B2B Marketing Metrics and KPIs: insights from experts at Sage, Sun Life and VitalPath

Speaker A: Today we are talking about metrics and KPIs and B2B marketing.

Speaker B: I think that K is a distinction because you cannot have all your metrics super important and main you need to define like one or two.

Speaker C: So in my experience I think there are three biases we see with the data and those are the only thing and give us a misleading picture on any metrics. Those biases are like time window bias. And another thing is selection bias. And the last bias I feel is aggregation. Like how are we aggregating our numbers?

Speaker D: To me like an OKR should really force prioritization. So it's more like a vision right for the company. Maybe it's your vision for your marketing team of okay, they should be longer term objectives.

Speaker A: Hello, welcome to the B2B Marketing Leaders Podcast. Today we are talking about metrics and KPIs and B2B marketing. How companies decide what to measure, connect marketing to business results. What is challenging right now in this area and how AI of course AI is changing the process. And I'm so excited today to have such brilliant experts with me. And let's start with the introductions. Yannick, please go first.

Speaker B: Hi. Thanks Olga. Thank you for the invite. Uh, excited to be here. So uh, I am now doing the marketing analytics at SunLife Financial Canada. We are one of the largest providers of life insurance and health insurance solutions in Canada as well as investment products such as like registered a uh retirement savings account and like other types of investment accounts. And I own uh all the marketing, the acquisition marketing reporting part. So all like the funnels, the campaign efficiency, the attributions and all that part, it's not exactly B2B to be honest. But despite the fact that we are like have a huge B2B uh part of our business and I support the retail right now. But the funny thing is with the complexity of the products that we saw I found that the final structures and basically everything we do in the acquisition maps pretty directly to what I used to do on the B2B side in my uh, previous roles which was so I for a couple of years ago I led the B2B marketing team for the online marketplace to work with the merch merchants selling everything on a platform and before that Ah, the B2B marketing team at Online Real Estate Classified working with like the real estate agencies and individual agents to bring the content to upsolid uh, products. So that's pretty much my last uh, I don't know many years of experience but most relevant to the today's conversation.

Speaker A: Um, thank you thank you, Nick. Yeah. And your experience in analytics is definitely something we need today. Yeah. Thank you so much. And Bridget, please go ahead.

Speaker D: Yeah, so my name is Bridget Locke. I am the VP of strategic marketing at Vital Path. So VitalPath, uh, is in the med tech contract development manufacturing space. So we focus on complex interventional catheters. So we, our customers are uh, medical device OEMs and you know, we're helping them bring their, their catheter devices to market and then we also manufacture them at scale. Um, so pretty much all of my marketing career has been within this B2B like contract development and manufacturing space. I'm so pretty used to long buying cycles. You know, typically a pretty audience has pretty much always been engineers.

Speaker A: Right.

Speaker D: So pretty educated audience that looks to educate themselves quite a bit before they uh, engage. Um, but I would say you know, since I've been with Vital Path, like my last couple of roles have been more focused on customer journey overall. So especially here at VitalPath Sales Analytics Reports into marketing. Um, so it's nice to be able to kind of really measure the full customer journey and have some influence over our metrics and analytics and kind of what we're trending, um, what we're measuring and how we're kind of like bringing all the pieces together into one big puzzle. Right.

Speaker A: Yeah, sounds exciting. And long sales cycle is everything to us. So it would be really interesting to uh, learn more from your experience. Yeah, thank you so much Bridget and Ravi, please go ahead.

Speaker E: Hi Olga. Thank you so much for the invite first of all and uh, uh, for a brief introduction. About Me so this is Ravi. I lead marketing data team at Sage. Sage is a uh, cloud based accounting, financial management, payroll and H software. So we provide such softwares for the small and medium businesses and uh, our uh, headquarters based in Newcastle uk. My role is to make it accessible for all the marketing stakeholders and meaningful for them so that they can connect activity to the outcomes. Uh, like if you look at the funnel, so if you look at the what we are doing and what is the outcome is coming and so that they can make a better decisions out of the data. So that's about me. I'm doing this data analytics for more than 10 years now with sales and marketing, mostly with marketing, but yeah and work with mostly B2B organization. So yeah, happy to be here and looking forward for this conversation.

Speaker A: Thank you so much Ravi. Yeah, ah, very, very exciting and uh, I'm impressed by all of your experience. Yeah, I'm uh, looking forward to start. So and my first, first Question will be about the practical use of metrics and KPIs. So we'll start with some success stories. Uh, could you please share an example where choosing the right metrics or maybe setting the right KPIs helped improved, improve B2B marketing results at your company, Maybe from your current company or your previous experience. Yeah, please share.

Speaker D: Okay. Um, I guess I can start with a little bit of an example. Um, that's somewhat recent, uh, from last year I guess. So we, like I said we're in the med tech kind of CVMO space and so our cycles are typically you know, three to six years. I mean it's like one to three years of top of funnel and then another potentially three years to really get to um, full scale manufacturing. Um, and so you know, and that's looking at kind of the complete device picture. Uh, but within our manufacturing and our capabilities we actually have one portion of our business that has a little bit of a shorter cycle. And um, so it's our components side. Um, so we do some laser processing and you know we had kind of a little bit of like a gap in our pipeline where we had a lot of things that were kind of bottom of funnel and then we had some things that were kind of like in development but you know just the, the cycle to get through like clinicals and FDA and all of those things can, can take quite a bit of time. And so we were trying to look at what could we bring in that would be a little bit closer to target I guess and, and could um, kind of help fill some of that gap.

Speaker A: Right.

Speaker D: So we were looking at our laser facility and how could we really start to promote those components to our like what would the subset of our audience be and who are maybe some former customers that we could re engage? You know, maybe there were some projects that got paused that we could try to reignite. And so you know we kind of did this whole campaign around, around our laser business and, and looked at what are our capabilities and our capacity and what kind of offer could we give that would be really attractive. And so we kind of gave this like quotes in 24 hours. Um, prototypes in two weeks is typically in our space. You know getting prototypes in hand for an engineer helps them really kind of make a lot of decisions on their project early and get them moving. And so you know typically components also are a little bit easier um, for our customers to integrate into their devices and make quicker decisions on. And so we did this whole campaign, you know it was really a three month campaign. Um, and we used really an integrated campaign strategy. And so we were measuring like the individual metrics of each of these campaigns. But then kind of overall what was the quality of these leads coming in and what was it leading to? And so, you know, within those three months we were able to generate enough leads that like over the next six months, you know, led to half a million of pipeline. And so it was nice to see that like even though the campaigns had stopped, like how was that journey continuing to progress and then how long? We didn't really know because we hadn't tried it before. So we didn't know, you know, like how much activity is it going to take to like continue the drip, I guess a little bit. Right, Drip effective of our audience continuing to engage with us in different ways and then leading to funnel and pipeline. And we still have some of those um, that were working now. So it wasn't like an instant, you know, turn the faucet on and see it all come through the bottom of the funnel. But it did help us generate some quick activity, activity and able to get some of those like smaller prototype runs to um, you know, keep some activity going. And we've seen that those have led us into actually more opportunity. Because sometimes it's like you get in and you prove yourself with a component and then they realize that we have other capabilities as well. Like we can integrate those components into a full catheter. And so it's opened the door to additional opportunities for us with those um, customers as well. Which is, you know, an even better outcome than the original goal of just generating like quicker turn opportunity.

Speaker A: Yeah. Great. And uh, the results are amazing and impressive. And my question is, uh, to you Bridget, um, what were the main metrics there? Leads or number of leads or maybe some account engagements, touch points or something else.

Speaker D: Yeah, I think initially it was really around total like cast a wide net of leads to kind of start to learn about this subset of our audience a little bit. And then from there we started looking at touch points more so of, of how are we influencing these leads that are starting to engage um, with you know, the integrated campaign strategy and, and how are they interacting with our different pieces of content and then what is leading to maybe them actually uh, coming through our website or maybe in an email campaign and then how are they interacting with us after that? So it's kind of like the sort, the leads, the um, touch points source, but also how are we influencing them?

Speaker A: Mhm. Sounds great. Yeah, thank you so much. And Ravi also wanted to share. Please go ahead I think it was

Speaker E: very similar to the example uh, uh, we had just heard actually. So it's like it's one of my um, older organization actually. Uh, so where we were working and obviously when we are marketing we just tried to look at topic funnel and uh, sometimes yeah we were not looking at the bottom of the funnel actually and things changes as soon as we started looking at uh, the bottom of the funnel. So for example MQL was increasing, the volume of MQL was increasing really good. But the uh, quality of the pipeline was not actually. It was not much impactful uh impact uh we were making actually in this sales pipeline. So we started looking at some more downstream data like whether the customer was ICB fit or not. What was the conversion from MQL to opportunity? What is the opportunity value uh when it comes from marketing, um, time to opportunity and pipeline by channel or segment. We started looking at those things and then we realized that we were making mistakes at the top of the funnel because sometimes it's like improving marketing performance change by what we call performance and how we look at our results. It's not just uh, what we do it just how we look at those numbers. And I uh, think that it depends a lot on that as well. So very similar to that.

Speaker A: Yeah, great example. And um, sometimes it's very easy to generate MQLs. But would they convert or not? This is.

Speaker B: I would like to piggyback on Ravi's point about not necessarily adding the metrics but looking at differently. And one of the recent great improvements that we have achieved in our acquisition practice is we actually finally combined everything we know from the sales team, all the feedback that we have about like every single lead and, and attributing that feedback to specific sources helps us recently to identify like actually huge difference by different lead source in MQL to SQL conversion rate and helped us to unlock lots of optimization opportunities just integrating those like that lower funnel information that we've been, I mean I wouldn't say ignoring completely but like under utilization, underutilizing it, bringing that up, looking uh, at that at like more granular level, attributing that to like pinpointing the specific comments from the, our sales representative to the, to the leads from the certain sources that helps us to improve that conversion to a sales uh qualified lead significantly.

Speaker A: Yeah, great example. And similar to Ravis as well.

Speaker D: Yeah.

Speaker A: And the next question is could you please share some um, case where the metrics you chose gave your misleading picture Because I'm pretty sure that sometimes we have situations where we choose some wrong metrics. Who would like to share?

Speaker C: I can start on that actually.

Speaker A: Yeah, please, please go ahead.

Speaker C: Yeah, I think when we say it's misleading, obviously every metric says something and I think uh, like Nick mentioned earlier, it's more about how we look at the data. Data is data, it says something. It's just how we want to see and look at that. So in my experience I think there are three biases we see with the data and those are the only thing can give us a misleading picture on any metrics. Those biases are like time window bias. Like we are measuring something not in

Speaker E: the correct time window.

Speaker C: Actually that campaign or that activity needs more time to mature so that we can measure it accurately. Then another thing is selection bias. Apple to Apple comparison. Are we comparing the numbers with like Apple to Apple or Apple to Orange? Ah, we will never be able to find right answer otherwise. And the last bias I feel is aggregation. Like how are we aggregating our numbers? And I think these are the biases which actually can make our data which can say that our metrics are misleading. But end of the day is how we look at that and I think that is more important.

Speaker B: So what I would probably love to add here is also just how like building on like the apples to apples part, something that actually proved really misleading in the past. So in one of our like optimization cycles in like actually in couple of, due to lots of different reasons, I wouldn't go to that. But we actually couldn't properly test our new creatives or new algorithms, uh, or new targetings and we had to just be down to a pre post analysis. And that on the high end side proved to be highly uh, impacted by a whole bunch of third party factors and actually skewed the measurement and understanding of the efficiency of what we've done. So this is something I would recommend to avoid at all costs to everyone.

Speaker A: Yeah, makes a lot of sense. Thank you Ravi and Nick. Uh, Bridget, would you like to add something?

Speaker D: Yeah, I think you know, for me as leaders, uh, in this space, I think you always have to kind of think about how you're managing the metrics to the rest of your organization too. So maybe what metrics or KPIs are measuring and kind of what their bias is or what their expectations are. Um, so I think, you know, for me I kind of have an example that I was thinking about where, you know, we recently had a event that we went to and we, we sponsored the event and then we had a panel speaking like a thought leadership session at the event. So we sent three people. Right. And so then everyone wants to know, okay, well, like, what were the results? You know, like, we sent, we invested in sending people there, we invested in doing, you know, preparing and doing thought leadership. So, you know, it's always like, well, how many leads did you get? Right. And I mean, if you think about the panel session itself, we probably had an audience of like 30, 35 people maybe. So it's not very impressive if you think about sending three people on airplanes to go to an. But, um, we did have two opportunities that became actual pipeline opportunities. So it wasn't just like two leads that, you know, wanted to start a conversation. They actually became pipeline opportunities, like directly from those events. And I say directly. I guess one was very direct. It was, you know, someone actually who attended our session. We were able to like schedule a, ah, design review with them and start discussions around, um, an actual opportunity. And then there was another one that came in kind of as like part of the ecosystem of the event. Right. And all the campaigns around it. So they had interacted with some of our LinkedIn posts leading up to the event and then had actually converted through our website. But then we did have someone at the event as well. We just didn't directly talk to them. And so it's kind of, you know, it was like one was directly sourced from the event and one was like more influenced through the event. Right. And so I think if you look at just how many leads were there, it's maybe not as enticing of a picture or maybe doesn't give you the ROI to say, oh, yeah, we should do this event again. But then when you look at the two opportunities that came out of it, um, you know, I'd rather get those two qualified opportunities than get 200 top of. Right. So I think it's just managing those expectations within your organization of, um, what are we doing and why and how are we measuring success versus just one metric of how many leads came out of it, like at a high level.

Speaker A: Yeah. Makes a lot of sense, you know, looking at our opportunities and sales.

Speaker B: Absolutely. Because I had the same experience was marketing, communicating like marketing metrics and like fancy marketing numbers to stakeholders across the organization only to cause the question of so what, I mean, that amazing lead amount. So, so what, what's next? So I think, yeah, the, the big, the big thing is to really connect whatever you're gaining and your, as a part of your marketing activities, to translate it to the language of all your stakeholders, some common understanding, common metrics. That make sense for everyone, like final sales or as Bridget said, those defined opportunities they get from the event. So something that's commonly understanded across all the teams involved in the process.

Speaker D: Yeah. Clear translation of what those are and how they influence your objectives and how, like for the year, for the organization. Right. Like, how are they actually moving the needle and making an impact? And that has to be under the audience. Your internal audience has to understand that. Right. And can draw those lines themselves as well.

Speaker A: Yeah. That's in B2B. It's not a game of numbers. It's not about as attracting as many leads as you can. Yeah. It's about attracting the right leads that, uh, will convert.

Speaker D: Yeah.

Speaker B: And also I found out that we ourselves as marketers, me personally. Guilty as charged. I often tend to forget that the people I work with are not all market marketers. They're like, uh. And I'm not even talking about the sales team. They're like finance legal engineers, like those who design products. So for them, sometimes it just. It's not that they're not out of context of like the scale. They just. They just don't know what that is.

Speaker A: Yeah. We need to speak the same language with them. Uh, my next question to you is about, uh, some unrealistic KPIs. Of course, probably you had to work with some unrealistic KPIs in your experience. Could you share these kind of examples and tell, uh, us what you did in this situation when you've got these unrealistic KPIs.

Speaker C: Now? I was just saying having a big target is a target is unrealistic APIs. But sorry, Nick is good.

Speaker B: Yeah. So I, uh, think. Would correct me if I'm wrong. I think. What do you mean is realistic? Not like the choice of metric, but something like the targets and them being too ambitious or something. And I think we've all been there was like the leadership setting up the target a little bit too high based on our understanding of what's possible and what's not. Um, of course been there not only in the current role, but probably at some point in every one of my roles it went differently. What I, uh, found throughout my experience to be really helpful flow is to unders. It's like to really go through the process was the people with all the stakeholders and, and like explain them all the levers and like. So let's say they say we need to double, uh, the sale amount next year. Very ambitious. Cool. Yeah. We want to conquer the market. Good. But is it actually feasible if you are looking at. Uh, so you need to explain, okay, so this is your sales number. This is like the twice higher. To get there you will need to go through all those steps and sometimes by the time, by when you are getting to let's say your ad spend and your advertising budget needed to do that with all those like drop down points, uh, they already realized that. Okay, let's maybe be more realistic. Sometimes it takes more time. Sometimes it takes like a quarter of like okay, not hitting the plan, not hitting the plan, not hitting the plan. And like, like six months of doing that to actually like reconsider and regroup and just re may revise the decisions uh, made in planning like six months, six months before it goes differently. But the clarity of the mechanisms to get there to like your final measure of success is this like money in the, into the account or sold devices or whatever. It is just helping people to understand all the math of all the steps. It actually is super helpful for everyone to plan more. I wouldn't say realistically because it's like not too ambitious but more plan something, something feasible.

Speaker D: Yeah, uh, I'm just gonna, you know, relate back to something Nick said earlier about understanding that you know, your colleagues and other people in your organization, you know, they don't do marketing every day and so they don't completely understand what it takes right to, or what we're doing every day, um, and how we're influencing kind of the overall impact. And so I think to your point Nick, a lot of it is on education. And so how can we do kind of like a reverse funnel exercise to show okay, this is, you know, maybe this unrealistic KPI or objective that you're giving us. And um, here's what it has taken in the past to get to this point. And so if you work backwards from that and kind of show the data, uh, I think that can be really helpful in trying to come to some sort of compromise. Um, or maybe at least like manage expectations of okay, here's what you've given us, here's what we're going to do to try to get there. But you know, let's like be a little realistic about the timeline maybe right. Or, or maybe the um, the end result. I also think that there's probably other ways that marketing can look at. Of course for me it's like looking at the whole journey and kind of the example I referenced earlier with where we're, we're looking at our component business. So maybe instead of starting from scratch and getting X number of new revenue in the pipeline it's well where can we look at our customers that are only buying components from us and where can we go in and educate our customer about our like full capabilities and maybe we can take advantage of you know, maybe they're having some issues with a different supplier and so they need a you know, someone to help solve some quality issues.

Speaker A: Right.

Speaker D: Like maybe there's some quicker whims along the way where while you're kind of working on the other from scratch, uh, marketing, um, contributions, you know where can you help like mid funnel help uh maybe grow some revenue or bring in some other value adds. Mhm.

Speaker A: Some up sales probably.

Speaker D: Yeah.

Speaker A: Um, sounds great. Thank you. Uh Ravi, please go ahead.

Speaker C: Yeah, I think very similar to Nick and Bridget actually. Um, whenever we see any realistic KPIs like too ambitious numbers to achieve. I think the only way to go is to understand is yeah reverse engineering. Uh, go back to the funnel. Okay. This is the target you want. Then this many opportunities we will need, sales will need. So these many activities marketing will have to do and for that marketing will have to spend this much. So I think it just, just have to uh, understand the whole funnel of how marketing and sales works and and talking to everyone um, with the same language. I think that helps a lot. Uh but again at least so I come from obviously data and analytics side I can say from my side I can just say what can be achieved and cannot be achieved with this number what is there. And uh, then it's up to the business what they want to go ahead with.

Speaker B: Another thing that I would add is what's important when setting the targets. And I know it sometimes uh, is missed uh from the picture when there are some ambition, some ambition goals in play is what's the actual size of the market. We want to double our client base but is there really twice more people out there who are our target audience? I mean in most of the cases there is but uh, to be honest, in one of my previous role we ran into the moment that was with uh one specific region. Uh was when I worked for the real estate classified and we ran into the leadership wanting to us to increase our partner base to a certain point. But we came back with there are no as many players on the market in that specific region. So I mean we can try but then we need to start some new real uh estate agencies for us to partner with them. Um, that's the only way to hit the target. So yeah of course at that point we reconsidered the goal.

Speaker A: Yeah it's a very good point. And if, even if there are more people in the market or more relevant companies, they usually work with someone with competitors. So it can be different strategy, how to attract them and uh. Yeah. What to communicate to them that they choose you over your competitors. Yeah, it's um, maybe another um, problem into challenge it's possible to solve. So uh, let's talk about how metrics and KPIs set up. Let's uh, start with the basics. What's the difference between metrics and KPIs in B2B marketing? Maybe too basic, but let's discuss it very shortly.

Speaker B: I think the difference is, I mean I've been to different organizations when um, things were called differently. Let's say for instance what I now have on my list titled Okrs for Nick was titled KPI's for Nick. Same type of things listed uh in one of my previous roles. So the naming conventions are different. But if we are talking about

Speaker D: the

Speaker B: difference is I think that K and KPI is a distinction because you cannot have all your metrics super important and main. You need to define like one or two in very very specific cases, maybe three things that will define the success of your business, the success of your sales strategy, the success of your acquisition strategy, all the others they will be just uh secondary indicators for different steps of the funnel. But the most important thing is just plan from what really defines the business. I don't know in most of the cases that will be like signed contracts or like uh deals or sales, some something final and everything else behind just the metrics on the way to get there and why it's important to just plan from that. Key thing is we talked and uh, both Bridget and Ravi mentioned uh earlier that okay there is no really point in big inflated number of leads let's say on top of the funnel because and if you set let's say for leads top funnel leads as the key indicator for your marketing team that potentially creates like misalignment and like okay, marketing drives leads, a lot of leads. Do they convert further down? Maybe, maybe not, who knows. But if we all plan for like okay marketing need to deliver leads and of a certain quality for sales team to generate X amount of sales from those leads. So and if we have that those sales as a shared metric for everyone involved that creates much better synergy across, across the funnel. I would say.

Speaker A: Mhm. Yeah it makes a lot of sense

Speaker D: for me. I kind of think of it as a tiered approach. So metrics are what we're measuring. Um but not all metrics could be KPI. Metrics help us understand if our KPIs are trending in the right direction. So multiple metrics are likely being analyzed to see if we're on track to hit our KPIs. And so for me, like we're looking at metrics maybe on a monthly basis or a more often. Like we're looking at them more often. And then KPIs are more like quarterly. You know, what are our metrics telling us? Like are we performing how we thought we should be? Um, or do we need to make adjustments? And then, you know, I think that's the next part of this conversation though is like are all of those helping us really achieve objectives a week overall as a business?

Speaker A: Mhm.

Speaker D: Yeah.

Speaker A: Thank you so much. And Ravi, would you like to add something?

Speaker B: Yeah.

Speaker C: I think it's very similar to textbook definition actually. So KPI is KPI tells us whether should be pleased or concerned and measure, um, metrics gives us those measurement like why, why this happening? So I think that's, that's a key difference. So we can say pipeline, pipeline is a KPI. But traffic read conversion, uh, icp, engagement, sales acceptance, these can be the metrics which will define that KPI. And we will know whether things are going the way we want to or not. So that's the basic difference actually. But people use it as they want to be honest.

Speaker B: Yes.

Speaker A: And uh, many companies now they talk about OKRs as well. So what do they, where do they fit into B2B marketing? And what's the difference from KPIs where it um, makes sense to use OKRs instead of KPIs. What do you think about that?

Speaker D: Yeah, I think for me okrs like force prioritization. At the end of the day it's like how are you using these acronyms internally at your organization? It might be a little different. Right. For each company and your leadership team. But to me like an OKR should really force prioritization. So it's more like a vision, right, for the company. Maybe it's your vision for your marketing team of okay, they should be longer term objectives that you're able to influence and that both your KPI are helping you understand if you're on the right track and you're going to meet those OKRs. And they're probably more like three year objective, especially in our space in B2B. I mean, you know, typically like within one year we're probably not hitting OKRs. But over time like we're working on influencing those and we're probably using KPIs to understand if we're on track or not. And the okrs overall though are something that like the whole organization should be able to get behind and should understand at uh, like a higher level and M they, and they don't need to like get into the nitty gritty details of how market but they can understand how we're tracking.

Speaker A: Mhm. Yeah. Especially with your long loan sales cycle of three to six years. It's very important.

Speaker D: Yeah.

Speaker A: Thank you so much Bridget.

Speaker B: Um, how I see it and yeah once again the naming conventions could be different in my experience. And that was, I don't know, uh, we do not operate with like such a long cycle. So uh, it's a little bit more dynamic because of course it's a different industry. So how I see it, uh, maybe not the entire organization but just my personal take on it is your okrs is more of a representation of your like current tactical focus on some uh, some like improvements for this year of this quarter more likely year. So it's a midterm thing and I know that in Bridget's world maybe one year is like a short term thing but it's like it's midterm for us. So let's say if we decide to invest our time and resources of the team in a certain like stage of the funnel or certain like segment that the OKR is just something that helps you to fill this short mid term targets to understand like your, this specific. Focused on a specific area time period and to say at the end of, in the end of it it was successful or not. While like the KPIs is something that defines your business and what you just track consistently.

Speaker A: M. Yeah, thank you so much. Um, makes a lot of sense. Would you like to add something as Well?

Speaker C: I think OKR, I think it was first founded in 1970s or something by intel, in intel actually by some uh, really good. I forgot the name but I think the idea of OKR is what I feel and how we use it um, is always objective. It gives a thematic goal, what you want to achieve actually. And it should be bound by time period. That's the main thing of an okr. It should be a time period. So um, for example our objective is to launch a mobile app in England. So they should be time period like within three months. And the key results would be okay we launch the app in England. That's, that's our thematic goal. Key results would be uh, let's achieve thousand downloads in first week. Having four Star or more than four star rating. Uh, on uh, second key results could be having more than four star rating, uh play store. And third key results could be ongoing drop off rate reduce, keep it as um, maybe less than 10% or something. So I think that's how we have used it in terms of okrs. Yeah, I think that that's my understanding of OKR and that's how we use it.

Speaker A: Thank you so much. And I want to talk about attribution and how to connect marketing measurements to pipeline and revenue. Usually especially in the situations when the buyer journey lasts several months for years, even like in the Bridget situation. And um, so it, and it's very complicated. It includes multiple touch points. How do you connect marketing efforts uh, to pipeline?

Speaker D: I think for us uh, you know, with our long buying cycle all about being able to like marry the sales and the marketing data. So we really use our CRM as kind of like our source of, of truth for everything. So we make sure that all of our marketing data analytics feed into our CRM and then that our sales team is really religiously using it, uh, to keep their pipeline updated and their opportunities updated. Um, our customer service team uses it for quoting. And so we have like really nice, you know, kind of real time, as real time as we can get uh, information on, on how things are moving through our funnel, uh, which is really nice. And you know, I think the more work that marketing has to do to like pull data and try and analyze it and match it up and like figure out what the data is telling you but then also try to like predict what to do next, the more you can just get all of that into one place from the beginning. It makes your lives a lot easier and um, it helps you tell that story uh, much more clearly. Especially Dan, to Nick's point earlier, like not everyone understands marketing or really knows what like this magic voodoo marketing stuff we do is. And so the more we can kind of tell that story upward and outward throughout the organization in kind of a clear way and show that dashboard more clearly in one place. Um, like for us to be able to have our sales apps, analysts like go into one place and have access to all the data and create like one really comprehensive dashboard is really helps us a lot and goes a long way for our leadership team and what we have to report to our board.

Speaker A: So everything, every touch point should be registered and written in the CRM and then you can, this is how you can track and that way um, it's

Speaker D: not just like source data, it's also all of the touch points along the way. So we're measuring like all the different ways that our leads and contacts interact with us and when they go to different organizations, um, we can have that history of data and how they've interacted with us and like what's influenced them in the past and things like that. So.

Speaker C: Mhm.

Speaker D: So that's. Yeah.

Speaker A: Um, it's always complicated. Multiple touch points. Yeah. Especially in such long sales cycle.

Speaker D: Yeah.

Speaker A: Thank you Bridget. Yeah. Nick, would you like to continue?

Speaker B: Yeah, sure. I would agree that the m. Most important thing here is how you set up your data uh collection at first and while let's say in the long term like larger sized opportunities, it has its pros and cons. So it's a. Yes, there are way much more touch points to document to actually map that journey from your first contact to the actual sale. But also at the same time your like number of those potential clients becomes lower. So it makes it maybe so it's, it's more complicated but the scale becomes lower. So it. There are like pros and cons. While let's say in uh more like mass markets you need more to. You need to think more, you less depend on let's say one sales manager to make all those like maps and entries in the CRM and document all the touch points with the, with the uh, potential client or partner or how you call them in your business. While let's say in more in marketing you need to go more. A little bit more technical. It becomes like the question of connecting different data sources. Connecting like transferring all the IDs, connecting all them. And it becomes a very complicated journey sometimes even so just an example. Recently we've been uh, trying to just m. Move and try like um, multi touch attribution for all our acquisition activities. And we tried to map like it's mostly online on uh, ah first steps of the funnel. It's mostly online in our business. And we were trying to map the um, specific touch points to like actual people profiles who tried to contact us, who browse like different pages, explore the products, then convert it, use different tools. And it becomes very complicated. First of all uh, it's online. There's no single person who uses just like one device to browse the information and just to connect it all together. That becomes a very complex task. And also a lot of, especially in our finance world there's like a lot of different systems involved and you need to ensure that you have the consistent tagging, consistent ID things that you can actually connect all your fragmented pieces of data to understand the actual journey to purchase one of your products. So um, that's challenging where uh, it's exciting actually to be now in the midst of the process of transitioning to that thing trying to understand how the multi touch attribution should look like in the specific case for the specific products, for the specific market. But yeah that's, that's very complex thing.

Speaker A: Can imagine. Yeah, it's always like that. But it worth it. So uh, Ravi, would you like to add something?

Speaker C: Yeah, I think I agree on that as well. It's tough and having all the data, it's not always possible, isn't it? So uh, it is always tough to identify when we have like long multitude journey, we have an event like Bridget mentioned earlier, one event, two different kind of opportunities to whom to give the credit for, like who should get attribution, uh on that, those leads actually. But yeah, having data which always is not 1/700% and to identify these things. But I think we can divide these into two things. Like there are leading indicators and lagging indicators. Uh so something which will come, we'll see as soon as we started our activity and that will be like engagement uh with our activity of marketing activity, whatever we are doing and how people, consumers are actually uh, uh, engaging with that and then lagging obviously revenue, revenue may come after a very, very long time. So I think uh, these, these are two important thing. But first class touch, uh, multi touch to identify this, to define these, to have it and let everyone agree on one definition. Like this is. What are we going to call last? I think that's, that's challenge isn't it? Uh and even the data is ah, a challenge. Yeah, we can have indicators but it is challenging.

Speaker A: Yeah, thank you Ravi. And uh, the question of the ROI of, of marketing activities in B2B I think um, it's connected to the previous one. So you've explained how you track different for example touch points and different activities from the beginning till the end. So in this case I think it's pretty possible to calculate the roi. So what do you think about it? Do you have something to add here on how to do it or if it's possible or not?

Speaker D: Yeah, I think you know, in the kind of complex space, whether you have a long buyer journey or not, I think it is possible. I think it's unrealistic though to think that you're going to have like a really precise ROI number and so you kind of have to manage those expectations of like it's going to be a little Bit more of providing evidence and telling a little bit of the story of your customer and how they came to be. Because likely you can't just attribute it to one source and maybe there's, maybe there is one case of really strong evidence of like well they, it came through our website as an rfq, but we all know that behind that There were probably 15 other touch points that influenced that person to finally convert through our website. Right. So to attribute it solely to ROI to our website, you know, we all know is probably a false uh, metric I guess. But um, but I think just being able to kind of set that stage for your business to understand how you're measuring your ROI and how you are measuring attribution and you know, whether you are kind of calculating the multiple touch points along the way and kind of giving them all credit in some way or if you're, if you are just picking the last touch point. I think it's just a matter of deciding what is going to help you prioritize your marketing and what's going to help you make decisions on where to spend money and invest in your marketing to like help achieve your okrs or help achieve your overall objectives. You just have to kind of decide how you are going to do it and what's going to work best for you and then just communicate that so that everyone's under the same understanding and can see again like how the marketing activities are, are driving that impact and they can draw that line themselves as well. Because you've made it really clear.

Speaker A: Thank you so much. Yeah, makes a lot of sense. Uh, Nick, you wanted to also to say something?

Speaker B: Yeah, so one, I think it's especially important when we're Talking about the ROI in like the B2B sector is ROI is the uh, uh, is equation with two variables. We have our marketing investment and we have, we get in return and sometimes we tend to forget or miss that part that the money or benefits or anything we're getting from the new sale, it's um, often not just like one transaction that's a long, that's a start of a long term partnership. And when we are actually thinking about the return on investment on the ad spend here or like the events or any acquisition investment, we need to look at that as like a longer term thing because sometimes you're signing up the contract and you will partner with that company for like 5 years, 10 years as I said, let's say even in our retail space, uh now and it's, it's B2C but we are, are selling the products that people will have that, those policies, let's say the life insurance for 30 years, 40 years going forward, and we will be getting those like insurance premiums from them for those upcoming years. So it's also to, it's important to see what are you getting at your like conversion point when somebody becomes your client. So it's not. Yeah, it's actually the beginning of much longer journey and yeah, this actually. And in lots of the cases that will make your ROI calculations dramatically different.

Speaker A: Yeah, it makes a lot of sense. Sometimes the first deal can be smaller. Yeah. And then um, some future deals with the same client can be larger and more consistent.

Speaker B: But then there always comes the question, of course, who gets the credit for the next sale? As it's like the account manager of that person or that organization or marketing who brought such a good lead also takes some amount of credit. But um, that's another problem.

Speaker A: It's a difficult, maybe philosophical question. Even

Speaker D: the conversation of like marketing's influence on branding and how you're internally educating your employees to represent the brand and how that's influencing your acquisition and longevity. So yeah, that's a whole other conversation.

Speaker A: Oh yeah, yeah, for sure. Yeah. Ravi, would you like to add something here on the ROI topic?

Speaker C: I think they improved pretty much everything actually. Still have to go with some assumptions and at the end of the day assumptions can be assumptions or assumptions. That's it. But another thing.

Speaker E: Yeah.

Speaker C: Um, we cannot calculate ROI completely. Like for smaller acquisition, like uh, where we have like trackable acquisition journeys. We can for longer uh, acquisition journeys for big enterprise customers. It is very, very hard to do that. So yeah, that's actually to somewhere I think what make and richer. Yeah.

Speaker A: Thank you so much. And uh, the end of our conversation today, I would like to ask uh, you about some automation and AI tools you use, usually in tracking metrics and KPIs. So um, the first question will be about the platforms or some tools you usually use. What is your tech, uh, setup on your company, current company, or maybe previous experience. What do you use?

Speaker C: So I think. So now we have uh, in Sage, we are using Snowflake for our data warehouse. And uh, we have really nice uh, LLM by uh, and AI technology by Cortex by Snowflake, which is very good um, for data people because we can just build a data model connected with Cortex analyst and then Cortex does affiliate, uh, and it's really smart tool tool and technology. Other than that I think, ah, we are piloting uh, Claude as well in our organization. Uh, in my previous Organization Ah now they have Claude and uh. I think that's that switch to AI and uh. I think pretty much it CRM and CRM. Our data layer is uh Snowflake and with AI we are using cortex and uh. For measurement with power Bi we have Copilot. Other than that we are also building our own uh. AI agents in aws. But yeah um. LLM is ah already there.

Speaker B: But this uh.

Speaker C: Is actually what we are using where we are using some already built solutions and some we are building for ourselves based on the use business use cases.

Speaker A: Mhm. Great. Yes. Some uh. Specific tools and AI of course as well. Yeah.

Speaker E: Um.

Speaker A: Okay. Richard would like to. Or Nick. Yeah.

Speaker B: I mean we use Claude and Copilot on a daily basis. Of course we are now thinking in the direction of a snowflake. It's early uh days yet so like I don't have a lot of details to share on that front. When. When the whole conversation always starts about the AI I always more excited to talk about like the principles ah. Rather than tools. In terms of what would you use AI for what not uh. When do you draw the line.

Speaker D: Um.

Speaker B: Which is I think yeah the more the very exciting topic.

Speaker A: Yeah. If you could briefly share with us.

Speaker B: Um. I am very um. Excited about what AI can bring to our daily practice. Of course great opportunities, everything. But what I see a lot is believe that just we will use AI it will come, it will change everything, it will make us life better. And then when you start okay, how which part of your funnel, what type of decision, what type of optimization, what what are you ready to give AI uh the freedom to make a decision for you or what decision you want to make yourself. So it's. It's insanely good for the automation of the work, for the optimization of the work for like all like a pipeline processing. But I think it's still go. We still need to take it with a grain of salt. We all know about like the sometimes the hallucination of the LLMs and all the very confident telling you something so wrong so that I think in a times when we rely so heavily on AI based optimizations that actually makes like the cost and uh. Like the power of your expert knowledge to validate the outcomes of the AI work. Really critical because like my own personal principle is don't dedicate to AI and I mean professionally not personally. Personally I sometimes rely on that too much. But professionally don't rely on AI and something that you do not completely understand yourself as like expert on the matter. Because otherwise at least at first stages you always need to validate if what you're getting is legit, valid result and then you can use that going forward. So that, that is very important part. And I have a feeling that with all the excitement about the AI usage in our professional practice, sometimes we tend to forget that part.

Speaker A: Mhm. And yeah, sometimes AI is not good, generative AI. I mean it's not good with calculations. So sometimes it makes mistake. It's a risk.

Speaker D: Yeah, I think um, we're kind of in a similar position. We're actually, the commercial team is evaluating like where it makes the most sense to start to utilize some AI tools. And you know, I think a lot of organizations are getting initiatives, right, Like AI initiatives that different functions have to kind of adopt and figure out how they're going to meet that initiative and like show some progress and like how do you tie some KPIs to it? Um, but so people, you know, we want to do it in a way that's going to be impactful for the organization and like help us achieve our objectives versus you know, kind of just adding metrics to show data for month to month. And so it's, it's more so about like, gosh, we do have a lot of data in our CRM, so how could we more quickly maybe look at that data and identify some trends and, or maybe like identify some gaps maybe where are we experiencing fallout that we didn't recognize before, you know, and maybe give us some predictive capabilities of, you know, or maybe giving us some, maybe insights on, you know, when we have kind of a new goal of like where should we start or, or you know, maybe it can give us some insights on where we've seen success before and we didn't even realize it. Maybe. Right. And so instead of like in my one example of, of our component business where we kind of were doing trial and error because we hadn't done it before before and we had to learn a lot along the way. Maybe, maybe we'd have some learnings up front in that kind of case. But you know, I agree with Nick that I think it really, there's a risk of kind of losing human contact, you know, in our space in a complex B2B environment. I just think that you really have to understand your business enough to know when an answer makes sense and when to question it because you know, it's easy for AI to just kind of like pull data together and give you an answer, but it doesn't mean it's the right answer for your business. And Your like the market you're trying to capture and your audience that you know so well. So you know, I think it can be helpful, but we have to contribute our human context and understand if result is, is accurate based on what we

Speaker A: know about our um, own business.

Speaker D: For sure.

Speaker A: Industry experience, industry knowledge and makes into marketing knowledge as well. Yeah. Makes a lot of sense.

Speaker D: Yeah. Don't worry about the risk of, you know, in this environment when you have a long buying cycle and your audience is highly educated and they're trying to educate themselves before they interact with you. You know, you spend so much time and the opportunity cost of like getting them so close and then potentially losing them because you lost the human context and you, and you have too much AI tool. Um, gosh, I would just hate to think about starting to use something because of an AI initiative and then you, you lose leads out of your funnel and it's so hard to build that trust and to get them back. So I just think you really have to keep that in mind too of how could they, um, be doing negative marketing for you. Right. And just really keeping your, your Personas in mind for where you are in your funnel.

Speaker A: Yeah. This highly intellectual audience feels some AI slob, um, vibe or feeling.

Speaker D: Yeah.

Speaker A: In this case it can ruin everything. Yeah. Makes a lot of sense to think about this kind of risk. Thank you, Bridget.

Speaker C: Yeah, I think the um, good in bed of AI and I agree with Nick and Bridget on that. But there are. It is a technology. End of the day, it is a technology. It is going to help in our world. That's it now how we use it. And uh, I agree with next point. If you are the subject matter expert and you are using AI, it's good. Otherwise what happens is AI says you can do it and it doesn't define that can actually. And people who are not subject matter expert, they think yeah, we can do it, but they don't ask why we should do it. Kind of a conversation actually that's. That happens. But with AI in terms of data and uh, analytics, if you say I think it is very much useful in terms of analyzing the huge amount of data, it can do it as quickly. Very, very quickly. We don't have to wait for data analysts and we don't have to wait for data scientists to do some uh, basic uh, analysis work on a data. So I think that is something where AI could be very much useful. However, hallucination very important. Otherwise with that bad data analysis they can give AI can give us a very convincing story and we will Assume it is right because it is so convincingly written there. And we're like yeah, it's correct. So I think putting some guardrails around our data, that is very, very important and human, uh, interpret. There must be a human intervention in terms of decision layer or wherever we are making any decision, whether we are bringing new data to that agent, whatever in terms of decision we are doing there. Uh, I think decision layer should be handled by human being, not an AI agent. Context, I think the way AI technology is growing, I think in six months time AI will have more context. And anyone else, that's what, that's what I think. Um, I think like because they will have more context because hundreds and millions of people are chatting with AI models nowadays, they will obviously learn from these chats. So they will have more context, more uh, yeah, spectrum of understanding uh, things than as human being we will be because we are limited to things. Right. But again I think uh, having those guardrails around the data, having understanding of what we are looking at, why we are looking at that and understanding that, I think that is very, very important today more than any anytime. I think it is very important today if we use AI just as technology to help uh, in terms of having the data. And yeah, I think it should be just using medium then rather than just like what I think uh, world is currently saying.

Speaker A: M. Yeah, makes a lot of sense. Thank you. And humans first. AI is a helper to us, but humans are the most important um, ah, elements here. So thank you so much for this very insightful discussion and I really appreciate your practical examples and your perspectives. These are very valuable. Yeah, thank you for them and so thank you for your time and expertise. Um, thank you, thank you for everyone watching us.

Speaker B: Thank you for having us here.

Speaker C: Thank you so much.

Speaker A: Thank you.