How to embed successful data commercialization strategy in 6 steps:
Strategic alignment - identify which data can contribute to achieving business goals, such as increasing revenue or improving customer satisfaction
Team alignment - establish a shared understanding of KPIs and their definitions across marketing, sales, and customer success teams.
Gap analysis - identify gaps in data and develop a plan to bridge these gaps to ensure you collect all data necessary for achieving business goals.
Data management - centralize your data in one place if necessary and make it sharable across teams.
Infrastructure and talent - asses systems, tools, and talent needed for data transformation and analysis.
Governance - designate responsible parties and establish processes to guide and champion the data strategy.
All right. So everyone, today we'll be talking about from data to revenue, how you can unleash your growth potential. And it's all about data today. So we already did some introduction, just a couple of bits more about myself.
So I'm indeed the founder of ThinkRavOps. I've been around for just over three years, three and a half years now. I currently work with startups and skill ups, PE-backed. My expertise has always been around customer journey design.
More so in the last year or so, I've been very interested in data monetization data commercialization, which we'll be talking about today, as well as tech stack and optimization. I put one of my favorite slogans in there we are in the people's business.
So RevOps, we do a lot of techy stuff. We talk about data, we talk about systems, but at the end of the day, we are absolutely a people's business.
So you can't do anything without the people. So there we go.
So what I wanted to do is I wanted to start with some key stats first. I'll do the first one. So 97 % today represents businesses in the world that are actually investing in data and AI projects.
It's quite a lot of us because we're talking about data and we're talking about AI for sure, especially in the last year, much more.
24% is actually the businesses in the world globally that are actually, in fact, data-driven, only 24 %. Most of us are quite not getting it.
The next one is 77 %. Now, 77 % are the number of businesses today, and if not, I think it's more, that have data issues, data concerns, especially around quality today that is stopping them, or they believe it's stopping them from actually being data-driven today.
72 % of leaders today are actually struggling with prioritizing the right metrics and KPIs. There are a lot of SaaS metrics and KPIs out there. How do you know which one to prioritize?
Most of us, probably try and use all metrics today, but we can't quite get it. We almost get analysis paralysis today because we're trying to do absolutely everything when we don't always have to do everything.
Depending on where we are in terms of the stage of the business, we should be able to prioritize some metrics over others or at least get to our MV KPIs, as I call them.
Other issues we have today are lack of objectives, and here we're talking about a lack of data objectives. So think about you set your overall goals, you set your OKRs. How do you actually compare with setting the right data objectives around it as well?
Data overload is a big thing as well today. We are accumulating a lot of data. So how do we get to a point where we understand how we can manage the data today? Data quality, we just talked about it, lack of resources.
Lack of resources can be both from a tech perspective but also from people and skills. the right skills in place.
Then finally, poor data integration is also a big challenge today. And I think most of you can probably relate when you have potentially HubSpot and Salesforce, and then you have Salesforce, maybe you also have ZoomInfo or Cognizm.
But those integrations are always a little bit challenging around getting the right data flow and ensuring that the right data are connected up. So we have a lot of challenges today, and these are challenges that are stopping us from becoming data-driven.
But there is a way for us getting to the next level. There's some work that we have to do and a lot of businesses are missing this today. This is what I want to talk to you about.
Data monetization. I don't know how many of you here listening today have ever heard of data monetization. Some folks here who are more on the data side and are interested in the data and geek stuff might have come across it.
I surely come across it maybe a couple of years, three years ago. I was really interested in understanding a bit more of that. Really, what is it?
Think about the customer data you have, interactions, and engagement. Think about just your funnel today, any marketing data coming in from your prospects to how you close customers to any data around your customers. So how do you take that data and transform it to be much more valuable to you? So that in effect you can use those data into insight and that insight actually provides you with potential more revenue streams.
And that is what data monetization actually means.
Initially, when I heard the first time about data monetization, I thought, is this even GDPR compliant? What do we mean by that? Because I always thought it sounded like you sell data to other parties or anything like that. That's how you monetize it.
Well, so that is definitely part of it. Because we have internal and we have external monetization.
So external monetization is what if you have the data itself as the product that you might sell to a business. And of course, with that, you need to look at whether are you GDPR compliant, etc. So that is something that I am currently not touching, but it's indeed part of monetization.
What I am talking about today is internal monetization. It's about the data that flows internally and those insights that you might have around your go-to-market teams, product development, HR, supply chain management, etc.
How do you utilize those insights for your internal benefit? And that part itself really is called data commercialization. So it's the process of taking that data, expecting the value, and then generating some revenue, or maybe we can podcast, which sounds good.
There are a lot of benefits in how you could use data and especially how you could use the transform data, so the insights. So are you going to create new market opportunities? How are you going to boost your revenue? Can you increase productivity, also efficiency, really, especially for those here in ops? Can you streamline decision-making? And especially since we've been talking about it for years and years. So data-driven decision-making, how do we do that?
But also it was quite trendy. Everybody's doing data decision strategies and whatever. But it's a big word for just tracking LinkedIn ads and UTM tracks, right?
Absolutely. Actually, when I worked at Adobe, we did have a big initiative around this as well. And it was called Dedom, I guess it's data-driven decision-making, but we call it DEDOM. And there was an internal push around that from the head unit to San Francisco, up to global. And it was something that still felt a bit fluffy and too high level and strategic and less, Okay, practically, what are we actually doing today to be able to get there?
So yes, there are a lot of benefits for sure in data monetization, or as I call it, data commercialization, because we're only looking at the moment, we're only speaking of internal, right? What's happening internally and how we can use that insight?
How do we get those benefits is the question, right? So there are a lot of benefits we just talked about here, but how do we actually get those benefits? A quote that I really like from Jeff Wiener is, data really powers everything that we do.
And most people do believe this. And as you know, most companies are having those data conversations on a daily basis because they're trying to figure out how we get to a place where we're actually getting value from the data today because it's always a mess.
Now, if data really powers everything that we do, then we have to do something.
First, we have to change the narrative a bit more.
And I think most businesses are getting there, but I don't think we're quite there yet because data is no longer just a byproduct. It shouldn't be an afterthought.
So most companies think about startups, only once they get to a certain stage then do they start thinking about, Okay, I have data coming in. How are we going to use this data? How are we going to set up our insights and how are we going to use the insights?
But even then, it's not a grand plan around that that's super embedded with their business objective. So we need to change the narrative from it's a byproduct or afterthought to actually it's a valuable strategic asset.
So that means if that's the case, that from the moment that you're thinking even about your services and product that you're going to be selling to the market, you're also thinking about data.
You're also thinking about the data you'll be accumulating as part of that process and thinking ahead of how you will be able to create a strategy around that that will get you and set you up for the future. So that's where we need to get to, really.
I was wondering, actually, because you mentioned startups, when is it statistically actually making sense to think about data?
Actually, you tell me, what do you think? When should a business start-up think about data and commercialization data strategy? When do you start thinking about it? What do you think?
I can tell you from our experience, even if when I was like me and another person running the agency, I had the HubSpot and I already started collecting that data because I was thinking always long term. I
was thinking, okay, maybe now I'm not doing editing with this. But at one point, I have such a good understanding of what these people did in time. That is crazy.
So I was thinking more about maybe the customer journey, or I was thinking more about I could reflect back or I could, in a discussion with a customer, I could already know the history that this customer or that this particular prospect had with me, and what content did they consume, so on and so forth.
And I can maybe put it in context. So that's what was my way of thinking. So definitely from point zero, I don't know.
And I would agree with you. That is the correct answer. Well, actually, you got that right. So most businesses, probably really start thinking about it after product market fit. At least when they're anywhere here, they really start thinking about, Okay, we're having data because now we're accumulating data.
What can we do with it? What does it tell us? How are we performing? But actually, going back to changing the narrative, if it is a valuable strategic asset, then it's as important as the product and services that you're going to be selling.
So at the moment to start thinking about that as well around here, around how are you going to go to market, what product, what services, your business model. It's part of that. Because if you think about your customer journey business model, your products and services, all of that contains data.
That's all information that will help you grow and will help you have the right direction in the business. So you should really start doing it from the beginning. I'm going to take you guys through, at least at a high level, because it's quite comprehensive even.
Data strategy. That's a big word that we hear as well, data strategy. And especially, you hear more about that on the enterprise side. So large enterprise organizations, they're always talking about data strategy. Now, unfortunately, even when they're talking about data strategy on their map, they're not necessarily doing a great job of it themselves either, by the way.
And in fact, I think, in my opinion, startups and scaleups have the advantage because in big enterprises who started thinking about data much later on in terms of really how am going to embed this into strategy and operations?
They're so far off because processes are so complex. They have so much more, like many more integration systems. They have so much more data as well to have to think about. Actually, as a startup or scale-up, you have the advantage that it's not too late. It might be still a challenge, but you can quite quickly get to a place where you have embedded that commercialization strategy into your overall grand plan. So data strategy is part of that.
So when I say commercial strategy, it is similar to data strategy, right? But it's just because I'm focusing on that in terms of my insights into the organization and how I'm going to use those insights to support my growth.
So data commercialization. I just talked a little bit about this.
What it really means is you have to think about, first of all, understanding your product and service, which you're already doing from the start. You are going through your business model, how I'm going to sell. You're looking at your customer journey. So you're looking at those touch points in terms of how your prospects and customers are going along each and every touch point.
And not just your customers, but also internally, the stakeholders, your good market teams, your product teams, how they're involved in that journey. And along those three, you have to start thinking about what's your data monetization or data commercialization strategy for each part of that.
You have to start thinking about, what data am I trying to collect here? Because why am I selling the product in the first place? So what do I want to know about it when people sell? Why did I choose this business model? So what do I want to know about it? Because do I want to know that actually it's working? It's the right direction that I'm taking when it comes to the business model?
And then the journey. I want to understand what are my customers doing today. Why are they selling? Why are they buying or not buying today? Is there something wrong with my customer journey? Where are the gaps? Where are the frictions?
These are all data points and information that you want to know across that. It's very important that as you think about these functions here, you're also thinking about the data part of that. It should go one on one. They're all one and the same. And they should be then embedded into your overall business or go-to-market strategy.
How do we embed a data commercialization strategy? There are a lot of steps, but I try to condense this into six broad steps. The first thing is really strategic alignment. What I mean with strategic alignment, which I talked about a little, is if you understand your key business objectives, in parallel, you need to ask questions about your data-driven objectives.
So I'll give you an example here, this one. So if you know your business objectives could be increasing revenue, improving customer satisfaction, reducing cost, etc. Then if you're defining your data-driven objectives against that, then it could be let's just take customer satisfaction. So I want to improve customer satisfaction. Your data-driven objectives could be to analyze customer feedback data to identify areas of improvement.
Now, if you know that, then you can go a step further because then the question you're asking yourself is, Well, how am I going to do that? Do I actually have the data points available today to be able to gather that information? And where is that information, by the way? What are the data sources? And are they coming in the right format? Or do I have to transform them?
Those are the questions you can start asking when you do that. So that's a strategic alignment. So you're really thinking about your business objective and your data business objective as one and the same, or you're matching them.
Then the next step is team alignment, which is actually divided into four areas, really. So it's us as a business understanding which KPIs are important to us, and also understanding the definitions around that. So it's universal, at least in our organization, we know exactly what we're measuring.
So if you're measuring MQL conversion rate, you know exactly what it means. You also know exactly in what time period we're usually measuring it. So you need to be very specific and everyone in the organization needs to know so they speak the same language.
But what you think is the difference? Here it will be mainly the alignment between, let's say, marketing sales and customer success. So if you think about the traditional HubSpot lifecycle stages, it could be the handoff from... Let's say, how do you measure MQL, SQL opportunity customers, and so on and so forth? And how do you do the handoff? This is what you mean by this team alignment.
Yes, exactly. And it's more. And beyond that as well is getting granular. And I would say granular, it's still part of the strategy. So it's, yes, we defined that and we have the team alignment, but it's about the definitions specifically.
Because I've worked with many businesses where different teams don't think of this, well, they don't think of the same KPIs, first of all. So it used to be, especially now we're getting better at it. We're trying to centralize and marketing our sales to work together. But before, they might have their own KPIs. And I don't think that it might link that it's one and the same and they have the same goal.
Now, what you need to do is you need to define that for the team. So everyone understands that. So even if you have, let's say, sales don't necessarily have a churn KPI, CS has, but they need to understand what their metric actually means and how it affects the entire organization.
So it's important to do that. Then the next step after that is you need to really predefine what decisions we want to make with the insights that we're going to get from it.
So once we define the KPIs, okay, so we get the numbers. So what? What are we doing with them actually? And you can actually start having conversations before and predefine, what type of decisions are we trying to make in the organization.
So what type of questions also can we ask in the organization? You can preempt those as well as part of your strategy.
And then finally, think about all the key activities you have in the business. Pipeline meetings, forecasting, projection meetings, QBR support meetings. All of those can also be predefined. So having an objective for what is it actually? What is actually a board meeting? Why do we do this? But also what do we need for it from a data standpoint? What are the key KPIs for it. And where are we going to get it?
Same for your QBRs, defining each one of them and then being able to also enable the entire organization. Because it's not just about leadership, it's that your sales guys need to understand exactly what the business is actually measuring, and what it means. S
o when they get to their pipeline meetings, they understand what the pipeline meeting is for, they understand the process, and they understand what KPIs or data points they need to bring to the table when you're having those conversations because they understand how it's linked to growth.
So that's the team alignment. So there are a few steps around aligning the team. So you can't stop with just, Okay, we define the KPIs now. We need to go beyond that and really define all your key activities, really.
Finally, once you got that, then you go into gap analysis and resolution. Now, this is basically, going back, we have our strategic objectives, we have our data-driven objectives, we have our potentially our MV KPIs, our Minimum Viable KPIs, because, again, there are so many metrics and numbers out there that you can probably track or measure.
But the best thing is to start with the minimum viable. What is important to you today at a minimum that allows you to do three things.
One, you have visibility into performance in those different areas if you think about the customer journey, as well as the business model, as well as the product.
And then two, you can actually it allows you to articulate why. Why is it up or why is it down? Does it give you enough insights beyond that? Because it's so what? Why is it? Then it can be a compass.
So it is able to direct or redirect you because you want to stay agile, especially as a startup. You want to quickly turn around decisions if it's not the right thing for you. And that's very important.
Part of that is then understanding with the gap analysis, what data points do I have today already that allow me to do that?
And what is the gap? So what am I not currently collecting in the journey? What data points in marketing? What data points in CS? Am I not quite getting it? And how, putting a plan, how do I get there? It's very important.
Then finally, we go into data management. And data management is how are you going to manage your data. And how are you going to centralize that as well, which is very important? Then finally, then we go into infrastructure and talent. Once you have a plan for that and you know what you're measuring you have to ask the question, where are you measuring it? Where are you collecting the data? Do you have the right systems?
If you have to transform the data into meaningful insights, are there analytical tools that you need to be using that you're not today? Maybe at the start, what you're doing is you're doing your Excel, using pivots and things like that, and you have hopefully someone who is skilled enough to be able to do that.
But at some point that doesn't cut it right you need to think about an infrastructure that allows you to really go fast.
And later on, we'll talk a little bit about AI as well. And especially AI, it's especially big now, and we talk about it so much more now, it's really going to help us accelerate that as well. Then finally, governance is important as part of your commercialization strategy. So who's actually governing all of this?
Because things are changing all the time, especially for startups. You probably need to re-review every six months, maybe every year is better. But you need to get to a central focus point where someone in the organization can own this and guide and champion this as well in the organization.
But it doesn't mean that they're the owner and they're only accountable for it because everyone in the business is accountable for the data that they manage on a day-to-day basis. Right? Okay. So where are we now?
So we talked a little bit about everything here. So okay, the good stuff, the juicy stuff, right? So benefits. We talked about some benefits that you can get around a boost in revenue, productivity, etc. But some stats that are actually out there are based on organizations actually using insights, becoming data-driven, and using insights to really help them grow.
There are stats out there. There are 20 % customers. That's right. Businesses are more likely to acquire customers.
23 times more than anyone else who implements this type of insights strategy in the organization.
Six times more is likely to retain customers. 58 % more likely to beat their revenue target.
And then 19 times more are likely to actually be profitable.
So there are some proven stats out there of organisations doing that. It's just that it's not enough of us doing it because we're not quite getting to a place where we're setting up the right infrastructure in the organization to be able to do that. It's absolutely possible, but we do need to think about the data or insight strategy from the beginning.
And if not from the beginning, you think about it now, do think about how are you going to embed this today in your business, in your over grand plan, in your vision, in your business strategy.
So how do you do that today and the steps for it as well? And I just mentioned a few of those here. Now, once you got all of that, and we just talked about the whole strategy in place, and okay, now we want to run and we want to do something.
So how do you actually drive insight-driven revenue? So how does it actually work today? Now, there are a lot of KPIs, some of the main KPIs right there.
We want to know, how do you take that into a potential funnel and then do something with it? And then hopefully it's just raining dollars or pounds if you're in the UK. So how do we actually do that? So how do we transform that into revenue?
Now, the difference between external monetization and internal monetization is external monetization is a bit more direct. Because if you're selling data as a product, there is a direct link to the value you're going to be getting back from it. So that's that transaction happening.
Now, if it's internal monetization or commercialization, then it's a little bit harder because how do you actually also quantify this for a startup? Because really it is influencing revenue. But there are ways of keeping track of the changes you're making in your execution that are based on insights that you've got.
But in order to get to a place like that, you need to be quite rigorous with your process around how you use insights. So a little process that I put together that hopefully, a lot do it, but we're not quite cementing the process into our day-to-day.
It could be potentially into a particular role. When I worked at Adobe, if you're selling to the SMB, especially the consumer side of things, it's very online-focused. So e-commerce. Now, how do you get you're not getting necessarily on a call, Hey, I'm going to call consumer and say, Hey, you want to buy something? You want to buy Adobe Cloud or anything? No, you don't do that.
So a lot of the selling that's happening is based on analytics. It's based on insights we're getting so we can just tweak little things, campaigns, how we do websites, what campaigns we're getting when we're going out there, where are we reaching our customers, how are we getting, how is our inbound looking like, can we do more, or all of those things is based on insights.
So even if you're a B2B, it is going to be in a similar fashion. And the handling of insights to drive revenue put this also in a different realm in terms of roles that potentially could sit there.
In ops, potentially around having a revenue target or influence revenue target around that. But really how you want to use data to drive revenue is these steps.
So it's really defining the problem first. So if you have set up your infrastructure to get the right insights, based on that, you define the problem. So let's say the problem is churn. So then you want to get the relevant data around that.
You want to analyze that data and understand a bit more about that. So why is churn happening? Where do we see the fault? Is it maybe that actually from the get-go when we're not onboarding them properly, is it because they're not just not adopting the product that we're selling to them? Why is that? Are there specific features that are missing? What is it?
So we're trying to analyze the insights in that way. Then we develop a hypothesis. Based on what we've analyzed, we now have developed a hypothesis around what the number is. So they're turning X %, which is above the target that we set ourselves.
And we know now also the data is telling us that the turning is because of X, Y, Z. So once we got that hypothesis, then we can test the hypothesis as well. Because then it's about, all right. So if we know the issue, what are we currently doing?
So we're saying maybe the onboarding is the issue. So are we putting some actions against fixing that today? So maybe we need to fix our onboarding process. We're not giving them enough training potentially. They're not getting the right support yet. We're not properly closing off onboarding, getting them to the next stage, whatever it may be.
So we then test the hypothesis and we go out there and we try to execute against it. And then you go back to iterate and refine. So that's how you can use the data to drive revenue. Because what it does is as long as you have the baseline established around... So we know the churn of X, now we know why and now we know what we can do about it.
Once we've actioned them, let's revisit back and understand where our turn now is. So is it up and down? Is it working what we're doing today?
In product, there is a lot of debate nowadays about this idea of adoption versus impact. Because if you think about, I don't know, tools like amplitude or mix panel and so on, all these SaaS companies were trying initially to make sure that there is an adoption of the tool.
And slowly on the adoption, you were making some hypotheses. Okay, if they are using this feature or that feature, or if they are logging in into the tool quite often, then that's an indication that you are actually using the tool, rather than focusing on, hey, is the tool making an impact in the business and actually achieving its business goals of why that tool was purchased.
So how do you solve that? Because that's a big issue I see that adoption is not enough. Adoption doesn't mean you make an impact. Adoption is... Maybe they are logging in because I don't know, you screwed up the tool, and then the reporting doesn't work and you're trying to fix it and you are on the tool all the time to fix it and have this huge friction versus having an impact. B y.
Yeah, for sure. It's actually interesting you talk about that. And definitely with a few of my clients, this is a hot topic. But we don't necessarily talk about adoption versus impact. It's adoption and impact.
So it is part of that entire customer lifecycle, which starts with onboarding. You close the customer, now in the onboarding phase. And onboarding has its own set of steps and it also needs to be defined what actually means that they're properly onboarded.
So you need to define that exactly. What are the things that need to happen or the customer needs to be able to do for them to say, I'm signing off, this is great, I'm onboarded because the right people are onboarded.
Because the people are onboarded, I'm able to do certain things in the platform already. It's already useful to me at some sort. So you need to figure out what exactly that is.
The next stage after that, after onboarding, is where you get into the adoption phase. And adoption is about, again, depending on the company, defining exactly what adoption actually means.
So adoption goes to they're actually using the tool, but indeed it's not just about their logging in.
Logging in is a good indicator, hopefully at the base level. But after that, you have to define exactly, depending on what your platform is, what are they actually doing in the tool.
You need to understand really what your leading indicators are in the organization for your platform. What is actually a successful adoption?
So define that as well. But again, like you said, it doesn't necessarily mean that you're going to not be adopted, that that's valid or impact. That's the next stage. Because you want to get them to adopt. Adopting really means that it's now embedded and ingrained into their, hopefully processes. First of all.
Now, beyond that is the impact. Now it's ingrained or embedded into their processes, into their day-to-day. Are they actually now seeing the value for what they actually bought in the first place? Because to start with, during the sales process, we sold them on that impact.
We sold them at that value. Now it's embedded into their processes. Can they actually see what that is?
That could be, I suppose, identifying those critical events. An example, when I worked for B2B SaaS for e-commerce s in conversion rate optimization and retention, one of the critical events was if somebody would generate an RFM model report for their e-commerce, that's more than advanced reporting because it looked at a bunch of cohort analysis and so on.
Then we know that, okay, these guys are mature enough. They are using the tool at its maturity. Now that's an indicator that we could open up a discussion about managed services. Because if you are looking at that level of reporting, it means that you have some specialists in the team who maybe understand that. And this is a trigger to have that discussion, right?
I think absolutely. Actually, for one of my clients, we did an exercise where we were looking at the platform utilization metrics. And we got to a point where we've... thinking of maturity, where we had to divide it into categories.
What is the minimum, which is base level, not just login, but are they doing releases, for example. Another base level would be how many licenses you sell to them and how many are they actually using. That's also based on level, right? At the minimum, that's what you understand from they're using an adoption, they're logging in.
Then you have the next level of maturity is then looking at what they can actually do in the platform, which is really based on your features. And you have some more simple features that most folks enrolling into the platform will be able to do it, or because that's maybe the baseline thing that they have to do.
Then you have more advanced features like the one that you just discussed. Companies need to categorize as well. And again, going back to using all data because it's impossible from the start for you to think as a business think about, Okay, right, I'm going to think about all the utilization metrics that we have out there that we should be measuring.
It is impossible to do it in that way. You can get to it at some point, but doing it in stages. So you have to define exactly what is minimum viable that allows us to at least do, again, the minimum so we understand better.
Once we got that really right, let's go to the next, and then let's go to the next, which allows us to understand more and more in terms of the maturity of our customers.
So we understand that performance better, which is going to help us for sure understand also more about the risk around churn. Thank you.
So how do you measure that whatever you're doing today and whatever insights you're getting that is informing you to execute is giving you return back, so an ROI back? So again, it is not an easy thing to do. It is complex, especially because it's also indirect.
So you can never really associate it directly. At least I haven't come across a metric that allows you to do that directly because there are so many things in place.
So if you have insights around the productivity of the team and you want to ramp that up now, that would be based on you potentially enabling the team. Let's say enabling the sales teams to ramp up, maybe do more. Or you might change the sales process slightly so that they can do things faster, things faster, and reduce the sales cycle, let's say.
But there's still people involved in these steps that are out there selling as well. So that's also the consideration of their own skills that they're using to maybe grow their accounts or their opportunities as well.
So you can never say it's a direct link. It is influence. But if you are able to measure that, if you are able to put a process in place where you're always analyzing the data, you establish the benchmark for that period of time, you decide on what are the key actions we need to do, and you execute on the action, then you measure the result because you have the benchmark.
You know our productivity was low, and you did a few things, maybe next quarter you review it again, and then it gives you the results, and you check your productivity again. If it's up, then you know, okay, I've influenced that number. And then you do it again and you iterate.
Now, most of us, we do this, but we also do it very ad hoc. It's not embedded, it's not ingrained into our usual processes because of several reasons.
Resource, for example, a lot of this obviously is going to come into rep ops to do or some operational team. But we're always putting out fires. We can rarely be so proactive in doing that. So maybe a company would say, Okay, I'm going to get a specific person, a role just doing that, which is okay too.
And you have the right skills and someone is more with BI unless you can keep coming back with that information to you. But again, it doesn't really scale. Now we have so many options out there around BI, but also especially getting into the realm of AI. So again, how do we get those benefits?
It's the next step. Maybe we start doing it and it's a bit more manual. We're using people to do the manual work, but now we need to do it at the next level. And when I say that, I don't mean that okay, the people who are doing it, we don't need them anymore, actually. I do believe that what AI does, enhances what people do in terms of elevating us.
We're able to do much more, be much more strategic. So to get to the next level, now we start thinking a bit about AI because it will make data analytics a little bit simpler. We can automate things. It helps with visualization, predictive modeling, and all of that. So stuff that most of you will know. So that's very important.
So how is AI evolving in this space? Rapidly, as you all know, there are so many tools out there and there are so many current tools you're probably using that are now adopting AI into their offering as well so that we can do more around data analysis, better recognition, etc.
I think based on what I said in terms of data commercialization, it is something, one, maybe you might not hear of those terms. I do think it's important that you use the term because you're probably doing some of it, not all of it. So it's really embedding strategy within your business overall business objectives. But using the term because it helps you change the narrative.
Because if you think of it as a very critical and valuable asset in your organization that also helps you with revenue generation, then maybe all of a sudden you think about it slightly differently and it becomes such an important agenda rather than an afterthought.
So then once you got there, then the next step is putting a strategy together to embed it. Then it's about how do I put the right processes together to actually have it almost as a role in the organization that allows us to continuously have those insights that help us grow revenue.
Then the next step is what else can we do? And that's the techy part. What do we do with AI today with automation, machine learning that helps us completely automate this so that we as people, all we do is take that and we can be more strategic around the execution of part as well.
And we can do more of that rather than just sitting and manually crunching numbers. We can be quicker in making decisions and we can be quicker in executing them as well. So yeah, I think that's it. Thank you.
Thanks a lot. Actually, we have a question we can stop this then? Yeah, we had a question about if you could elaborate a little bit more about reporting dashboards and for different stakeholders as part of the data strategy. Often times we have this situation when people want to track everything, they want real data, but they cannot review and take action every day. So what's your take on that? So again, going.
Back there are so many data metrics, and KPIs that we can track and measure, but we can't do it all. You can think that you want to do it all, but every business struggles with them. Most businesses today, indeed, don't get to do it and they don't get to do it and they don't get to do it every day. And it's usually ad hoc. It's usually right.
There's a meeting, we have a board meeting. Everyone is trying to hassle and get the right numbers. Maybe the board is asking you an ad hoc question that you didn't prepare for, a spanner in the works, and now we need to look for something else. So that is why it's important from the get-go as a business to really set your strategy around what does the business want to measure? What are, again, our minimum viable KPIs, the measures that we want to measure?
That should be something that we can do on a regular basis. Everything else sure can be ad hoc, but there needs to be something that you can do on a monthly basis, quality, whatever your cadence is, that you should be able to do.
And it should be something that's doable. And also if you don't have the right resources, so tech, or talent, this is something very important. It should be something that the organization needs to invest in because if you want to do it regularly. Otherwise, there will be gaps. It's going to be inconsistent.
You won't be able to get to do it continuously. So this is a lot of things to think about. But again, going back to the strategy and it's actually stopping what you're doing now, maybe not the AU, but have a stop and think and speak to the leadership team as a business to then decide, Right, how do we move forward from here? How can we get to a better place? And that's very important to do. Awesome.
Thanks a lot. So, Katrine, how can people learn more about what you teach, what you learn, what you do? Yeah, sure.
Well, my company ThinkRevOps. So you can absolutely go on the website. We actually just launched today, the new site is live today. I don't think we officially had a launch post yet. It's been busy. But officially live today, finally, I've been pulling my hair for the last two weeks, three weeks just to try and get it out there. But please do visit our website.
Please do look at our website. It's changed now because we've grown as a team as well. We have much more offerings, but just have a chat with us. You can schedule a call if you want to know more about this. Schedule a call with us and any of us is happy to have a discussion with you.
One of the things about our team, we're always happy to share knowledge. I think knowledge should be free. Any questions you have, please reach out to me. Also on LinkedIn, of course, you can find me and I believe the link is there, perfect. There we go. Thank you.
Thanks a lot, Katrine.
about the author
I help B2B companies drive revenue with Demand Generation Programs. RevOps Evangelist and marketer who thinks outside the funnel. Creating marketing programs aligned with how people want to buy, not in a way companies want to sell to them.