S7E7 – Why Human Psychology Still Wins in B2B SaaS Sales (Even in the Age of AI) with Desiree-Jessica Pely

In today’s crowded SaaS market, having a great product simply isn’t enough. understanding why human psychology still wins in B2B SaaS sales is very crucial. Many companies generate significant interest, such as leads, web traffic, or downloads, but still struggle to convert that attention into reliable revenue. The real issue isn’t a lack of data; it’s a misunderstanding of how B2B buyers actually make decisions.

In this episode of Grow Your B2B SaaS, Joran Hofman hosts Jessica Pely, co-founder of Loyee.ai and former fintech CTO, to discuss why great products alone do not win in SaaS. Jessica emphasizes the need to align go-to-market strategies with real buyer behavior. Her approach combines behavioral science, data, and AI and delivers a clear takeaway: sustainable growth comes from better targeting based on behavioral signals and executing with focus.

From Academia to Startups Jessica’s Journey

Jessica’s insights stem from her academic background in behavioral economics, where she worked alongside a Nobel Laureate studying financial decision-making. A key learning? Buyers aren’t purely rational. Emotions, biases, and social influences matter. As a fintech CTO, she inherited a massive list of 10,000 potential clients, most irrelevant. So, she shifted from “spray and pray” tactics to behavior-driven targeting. Her strategy prioritized companies showing real pain points her product could solve, not just surface-level firmographics or intent signals. That pivot led to a more accurate, scalable go-to-market model.

Interest Does Not Equal Intent The Big Misunderstanding

One of the most common SaaS mistakes is confusing curiosity with intent. Website traffic and content downloads are often mistaken as buying signals. Jessica argues that the root problem lies in poor targeting and incorrect assumptions. Many teams chase huge total addressable markets with generic messaging or pursue flashy brands that don’t need their solution. This creates pipelines full of false positives and wastes sales team energy. Real success comes from identifying who’s actively feeling the pain your product solves, not who looks good on paper.

Behavioral Psychology Reveals the Buyer and Seller Gap

Understanding buyer behavior is crucial. Status quo bias means many buyers stick with what they know, even if it’s not optimal. Loss aversion makes them hesitant to change unless they’re in real pain. Ironically, those who’d benefit most often resist the hardest. On the seller side, overconfidence is a major issue. Sales teams frequently misjudge deal stages or timelines, relying on gut feel rather than data. Jessica emphasizes that while AI tools can help identify pain, human coaching is vital to address seller biases and improve forecasting.

Misusing AI in Sales Common Pitfalls

AI is becoming standard in SaaS sales workflows, but misuse is rampant. Jessica warns that many teams treat AI like a “magic button” rather than a tool requiring direction. Companies often implement AI without clear goals, be it efficiency, better insights, or targeting, and expect instant results. AI adoption, she says, should be treated like an R and D project. It needs experimentation, onboarding, and ongoing feedback. Without structure, AI tools like ChatGPT can drift off-message. Jessica compares AI to hiring a junior employee, it needs a job description, supervision, and training to add value.

Redefining Your Ideal Customer Profile

Most teams define their ICP too broadly, by industry, size, or tech stack. Jessica recommends a tighter focus. Your ICP should reflect the subset of your market actively experiencing the specific pain your product addresses. If you’re unsure, start by studying your best customers. What patterns, challenges, or workflows do they share? Use those insights to find similar twins. This behavior-based targeting ensures you’re engaging with companies most likely to buy and benefit, not just those who could.

Use Jobs to Be Done for Real Opportunity Discovery

To identify real sales opportunities, Jessica promotes the Jobs to Be Done framework. Instead of focusing on static signals like vertical or tech stack, ask what job is the user hiring your product to do? For example, if you offer verified phone numbers, knowing a company uses ZoomInfo isn’t enough. A more useful indicator might be outbound account executives working without SDRs, a clear operational need your product solves. Her team analyzes five years of job postings to uncover these behavioral clues, enabling precise, relevant messaging.

Start Targeting Before Product Market Fit

Targeting isn’t something you start after finding product market fit. Jessica encourages teams to form early hypotheses about their ideal customers and iterate fast. Analyze usage data, churn, and retention to refine assumptions. Ask who is consistently getting value from your product right now? These early adopters are your blueprint. Focus on the smallest viable segment where you can deliver meaningful results. Targeting early doesn’t just speed up learning, it also helps you build better products by aligning closely with real-world user needs.

Turn Customer Data Into Targeting Signals

If you already have customers or pipeline data, mine it for strong targeting clues. Jessica breaks this down into three areas: business model and initiatives, team structure, and tech stack. Look at your highest-revenue, most loyal, or fastest-expanding customers. What do they share? Identifying these patterns helps you build predictive lead scoring. Layer on qualitative insights like hiring patterns or company culture for deeper understanding. AI can help spot hard-to-see trends, but don’t treat it as a checklist. Focus on signals directly tied to the pain your product solves.

Review and Refresh Your Targeting Frequently

Many companies only update their ICP or targeting once a year. That’s too slow. Jessica advises reassessing at least quarterly. Markets evolve rapidly. New competitors emerge, tech stacks shift, and customer needs change. Your ICP from six months ago might no longer be valid. Treat targeting as a living process. By revisiting it often, your team stays agile and aligned with today’s buyers, not yesterday’s assumptions. This dynamic approach gives you an edge in fast-moving markets.

More Data Does Not Mean Better Decisions

SaaS teams often drown in metrics and dashboards. But more data doesn’t always lead to smarter choices. Jessica recommends narrowing your focus to ten to fifteen key signals that truly predict success. Rank them by clarity and impact. Build messaging around the strongest indicators. Sometimes, one strong signal like a hiring trend can be enough to justify outreach. In other cases, it takes a cluster of signals. The point isn’t to capture all the data, it’s to act on the right data, with confidence and clarity.

Align Human and AI Efforts with Value and Intent

Jessica offers a simple framework for balancing AI and human efforts: match actions to value and intent. If a lead is high-value and high-intent, use human-led, relationship-driven sales. If they’re high-value but low-intent, use AI and marketing to warm them up. Low-value, low-intent leads aren’t worth the effort. For high-intent but low-value leads, consider product-led motions like trials, just don’t over-invest human resources. This model helps teams focus energy where it drives real returns while still leveraging automation.

Automation Alone Will Not Win Human Connection Matters

The future of sales is increasingly automated, but that makes human connection more valuable, not less. Jessica believes personal relationships will become a key differentiator. In a world flooded with AI-generated messages, thoughtful, authentic outreach stands out. People still buy from people they trust. Companies that can combine automation for scale with human touch for impact will outperform those relying on tech alone.

Growth Strategy by ARR Stage

Jessica breaks down her growth playbook by company stage. From zero to first customers, lean on your network and iterate quickly. Up to five hundred thousand in ARR, focus on companies similar to your early adopters. Between five hundred thousand and three million, scale what’s working and expand your reach. From three million to ten million, open up the market but stay focused. Document everything, wins, failures, pivots, so your team and tools learn over time. This stage-by-stage approach ensures growth is intentional, not chaotic.

Final Takeaway Focus Beats Volume

The ultimate lesson? Success doesn’t come from doing everything, it comes from doing the right things. Top SaaS teams don’t chase every lead or trend. They target the right customers, use behavioral insights to guide their efforts, and balance tech with human touch. Focus beats volume. Every time.

Key Timecodes

• (0:00) – Cold Open: Signals vs. Noise in Go-To-Market, Sales Overconfidence in B2B SaaS
• (0:49) – Guest Intro: Jessica Pely – LOI AI, Behavioral Economics Meets SaaS
• (1:30) – Origin Prompt: Behavioral Targeting in SaaS Sales
• (1:43) – PhD to CTO: Rational Biases & Enterprise Sales Strategy
• (2:58) – Founding LOI AI: Identifying Pain-Driven Accounts & Buyers
• (3:13) – Conversion Struggles: Interest ≠ Paying Customers in SaaS
• (3:38) – Targeting Models: Spray-and-Pray vs. Signal-Based Go-To-Market
• (4:56) – Chasing Logos: How Social Bias Derails SaaS Sales Focus
• (5:05) – Psychology in B2B Sales: Biases from Both Sides of the Table
• (5:21) – Buyer Biases: Status Quo, Risk Aversion, Loss Aversion
• (6:50) – Adoption Dynamics: Early Adopters vs. Most-in-Pain Accounts
• (8:23) – Sales Overconfidence: Deal Cycles, Forecasting & Coaching
• (8:30) – Sponsor Break: SaaStock Dublin – Founders, VCs, Meetings
• (9:39) – AI in Sales: Misconceptions & The Human Element
• (9:58) – 3 AI Use Cases: Automation, Insights, Autonomous Decisioning
• (11:17) – AI as R&D: Hire AI Like a Junior, Align with GTM
• (12:54) – Garbage In, Garbage Out: Build Your Sales Knowledge Base
• (13:43) – ICP vs. TAM: Best-Fit Profiles & Signal-Based Markets
• (15:15) – Customer First: Twin Companies & Lookalike Targeting
• (16:02) – Competitor Displacement: Migration Targeting via Pain Points
• (16:47) – Too-Broad Signals: Salesforce ≠ Clear Jobs-To-Be-Done
• (17:37) – JTBD + Job Ads: Scraping for AE Needs & Verification Pain
• (19:27) – Early-Stage Focus: Iterate, Learn, Focus on Fit
• (21:00) – AI for ICP Scoring: Cut Through Noise with Fit + Pain
• (22:38) – Qualitative Signals: Culture, Pricing, Sales Motions & ML
• (23:48) – Operating Rhythm: Reassess ICP Quarterly
• (24:29) – More Data Isn’t Better: Limit GTM Signals to 10–15
• (25:45) – Human vs. AI Outbound: 2×2 Matrix for Outreach Strategy
• (28:33) – Growth Principle: Focus Over More – Execute Deeply
• (29:01) – Future of SaaS Sales: Automation + Human Differentiation
• (30:02) – Stage-Based GTM: Scaling from 0 → $10M ARR
• (31:24) – Document Everything: Train AI, Onboard Faster
  

Transcription

[00:00:00.000] – Jessica Pely

A lot of companies now in the go-to-market talk about signals, data, and insights, and they always think more is better, but actually it’s the opposite because it just creates noise. If you have just more of something, it just creates noise. It’s not a signal anymore. But there is the false belief that these companies might be interested, and no one really puts in the effort to try to understand if there might be any interest. Overconfidence is a big factor A lot of sales leaders are constantly fighting overconfidence with their sales reps because they ask, Oh, is the deal about to close? Yes or no? They always overestimate how long in short the deal cycles are, for example.

[00:00:49.440] – Joran

Behind every SaaS sale, there’s a human decision and understanding the psychology behind why people buy can unlock faster and smarter growth. My guest today is Jessica Pely. She’s the co founder of LOI AI, a platform that empowers go-to-market teams where they turn complex market signals into precise, Actionable Insights. Jessica herself has a PhD in financial economics, a working background in computer science, and now building her own startup. She’s combining the academic research, computer science, and entrepreneurial execution, which is a really nice combination. Welcome to the show, Jessica.

[00:01:25.640] – Jessica Pely

Hey, Joran. Thanks for having me here today. I’m super excited to be here today.

[00:01:30.280] – Joran

Nice. We’re going to start with a story. Can you take us back to the moment you first realized targeting clients with behavioral insights could actually change how SaaS companies sell and grow?

[00:01:43.020] – Jessica Pely

I did my PhD in financial economics, and I was working with a Nobel Laureate that basically got a Nobel Prize for understanding that if we invest money or if we purchase products, we are not always very rational and their bias is behind I left academia and had a fintech company. At this fintech company, I was the CTO, but I needed to sell to banks and hedge funds. My co founder got me a huge list of 10,000 banks and hedge funds. I realized, of course, I can now call everyone and email everyone, but that will not bring me to my goal of selling. That’s where I realized I really need to find the banks and hedge funds and the decision makers within that have the pain point. That’s a human question, a social question. How can I find those companies that might be slightly interested in what I am selling at my fintech company? That’s where I first realized, Okay, actually, what I was selling to banks and hedge funds, like what companies to bet on, what people to bet on, I can use now as a sales rep myself. I just need tools to help me out with that.

[00:02:58.200] – Jessica Pely

That’s basically how I ended up founding this company to basically just focus on that, identifying companies and people who to target that might have a potential pain point. That’s a behavioral question at the end of the day.

[00:03:13.660] – Joran

Nice. We’re going to dive deeper into this topic. I’m going to take it one step back. A lot of SaaS companies struggle to turn interest into paying customers, right? I mean, you already mentioned you need to focus on the companies which have certain pain points. Why do you think people, our SaaS companies, have so much trouble turning interest into paying clients? Is it purely because they’re not focusing on the right companies or is there anything else?

[00:03:38.050] – Jessica Pely

I think it’s a big part of it. They go in with their spray and pray motion and they look at their TAM, mainly graphics and say, These companies should be interested in what I’m selling. Then they blindly, chicken with their heads, cut off, run after these companies. But there is the false belief that these companies might be interested. No one really puts in the effort to try to understand if there might be any interest. Then they approximate interest with intent data, like someone hopping on the website, for example. I think that’s where it’s a little bit broken. Why is it broken? First of all, what does behavioral economics teach us? Well, I don’t have the knowledge to derive the interest. Maybe the sales enablement is not there. Usually, if you go more towards enterprise sales, you get better education, how to derive pain points of your buying persona and ask very strategic about it. Usually, the sellers are more experienced. But if you have a large market, you cannot research every company and every decision maker to derive, Okay, could this person have a potential interest in what I’m selling? Then there are all these social biases.

[00:04:56.940] – Jessica Pely

I want to sell to the cool logo. I don’t really care if they might have an interest, but I know it’s a cool logo to have, so let’s go after it.

[00:05:05.660] – Joran

Where do you see human psychology, biases, emotions really play in B2B SaaS? You already mentioned in the beginning with the Nobel Prize, everybody has their bias or emotion when purchasing a product. Sales people have a bias when they try to sell to bigger logos. Tell me more about this. How do you see certain things?

[00:05:21.900] – Jessica Pely

First of all, we have to distinguish their bias is on both ends at the seller, but also at the buyer. I think what we are trying to solve with our company is making sure on the buyer side, what is the bias there? They have a status quo bias. Companies, if something is going well, they stick on what they’re doing. We all know that if they are using already certain tech stack, certain tools, it’s just so difficult to switch them over to other tools or new tools because it’s easy to keep the status quo. The status quo bias comes from people are usually risk averse and lazy. That brings us to, Okay, as sellers, we really need to convince them and show them ROI that if they change something, it will benefit them in the future. But we actively always fight status quo biases. The other thing is people sometimes are not even risk averse, which means that I don’t unlike losses and unlike gains to the same extent, but they are loss averse, meaning that if something is going okay, you will not take any risk. But if something is going really, really bad, you will take that extra risk to prevent being in that loss.

[00:06:50.940] – Jessica Pely

That’s a little bit difficult to understand because if you are selling tech, they always say, Who are your early adopters? Because they’re all early adopters will push you getting in, your champions will bring you in, but they irrationally love your product. They want it, right? But technically, the companies would benefit the most who are struggling the most in one domain, but these companies who are struggling the most, and I in the lost domain, that are the least personas, least buying committees that actually bring you in. These are all these different dynamics that you have to understand. If you are a founder or building a sales team, what are really the biases that we need to conquer here? And what is our strategy? Now, on the sell side, overconfidence is a big factor that I’m hearing a lot. A lot of sales leaders are constantly fighting overconfidence with their sales reps because they ask, Oh, is the deal about to close? Yes or no? They always overestimate how long and short the deal cycles are, for example. So There is a whole different set, but I think that is going a lot into sales enablement. Where our product helps is basically just providing the knowledge about which prospects might have a pain point and selling into that pain point, giving them the expertise into how to best conquer the market, how to best conquer the accounts.

[00:08:23.040] – Jessica Pely

But we don’t work much on their own psychological biases. That’s where the human coach is still the best option.

[00:08:30.880] – Joran

This podcast episode is brought to you by SaaStock, my personal favorite SaaS event and community. The Europe event is taking place on the 14th and 15th of October in Dublin, and it’s going to be two full days of investor match making talk from other founders and meetings, which will be a huge focus this year as they’re planning to have over 15,000 pre-scheduled meetings before the event. As a listener, you’re getting an exclusive 30% discount through the link in the show notes. If your company is already doing above 1 million AR, you might even qualify for a completely free ticket while they last plus up to €400 reimbursement towards your travel expenses. Discounted and free tickets are limited, so Feel free to pause now, hit the link, buy your ticket or get your free ticket, and then I will see you in Dublin as ready as this will have a boot there as well. So make sure you get your ticket and then come back to this episode. We’re going to dive a lot deeper into you how to sell correctly? Let’s end with your last statement, the human coach. Because nowadays in sales, you see a lot of AI being used, right?

[00:09:39.680] – Joran

I think a lot of companies are using it wrongly. Let’s first focus on what they’re doing wrong, and then we’re going to tell them how to do it right after. Because where do you see AI going wrong in sales? And how do we keep that human side in sales? Where do we need to keep it?

[00:09:58.060] – Jessica Pely

Yeah, that’s a good question. And a difficult question to answer because everybody out there is different. How I like to bucket it, where can we use AI and leverage AI? I studied computer science, I had a machine learning and AI classes. You can do so much at different angles. Right now, when we look at what is going on in the field, you use AI to automate tasks, automate workflows, manual tasks. You can use AI to give you better data and insights or to completely make decisions for you autonomously. Companies, when they bring in AI, they really think AI is like a magic and will solve all their problems. But they never think, what do they actually want to optimize with AI? What of the three potential values? Within those potential values, what’s the outcome we want to achieve with AI? The problem is they don’t even know how they want to achieve that outcome without AI. It’s really difficult to then have expectations that AI is like this magician that would solve it all. That’s just from a concept. Where’s AI? I think there is no right or wrong answer. People that bring in AI don’t see it as R&D.

[00:11:17.280] – Jessica Pely

They see it more as a SaaS tool to bring in, and it should work and solve their problems. But right now, it’s more an R&D because the vendors that are selling AI products are still learning and growing. The buyers of the AI products, they didn’t sit down to really think of where can we get help. Instead of seeing AI as a magician, they should see AI rather as a junior that you are hiring, that you have to also hire for a certain job to be done, certain project, and where you have to have patience to onboard them. I think where AI goes wrong is that no one takes the time to really educate, coach, and learn with the AI and putting in the R&D investment that it’s really needed beyond the financial components, the experimentation, the ideas, the creativity. Number two is if they use AI and it’s going, let’s say, well, then usually it’s reps using ChatGPT. Everybody knows how to use ChatGPT, right? It’s just prompted and get something out of it, but then it’s not really guided It. Do you know if ChatGPT aligns your reps towards your strategy? I think that’s the problem, that you have this bottom-up usage of AI tools, but does it really align with the strategy you want to focus on internally.

[00:12:47.880] – Jessica Pely

You need a strategy and the AI tools work towards that strategy with the humans using those AI tools.

[00:12:54.560] – Joran

Yeah, that makes a lot of sense. Last week I had an interview with Andrew Kaplan, and we even We’re going to do a bit the same, like garbage in, garbage out with AI. If you’re going to do anything and you need that job to be done, educate the AI. If you’re just going to use ChatGPT, they don’t have context. Ideally, build up a knowledge base, a document, even a Google Doc, implement everything, keep track of your experiments, keep track of whatever you’ve been doing in sales, what is working, what isn’t, so you can train it based on your own data and not somebody else’s. Yeah. Let’s now go into how SaaS founders’ company should be doing it. If you were to advise a SaaS founder, looking the scale and that start at the beginning, what are two or three insights about their ICP or target list, you would recommend them testing immediately.

[00:13:43.180] – Jessica Pely

Everybody talks about ICP Companies, and what they have in mind is industry size, maybe the tech stack that someone is using, and that’s it. You have a large market to go after, but it’s not very strategic because these companies are It’s super different culturally, for example, or might have different pain points, might be in different stages of a company cycle. What is important is that companies think that ICP is the TAM, but what the ICP should be, it should not be not even the TAM, it should not be even the sum, but usually even a fraction of that, which companies have the pain points and show the signals within my TAM because that’s basically what you should be targeting. That’s actually your real ICP. But people use ICP oftentimes as a synonym for TAM, but it should be actually for the really tiny fraction of your sum. People call it now, okay, signal-based market or a best-fit customer. Instead of ideal company profile, it’s like best company profile. What is your best match? Really identifying the insights, pain points, behavioral cultural factors that go beyond thermographics, that your product will succeed fastest. Oftentimes, smaller companies, they don’t know.

[00:15:15.560] – Jessica Pely

Then what we recommend is, okay, look at your current few customers and just get similar ones into the game. If you close one smaller company in a specific space selling a certain product with a sales motion, you need to have super similar ones and go after them first. Basically, twin companies.

[00:15:40.200] – Joran

Exactly. If I could give an example, we have an affiliate management platform. We know if somebody uses one of our competitors, they have a clear pain. They don’t get inbound request from affiliate where we have a network, they don’t. We’re currently focusing on targeting them to migrate them over to us, which is working really well. Is this a best fit client example?

[00:16:02.940] – Jessica Pely

That can be it. If you see that especially this one tool gives them so much pain and it’s a manageable size of companies that use that tool, then I think it’s a great way of entering into the company because you can easily pitch to it. Knowing they have this tool, you can easily pitch to it. You can have a very simple message. As you just mentioned, Hey, you’re using this tool, but you’re missing out on You might have this pain point. We are solving that. It’s a very crystal clear messaging. That’s super valuable.

[00:16:38.320] – Joran

Yeah, any company can do that. You always have a competitor or a complementary tool. You have something where you can start. Other places where we can start.

[00:16:47.680] – Jessica Pely

Let me give you an example where it might not work so well. Imagine you are having a tool that you know that a company has a pain point with. Let’s say they’re a Salesforce user. The vast majority uses Salesforce, so it doesn’t shrink your market much. The pain points within Salesforce usage are different. What is the pain point with Salesforce you can solve for? Is it lead routing, ABM, outreach. You have to be super, super specific. Then the inside of using Salesforce is not so valuable anymore.

[00:17:22.220] – Joran

It makes a lot of sense. It’s almost that saying nobody ever got fired for hiring IBM. Same with Salesforce, I guess. You need to know exactly what you fix if they use such a big platform like them.

[00:17:37.020] – Jessica Pely

We always say, what’s the job to be done you’re selling for? Because as a SaaS company or an AI company, there’s always a job to be done you sell for. Then that can be a great predictor of the pain point. What we do in our platform is we scrape job ads five years back and we really try to understand which companies have It’s a job to be done. That gives us a slight insight about a potential pain point that we can solve for. For example, just very simple, I’m selling verified phone numbers, and I want to know which companies suffer from poor phone numbers. I can look out for who’s a ZoomInfo user, who’s an Apollo user. But it wouldn’t give me… I don’t know if these users are actually using the a phone number feature at ZoomInfo, but it might be a good fit, but the market might be too big. Maybe it’s the full cycle AEs that cannot deal with wrong phone numbers. Give me a list of companies where the AEs are full cycle. They are very limited in time compared to SDRs. Let me know if they’re actually doing cold calls, because not all of AEs do phone calls, but if they’re full cycle, there’s a high chance that they’re doing phone calls, and they are definitely impatient.

[00:19:00.670] – Jessica Pely

They want the right phone number. How do we get that data? We scrape the job ads. Are there any full cycle M, A, E that need to perform phone calls with prospects and build relationship? That’s a great insight. I can sell them and pitch them because I can tell them, Hey, I know you are limited in time and you still need to do these phone calls because you don’t have SDR, so make sure you get the best phone numbers you get from us.

[00:19:27.240] – Joran

I think this is a really creative way to get the What signal you’re looking for, the pain point you’re looking for. When you go into specifics, should a SaaS founder or SaaS company focus on this early? You mentioned, Don’t spray and pray, and definitely focus on your best fit client. Should you Are you doing that super early stage or is that something which is going to come later when you know who is your best fit client?

[00:19:53.320] – Jessica Pely

You should start thinking about it earlier because you want to grow fast. You don’t know yet how to grow fast, so it’s difficult to use data to help you out with that. But you need to have at least some certain assumptions and hypotheses to really understand, okay, who could potentially have this pain point? Test and iterate. If it hits the nail, great. If it doesn’t, iterate, iterate experiment. But have it always think about what is my best customer profile? Not what is my TAM, what is my ICP? Really think of who can benefit the most from You will see early stage that a lot of customers churn, a lot of customers don’t get the value out of the product that you’re offering. Use this knowledge, what is not working, put it into your assumptions and into your equations. But always think of, Okay, what is the smallest set of companies I want to sell to that might have this pain point.

[00:20:51.040] – Joran

Who is interning and staying with you and actually using the product. You can use the data you already have internally as well.

[00:20:58.400] – Jessica Pely

Exactly.

[00:21:00.000] – Joran

This is what you do as a company, right? You mentioned an example where you could look at job listings yourself, but how can AI help reduce noise to find that best fit client and maybe even do lead scoring on ICP level or something like that?

[00:21:15.300] – Jessica Pely

If you have already a customer base or a pipeline that is working, you’re creating opportunities, you can just look at those. You can look at closed-warn versus closed-loss. You can look at closed-warn and the the revenue that they’re making. You can look at closed one and the upsell they’re doing. You can also just start with, Okay, this is a good pipeline because we are having deep conversations, and this is a bad pipeline. That’s your outcome variable. Then you can use machine learning models, AI, or tools like ours to really then get all the information on these companies, progress in your funnel, either positively or negatively, get all the information on them. I always recommend break it down into three parts. What are their business model and initiatives? What are their people and processes? What is their tech stack? Try to find commonalities within these three buckets. That’s always a great starting point. Then look for these cultural factors that are really only specific to your product. Where I can help is obviously you can do a simple descriptive statistics. Look at, okay, what is the company size of the industry? But you really need those qualitative insights.

[00:22:38.160] – Jessica Pely

What is their pricing model? What is their sales motion? What are their business initiatives? What do they want to do with their fundraise money? What are the people doing that they hired? Go into those qualitative factors. That’s where a little bit our human cognitive skills have some limitations. You can use machine learning models to help figure out what are the signals and insights on a more qualitative level, and then use those insights and score based on that, your whole market, or even your starting pipeline. Nice. Exactly what you have, your company score high when they use the specific tool. In my example that I gave, companies should be scoring high that have full cycle AEs that need to phone call and other factors that matter. It needs to be more qualitative, more cultural.

[00:23:31.340] – Joran

I was bucket three, tech stack. You were bucket two of people. Then bucket number one, business model and initiative. What are they doing and how? You mentioned spending money when they raise, for example. Do they even look at things? Do they have the pain points you’re trying to solve?

[00:23:48.200] – Jessica Pely

Companies, when they are large, they do it once a year. They sit down and discuss, who should we going after and why? That’s actually something you should do more often and more automated because things change fast nowadays.

[00:24:03.780] – Joran

If we put a time frame on it, like do it more often, what does it mean in your case? Like quarterly then?

[00:24:11.300] – Jessica Pely

I think at least quarterly to reassess if something is working or not.

[00:24:16.000] – Joran

Yeah, nice. Now you have the three buckets, right? Nowadays, you can get a lot of information, if not all the information you want. Is more information always better?

[00:24:29.080] – Jessica Pely

No. Absolutely not. A lot of companies now in the go-to-market talk about signals, data, and insights, and they always think more is better, but actually it’s the opposite because it just creates noise. If you have just more of something, it just creates noise. It’s not a signal anymore. You have to reduce it to the least you need to make a good guess. For you, it’s very good because you just have one strong signal that seems to work. For other companies, it might be 5, 10. Honestly, we don’t recommend more than 10 or 15 because even think of it for the reps, it’s so confusing to deal with all it. What’s the strongest? What’s the second strongest signal? You really have to be strategic about it. Don’t throw more information or more data at people because that just creates noise and confusion and it doesn’t get you anywhere.

[00:25:25.380] – Joran

Let’s assume we have found our best fit clients. We have this fraction of some. Probably you don’t want to automate everything on them. Where do you see the balance running AI on them or AI outbound versus human led? Probably has to do with ACV as well, but how do you see it?

[00:25:45.420] – Jessica Pely

What I see working now very well in the market is a two times two matrix. Think of it like you have one axis where you have, is this a high value prospect versus a low value prospect? This is basically, is it fitting in your It’s your best customer profile in your ICP? Do they show, and the other axis, show intent or not? Think of it like these quadrants. If it’s a high value prospect and it’s a high intent, let’s say you had an engagement with them previously. They are indeed on your website. Then I would say if it’s a super high-scored prospect and you know it’s super high value because it’s in your best customer profile, then Then actually, you should use still humans to build the relationships to sell them. If it’s a high-value prospect but there is low intent, honestly, these leads need to be warmed up. It would be a waste of resources to put a sales rep on it. How do you warm them up? You can use marketing tools, you can use AI SDRs like outbound to warm them up. Whatever it is, you have to create somehow awareness that you exist.

[00:27:02.100] – Jessica Pely

But you know it’s worth it because it’s a high-value prospect for you. Now, on the lower end, let’s say there is no intent and not a high-value customer, Just drop it. Don’t care about them at all. Don’t waste any money for it, not even anything. If it’s high intent but a low value for you, you can explore. You can maybe have a PLG motion offer, a free trial, or reassess then your ICP We have these low value prospects should be in actually the high value category. Within those high value prospects, have them in three buckets or prioritize them. The ones where you really want the logos, I would say, do a mixed approach of warming up with AI but have a human. The ones that are not a good fit or a tier 3, a lower prioritized, use AI SDRs where you really don’t have any negative outcomes of burning those leads.

[00:28:06.220] – Joran

Yeah, that is focusing on the best quality. So human focus on a high value prospect and high intent. Sounds logical. Don’t automate everything. I’m going to try this out. I want to see if you can finish this sentence for me. So if a SaaS founder wants to kill their SaaS faster, they should always remember Yeah, more is not better.

[00:28:33.380] – Jessica Pely

Focus and double down on what’s working. Stay focused. It’s not about doing more, but focusing and executing on the few.

[00:28:43.800] – Joran

Yeah, that comes up lately where we have so much data nowadays that more is definitely not better. If we look at the future, what should SaaS founders prepare for in the next one, two, three years?

[00:29:01.920] – Jessica Pely

A lot of it will be automated. I can be prescriptive in what they should be focusing on, but I think keep doing what you’re loving, keep focusing on the pain point and the problems you are solving, and really keep the relationships because we don’t know how humans will be buying in two and three years from now. But one thing I learned when coming to the US, people here really buy from people they like and they connect well with. I think we should always focus on human connection. It can never be a wrong thing.

[00:29:41.840] – Joran

It can be a way to stand out. As you mentioned, a lot will be automated. If you focus on the human connection, you will stand out from automated companies. Last question. These are more towards like, revenue stages. If you have to give advice to a SaaS founder who’s just starting out and growing from zero to 10K the MRR, what advice would you give him here?

[00:30:02.960] – Jessica Pely

Okay, so I would break it down. What are your first three customers? Then how do you get to 500K to 1 million to 3 and to 10? Really think along It’s a line, Okay, how do you get your first three? Maybe through the network. Then, How do you get to your first 500? Just double down on the heavy users that you have from your first three. Who are their twin companies? Who are their competitors? Really We just narrow down on them. Then going from 1: 00 to 3: 00, we are not there yet, so I can just share what I’m working with from our customers. Maybe what is working is okay. Now, from having exploited the twin companies, now really hone into the signals, those companies were showing the pain points and scale that. Going from 3: 00 to 10: 00 is opening up the market, focus on the sum, and use tools, tech, and AI to penetrate those markets faster. I always have a good strategy for it. I think the first 3 million is a lot of experimentation. Try new things and always hone what is working and forget what is not working.

[00:31:20.550] – Jessica Pely

Just use it as information to inform your next experiment.

[00:31:24.700] – Joran

Exactly, because that’s indeed what Andrew said. It doesn’t matter if it doesn’t work, but document everything. As You mentioned as well, AI is going to be a big part, right? So you do want to document everything. If somebody comes on board or you want to ask your AI what worked, if you document it, it will be captured. Nice. Let me try to summarize. If we take it all the way to the beginning, if you want to grow your SaaS, you need to find companies where you can actually help them to solve their pain points. So don’t spray and pray. Bias is going to be present at seller and the buyer side. But in the end, again, sell into the pain points, focus on the job to be done. If you are thinking about using AI, first ask yourself the question, what is the outcome you want to reach without using AI? And then see if you can leverage AI to actually do it. If you’re going to determine your ICP or even, I guess, your best fit client, as you call it, be strategic about it. So it’s going to be a tiny fraction of your sum.

[00:32:20.580] – Joran

So find super fit clients based on pain points and use insights like business model and initiatives, people, and tech stack. And remember, where more data could be more noise. So keep it as compact as possible. If you then are going to take it one step further, you can put your clients into a matrix where ideally humans focus on high value prospects with high intent. Warm up high value prospects with low intent. You can use some automation and marketing in here. And then think about high intent and low value of implementing PLG, self-serve, or any automation, or maybe reassess if they’re actually low value or not. When we look at the future, people still buy from people. So keep focusing on that connection. It is going to work and might even help you stand out later on. Zero to 10K MR, focus on your network. So get clients in from your network. And I guess you chopped it up a bit deeper, but 10K to 10 million AR, hone in on signals, experiment a lot, opening up your market and focus on your entire sum. So not that tiny part of it anymore.

[00:33:28.340] – Jessica Pely

I’m like, wow, you You’re doing really good. I could not even summarize that.

[00:33:33.140] – Joran

All your words. People love this episode and they want to get in contact with you. What should they do?

[00:33:41.790] – Jessica Pely

Contact me on LinkedIn. I’m pretty responsive. Just mentioned that you listen to this podcast or drop me a note why you would like to connect, how I can be helpful. I’m an early stage founder, so always happy to chat with other early stage founders, but I also love, obviously, to chat with later stage founders and see if any collaboration opportunities or just me learning from them.

[00:34:05.360] – Joran

I refer to you as Jessica Paley at the beginning, but people can find you on LinkedIn as Desiree Jessica Paley, so keep it in mind when searching. We’re going to add a link towards your company, which is loye. Ai, so people can reach out to you five both ways.

[00:34:22.440] – Jessica Pely

Thank you. We’re in the middle of a rebranding process, so I will let you know our new company name soon, so don’t get confused.

[00:34:29.760] – Joran

Pick me then I’ll change the link and change the domain. For people on Spotify, if you haven’t done so, please leave us a review so we can boost the algorithms and we can help more SaaS founders. If you’re listening to any other platform, do the thumbs up rating whatever is on the platform. But for now, thanks again for coming on, Desri.

[00:34:50.860] – Jessica Pely

Thank you so much, Juran. It was a pleasure talking to you.

[00:34:54.320] – Joran

Thanks. Thank you for watching this show of the Grow Your  B2B SaaS podcast. You made it till the end, so I think we can assume you like this content. If you did, give us a thumbs up, subscribe to the channel. If you like this content, feel free to reach out if you want to sponsor the show. If you have a specific guest in mind, if you have a specific topic you want us to cover, reach out to me on LinkedIn. More than happy to take a look at it. If you want to know more about Reditus, feel free to reach out as well. But for now, have a great day and good luck growing your BTB SaaS.