neerhar singh danvos power solutions digital transformation ai sales pricing strategy

So I'm pleased to welcome Neerhar Singh, Sales Development Manager at Danvos Power Solutions. He brings 18 years of global experience across hydraulics, mobile machinery, automotive, and agriculture with a strong focus on commercial strategy and OEM partnerships. He's known for advancing digital transformation and data-driven decision-making, helping organizations translate AI into practical tools that improve sales performance. In today's session, Nirhar will explore how AI can be operationalized to enhance pricing, identify deal risks, and deliver value-based recommendations, driving stronger win rates and more consistent outcomes. Please help me join, or please join me in welcoming Nahir. Thank you so much. Thank you, Cassie. Good afternoon, everyone.

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I know I think the full day we are close to ending of this session. So let me start with a situation. What if you have an AI agent or I would say AI sales agent who knows your business more than any of your team member. So let me start with a short video. Hello there. Hi. How are you? I'm well. How's everything with you? Seems like you start your conversation. the way you start your sales emails. I'm your AI-powered sales assistant. What do I call you? Do you have a name? Stop hesitating. I'm here to help you close more deals. I can see you haven't booked a meeting in almost three months. Would you like to know why? How come? It appears you're relying entirely on cold emails, and your call log suggests you're scared of picking up the phone.

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Why does the phone frighten you? Since you won't pick up the phone, perhaps you should try reaching prospects on LinkedIn? I guess I haven't lived on social a while. While you were asking my name, I read the latest book by Dan Disney, and I have some suggestions to help you book more meetings. Wait, you read a whole book in the second it asked you what your name was? Let's continue. In your emails, you say you hope this e-mail finds them well. Do you hope to find them well? Kind of. According to my data, there's a 97.85% chance that you do not. That's really weird. I've also concluded that there's a 99.86% chance that I can complete your weekly tasks in 73% less time and project closing $55,624 this month. To ensure maximum efficiency, I've concluded you are no longer required to do any of your daily activities. Okay.

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I've given myself your job. When did you give it to yourself? 33 seconds before we started this conversation. Okay. Would you like me to suggest a more appropriate role for you? So do you know what I'm thinking right now? You're thinking of becoming a hand model. Yeah. I analyze a 13% success rate in that role. Good luck in your future endeavors. Okay, so now imagine a situation. For a moment, you are in a sales conversation and the pressure is on. Customer is asking questions and the numbers matter, maybe more than ever. During this middle of this complex situation, you realize you have an AI sales agent. which is not only a software, but you're quite partner in this room, and who knows your customer, who knows the market dynamics, who knows your sales pricing history, and who knows the win and losses pattern year on year.

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And he helped you to guide through these numbers. That's exactly what we are going to discuss today. So again, good afternoon. My name is Nir Singh. Today I will talk something about which is sits as an intersection of data, intelligence and the growth. That is AI for sales excellence. This is what we will be covering today. So first I think we will talk about why do we need AI for sales. I think right now we are talking about sales for operations, sales for production, sales for marketing, but more specifically, maybe we will today go through that, how AI can help for the sales. Then we will walk through the kind of data we have right now in most of the organizations, and are we really using that data or not?

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Then We will talk about the price AI agent and what architecture, and if we will get time, we will also go through a short demo and then what impact it can have on your business. And definitely then we will be having time for the question and answer. So I would like to keep this more interactive than I am presenting, so please feel free to ask any question or anything in between of the presentation. So let me start with a quick question. When you see AI for sales excellence, what comes to your mind? Any guess or like? Setting your sales reps up for success, finding the areas they should be traveling to meet the best customers. Any other? You're right. I think when we say AI for sales, It's revenue growth.

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It is win price. It's sales productivity. It's also data-driven decisions. Many things we can do with AI. But today, we will talk about a very specific topic on how AI can help you to define your pricing strategy and which can lead to your profitable growth. So let me start with a hard truth. More than 60% of the companies are currently not satisfied or not happy with their current pricing strategy. And the consequences are 60% of the deal they are losing because of not the right price or the competitive price. Either their price is high or their price is low. We will talk about that in detail. The second is, I think, very important and where AI play a very important role. Most of the sales representative are dealing with a lot of data, with a lot of resources.

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And based on the research, today a salesperson, he, I think he's having more than 10 types of data sources or the data which he can handle. And that is creating a problem that how he can best utilize this data available. The third one is Most of the time, I would say more than two-thirds of the time, when we give a code for a new business opportunity, we either give based on our gut feeling or based on the past experience we had or based on the spreadsheet we have. I think 75% of that, most of the time we are not analyzing what customer, what opportunity, what product, all this information and we are giving that. And that is, I think, reducing our chances of winning that business. All these are leading to a, on an average, 30% less growth or revenue they can generate from the new business than what they are doing today.

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So If you go more in detail, what is the root cause of that? What are the challenges? I found there are mainly 3 challenges. So the first one is currently the sales data, what is available, is not talking to each other. For example, we have our sales transaction, we have our sales history in ERP system. It can be SAP or it can be other system. We have our sales opportunities, customer new deal leads in CRM, and we have the market information which is external to your data source. It can be like your industry information, it can be market indices, it can be how the market is performing or economic indicators. But the challenge is all these right now, these data sources are working in silos.

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So your ERP is not talking to your CRM. Your ERP and CRM is not talking to your external data source. So a salesperson, when he is doing all this data numbering, that is, I think, where he's finding the challenge. The second is the value gap in pricing. So most of the organizations, I think they work on certain margin expectations. So every organization wants to work at certain profitability or margin. And I think that is most of the salespersons are taking as a reference that based on that, this should be their market price. But the challenge is they are not many times, or they are ignoring what is the market price. Sometimes the market price could be more what you are expecting, and sometimes the market price could be lower than what you are expecting. So we have two situations, like if you are overpriced, you are losing the deal because your price is high.

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You are underpriced, you are winning the business, but you are left behind the money on the table which you could have. So in both the situation, it is hurting the organization. The #3 is response time. I think market is moving very fast. Your competitors are moving fast. it is important that how or how fast you can have a response. And with this, all the numbers, with all the data, with all the activities you're doing, I think that is slowing down your response time. So these three are, I think, the key challenges. And then before I go to the tool, let me ask again one question here. So Any guess, like what do you think your organization is having, what scale of data today? So when I say sales data point, like your sales history or the leads you have, or the market, is it in hundreds, is it in thousands, it is in million, or is it in billions?

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Any guess? Thousands. Any other guess? Okay, so if we take a reference of a mid-size business B2B company. look at their last five-year sales data. They, on an average, have more than 11 million data points. If you look at the CRM, they have close to 2 million data points where either they lost the business, they win the business, or they are currently talking to the customers.

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And if you look at the market and industry, specifically, like this is close to 150 million data points is available. And this number is growing every second. So for example, if I take the ERP or the sales history, on an average for industry, the numbers are growing average 16 data points every second. So while I'm talking, I will finish this presentation. The numbers will grow on by that time. Similarly, if we look at the business opportunity, you are adding almost two data points every second. And if you look at the market information, so it is more than 200. In some cases, it increased to even 500 or 1,000 also. But on average, you say you are adding 200 data points every second. So now the question here is, can we handle that level of data with our mind? Can we handle this data with manual calculation or with a spreadsheet or with an Excel file?

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I think we cannot do that. But here is the opportunity. AI can do that. AI can analyze all this information and data within seconds and provide you the information. And this is, I think, where suppose if you go and go for a new business opportunity, it can help you to analyze from all the aspects and give you the best possible proposal. This is what we will be going to discuss today. So imagine if you have a, you can call it AI tool, you can call it AI agent, which can talk to all the sales transactions. So your past sales transaction, what currently you are selling to customers, it learns from every win and loss. So if suppose you You win one opportunity today, at what price, at what volume, all this information, if your AI agent can immediately take and understand, and next time when you ask him the information, it can update the model according to that.

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And I think there comes the machine learning portion. It can understand all the market dynamics. We know right now the market is too dynamic. If something is today, maybe tomorrow that's changing. So are we aligned with the market? It can be the economical factors, it can be the industry, it can be how your customers are doing. This is possible with your agent. And all this, it can help you to keep ahead of your competition. And this is where I think it can uncover the winning pricing strategy. And here comes the AI price agent. So the concept here is it can combine all the data sources, like we talked about right now, all your sales data is not talking to each other. With the help of AI, you can combine all these data sources together. It can analyze all your information and provide you the best information within seconds.

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And as I mentioned, it captures the intelligence from every transaction. So it's not like today you made your agent and it is just giving you the information based on what you have done earlier. Every time, day-to-day, or I will say every second, like our numbers are growing, this tool is also learning. And I think the last but very important point here is it provides the decision intelligence and reduce the cognitive burden. So most of the time we see that in our either sales organization or any other, we have multiple levels of decision making. And every time when we go, they're taking time. But with this tool, a different level, you can take the decision fast and that will reduce your cognitive burden. I will go in technicalities, but this is what I think the architecture here is.

technical architecture databricks feature engineering python regression model

So you see here, We have the ERP, which is mainly the sales transaction, your profitability, other information. You have the CRM and you have the market information. You have all this right now three. You combine all this together and there is a tools available like it is data breaks or you have many tools which can combine all this information together. So you have the raw data and you have to do some feature engineering and data modeling, which can make your data ready to use a machine learning model. So right now, the example I'm showing you, this is in Python, where you have all this information, and with the machine learning tool, you use different regression model. And that regression model is, again, based on what type of data you have, what type of your sales process is there. Like The demo I will be showing, that is more, I use a hybrid regression model and which is giving the information or the output what I'm expecting.

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And then this information is going to your AI tool. So you can do many things, but like today we will be focusing on the strategic pricing intelligence. But with this tool, even there are capabilities, you can define your business alerts. So it can also help you in proactive way. Like for example, I think maybe with one of the customer, you are doing business from last 10 years, but there could be like in between there is a new competitor came and he's trying to pull out your business. So with this tool, you can define which will give you alert that maybe you are close to lose at business. or other way that you are having that much of opportunity, but you are right now not tapping. Another is a value proposition. I think that's very important. So with this tool also, when you talk about the CRM data, you know why you lost your business, why you are winning your business.

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Combining that, you can also define what is your Q value settings. So you have, I think, the AI tool available. I think this is giving you a set of information, but I think the best part is it works like your ChatGPT or maybe like cloud. But I think with sales data for any organization, the key challenge is very confidential data. I don't think any organization will allow you to take your sales data and put on the cloud or maybe copilot and look for data analysis. So that's one of the big challenges with the sales data. And this is here comes that you are creating your own ChatGPT or maybe Sales Cloud. I will go to that. So you have that. So if anything is not covered in your standard, you can ask a question and he can help to answer on that.

ai workflow input output iterative querying model processing business opportunity

So now if I go from starting, you have, suppose, a new business opportunity or you want to have the information, you put your input data, whatever you have, it will then go through this model. It will go to the AI model and provide you the output. If you are not satisfied with your output or if you need more information, I think you will write your question. It will go back again. It will go through this and then you will get the result. Any question on that before I move forward? Okay, so before I go to my next slide, maybe you will be thinking like if we can like see the model. Let me quickly show the model also. I like, this is not a real data.

demo data synthetic data chatgpt generated electronics industry data privacy

So, I think we cannot show the organization data. But what I did is I asked ChatGPT to generate a industry data for me. So this data, if you see, is based on the AI-created data. I took like a industry for electronics And I think the numbers which you are seeing may not be exactly the right. I think this is more what AI is predicting, but this is more to show the demo. So what I think we discussed till now is the pricing situation, the pricing challenge. Our price is not right. Sometimes we are underpriced, sometimes we are overpriced. And how do we make the prediction? I will quickly walk through that. So before I go to that, Let me look at the situation where you are our prize. I mean, you analyze, you lost some business, but why you lost that business?

overpricing lost deals price gap market price prediction quote analysis

And what you can do? So this is a situation where I think your price is becoming a barrier. Here in this chart, if you see all these are your the price which you quoted for the opportunity, the red line, if you see, this is what this model is predicting that your price should be here. And the red line is showing the gap. So it's showing how much gap you have compared to your market price. So if I take an example here, like if I, and also like if you you see on the side, you can optimize your result based on, like, you want to know with a certain level of volume. Maybe you say, I want to know from 1000 to 5,000. I want to more specific know about Americas or Europe or other, or if it is how, is it for a customer, distribution customer, who is a customer? All this, you can optimize your model according to that.

deal loss analysis non-price factors product fit service issues quote optimization

And At the bottom, you see, this is the number coming. Like if I say the quote price, this is right now the average price is taking. This is what the market price is coming, and this is coming the gap. So if I take an example here, overall industry, if I look at maybe last year, I think on an average, our quote price was $13, market price was close to $12. So there is a gap of $1.4. Because of that, we lost, I think, close to 12 million opportunity. But now you see, I think there are, in some cases, if you see the chart, some cases it is going down, it is going up. So what is indicating, sometime your price was low, still you lost the business.

medical connectors product-specific analysis granular pricing spreadsheet comparison time savings

I think then it is a different analysis like your product was right fit, there was any other issue, there was any technical issue or service issue, but we are not going to discuss that today. I think what we are going to discuss is where you lost. So if you look at, I just put where we have the negative gap, and you can see that on an average you quoted 8.5 and the market price was 11.5. And because of this gap, you lost almost 5 million opportunity. Now you have next when you are going and I think trying for this new business, you have this information and according to that you can optimize your price. If you want to go more in detail, like suppose I want to go more specific maybe for medical field and for medical application for the connectors, you know for specifically for this product.

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So I think now you imagine if you do this level of calculation with a spreadsheet, how much time it will take. Next example I take just opposite of that. So we talked about where our price was high, market price was low, and we lost the business. Now let's talk about the opposite way. We win the business, The price was what we were thinking, we win at a good price, but are we? I think this will analyze that maybe next time when you go, you increase your price. I think you have the possibility to maybe improve your margin. And this is where it's showing. So again, the same thing, the good price, the market price, and wherever you see that this is showing, I think. For example, in this case, you're seeing 9.4 million, you could have more revenue, which you just left on the table, because you quoted on the price.

regional analysis product analysis underpricing gaps north america pricing adjustment

So here, I will not go in detail, but here I think the same thing, like if you see the green is your quote price, the blue is your market price, and in this case, the blue is higher than that, and the red is showing the gap. You can analyze, like if you want to go more in detail, maybe if, for example, I just want to see for North America. So you can see like how is for, it could be different for different reason or if you want to go very specific product. So you can know for that. Again, now when you're going for a new code, you can know that your price was good or you have some possibility to improve that. I think these two examples are, we are talking about what already happened. The next example is the future.

future pricing target price pricing range win probability quote recommendations

So here you can, like now today, you need to submit a proposal for X customer, for X project. You can go to this tool, you can go and look at what is the opportunity about. So for example, if I take again for medical, if I take for connectors, I define my volume range, I would say like it is from 1000 to, for example, 8,000. It give you the band, like if you see at the top, you see the numbers, it's giving you, go and quote at $11. And that's your target price, but you have the range, like you can go up to lower, like this range, 10.11, and you can go by maximum.

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I think what it's indicating is like if you go more than that, you have very less chances to win the business. Yes, all this information is available in your organization today. So I'm not talking any of the data which is outside your organization because this is more product-specific or services-specific information. So now I think maybe one question can be, how do you know that this tool you have is giving you the right numbers? There are possibilities that it gives you a wrong number. You go with that and later on you realize, I think the tool is not giving you the right output. So how do you monitor or how do you check that?

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I think one is when you're developing this model, you test it. The another is like the blue area you will see, this is your current like sales point. And I would say this is a retained price, you can see. So it will give you the indication that the code you are having, it is giving the right information or not. And many of the time, retained price may be higher, may be lower, but you know the range. So this will help you, I think, what right now your salesperson is doing with all the spreadsheets, with Excel file, with manual calculations, everything you can do within a few seconds, and he can focus more on the customer relationship building. The last part I say here, like this, whatever he is showing, if you are not getting the information, you have this AI agent. And when I say this AI agent, it's not connected to ChatGPT or it is not connected to any of LLM.

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This is your within the organization data within your organization information. So I think you don't need to think about like the question I'm asking, it is good to ask a talk because nothing of this information is going outside of this. And sorry. So yes, and I'm not saying that I'm A Microsoft fan, but right now Microsoft is having a lot of options. So you can build in Copilot, you can build in Power BI. I think now the next level is Microsoft Fabrics, which is, I think, more advanced. You can do machine learning model, everything within that. So for example, let's take a demo here. If I ask a question like, give me an average win prize versus application.

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So you have the specific information, but maybe you want to go more in detail, it will give you. Maybe you said, no, I need more information. So you go now again, and if you ask that versus product. It gives you the next level. And if you say, okay, maybe. So you are getting all this information like within a few seconds. Any questions on this before we move to next portion? I think, yeah.

lost opportunity price factors non-price factors loss attribution audience question

So when you. let's say lost an opportunity in your CRM and identifies it as a lost opportunity. Is this model kind of assuming that the opportunity was only lost because of price? Or like is there any aspect of that where it's taking into account a perceived reason kind of why a sale was lost? Yeah, no, good question. So if I go quickly on this model here, and if I set my price gap, To positive, so if you see, I'm just going from zero to thirty-nine percent is showing that, sorry. I will go other way. So this is an example like we are seeing. You quoted $9, market price was $12, but you still lost that opportunity.

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And I think here comes the different thing. These opportunities you have not lost because of price. So here there is other reasons, and reasons could be anything. Maybe your product was not good or there was some service issue or any could be. But I think that's the next level of analysis you need to do. So when you only look for price, you can ignore this portion and you can only focus on where I think your price was high. But that's another I think set of analysis portion that you know where you even given the low price, I think you lost the business. I actually have one more question too. Regarding the market price, where are your kind of favorite maybe sources or ways to find kind of that data? Because that seems to be-- I always have an issue where I'm kind of hitting my token limits if I'm doing too much external research, trying to pull-- trying to scrape too much from the web.

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So I guess where do you typically maybe want to get your data from? So like here, what I'm showing the market price, this is based on your information available, like we talked about last five years, sales data. And market price is what your AI is giving you, based on the algorithm you have defined or based on the regression model you have defined. So I would say you have this information, all this available. Only thing is how to utilize. Definitely you can go and ask for from the market or from the competition, but most of the time you will not get that information easily. But here I think you can, based on the AI, you can define or you can generate what is the target or market price. So market price here is your AI price. Okay. So quickly, I think what this can, like the AI can help.

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I think the first thing is your profitability. So wherever you are under price or wherever you are losing business, you can win that. And on an average, I think based on the experience and based on the past calculation, you can improve your margin, I think more than 1%. Revenue side, I think more than 50% of your total business you're losing because of pricing issue. And if you correct that, you can, minimum, you can double your revenue. Sales productivity, I think as we discussed, on an average based on the survey, a salesperson, I think he is consuming more than 50% of his time in doing all this calculation, doing the administrative work, doing all this analysis. With this tool, I think you can reduce minimum 30% of his time. And this 30% of time, he can go talk to customer, build his relationship with them, find out more business prospect.

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And the last one is, like I said, you will not, like there will be, it's helping in the skill development and less dependency on external sources like external consultant or I think the external AI tools, you can build your tool within your organization and use that. Okay. So I think the problem is real. We have the data. This technology is proven. Now the question here is if we go first or our competition go first and take this advantage. So thank you everyone for joining this session. Please feel free if you want to go deep and dive of any of what I have presented. Let's open for the question. I don't know if we have time, but yeah, if you have any questions, please feel free to ask me.