Learn proven ways to ensure your sales team has confidence in your guidance recommendations.
About the Speakers
Joe Chamberlain is a Solutions Architect in the PROS Professional Services MDS team with experience in sales and retail management. During his career with Lowe's, Chamberlain held multiple management positions, each subsequent in expanding functions, and was responsible for building people development roles. Chamberlain graduated cum laude with a Bachelor's Degree in Marketing working full-time as a Sales Specialist and completed a Master’s Degree while working in Senior Management.
Hannae Samailovic is a Customer Delivery Manager in the Professional Services organization. Hannae's focus at PROS is ensuring that the value promises made on the sales side of the relationship are delivered during the implementation. Hannae is passionate about customer experience, value attainment, and the adoption of pricing solutions.
Amol Modgi is a consulting professional with extensive experience in implementing enterprise software solutions to drive business strategy. In his current role, he specifically focuses on implementing pricing and CPQ (Configure/Price/Quote) solutions across manufacturing, distribution, and chemical industries. Modgi is a people leader with experience in building and scaling global teams. Modgi is a Customer Delivery leader with a track record of delivering 25+ CPQ/Pricing enterprise solutions.
Hannae Samailovic: Welcome to the driving adoption through the use of data session. If you attended the pricing panel discussion before this, then you know that a successful partnership with sales is key to creating better pricing discipline. I'm Hannae Samailovic, a customer delivery manager here at PROS. In my role, I work with a lot of customers going through pricing transformations, and I've seen the many levers that you can pull to ensure that you have a successful pricing transformation. Focus on good change management and adoption, however, has been the most impactful. So let me start with the question to all of my pricing managers out there. How many of you have made a price adjustment, done all of the modeling, received all of your downstream approvals, only to not recognize the full uplift. The next best step is to go in and do a root cause analysis, try to identify why you're not receiving that uplift....
Hannae Samailovic: Oftentimes that leads to you uncovering that a lot of your sales reps are defaulting to the old way of pricing. Whether that's looking at the last price paid or locking in a margin with a special price agreement that they think is the best price for the customer. In this session, we're going to explore how you can drive pricing adoption through a data driven approach and how this helps to instill confidence in your sales team and drive better pricing discipline. So let's talk about how you get that buy-in. The best way to ensure pricing guidance adoption is to instill confidence in the guidance. Trying to convince sales reps, especially those with lengthy tenures, that a black box is smarter than they are won't get you very far. PROS guidance allows pricing managers to easily display the underlying segmentation framework and allows the seller to quickly understand the guidance recommendations. So let's watch as the following scenario unfolds over his own call. Amol is a sales rep at a sales parts distributor and he has some questions for Joe who's playing a pricing manager. Take note of how the solution is used to justify the pricing.
Joe Chamberlain: All right, thank you, Hannae. All right. Hey Amol, how's it going?
Amol Modgi: Hey, I'm good, Joe. Thank you for agreeing to meet with me.
Joe Chamberlain: Yeah, no problem.
Amol Modgi: And I have some questions around the deal that I'm working on. So I'm going to tell you the customer that I'm working with, it's called Allstate Auto Parts and they're an existing customer and they're trying to buy a line of manifolds from us. And I am, I was putting together this deal for them in our CRM and I saw the prices for them. And I know we have launched this new tool that is giving us pricing recommendations. And somehow I don't believe in those prices. The reason being, I've never sold them at that price. And I was actually wondering that system would either show me just the last sold price, or I'm just wondering if we have not given them enough discount provided authenticating that they are actually an existing customer. So I don't really believe these prices. Can you please help me with that?
Joe Chamberlain: Yeah. Okay. Yeah. So let me pull up the application here. Can you see my screen?
Amol Modgi: Yeah, I do.
Joe Chamberlain: Okay, cool. And yeah, so thanks for reaching out to me, man. And you're thinking about this the right way. And so one of the things that this tool kind of allows us to do is to provide the justification and answer that question of, you know, hey, why are we doing this price increase? And so that's one of the things that we'll jump in here and we'll take a look at. [crosstalk 00:03:55] So you said you were quoting is a line of manifolds.
Amol Modgi: That's correct. Manifold and for all set use autopilot. Yeah. [crosstalk 00:04:05]
Joe Chamberlain: Does Tom still work over there?
Amol Modgi: Oh, yeah, he does. He actually recently got promoted and he changed his role now.
Joe Chamberlain: Nice. Well, yeah if you run into them on a Zoom call or anything in the future, you'll have to tell him I said hello.
Amol Modgi: Yep, absolutely.
Joe Chamberlain: All right. So let's see what we've got. Okay. All right. So we've got some different recommendations here. And so what we need to do now is we need to specify our selling conditions, the channel deal type and priority. And before we do this, are you familiar with the segmentation model we landed on and why it is that we specify these characteristics when we're getting a recommendation?
Amol Modgi: I mean, they look familiar, I'm sort of familiar with them, but if you can refresh my memory a little bit, that will be helpful so I can just add them together.
Joe Chamberlain: Yeah. And so when we did our study, what we found was that these attributes were the most significant that we had in terms of determining what factors cause us to price differently. And how can we group our transactions and our customer interactions in a way that gets more granular so that we're only looking at the most similar customers and selling scenarios when we're providing these recommendations. So it's a way of further enhancing the quality of the peer group, which we're going to look into here in a sec, that's going to show where these recommendations.
Amol Modgi: Right. Okay. Got it. So then these are the essentially attributes that drive our pricing behavior. And that makes sense to me because I know we price differently between different channels or based on our data type..
Joe Chamberlain: Yeah. And I'll use attribute, selling condition interchangeably.
Amol Modgi: Okay.
Joe Chamberlain: All right. Okay. And so for this one, was this online or retail?
Amol Modgi: This was a retail deal.
Joe Chamberlain: Okay, cool. And spot or contract?
Amol Modgi: It's a spot code.
Joe Chamberlain: Okay. And are they in a hurry?
Amol Modgi: Nope. It's a normal order. They buy from us all the time.
Joe Chamberlain: Right. Cool. Okay. So yeah, let's drill into the normal here. And so what I have here now is the peer group. So you can see from this list, we have Munich Auto Shop, GT International, Derrick. And this is going to be the peer group of similar customers who have purchased manifolds from us. So here is where we're going to have the justification for the recommendation. This recommendation is going to be based on where Allstate has been historically in relation to those peer groups. So just like, kind of as your eyeball on this, do these customers make sense as peers in your mind?
Amol Modgi: Yeah. Yeah, they are. No, this is really interesting because now I'm seeing, you know, looking at some of these directors and I see Munich Auto Shop and GT International Motors, they are definitely their peers. I have done business with them. They are exactly like [inaudible 00:07:04]. And I actually see that they are paying way higher than what Allstate is used to paying. Right, so they pay 273 and Allstate is like 236. So yeah, that makes sense now. So yeah, we should be actually raising their prices. And just so that I understand this clearly, Joe, so we are recommending a price increase from 236 to 242. On the right-hand side, I'm seeing that this is a 2.4% increase, which I think is probably reasonable and I should be able to get that and maybe propose that price, but I'm just wondering if this tool has the ability to change how quickly we are increasing in the prices or how quickly we are reducing the prices. You know, just like if it's conservative, aggressive. So do you know if this can do that?
Joe Chamberlain: Right. Yeah it can. And so I'll go on and show you that real quick. And so you can see here that the price increase is going to be relative to what they've paid historically. So it's always going to take that into consideration. And so that's one of the kind of the biggest things that we focus on is making sure that when I recommend you an increase I'm saying, hey, go from 236 to 242. You can get your head around that, the customer is not going to freak out, it's something that's reasonable. As you can see on the line above, in the case where it's a high priority order, the increase is almost at 6%. So it's it's going to vary a little bit.
Joe Chamberlain: But in terms of like of how we control that, let me show you a workflow that we do have. And so, as you can see here, we're set at the moderate level, which is the standard that we decided on during the implementation, but if it ever comes to a point where you're seeing it and it's too aggressive or there's market forces going and we need to be a little more conservative. We can do that in here for a period of time, or we can alter our strategy long-term depending on the results and the feedback that we get.
Amol Modgi: Oh, got it. Okay. No, that makes sense. This is fantastic because I can completely see how this can be useful based on how much [inaudible 00:09:14] changing, we can go more conservative or more aggressive. So thank you, Joe. This is really helpful. Now I see how Allstate Auto use auto-parts compares with some of its peers for that specific product. But now that we are on this topic, I actually want to ask you one more question. And I'm actually working on one other deal for a new customer. They're called Japanese Automotive, and they're also buying the same line of products from us. But the thing that is interesting about them is they have never done business with us for this product. So they have never bought from us. And I'm wondering if I have to negotiate with them, or if I want to propose a price, what should be the price? And I'm wondering if this application can really recommend the price, if we don't have any transactions for them, or they have never bought anything. So can you please help me with that?
Joe Chamberlain: Yeah, definitely. Yeah. So we do have a workflow in the application to get there and I can show you that, but the idea with that is because that question comes up all the time, right? It's like if I haven't bought this stuff before, how are you going to recommend a price? And so kind of the idea with it is, is that by determining a peer group, right? Like if you and I are very similar in the way we buy things, like we buy oil filters and air filters and other products kind of in the same way. And they've determined that we're similar customers. If you are buying tie rods or something else. And I'm not buying those based on the knowledge that we have of you, we should know in a general way where I should be priced. And the idea is that similar customers are buying. We have the data and based on that, we can make a good prediction of where you should be.
Amol Modgi: Got it. Okay. That will be cool to see that.
Joe Chamberlain: Okay. Yep. And so I'll show you kind of what I'm talking about here. So it's manifold, you said, and-
Amol Modgi: Correct. [crosstalk 00:11:13] Japanese Motors, yeah.
Joe Chamberlain: Okay. All right. Yep. And yeah, just like you said, there's, you know, we don't have any history, so there's not a specific recommendation for them, but what we need to do is specify our selling conditions to give the correct recommendation. And so are you looking at doing online or is it-
Amol Modgi: This is an online deal, yeah.
Joe Chamberlain: Okay. And spot or contract?
Amol Modgi: Spot, as well. Yeah.
Joe Chamberlain: Okay. And what's the, are they in a rush?
Amol Modgi: It's just, no, it's normal priority.
Joe Chamberlain: Okay. All right. So let's see what we got. Okay. All right. So here what we is kind of that peer group, like I was telling you about. So we've got British Motors, American Motors, Mendoza. And so these are customers that are similar that have been purchasing these manifolds. And so from this we can get a general idea of where Japanese Motors should be. Does this make sense? Do you feel like that's reasonable?
Amol Modgi: Yeah. That is completely reasonable. No, this makes sense. So it gets compared against its similar customers and how we set up customers and it will provide a recommendation. So this gives me a lot of confidence and understanding of how those prices are coming. And I think I feel a lot more confident now going back to my customer and proposing those prices. So thank you. This has been very helpful. Thank you, Joe.
Joe Chamberlain: All right. Hey, no problem. Yeah. And definitely thanks for reaching out. And if anything else comes up in the future, don't hesitate to call.
Amol Modgi: Will do. Thank you, Joe.
Hannae Samailovic: Thanks Amol and Joe for showing us how powerful the PROS guidance tool is in instilling confidence and recommendations. In the workflow that we just watched, we saw how Joe was able to show the underlying data that led to the pricing recommendations. Joe also educated them all on the underlying segmentation and he got buy-in on both the customer groupings and the segmentation by taking him through a real life business scenario. Lastly, he showed how the AI guidance application was driven by Amol's own winning deals. So why is going through this type of exercise with your sellers important? It's really important because it drives adoption of the tool and trust in the pricing that's coming out. Based on an AMR research study, customers that invest in change management and keep adoption as a high focus item in their pricing strategy are 50 times more successful when rolling out pricing optimization software. Before we transition into questions, Joe and Amol, would you guys like to introduce yourselves?
Joe Chamberlain: Yes. My name is Joe Chamberlain and I'm a solutions architect here on the professional services team at PROS. I have a lot of expertise and guidance implementations.
Amol Modgi: Hello everyone. My name is Amol Modgi. I'm a director of professional services at PROS and I'm responsible for leading and managing implementation of PROS solutions across manufacturing and distribution industries.