AI for Sales Prospecting
Table Of Contents
- Leveraging AI for Identifying and Qualifying ICPs (Ideal Customer Profiles)
- AI-Driven Personalization: The Key to Effective Outreach
- Optimizing Outreach Timing and Channels with AI
- Measuring the Impact of AI on Your B2B Prospecting
- Overcoming Challenges in AI-Powered B2B Prospecting
- Putting AI-Powered B2B Prospecting into Action
- The Future of AI in B2B Prospecting: What's Next?
How to Use AI for Sales Prospecting - A Guide for B2B Sales Reps
Did you know that companies leveraging Artificial Intelligence for sales prospecting are seeing an average increase of 50% in lead generation and appointments, according to Harvard Business Review. We're not talking about marginal improvements to sales strategies here – this is a massive shift in the sales landscape.
But that's just the tip of the iceberg. A recent study by McKinsey found that early adopters of AI for sales prospecting are already seeing revenue increases of 3-15% across their full customer base. Not only that but over 40% of the highest-performing sales teams are already doing B2B prospecting using AI. The writing is on the wall – adapt or get left behind.
I've seen companies transform their prospecting processes, dramatically increase their conversion rates, and achieve growth they never thought possible. And trust me when I say this: we're just scratching the surface of what's possible.
Leveraging AI for Identifying and Qualifying ICPs (Ideal Customer Profiles)
The cornerstone of effective B2B prospecting is identifying and qualifying your Ideal Customer Profiles (ICPs). AI has transformed this process, making it faster, more accurate, and scalable. Let me take you through how we at LavaReach harness AI's power for this crucial first step.
Imagine you're a sales rep faced with a list of 100 potential accounts. Traditionally, you'd spend hours, maybe even days, manually investigating each company. You'd search websites, LinkedIn profiles, and news articles, piecing together a picture of each prospect. It's a time-consuming process that often leads to information overload and decision fatigue.
Now, what if you could feed that same list of 100 companies into AI-powered tools like ours at LavaReach. Within minutes, not days, you have a comprehensive analysis of each company. The AI has pulled information about their size, revenue, recent funding rounds, technology stack, key decision-makers, buying process and recent news or initiatives. It's not just faster, it's a ton of research that would be impossible for a human to achieve in a reasonable timeframe.
But here's where it gets really REALLY interesting. AI doesn't just gather information. It analyzes it. It can identify patterns and connections that might escape even the most diligent sales rep. For instance, it might notice that companies in a certain industry who've recently changed their tech stack are more likely to be in the market for your product. Or it could flag companies that have recently expanded into new markets, suggesting they might need your services to support their growth.
BUT the true power is unleashed when we apply predictive lead scoring. By ingesting your historical data – including information about deals you've won and lost – AI learns to predict which new leads are the most promising leads likely to convert. It considers factors like company characteristics, engagement history, technographic data, and firmographic changes. With data driven insights, you get a prioritized list of most promising prospects, allowing you to focus your energy where it's most likely to pay off.
But don't worry – we're not talking about replacing the human element in sales prospecting. Far from it. What AI does is free up your time from the tedious research tasks, allowing you to focus on what humans do best: building relationships and closing deals. If you're spending two hours on prospect research now, AI tools might cut that down to 20 minutes. That's an extra 100 minutes per day that you can spend on actual selling activities.
And let's not forget about the rapidly changing nature of business information. A company that wasn't a good fit last month might be perfect now due to a recent change in leadership or a new funding round. Our AI tools excel at pulling in real-time information, continuously monitoring news sources, company websites, and social media platforms. They alert you to relevant changes as they happen, ensuring that when you reach out to a prospect, you're always working with the most current information.
The beauty of AI in this context is its ability to handle complex, multi-dimensional queries. You're not limited to simple data points. You can ask the AI to identify companies that have recently expanded into a new market, are facing challenges that your product specifically addresses, have a technology stack compatible with your solution, and have shown interest in similar products or services. Try doing that manually, and you'd be drowning in spreadsheets and browser tabs.
At LavaReach, we've seen clients reduce their research time by up to 80% while simultaneously increasing the quality of their leads. It's not just about efficiency. It's about effectiveness. By focusing on the right prospects from the start of sales pipeline, you're setting yourself up for higher conversion rates and, ultimately, more closed deals.
But here's where it gets even more interesting: the more you use AI for this process, the better it gets! Machine learning algorithms improve over time as they process more data. So the longer you use an AI-powered prospecting tool, the more accurately it can identify and qualify your ideal customers.
AI-Driven Personalization: The Key to Effective Outreach
Now that you know how to use AI to identify and qualify your ideal prospects, the next challenge is reaching out to them effectively. This is where the concept of personalization comes into play, and it's an area where AI can be both a powerful ally and a potential pitfall if not used correctly.
Let me be clear here: we're not yet at a point where AI can effectively personalize cold outreach at scale. I know, I know - you've probably heard vendors claim they can do just that. But here's the thing: most fully AI-generated "personalized" messages still feel generic and impersonal. They lack that human touch that's so crucial in B2B relationships.
So, does this mean AI has no role in personalization? Far from it. We've just shifted our focus to what AI does best: in-depth research that enables human sales professionals to craft truly personalized messages.
Think about it this way: the key to effective personalization is having deep, relevant information about your prospect. This is where AI really shines. Our AI prospecting tools can gather a wealth of data about a prospect in minutes - recent company news, key initiatives mentioned in annual reports, the prospect's role and career history, company pain points and challenges, recent social media activity, or published content.
Having this AI-gathered information, your sales reps can craft highly tailored messages that resonate with each prospect. The AI does the heavy lifting of research, helping your sales team to focus on what they do best: crafting the perfect sales pitch.
But AI's role in personalization doesn't stop at research. While we don't recommend using AI to write your entire outreach message, it can be invaluable in generating ideas for personalization. For example, our AI can summarize a company's mission in a concise, engaging sentence, identify recent achievements or milestones worth mentioning, suggest relevant talking points based on the prospect's industry or role, or highlight potential customer pain points your product could address.
These AI-generated ideas serve as a springboard, inspiring your sales reps to create truly personalized and impactful outreach.
Moreover, AI can help tailor your messaging based on specific attributes of your prospects. Imagine being able to adjust your value proposition based on specific job titles, highlight different product features depending on the company's size or industry, or suggest relevant case studies or social proof based on the prospect's situation. This level of customization ensures that your outreach is always relevant and valuable to each specific prospect.
Yeah, this sounds great, but how do I implement this in my sales process? Here's how we do it at LavaReach:
First, we use our AI to gather comprehensive information about a prospect. This includes everything from basic company data to recent news, social media activity, and industry trends. Then, we have the AI analyze this information and generate a summary with key points of interest - things that might be relevant for outreach.
Next, this information is displayed to the sales reps in an easy-to-digest format. The AI might suggest a few potential talking points or angles for personalization. But - and this is crucial - we leave the actual writing of the message to our human sales reps.
This approach enables sales reps craft outreach messages that are deeply personalized and relevant, crafted with the insight and creativity that only humans can provide, but informed by the comprehensive research capabilities of AI. It's the best of both worlds!
Let me give you a real-world example. We had a client in the cybersecurity space who was targeting financial institutions. Our AI flagged a recent news article about a major bank facing a cybersecurity breach. It also noted that the prospect company had recently hired a new CISO and was in the process of overhauling its security infrastructure.
Our client's sales rep crafted a message that referenced the recent industry breach, acknowledged the prospect's proactive steps in hiring a new CISO, and suggested how their solution could support the ongoing security overhaul. The message was personal, timely, and highly relevant to the prospect's current situation. This resulted in a response within hours and a meeting scheduled for the following week.
This is the power of AI-driven personalization when done right. It's not about letting AI write your messages for you. It's about using AI to equip your sales team with the deepest, most relevant, actionable, and most up-to-date information possible, allowing them to craft truly personalized outreach that resonates with prospects.
Optimizing Outreach Timing and Channels with AI
Now that we've covered how AI can supercharge your research and personalization efforts, let's talk about another critical aspect of B2B prospecting: timing and channel selection. Because let's face it, you could craft the most brilliant, personalized message in the world, but if it lands in your prospect's inbox at 2 AM on a Saturday, it's probably not going to get the attention it deserves.
This is where AI really starts to flex its muscles. AI systems can analyze vast amounts of historical engagement data to predict the best times to reach out to specific prospects or industries. It can organize for you instead of you having to organize information yourself.
For example, you're targeting CTOs in the fintech industry. AI might notice a pattern that these busy executives tend to be more responsive to emails sent on Tuesday mornings, right after they've had their first cup of coffee and cleared out the weekend backlog. On the other hand, marketing directors in e-commerce companies might be more likely to engage with messages sent on Thursday afternoons when they're planning for the week ahead.
But it can get even more granular than that. AI can learn individual preferences over time. Maybe John Smith always checks his email first thing in the morning, while Jane White tends to catch up on correspondence after lunch. With AI, you can tailor your outreach timing not just to industries or roles, but to individual prospects.
Now, let's talk about channels. Email is just one of many ways to reach out to prospects. There's also LinkedIn, phone calls, Facebook, X/Twitter, even good old-fashioned snail mail. But how do you know which channel is best for each prospect?
Once again, AI can help here. By analyzing historical data and current trends, AI can suggest the optimal channel mix for each lead. For instance, it might recommend starting with a LinkedIn connection for C-level executives, followed by a personalized email, while suggesting a different approach for mid-level managers.
But here's where it gets really interesting. AI doesn't just look at historical data. It can also identify real-time triggers that signal the perfect moment for outreach. We call this "trigger-based selling," and it's incredibly effective.
Think of a scenario where you're selling a project management solution. Your AI system alerts you that a company you've been targeting just hired a new Director of Operations. This could indicate that they're scaling up their projects and might be in the market for a better management tool. Or perhaps it notices that a prospect company just announced a major expansion into a new market – a perfect time to reach out with your global communications platform.
These triggers could be anything from job changes within target companies, recent funding rounds, company expansions, or even mentions of specific pain points in company communications. By alerting you to these triggers, AI can help you reach out to prospects at the perfect moment, when your solution is most relevant to their current situation.
I've seen our clients increase their response rates by 30% or more just by optimizing their outreach timing and channel selection.
But AI doesn't just help you time your initial outreach. It can also optimize your entire multi-touch campaign. Because let's be honest, in B2B sales efforts, it often takes more than one touchpoint to get a response.
AI can help you orchestrate a seamless multi-channel approach. It might suggest starting with a LinkedIn connection, followed by a personalized email two days later, then a phone call the next week, and perhaps a piece of valuable content shared via X/Twitter after that. The AI continuously analyzes the response (or lack thereof) to each touch and adjusts the prospecting strategy accordingly.
This sounds great, but isn't it a bit... impersonal? Are we just letting AI dictate all our interactions? That's a valid concern 100%. But here's the thing: AI isn't replacing the human element in sales prospecting. It's enhancing it.
Think of AI as your incredibly efficient personal assistant. It's doing all the heavy lifting of data analysis and pattern recognition, freeing you up to focus on what humans do best: building relationships and closing deals.
The AI might tell you that Tuesday at 10 AM is the best time to reach out to a prospect, but it's still up to you to craft that perfect message or make that compelling phone call. The AI might alert you to a trigger event, but it's your expertise and human touch that will turn that opportunity into a sale.
In essence, AI-powered timing and channel optimization is about working smarter, not harder. It's about reaching out to the right person, at the right time, through the right channel, with the right message.
Measuring the Impact of AI on Your B2B Prospecting
Alright, we've talked about how AI can revolutionize your research, personalization, and outreach timing. But how do you know if all this AI wizardry is actually making a difference? After all, in sales efforts, the only results that really matter are the ones that show up on the bottom line.
At LavaReach, we're big believers in the power of sales data. But we also know that not all sales data is created equal. When it comes to measuring the impact of AI on your B2B prospecting efforts, you need to focus on the metrics that truly matter.
First up, let's talk about efficiency. One of the most immediate and tangible benefits of AI in prospecting is the time it saves. We had a client who was spending an average of two hours per day on prospect research. Within a week of implementing our AI tools, that time dropped to just 30 minutes. That's an extra an hour and a half every day that their sales reps could spend on high-value activities like actually talking to prospects and closing deals.
But it's not just about saving time. It's about what you do with that time. That's why you also need to track the number of qualified leads generated. With AI, you should be able to identify and qualify more leads in less time. We've seen clients double or even triple their number of qualified leads without adding a single hour to their workday.
Now, let's talk about engagement. After all, generating leads is only valuable if those leads are actually responding to your outreach. This is where we start to see the real power of AI-assisted personalization and timing optimization.
Email open rates are a good place to start. With generic, poorly timed outreach, you might be lucky to see open rates of 5-10%. But with AI-informed personalization and timing, we regularly see clients achieving open rates of 40% or higher. That's a lot more eyeballs on your carefully crafted messages.
But opens are just the beginning. What really matters is responses. We track positive response rates - that is, replies that express interest or ask for more information. With traditional methods, a 5% positive response rate is often considered good. But with AI-powered prospecting, we've seen clients consistently achieve rates of 10%, 15%, or even higher.
One client saw their positive response rate jump from 3% to 12% within the first two months of implementing AI-powered prospecting. That's a 300% increase in interested prospects, without sending a single additional email.
Of course, responses are great, but what we're really after are meetings. We've seen many clients increase their meeting booking rates by 50% or more using AI-powered prospecting techniques.
But here's where it gets really interesting. AI doesn't just help you get more meetings - it helps you get better meetings. By focusing on highly qualified leads and personalizing your outreach, you're more likely to book meetings with qualified prospects, who are genuinely interested in your product or service.
This leads us to what I consider the holy grail of sales performance metrics: conversion rates. You need to track both lead-to-opportunity conversion (how many leads turn into genuine sales opportunities) and opportunity-to-win conversion (how many of those opportunities turn into closed deals).
One of our clients saw their lead-to-opportunity conversion rate increase from 20% to 35% after implementing AI-powered prospecting. Even more impressively, their opportunity-to-win rate went from 25% to 40%. Think about what that means for their bottom line - not just more leads, but a higher percentage of those leads turning into actual revenue.
"That all sounds great, but what about the cost? Isn't AI expensive?" And that's a fair question.
Consider your cost per lead and customer acquisition cost (CAC). Yes, there's an upfront investment in AI tools. But in almost every case we've seen, that investment pays for itself many times over in terms of increased efficiency and effectiveness.
One of our clients, a mid-sized IT company, saw their cost per qualified lead drop by 40% within three months of implementing AI-powered prospecting. Their CAC decreased by 25% over the same period. And because the leads were higher quality, the lifetime value (CLTV) of the customers they acquired actually increased.
But here's the really exciting part: All of these metrics tend to improve over time. Why? Because AI systems learn and get better the more they analyze data. The longer you use AI-powered prospecting, the more accurate and effective it becomes.
Now, I'm not saying AI is a magic bullet that will solve all your sales prospecting problems overnight. It's a tool, and like any AI driven tools, their effectiveness depends on how you use it. But when implemented correctly, AI powered tools can dramatically improve every aspect of your B2B prospecting efforts.
Overcoming Challenges in AI-Powered B2B Prospecting
Now, I've painted a pretty rosy picture of AI in B2B prospecting so far. And don't get me wrong – the potential is truly exciting. But I wouldn't be doing my job if I didn't address the elephants in the room. Yes, there are challenges when it comes to implementing AI in your sales process. But here's the good news: these challenges are not impossible to overcome.
First thing is sales data quality. Always. You've probably heard the phrase "garbage in, garbage out." Well, it's never been more true than when it comes to AI. Your AI is only as good as the data you feed it. And let's be honest, most companies' CRM data looks like it's been through a blender. Duplicate entries, outdated information, inconsistent formatting, etc.
But don't wait for perfect data to start using AI. Start with what you have, and use the AI implementation as an opportunity to clean and enrich your data. It's a virtuous cycle – the cleaner your data gets, the better your AI performs, which in turn helps you identify and clean up more data issues.
Next challenge: integration with existing systems. I can't tell you how many times I've heard sales leaders say, "This AI stuff sounds great, but we've already got a CRM, a marketing automation platform, and six other tools. How's this going to fit in?"
It's a valid concern 100%. The last thing you want is another siloed AI tool that doesn't talk to the rest of your tech stack. That's why at LavaReach, we've prioritized integration capabilities. Our AI tools can analyze data from and push insights to popular CRM systems like Salesforce, HubSpot, and Pipedrive. We've also built APIs that allow for custom integrations with other tools in your sales and marketing stack.
Now, let's talk about a challenge that's often overlooked: the human factor. I'm talking about resistance to change. Let's face it – sales is an industry steeped in tradition. Many successful sales professionals have mastered their craft over years or even decades. And now here comes this AI, threatening to disrupt everything they know.
I remember one client, a seasoned sales director, who initially mocked the idea of AI in sales prospecting. "I've been doing this for 20 years," he said. "No computer is going to tell me how to do my job."
Fast forward three months, and this same sales director was our biggest champion. What changed? We didn't force the AI on him. Instead, we showed him how AI in sales prospecting could make his job easier and more effective. We started small, using AI to help with research and lead scoring. As he saw the results of leveraging AI – more qualified leads, less time wasted on dead ends – he gradually embraced more AI-powered features.
So what's the lesson here? Implementation is as much about change management as it is about technology. You need to bring your sales team along on the journey. Show them how AI can make their lives easier and help them hit their targets. Provide training and support. And most importantly, emphasize that AI is not here to replace them, but to empower them.
Let's address another elephant in the room: ethical concerns. As AI becomes more prevalent in sales prospecting activities, questions about data privacy and ethical use of information are increasingly coming to the fore. And rightly so. The last thing you want is for your AI-powered prospecting strategies to cross ethical lines or run afoul of regulations like GDPR or CCPA.
At LavaReach, we've built privacy and ethical considerations into the core of our AI systems. We're transparent about data collection and use. We ensure compliance with relevant data protection regulations. And we've implemented strong data security measures.
Finally, let's talk about expectations. In the age of ChatGPT and other highly publicized AI breakthroughs, it's easy to develop unrealistic expectations about what AI can do.
The key is to set realistic expectations. AI is incredibly powerful, but it's not a magic bullet. It can dramatically improve your prospecting efficiency and effectiveness, but it can't replace the need for human creativity, empathy, and relationship-building skills.
In fact, I'd argue that AI makes these human skills more important than ever. As AI takes over more of the routine, data-driven aspects of prospecting, the ability to connect on a human level becomes your key differentiator.
Putting AI-Powered B2B Prospecting into Action
Now that we've explored the current landscape and challenges of AI in B2B prospecting, you might be wondering, "What's my next step? How do I actually start implementing this in my organization?" Let's break it down into actionable steps that you can start taking today.
First things first, take a step back and assess your current situation. Before jumping into AI-powered prospecting, you need to understand where you're starting from. What are your biggest pain points in prospecting right now? Where are you and your team spending the most time? How successful are you at converting leads to opportunities? And perhaps most importantly, how accurate and up-to-date is your customer data?
Sales prospecting AI driven tools are most effective when aimed at specific, well-defined problems.
Once you've got a clear picture of your current process, resist the urge to overhaul everything overnight. Instead, pick one area where AI could make a significant difference. At LavaReach, we often recommend starting with AI-powered lead scoring. It's a relatively straightforward implementation that can yield quick, measurable results.
Now, let's talk AI tools. There's no shortage of AI-powered sales solutions on the market, each promising to revolutionize your sales process. But choosing the right one isn't about finding the most advanced AI – it's about finding the right fit for your needs. Look for solutions that play nice with your existing sales tech stack.
Before you implement any AI solution, though, you need to have a heart-to-heart with your data. AI is only as good as the information it's trained on, so take the time to clean up your CRM. Remove those pesky duplicates, update outdated information, and establish clear manual data entry protocols moving forward.
Bring your sales team on board. Invest in comprehensive training that goes beyond just the technical aspects of using the new AI tools. Your sales team needs to understand how to interpret and act on AI-generated insights. Remember, the goal isn't to replace your sales reps with AI, but to give them superpowers.
Once you've implemented your AI solution, keep a close eye on the results. Are you seeing the improvements you expected? Are there unexpected challenges or opportunities cropping up? Use these insights to continuously refine your approach.
Track your KPIs closely and share the wins, both big and small. Did AI help you land a major account? Did it save your team hours of research time? These success stories will build enthusiasm and buy-in for your AI initiatives, paving the way for broader adoption and even greater success.
At LavaReach, we've seen companies transform their sales processes and achieve remarkable results through the intelligent application of AI. But we've also seen companies struggle when they try to force AI into processes that aren't ready for it, or when they neglect the human element of sales processes. The key is to find the right balance – to use AI to handle the data-heavy, repetitive tasks that computers excel at, while freeing up your human sales team to do what they do best: build relationships, solve complex problems, and close deals.
The Future of AI in B2B Prospecting: What's Next?
We've covered a lot of ground in this AI prospecting guide, from the current capabilities of AI in B2B prospecting to the challenges of implementation. But let's face it – in the world of AI, the only constant is change. So, let's take a look at what the future might hold for AI in B2B prospecting.
First off, let's talk about Natural Language Processing (NLP). The advancements we're seeing in this field are incredible. Remember how I said earlier that AI isn't quite ready to write fully personalized emails? Well, that might not be true for much longer.
The latest large language models are getting scary good at understanding context and generating human-like text. At LavaReach, we're already experimenting with AI that can draft initial outreach messages based on prospect data and company messaging guidelines. It's not perfect yet, but it's improving at a rapid pace.
These NLP models are becoming increasingly multimodal. They're not just processing text, but also images, audio, and video. In the near future, your AI prospecting assistant might be able to watch a prospect's keynote speech at a conference and extract relevant insights for your outreach.
Next up: predictive analytics on steroids. The AI we're using today is pretty good at predicting which leads are most likely to convert based on historical data. But the next generation of AI will take this to a whole new level.
I'm thinking about AI that can predict not just which companies are likely to be interested in your product, but when they're most likely to buy, what specific features they'll be most interested in, and even what objections they're likely to raise. I believe it's doable.
One area I'm particularly excited about is the potential for AI to identify "hidden" opportunities for potential customers. These are prospects that might not fit your traditional ICP, but have a high likelihood of conversion due to specific circumstances or needs.
Another trend to watch: the rise of AI-powered sales assistants. I'm not talking about replacing sales reps – far from it. But imagine having an AI sales assistant that can handle initial prospect engagement, answer basic questions, and qualify leads before handing them off to your human sales team.
These AI assistants could engage in natural language conversations via chat or email, understand complex queries, and provide relevant information about your products or services. They could work 24/7, ensuring that no lead goes unattended, no matter when they reach out.
These AI assistants would also be learning from every interaction. They'd continually refine their understanding of what makes a qualified lead, what objections are most common, and what messaging resonates best. This learning would then feed back into your overall sales prospecting strategy, creating a constant loop of improvement.
Now, let's talk about something that might sound a bit Sci-Fi: emotion AI. Advances in computer vision and voice analysis are making it possible for AI to detect human emotions with increasing accuracy. In a sales context, this could be game-changing.
Imagine an AI that can analyze a prospect's facial expressions and tone of voice during a video call, providing real-time insights to the salesperson about the prospect's emotional state. Are they excited? Skeptical? Confused? This kind of emotional intelligence could help sales professionals tailor their pitch on the fly, addressing concerns before they're even voiced.
Of course, this raises important ethical questions that we'll need to grapple with as an industry. But used responsibly, emotion AI could help salespeople be more empathetic and responsive to their prospects' needs.
"This all sounds great, but how do I prepare my team for this AI-powered future?" It's a great question, and the answer is simpler than you might think: focus on uniquely human skills.
As AI takes over more of the data-driven, analytical aspects of sales prospecting, the skills that will become most valuable are those that AI can't replicate (at least not yet). Things like emotional intelligence, creative problem-solving, and the ability to build genuine human connections.
Also, foster a culture of continuous learning. The pace of change in AI is rapid, and staying ahead of the curve will require ongoing education and adaptation. Encourage your sales team to stay curious, to experiment with new AI tools, and to always be looking for new ways to leverage technology in their work.
As we wrap up this guide, I want to leave you with one final thought: the future of B2B prospecting isn't about AI replacing humans. It's about AI and humans working together in ways we're only beginning to imagine.
The companies that will win in this new landscape won't be those with the most advanced AI. They'll be the ones that find the right balance between artificial intelligence and human intelligence, leveraging the strengths of both to create truly exceptional sales experiences.
At LavaReach, we're committed to being at the forefront of this AI revolution in sales prospecting and sales engagement. We're constantly innovating, experimenting, and pushing the boundaries of what's possible. But we never lose sight of the fact that at its core, sales is about human connections.
About Daniel Zhao
Daniel Zhao is a multiple time founder with years of first-hand experience in B2B sales and revenue leadership. He has a consistent track record of helping companies experiment and implement outbound in SaaS and other industries. Throughout his career, Daniel has set up numerous outbound motions for the first time for companies that previously had not found success with sales led customer acquisition.