Why Your Analytics Aren’t Driving Real Business Impact with Jason Perkowski

Marketing analytics operations transforms raw campaign data into strategic business insights through proper data governance, multi-touch attribution models, and optimized tech stacks. In this roundtable, Equifax Marketing Analytics Manager Jason Perkowski explains how to build data warehouses, implement attribution reporting that accounts for the complete buyer journey, and optimize growing marketing technology stacks while maintaining centralized data governance and cross-functional collaboration.
 

Jason Perkowski

He has 15 years of experience in marketing analytics and data operations within B2B marketing. He leads marketing performance measurement and data operations at Equifax and serves as an advisory board member of Customer Data Alliance.

Transcript

Kawal: Hello.
Kawal: Welcome to the 3rd episode of our roundtable series, Ops in motion.
Kawal: I’m Kawal, co-founder at Digital DI Consultants, joined by my co-founder, Shiv Panchagiri, and we are guiding the conversation on how teams today can use analytics to not just report but truly drive strategy.
Kawal: In this series, we deep dive into systems, strategies, and stories behind marketing operations and data analytics.
Kawal: We will also explore how to bridge the gap between the data and marketing.
Kawal: And today we are, we are thrilled to be joined by Jason Perkowski, who is the marketing analytics manager at Equifax.
Kawal: He has 15 years of experience in blending marketing know-how with deep data expertise, and Jason has a business acumen in his thinking and his technical expertise.
Kawal: He’s just a data pro, who’s someone who can turn insights into impact.
Kawal: So let’s jump in.
Kawal: And Jason, would you like to introduce yourself?
Jason: Thank you for the very kind introduction there, Kawal.
Jason: So, yeah, my name is Jason Perkowski.
Jason: I’m a marketing analytics manager at Equifax.
Jason: I work within one of our B2B marketing groups, and I’m in charge of marketing performance measurement and data operations.
Jason: So I work, you know, very closely with department leadership, demand generation, other marketing groups, as well as data and technology to really measure the effectiveness of our marketing programs.
Jason: And really understand and measure that, right?
Jason: So I have a long career spanning many data-related roles, including finance, BI, software engineering and consulting, and now, you know, marketing analytics mops.
Jason: So, excited to be here today.
Kawal: Wonderful.
Kawal: Hey, Jason.
Kawal: I have a question for you.
Kawal: Well, I was very keen to understand what your take is on the evolution of marketing operations over the past 7 to 8 years.
Kawal: And also, what is your process of turning raw campaign data into something useful, something useful to sales and leadership teams?
Jason: Sure, sure.
Jason: Yep, great question.
Jason: So, you know, marketing operations, you know, like many other areas, has really evolved a lot due to the growth in technology and automation.
Jason: So, it’s kind of evolved from more of a, a back-office kind of area to a more strategic one.
Jason: You know, an area, if you know, done well, can really be a growth driver for the company.
Jason: And that shift, like we’re saying, has been driven by, you know, technology and automation and data, and right, and how much more targeted and efficient we can be in reaching the right audience.
Jason: And, you know, this has improved both, you know, traditional broad-based demand gen, as well as, you know, newer, ABM-related motions where we’re targeting, you know, smaller cohorts using intent data for very personalized messaging.
Jason: And, you know, to me, it kind of comes down to 4 things or 4 areas, people, process, technology, and data, right?
Jason: And so you asked about data.
Jason: So that’s, you know, really foundational to all this.
Jason: So, you know, my process of turning raw campaign data into something useful for leadership really starts at the source, right?
Jason: So, first, you need to have a CRM with clean and governed data.
Jason: Data, you know, that the business needs to know.
Jason: Then, you know, ideally, if you have like the budget and resources for this, I recommend ingesting that data a, into a data warehouse, right?
Jason: So BigQuery, Azure, those kinds of things.
Jason: Once there, we can curate that raw data into tables that are optimized for reporting.
Jason: And then finally, you have dashboards that you build on top, to make easy-to-understand dashboards for the business.
Jason: And, you know, it’s, it’s about the dashboards, but it’s really also about impact, right?
Jason: And what I mean by that is when you’re designing things for sales or leadership, you have to have that stakeholder in mind to understand what they want and need.
Jason: And I work in marketing, so, you know, something that’s very important to us is attribution, right?
Jason: So that’s taking the data and, you know, showing, hey, these campaigns had these influences on these sales opportunities, right?
Jason: So that’s just an example of how to make it real, for the business.
Shiv: Nice.
Shiv: Well said, Jason.
Shiv: I mean, you really articulated it very well.
Shiv: I mean, that’s what we’ve been seeing all this for so many months and years.
Shiv: OK.
Shiv: I have another question, Jason.
Shiv: So, since you have a good understanding of marketing analytics, what’s one data insight that you’ve uncovered that completely changed how a team approached their strategy?
Jason: Great question.
Jason: And, you know, one thing that comes to mind is actually, like in my department, when we uncovered, you know, just how many touches there are in the buyer journey and what some of those were.
Jason: So seeing the whole customer journey and the many touches completely changed, you know, the insights we’re able to get from our campaigns.
Jason: It’s, for example, initially, we’re just looking at like the first touch or the last touch.
Jason: But we evolved to like a multi-touch model where we’re able to see, you know, all those different touches and split up the influence among many.
Jason: So this allows us to uncover really the importance of certain channels, right?
Jason: namely, in our case, our webinar series and our trade shows, and really being influential and driving awareness and influencing opportunities.
Jason: And you know, there’s still a lot more we can do.
Jason: We’d like to understand the sales touches, you know, how many of them, what kind of touches those are, etc.
Jason: so to summarize, I’d say one insight is really just, you know, discovering, hey, there are all these channels, that are influential on the sales process, and here they are, and, you know, here are, here are their win rates when, you know, a customer attends these two shows or they, they attend a webinar of ours, our win rate has improved, right?
Jason: So getting these kinds of like.
Jason: The compound insights have really been instrumental in telling us, you know, what marketing programs we need to lean more into and which ones we need to improve.
Shiv: Right, right.
Jason: So, OK, well, you know, you’ve been in marketing operations for, you know, over a decade now.
Jason: So, you know, what kind of changes have, have you seen in the tech stack, and what are some ways to optimize them fully?
Kawal: Absolutely.
Kawal: That’s again a great question, and it reflects how much we have seen the space has evolved.
Kawal: When I started my career in marketing operations, which was over a decade ago, the tech stack was very simple.
Kawal: We had CRM like Salesforce, and we had email sending out platforms like Marketo, Eloqua, and maybe one or more tools like webinars, hosting platforms, or you have form builders or some landing page builder tools.
Kawal: And then, the focus was very simple to just send emails out and create automation and list-based.
Kawal: Targeting those emails and then when they are sent out, score them and route them to the sales, but today the stack has evolved a lot.
Kawal: Now we’re not only managing the email, campaigns and the CRM, but we have multi-touch attribution.
Kawal: We have ABM tools.
Kawal: We have customer data platforms.
Kawal: We have intent data providers.
Kawal: We have data orchestration platforms as well.
Kawal: So, I mean, a lot is going on and of course, AI.
Kawal: Power dynamics and enrichment tools.
Kawal: I mean, I can go on.
Kawal: Still, there is so much to add to this list, but yeah, this evolution that we have seen.
Kawal: So this has caused a big shift, that is not just in terms of tools that have been added to the stack, but also the expectations.
Kawal: So MOPs is today expected to connect the dots between different teams and departments like marketing, sales, customer support, and product.
Kawal: And make it all measurable.
Kawal: So now the question arises, like, how do we optimize this growing stack?
Kawal: So, what I have seen in my experience is that these are the five things that make it work. So, number one, start with the strategy and not just look at the tools.
Kawal: So nowadays organizations look at tools like, OK, we have these kinds of tools that’re going to do this and that, that’s gonna manage the lead, that’s gonna manage the funnel, but that’s not right.
Kawal: We’ve got to get the strategies right.
Kawal: So, you don’t need 15 integrations for something to work.
Kawal: You need the right tools to be integrated with the real process, and you need to see how it fits into your business and how it shows you the complete customer journey.
Kawal: And the second point would be centralized data governance.
Kawal: So the more the tools are, there’s higher the risk of fragmentation.
Kawal: So you want to define your source of truth.
Kawal: There has to be one tool that is the source of truth, and you need to build integrations with hygiene in mind.
Kawal: When I say hygiene in mind, I mean lead management.
Kawal: You have naming conventions, you have a life cycle.
Kawal: You have ownership.
Kawal: So you’ve got to define all of these initially so that people are clear, teams are clear about what.
Kawal: Who is looking at then standardizing the processes?
Kawal: So don’t let every team work in silos.
Kawal: There has to be a conjunction between cross teams, and there has to be shared frameworks.
Kawal: So again, the lead management impacts everyone, right?
Kawal: Campaign execution is also impacting all of the teams who are working together, marketing, sales, customer support, and the way the data is handled.
Kawal: So it’s very important to have a shared framework there.
Kawal: And then auditing.
Kawal: So, since we have so many tech stacks together, we need to audit them regularly.
Kawal: So it could depend on how big your database is, what type of, you know, marketing you do, and how big the organization is.
Kawal: You can, you know, audit, try auditing it maybe once in 3 months, once in 6 months, or once a year.
Kawal: And of course, training your people with the evolution in tech stack.
Kawal: I mean, it’s very difficult to be, you know, cope with all of these tools, so you have to keep learning.
Kawal: So you need to train your team and prioritize creating internal documentation, and then ongoing, cross-functional collaboration is very, very important.
Kawal: And, yeah, so these tools have evolved, so, but the goal remained the same.
Kawal: We still have to see the alignment that is being driven by these tools, and we have to have the visibility and the real outcome, and the proper outcome is very, very important.
Kawal: So when you get that right, I think the stack becomes just an enabler, and there’s no bottleneck, there’s no issue, or there’s no fallback that you’re seeing in your processes to work.
Kawal: That’s, ‘s what my take on that was.
Jason: Yeah, so many great points.
Jason: I mean, it’s a, so many tools, so many high expectations now, there’s so much involved, so great points there.
Jason: So, Shiv, you know, what, what do you think is the highest hidden cost of bad data, you’ve seen in a campaign or pipeline, you know, this, this data that Kawal was just referring to.
Shiv: So, well, interesting you ask that, Jason.
Shiv: Well, the fact is, this has been a problem forever, and unfortunately, even today, in 2025, not enough attention is given to minimizing the impact of inefficient and ineffective CRM data on marketing, sales, and customer service campaigns, right?
Shiv: Think about it, it eventually affects the company’s bottom line, and that is revenue.
Shiv: Well, there are many effects of bad data.
Shiv: However, a few that I can really highlight are, number one, wasted budget and its direct impact on campaigns, right?
Shiv: Communications are sent to invalid and outdated contacts.
Shiv: You, you are reaching out to companies and contacts that are not part of your ICP, so it, it has a direct impact on your, on your budget.
Shiv: The number 2 is that you’re leaving out great opportunities on the table.
Shiv: I know those are unfulfilled ones that lead to the sales cycle.
Shiv: You’re, you’re losing a lot of deals, and then of course it, it eventually affects your pipeline.
Shiv: The number 3 is poor customer service.
Shiv: Now, what happens is if your CRM has data duplicates, if it has inaccurate contact and complete details, the lead efficiency of your customer service kind of comes down drastically.
Shiv: And the number 4, which I feel many people kind of neglect, is the alignment between the teams, right?
Shiv: Now, let’s say for example, the alignment between the sales, marketing, and customer service teams, and in fact with the leadership team as well.
Shiv: So you’re always risking the operational inefficiencies due to bad data.
Shiv: And the number 5th that I, I can certainly pinpoint is ineffective reporting and, and, and decision making because of the CRM data.
Shiv: Well, these are some of the views that are affecting the organization as a whole.
Shiv: The list kind of goes on, but eventually, as long as you’re able to take care of the CRM database, it helps you in many ways, right?
Shiv: Now, on a different note, I have a, a, a question for Kawal, right?
Shiv: Now, Kawal, when you are asked for attribution reporting, now, what do you say when the data isn’t telling the whole story that you would ideally want it to be saying?
Kawal: Right, that we have, we all of us have faced, I think Jason, Jason could relate to it very well.
Kawal: We have faced it so much in marketing operations.
Kawal: So when I am asked for attribution reporting, and the data isn’t telling the truth, I would always start with the context.
Kawal: I would always refer to that and say that the model, what, what the model is showing up here right now is not the complete model, and it is not capturing all the details, all the, you know, attributions, all the touches that we call.
Kawal: But, because even the models like First Touch, Last Touch, Multi-touch, they are not gonna track or, you know, capture all the interactions that happen.
Kawal: So there are many interactions which are offline, like you meet someone outside of the office, you cannot track them in the tool, and you have Slack introductions or, you know, Slack messaging, and you have somebody sending you a webinar invite.
Kawal: On WhatsApp, maybe on Slack, or on LinkedIn, you call it dark social also, so there are sales interaction that happens over coffee.
Kawal: Someone met you at lunch or coffee, and they had some discussions about sales.
Kawal: So I mean, that’s a rare thing that somebody goes and adds that into the tools.
Kawal: So all of those are missed.
Kawal: So that’s why the attribution cannot be complete.
Kawal: So I would add in like quantitative.
Kawal: Inputs like when it was what the sale is hearing on the ground, right, what the sales team is hearing, so I would ask them and try to add those contexts and I would say what content’s being referred in the call and then I would also add any spike that correlate with the untracked effort, any other untracked effort that I feel could be outside of.
Kawal: So I would try to collate all of that and try to put it in, but again, we cannot add everything.
Kawal: And I’m gonna be transparent with the stakeholders, saying that OK, this dashboard is directional but not definitive.
Kawal: So let’s be clear on this.
Kawal: And I would say attribution isn’t about proving what marketing value is or how well the marketing is working.
Kawal: It’s about building confidence in where to invest next.
Kawal: So it’s going to give you that direction, like where you can invest next, which process, which thing, which marketing mix is working better.
Kawal: So it’s about something, you know, supporting smarter decisions, and sometimes that means looking at the bigger picture and getting the full journey and not just what’s in the data.
Kawal: So that would be my take on attribution reporting.
Shiv: Well, wonderful.
Shiv: That’s, ‘s pretty impressive, Kawal, and thanks for touching on some important aspects within attribution, right?
Shiv: But Jason, this is for you.
Shiv: we all know that you’re an, you are an advisory board member of Customer Data Alliance, right?
Shiv: Can you please help me understand more about your role and exactly what you’re doing there?
Jason: Sure, sure.
Jason: So yeah, at first, you know, a little bit about the group, you know, it’s an association that was created to help, you know, practitioners like us, as well as business leaders, to address a lot of things, frankly, that, you know, we discussed today, in their organizations.
Jason: So, you know, my role on the board is really about bringing a practitioner’s perspective, you know, someone in the trenches of marketing data and operations. I collaborate with other members, other members with, within the group, to help steer the group’s content and direction, especially around best practices, for activating customer data.
Jason: I also promote the group, you know, within my network, to help secure, you know, future speakers and sponsors for our events, because that’s a big part of our events, which is the speakers that come.
Jason: So, you know, at events, we talk about a lot of things about building a strong data foundation, as we talked about today, how to make data more accessible across teams and how to drive better personalization and measurement.
Jason: And there’s particular emphasis on, you know, this cross-functional collaboration, between, you know, marketing, technology, and product, to really build an understanding of these technologies, you know, all the technologies we discussed today, and the use case.
Jason: is to ultimately activate this stuff within the business, right?
Jason: This is complex stuff.
Jason: It needs cross-functional collaboration to really, frankly, be successful.
Jason: It’s a great opportunity to learn about industries and the other people, and help shape conversations that move the space forward.
Shiv: Sounds like, well, sure, well, it sounds like a, a great group, right?
Shiv: I mean, maybe people can reach out to you or maybe other members of this association and see how they can collaborate with you all.
Shiv: Well, that, sure.
Shiv: And what’s the best way to get in touch with the association or the group?
Jason: You know, reaching out to me, we have a LinkedIn page and a website.
Jason: We’re called Customer Data Alliance.
Jason: You can find us, you know, find us there.
Jason: Chris Adelman is the founder of the organization.
Jason: He’s on LinkedIn, so just feel free to reach out to really, really anyone, through our website or through me.
Shiv: All right, sounds good.
Shiv: All right, thank you.
Shiv: Well, well, we are coming to the end of an amazing discussion.
Shiv: We covered many aspects of B2B marketing, starting from the evolution of marketing operations and tech stack in the past 7 to 8 years.
Shiv: We’ve also covered the role of data analytics in marketing and the consistent challenges with the CRM database.
Shiv: Jason, it was a pleasure having you on the show, and you’ve been fantastic with your insights.
Shiv: And thank you, Kawal, for the wonderful insights about the many aspects of marketing operations and data analytics as such.
Shiv: Thank you again.
Kawal: Thank you.
Shiv: Thanks, Jason.
Jason: Thank you.