up the relevance of those signals using a combination of AI and natural language processing.”
Standalone intent is priced per keyword.
Standalone intent has been generally available since December and already has several clients. However, due to the calendar, the firm held off on announcing the service until late January.
Demandbase Intent by the Numbers (Source: Demandbase)
“We view this acquisition as a great opportunity for both Volaris and ClickDimensions to provide innovative solutions for the ever-evolving MarTech and sales enablement industries,” said Jay Hoffman, Group Leader at Volaris. “I’m eager to work alongside ClickDimensions’ leadership team to propel their strategy forward and continue to serve their global footprint of customers and partners. We’re pleased to welcome ClickDimensions to the Volaris Group.”
ClickDimensions Marketing Automation supports email marketing, campaign automation, surveys, events, landing pages, forms, SMS, and Social. While the company now offers marketing automation and sales engagement, its long-term plans are to provide a “full RevTech solution” built on the Dynamics platform. This future platform and services organization will be built both organically and via acquisitions. It will support embedded AI for sentiment analysis, next-best actions, opportunity scoring, and activity capture.
ClickDimensions is a Microsoft VAR and consultancy with a global footprint across 76 countries and 3,500 customers. Its solutions integrate with Microsoft Dynamics CRM and Sales. Half of its revenue is derived in the EMEA region, with most of the rest in North America. It serves a broad set of industries, with no segment representing over 25% of its customer base.
ClickDimensions also offers consultancy services that provide “fractional access to skilled resources that are in short supply.” Services include onboarding, training, and execution; demand generation; customer data services; and marketing operations.
“With the strength and global scale of Volaris behind us, we envision a bright future for ClickDimensions and the customers and partners we serve,” stated Mike Dickerson, CEO at ClickDimensions. “We look forward to continuing development of our product suite and further solidifying our long-term commitment to the Microsoft ecosystem, innovation, and SMB market.”
ClickDimensions will continue as an independent organization.
“Small to medium businesses are just as keen as large enterprises to acquire and build deep, loyal customer relationships, and ClickDimensions has put the capability to do so into the hands of thousands of companies worldwide,” said Rob Palumbo, Managing Partner at Accel-KKR, which invested in ClickDimensions in 2016. “It has been gratifying to help ClickDimensions grow just as the company itself has helped thousands of small to medium businesses grow and expand. We look forward to cheering ClickDimensions and Volaris from the sidelines.”
Happy New Year. While off on vacation last week, I published an interview with Salesloft SVP of Product Management Frank Dale concerning Ethical AI. He joined Salesloft in November 2019 when Costello, the opportunity management firm he founded, was acquired by Salesloft. He has served as either CEO or COO at several investor-backed software companies, including Compendium, which Oracle acquired.
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.
SalesLoft CEO Kyle Porter has long emphasized authenticity and personalization in sales conversations. Do you see Generative AI potentially undermining trust?
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.
Dutch lead generation vendor Leadinfo is acquiring WebProspector.de, a German visitor tracking company, with the deal closing on November 1. Deal terms were not disclosed.
The acquisition expands Leadinfo’s presence in Germany. It acquired Leading Reports in April and opened an office in Düsseldorf in July to improve its sales and support in German-speaking markets.
Leadinfo claims to be the market leader in Benelux and the most prominent international provider in the D-A-CH region. It consolidated its position in the Netherlands over the past year with the acquisitions of Leadexpress (December 2021) and LeadElephant (January 2022).
Leadinfo supports over 3,000 customers in Benelux, D-A-CH, Great Britain, and Scandinavia. Customers include Lavazza, Quis Machinery, Channable, and Creditsafe.
Both Leadinfo and WebProspector.de have focused on their partner networks, with Leadinfo supporting over 1,100 partners.
WebProspector.de clients will migrate to the Leadinfo platform and benefit from a broader set of integrations. Leadinfo supports over fifty platforms, including Slack, Salesforce, Microsoft Dynamics 365, HubSpot, and Salesloft.
CRM integrations are bi-directional, with Leadinfo performing duplicate checking against lead and account records. When sending to CRM, companies may be added as Lead or Account record types. Additionally, company profiles include deal and task information gathered from CRMs, and users can create new deals and tasks from Leadinfo.
team.blue visitor information displayed in the Leadinfo service.
The Leadinfo platform supports a visitor inbox, real-time lead alerts, web forms, visitor browsing recordings, and triggered workflows. In addition, its programmatic functionality supports Google Ads and LinkedIn retargeting.
Liquid Content, its site personalization functionality, adjusts websites based on firmographics. As a result, marketers can customize the text, video, content, and images presented to the visitor, allowing them to segment their website presentation.
Leads are enriched by IP address matching against a reference database of 220 million global businesses. Firmographics are gathered from global registry filings and web crawling. Firmographics include address, phone, year founded, industry codes (US SIC 87 and local codes), sizing data, company logos, social media links, business descriptions, etc. Legal information includes the registration number, entity type, ultimate parent, group size, and employees in the group. Business descriptions are generally available in the local language. Other information includes a pinned Google map, page view information, Leadinfo’s lead score, and Leadinfo tags.
Leadinfo company profiles include a proprietary lead score based on visitor behavior. However, lead scores do not yet adjust for firmographic fit; thus, a company may score high based on behavior but may not be a qualified lead.
Leadinfo displays HubSpot Deal and Task information. Sales reps can also create new tasks and deals from Leadinfo.
In July, Leadinfo was acquired by team.blue, a European cloud services provider with an office in fifteen European countries and Turkey. CEO Han Kleppe and the rest of the management team continue to run Leadinfo as an independent subsidiary.
Leadinfo is growing rapidly and was recently named to Deloitte’s Fast 50 technology list for the Netherlands.
Avoma added scheduling and reminder functionality to its conversational intelligence platform.
Conversational Intelligence vendor Avoma added a meeting scheduler to its service. The new functionality streamlines pre-meeting workflows with automated booking, meeting reminders, and “agenda alignment” before the meeting.
Avoma Scheduler supports multiple links for “various purposes and durations based on your availability, time zone, [the] context you want to capture, and more.”
Avoma supports Outlook and Google Calendar using an OAuth integration.
Unifying the meeting process reduces toggling between applications and the likelihood of missed context or action items.
According to Avoma, no-shows and cancelation rates average up to 40% of meetings, resulting in wasted time, longer deal cycles, and reduced conversion rates. Thus, reminders allow for fewer no-shows and automated rescheduling.
Avoma argues that the meeting process is disjointed with separate tools for
Scheduling meetings
Setting agendas
Taking notes during meetings
Recording, transcribing, and analyzing the call
Sharing meeting notes after the call
Syncing meeting intelligence with the CRM
Different departments and customers have different tech stacks, so meeting intelligence can end up siloed. Furthermore, juggling multiple tools leads to lost context and action items slipping between the cracks.
“There are already several scheduling tools available, but they are point solutions, requiring people to navigate several tools before, during, and after a meeting. Our approach is to help you save costs as well as avoid the multiple tool fatigue by simplifying end-to-end meeting workflows. This helps you offer a seamless experience to your customers.”
Avoma CEO Aditya Kothadiya
SEPs Outreach and Salesloft and chatbots from Drift and ZoomInfo also offer integrated meeting scheduling within their conversational AI platforms.
The new 6Sense Conversational Email Campaign Builder
At the 6sense Breakthrough Conference, 6sense unveiled its new Conversational Email module. Conversational Email employs AI models, psychographics, technographics, intent data, and predictive analytics to deliver “hyper-personalized, hyper-relevant emails to qualify and convert leads to sales meetings.”
Conversational Email supports campaigns across functions and the buyers’ journey. Marketing can send “personalized peer-to-peer nurture emails from multiple AI personas, and Operations can systematize meeting conversion and scheduling for qualified accounts. Sales teams can operationalize best practices and “scale across segments much easier.”
Marketing can also deploy Conversational Email to revive dormant accounts, qualify and convert inbound leads, and boost webinar and event registrations, participation, and follow-up.
6sense claims that beta customers enjoyed a 50% reduction in deal cycle times for marketing-sourced opportunities and a 1.5X increase in average deal size.
“A look at the basic process Conversational AI uses to nurture leads and turn them into sales opportunities.”
AI tools include a Visual Conversation Flow Builder, Email Assistant, and Qualification and Sales Handover. The Email Assistant employs AI to “effortlessly engage the correct buying team members, schedule follow-up based on out-of-office replies, book meetings with the right owners, and send targeted content.”
Dynamic content consists of multivariate blocks tailored to specific keywords, segments, personas, or products for personalizing messages.
Additionally, Conversational Email supports automated workflow triggers based on account buying behavior and contact activity.
“This launch is one of our most significant product updates yet,” said 6sense CTO Viral Bajaria. “Every company has overlooked and underworked, yet high-quality, leads. Critical outreach happens too late or simply never at all, which leads to missed revenue opportunities. The early results from customers in our beta program using 6sense Conversational Email demonstrates the impact: reduction in deal cycles, increase in average deal size, and new pipeline generated. While others in the market focus on sending emails, we are the first to focus on writing relevant emails and responding in ways that lead to more quality pipeline, more efficiently.”
6sense Contextual Targeting improves engagement and recall.
6sense also rolled out Contextual Targeting, which places ads alongside similar digital content. A study by Spark Neuro found that contextually relevant ads generate 43% greater engagement and double the ad recall.
In addition, 6sense offers over 100 new custom contextual topics for B2B marketers. “Advertisers won’t need to settle to use contextual segments that are largely designed for consumer marketers,” stated 6sense.
6sense claimed three benefits to Contextual Targeting. It:
Respects user privacy by targeting audiences without using behavioral or data profiles
Provides ready-to-use contextual topics built specifically for B2B
Eliminates wasted ad spend on buyers that aren’t likely to engage
Another new feature is Campaign Forecasting which estimates a campaign’s daily audience, daily impressions, and daily spend. Campaign Forecasting helps marketers assess campaign budget and reach before launching the campaign.
6sense also announced at Breakthrough that a sales intelligence data application would be released in Q1. 6sense, which bought data company Slintel last October, will offer global contact data, intent data (3rd-party data, anonymous web visitor insights), firmographics, psychographics, and technographics. In addition, the “intuitive” UX will provide “actionable insights and [an] orchestration layer necessary to identify, prioritize and engage with accounts in-market.”
“With B2B buying committee members increasingly choosing to remain anonymous through most of their journey, sellers need insight to earlier signals for their sales outreach to be effective. With our latest advancement in 6sense Sales Intelligence, we bring industry-leading intent data, contact data, and AI insights to help sellers efficiently identify priority prospects, personalize their interactions, and take timely action with ease to drive meetings and conversion of pipeline to revenue.”
Amar Doshi, SVP of Product & UX at 6sense
The Breakthrough Conference was billed as “an inside look at best practices to leverage AI and big data to accelerate revenue generation efficiently.”
“The Proceed with Confidence focus of our 2022 Breakthrough event couldn’t be more timely. We heard from more than 50 sales and marketing speakers at this year’s event that 6sense Revenue AI is the must-have competitive edge they can’t grow without,” said 6sense CEO Jason Zintak. “B2B companies are losing revenue opportunities and leaving money on the table. To deliver a better buying experience in today’s selling environment, it’s imperative to leverage AI along with pre-intent data, intent data, and predictive analytics to know which accounts are in market to buy your product or service, when and how to target them, and what messages to deliver to best engage.”
“Being able to provide our customers with 1st-party intent data on the largest healthcare technology and information audience on the web is a true game changer in our industry,” said Sean Brooks, Co-Founder of Xtelligent Healthcare Media. “Sales and marketing teams will now have direct access to entire healthcare buying teams, including Clinicians, Line of Business, and IT Decision-Makers, to find more opportunities and accelerate technology deals.”
Priority Engine for Healthcare Highlights for Top Accounts.
Priority Engine for Healthcare supports over 400,000 opted-in healthcare contacts, including Providers, Health Systems, Payers, Pharmaceuticals, Life Sciences, Accountable Care Organizations, and Federal/State Healthcare Agencies. TechTarget claims that 90% of the US healthcare system is covered. Xtelligent said its audience contains “70% Business & Finance Executives and Clinicians who have critical involvement across healthcare technology purchases that are becoming increasingly complex.”
The service is available for ten segments:
Analytics
Electronic Health Records (EHR/EMR)
Healthcare Security & Compliance
Health IT Infrastructure
Life Sciences
Patient Engagement
Payer
Pharma
Revenue Cycle Management
Telehealth
Intent data is gathered from 14 B2B healthcare media properties. HealthTech sites include EHR Intelligence, Health IT Security, and Health IT Analytics. Clinical research and medical sites include LifeSciences Intelligence, PharmaNews Intelligence, and HealthPayer Intelligence. Over 400 healthcare topics are covered, with roughly half focused on Healthcare Tech.
Priority Engine for Healthcare also offers visitor intelligence and content view tracking. Healthcare intent data from BrightTALK, TechTarget’s digital webinar and event platform, is also included.
Xtelligent, also based in Boston, has a similar content model to TechTarget. When acquired last year, it had over 1.5 million healthcare-related visitors per quarter across ten websites, but lacked a platform for enabling its contacts and intent datasets.
Xtelligent content focuses on healthcare-related software and technology decisions, aligning with TechTarget’s enterprise software focus but in an adjacent market. Xtelligent topics include telehealth, healthcare analytics, revenue cycle management, healthcare IT security, and electronic health records.
The new intent topics identify HealthTech content consumption at the account and prospect levels, gathered from TechTarget’s 150 enterprise and health technology websites.
“By expanding the amount of permission-based, relevant 1st-party purchase intent data our customers have access to and delivering a full suite of marketing, sales, and go-to-market services to engage real buyers, we help companies of all sizes achieve better results at scale in this market,” said Michael Cotoia, CEO, TechTarget. “As a leader in coverage of B2B enterprise tech for more than 20 years – combined with working very closely with our almost 3,000 customers – TechTarget has unique visibility into the buying dynamics across every major sector of the market. Our experience positions us well to bring our model to adjacent vertical markets with similar attributes to enterprise B2B tech – long/complex-sales cycles, large purchases, multiple members of the buying team, and a strong need for 1st party data to enable marketers and sellers – just as we have done in healthcare.”
Nektar.AI supports automated activity capture and CRM sync across the full customer lifecycle.
After two years in stealth mode, Revenue Operations solution provider Nektar.AI was unveiled earlier this month. Nektar looks to solve the “CRM data leakage” problem whereby critical lead, contact, and opportunity data is omitted or decays. For example, Nektar recognizes meeting attendees missing from the CRM and automatically creates contact records that include email, direct dial phone, title, and buying committee role. Out-of-date or missing data negatively impacts both the sales process and operational functions such as analytics, automated recommendations, and pipeline forecasting.
Nektar offers a no-code platform that applies natural-language processing against email, calendar, chat, and social touchpoints, capturing revenue activity data across the full customer lifecycle and syncing it with the CRM. Automated activity capture improves rep productivity, allowing sales professionals to focus on selling instead of data entry and updating.
“Sales teams depend on their CRM data to gain insights into team productivity, pipeline insights, and revenue forecasting. Despite a CRM being an important system of record for modern go-to-market teams, it still grapples with the problem of poor user adoption and missing data. As per estimates, 40-50% of sales activity data remains missing from a CRM, while 27% of the data that’s available in a CRM decays every month. This leads to major data and productivity leakage,” stated the firm.
Furthermore, deals are becoming increasingly complex, with larger buying committees and sales teams communicating across an expanding array of sales channels. While most of these conversations are now digital, they take place across disparate platforms and channels, resulting in data fragmentation. Nektar’s mission is to collect this fragmented intelligence and feed it into the CRM, making it available to the revenue team without requiring sales resources to key this intelligence.
“There are a lot of reporting and analytics solutions out there. Other solutions have been investing primarily in the downstream problem with respect to visibility and analytics, an important problem to solve. But the core problem actually starts upstream, which is where activities are taking place and where data is being generated. A lot of that data doesn’t make its way into CRM, which actually results in downstream problems. If there’s poor data in, you will have poor insights available,” explained CEO Abhijeet Vijayvergiya to GZ Consulting.
“We found our product-market fit when we found that more than 50% of critical revenue activity data is not going into CRM,” continued Vijayvergiya. “This data leak results in productivity leaks, and that results in revenue leaks.”
Furthermore, as firms make staffing cuts, “they’re trying to do more with less” and losing implicit knowledge that never made its way into the CRM. Nektar allows companies to recover much of this lost activity and contact intelligence, boosting a firm’s ability to manage revenue operations during a recession.
“There are tools like Gong and Clari and some other forecasting solutions which provide good insights,” expanded Vijayvergiya on Nektar’s value proposition. But these systems are not resolving the data leakage problem.
Nektar claims 95% accuracy in its data capture and syncing processes. The service can go live in ninety minutes and begins providing time to value in three days as it gathers both historical data missing from the CRM and populates it with ongoing activity capture.
Nektar operates in the background, collecting and syncing data. Thus, there are no training sessions or additional UIs to learn. Sales reps do not need to toggle to other platforms, and their data entry work is significantly reduced.
“Nektar plugs the CRM data leakage without a user lifting their finger. We basically eliminate the need for user adoption and give all the time back to salespeople to go and sell while relieving their administrative burden,” said Vijayvergiya.
While there have been third-party solutions to populate and enrich account and contact data records for over a decade (e.g., Dun & Bradstreet, ZoomInfo), these vendors were blind to the demand unit unless a sales rep entered all members. Nektar is complementary to third-party DaaS providers, Revenue Intelligence vendors (e.g., Clari, Revenue Grid), Conversational Sales (e.g., Gong, Chorus), and business intelligence vendors (e.g., Tableau).
“We are aware that some of these solutions have their own activity capture system, but most of their activity capture solutions work in silos for certain sets of users who adopt their solution,” continued Vijayvergiya. “Users are not adopting the solution, or data loss happens anyway. A solution which we replace, more often than not, is Salesforce Einstein Activity Capture.”
Nektar supports email and domain exclusion lists to prevent mining confidential information (e.g., legal, investor relations, partner development).
Nektar is generally available as a native Salesforce solution, with HubSpot and Microsoft Dynamics on the roadmap. In addition, all of its system integrations are native, providing higher quality and performance.
Nektar.AI closed a $6 million seed round last summer, raising its total funding to $8.1 million. The round was led by B Capital Group, with Nexus Venture Partners, 3One4 Capital, and angel investors also joining.
Nektar has not disclosed pricing, but it is per user per month with SaaS-based pricing billed quarterly or annually.
Nektar is a fully remote company with 32 employees across seven countries with plans to hit fifty employees by the end of this year. Although emerging from stealth, it already supports over 1,500 users.
Nektar’s goal through year-end is to focus on its go-to-market strategy and North American hiring.
Nektar activity tracking at the account level
Data capture as an AI tool is becoming increasingly important. It probably isn’t a standalone offering but an underlying capability for populating the CRM with harvested digital intelligence, monitoring buyer engagement, and building out the buying committee. As such, it is core to both CRM data enrichment and revenue intelligence.
I have seen a series of data capture announcements from vendors of all sizes: big (Microsoft Viva Sales), medium (People.AI, Introhive), and small (e.g., Nektar, Winn.AI). I also covered People.AI and Winn.AI this week.
