Data Chief Podcast
THE DATA CHIEF | EPISODE 13

The Joy of Data Done Right with Alight Analytics (Part 2)

Michelle Jacobs

President & Co-Founder

Alight Analytics

Current Episode
EP13: The Joy of Data Done Right with Alight Analytics (Part 2)
EP13: The Joy of Data Done Right with Alight Analytics (Part 2)
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This is Part 2 of Cindi’s interview with Michelle Jacobs, President and co-founder of marketing measurement agency Alight Analytics.

Listen to Part 2 of their interview here.

On this episode, Michelle and Cindi discuss how data can turn marketing from a regretful expense to a worthwhile investment, the benefits of learning to ask the right questions of your data, and how to determine a customer’s wants and needs without invading their privacy. Plus, they talk about women in analytics and what they expect for the future of the industry.

Key Takeaways:

  • Avoid the Data Death March. Like single-use plastics, individually prepping massive amounts of data using yesterday’s methods (like Excel and PowerPoint) for each and every meeting, presentation, and report is wasteful. The right technology can free up time and resources by centralizing this data and allowing it to be multi-purposed on-demand across all departments.
  • From top-to-down best practices. Think about the question you want to answer first.
  • Data turns marketing from an expense into an investment. With the right data to trace factors that go into lead generation, clients can quantify exactly how their marketing dollars make a difference beyond black-and-white sales figures.
  • Messy data vs. clean data vs. no data. Data quality is good enough when it's directionally accurate.
  • Consolidation is your friend. If you’re a marketer and you’re looking to make educated decisions, get your marketing data in one place so that you can look at it holistically and understand how your entire ecosystem is performing.

Key Quotes:

We really preach a theory where you’re starting from the top down. So you’re figuring out what questions you want to answer first. … Once you determine what that is and outline that, then it becomes really easy to know what data you need to pull. So you know what questions you’re trying to answer then that leads to the sources that you need to pull from. … So it’s really about where you’re spending your efforts and your money from a marketing standpoint and making sure that all of those touchpoints are included so you have a completely full picture of where all of your money and your efforts are being spent.

I think it’s easy to get stuck in this thinking of, okay, we need analytics and we don’t know what that means exactly, so we’re going to go hire a bunch of people. … And then I think they realized, okay, I’m just throwing money and people at the problem. And this is actually a system problem, not a people problem. And it’s a strategic problem, not an effort issue.

Is messy data still better than no data?

If it’s consistently messy, then it’s usually directionally accurate! The reality is no data is going to be a hundred percent perfect. But as long as you’re consistent about how you’re gathering it, what you’re doing with it, then you can use that to make some accurate assumptions about the data and decisions. So think about if the data quality is enough to be directionally accurate.

Is flipping the 80 percent of time we spend preparing data with the 20 percent of time we spend analyzing it just a matter of up-skilling our teams?

I think that’s part of it too, for sure. It’s the two pieces, right? It’s investing in a solution that can get you all of the data together [in a way that’s] consumable, make sure it’s accurate, make sure it’s updating automatically. Let’s get our solution in place, and then let’s make sure we have people who are either trained or that we can train that understand marketing, understand our campaigns, and can analyze it in a way that allows us to do something with that data.

Do clients trust their gut more than the data?

It’s not as bad as it used to be, but we’ve had clients over the years specifically that we have shown them, analysis after analysis, that says “Stop doing direct mail.” And they tell us, “I know that says to stop doing this, but my gut says it’s working.” … And I’m not picking on direct mail at all. It could be, you know, Ad Words. It could be whatever, it’s just this was one specific example where it wasn’t working for them anymore. I mean, it works for other people, but we do have a lot of attribution models that our clients can use out of the box, and you have to try to put your feelings aside and trust the data. And that can be very difficult to do.

My business partner, Matt, he speaks a lot in educating marketers and he has this slide that shows the Data Death March … it’s getting the data out of these systems and then putting it into Excel and then putting it in the PowerPoint and then you try to analyze it, and somebody has a question — you have to go do the whole process over again. So this Data Death March is why almost all of our clients come to us; they’re typically in that situation where they’re manually working through data and doing exactly what you said, they’re spending 80 percent of their time just getting data ready for an analysis and 20 percent of their time, if they’re lucky, actually analyzing it.

Is PowerPoint where data goes to die?

Over all these years, we’ve all [gotten] this great data together and [we’ve made] these amazing charts, and [we’ve] put it in PowerPoint and [we’ve presented] it. We never look at it again. It’s just not sustainable. It’s not as great as having something that updates for you every day that you can look at.

Not only is technology moving faster, but from a marketer’s standpoint, there [are] more and more ways to get your messages to people than ever before. Every day, there’s a new source, a new channel — that’s [a pain point] that we’ve heard [about] from people who try to manage all of this data themselves without relying on a partner.

How the data proves that marketing is more of an investment than an expense:

Marketing has always been treated as an expense, so we tell people it’s an investment. When you have analytics, marketing can be an investment — it shouldn’t be the first budget that gets cut. You should show them that it’s actually working for the company.

As a woman in data who understand the importance of diversity in the field, what does Michelle do to promote this diversity?

We try to hire as many women as we can possibly find for any of our tech jobs, and the honest truth is: there’s just not enough out there. We try to network and do other things to encourage women to know who we are and to apply.

How might we entice more women to enter the field?

I think it’s a pipeline problem. … I’ve had conversations with a lot of younger women who were going into college and trying to figure out what they want to do with their life. I tell them to learn how to code, and they don’t want to. … I try to encourage it, [but] I think it’s an age old question of women tend to go more into the arts and marketing kind of careers and men tend to go more into the math and science tracks. And I think until we’re able to really redirect that at a young level, we’re going to continue to have this challenge.

Advice Michelle would give to a new or aspiring data professional:

If I were starting over, I would be as hands-on as possible. I would build my own website. I would put analytics on it. I would buy some ads. I would spend a few dollars on Google, buy some ads, making sure my organic search is optimized. … And while that’s really marketing specific, the more you understand about that underlying data and the strategy behind marketing, the much better you’re going to be at your job and a lot easier it’s going to be.

One of Michelle’s biggest takeaways:

If you’re a marketer and you’re looking to make marketing decisions, get your marketing data in one place so that you can look at it holistically and understand how your entire ecosystem is performing.

Bio:

Michelle Jacobs is the president and co-founder of Alight Analytics, and she's on a personal mission to revolutionize how marketers use data.

Alight's marketing intelligence platform ChannelMix fuels a suite of next-generation solutions that enable any marketer to turn marketing from an expense to an investment. Alight’s solutions combine the speed and precision of software with the flexibility and expertise of consulting, delivering an experience that’s utterly unique in its industry.

Before co-founding Alight, Michelle drove marketing, advertising, and Web analytics strategies for leading companies such as H&R Block, American Century Investments, Saatchi & Saatchi, and Toyota. Michelle is a sought-after speaker and panelist with a unique perspective both on marketing analytics generally and being a woman in data and MarTech specifically.

 

Key Takeaways:

  • Avoid the Data Death March. Like single-use plastics, individually prepping massive amounts of data using yesterday's methods (like Excel and PowerPoint) for each and every meeting, presentation, and report is wasteful. The right technology can free up time and resources by centralizing this data and allowing it to be multi-purposed on demand across all departments.
  • Data turns marketing from an expense into an investment. With the right data to trace factors that go into lead generation, clients can quantify exactly how their marketing dollars make a difference beyond black-and-white sales figures.
  • From top-to-down best practices. Think about the question you want to answer first.
  • Messy data vs. clean data vs. no data. Data quality is good enough when it's directionally accurate.
  • Consolidation is your friend. If you're a marketer and you're looking to make marketing decisions, get your marketing data in one place so that you can look at it holistically and understand how your entire ecosystem is performing.

Key Quotes:

Is messy data still better than no data?

If it's consistently messy, then it's usually directionally accurate! The reality is no data is going to be a hundred percent perfect. But as long as you're consistent about how you're gathering it, what you're doing with it, then you can use that to make some accurate assumptions about the data and decisions. So think about if the data quality is enough to be directionally accurate.

What does Alight Analytics do?

Alight Analytics is a marketing analytics provider. We have analytics solutions that are tailored to agencies and brands to give them a turnkey way to really manage their marketing spend, their marketing campaigns, and have a clear cut way to get insights into what to do next.

How Michelle became a data chief:

I started my career in advertising working for Saatchi & Saatchi, a global ad agency, as a media buyer. So I was still in the numbers for sure. As time went on, I ended up at American Century Investments where I was in their third-party division and got really interested in the websites. That's when things really started to blow up in the Internet space and Web analytics started to become a thing then ... the websites for brand new digital advertising was brand new and analytics was brand new and I was young. So I just learned the ropes, taught myself, and it turns out that I had a knack for it. And so I really transitioned my career to be more analytics focused.

What it was like to start shortly after Y2K:

Google Analytics wasn't around yet, so it was actually Omniture back then, which is now owned by Adobe. So SiteCatalyst was the first tool that we used to mine data. And, of course, Excel.

The range of data sources that a marketer really needs to pull from -- especially in the age of COVID-19:

We really preach a theory where you're starting from the top down. So you're figuring out what questions do you want to answer first? Let's think about what my marketing is trying to do. What are we trying to accomplish? What are our goals? What are our strategies? Once you determine what that is and outline that, then it becomes really easy to know what data you need to pull. So you know what questions you're trying to answer then that leads to the sources that you need to pull from. So if the question is for retail -- how many sales did I do? -- it always goes back to sales. So you're going to need to pull in data from your CRM systems. You're going to need to pull in data from all of your marketing activity, whether that's your online advertising, your email systems -- it could be TV, radio, print with everything that's going on. Now there's probably less radio out there than before, but certainly there's probably more streaming ads that are running now than historically. So it's really about where you're spending your efforts and your money from a marketing standpoint and making sure that all of those touchpoints are included so you have a completely full picture of where all of your money and your efforts are being spent.

Trying to discern what a customer really wants and needs without invading their privacy:

What we tell everybody is to break it down and start at the campaign level or the channel level -- don't worry about the individual. There's a lot of ways you can address the individual. You know, what does Cindi do? We tell people: Don't worry about that yet. Let's start with, how did my marketing ecosystem do? How did my channels do? So I want to see how did Facebook perform? How did LinkedIn perform? How did my Google Ad Words perform? I want to look at all of my channels and my sources and get all of that together first. And so that's why we've created things like our paid social out of the box dashboards, or our paid media out of the box dashboards, or our lead generation dashboards, or eCommerce, because you can look at things at that high level and really start optimizing at the channel level. So that's really our focus and where we have people start, because that's a lot more consumable and a lot more easy to manage than trying to optimize for Cindi specifically.

What does Michelle think of Gartner's prediction that by 2023, 60 percent of CMOs will slash the size of their marketing analytics team because 50 percent will fail to realize the potential improvements?

I don't know that I necessarily agree. It goes against what we have been hearing, that people are going to be slashing their budgets. I think maybe they'll be redirecting and getting smarter about it -- maybe just getting smarter about what they're spending. I think people go through these phases where they decide they need to do analytics. And I would have been in the same boat as a lot of marketers; we weren't trained on analytics in school, again, us older marketers. That wasn't a thing when we were in college. And so we're still figuring out how to piece it all together and what it means for our organizations.

I think it's easy to get stuck in this thinking of, okay, we need analytics and we don't know what that means exactly, so we're going to go hire a bunch of people. We're going to hire a bunch of data scientists because everybody's telling me I need a data scientist. I'm going to hire data scientists. I'm going to hire a team of people to pull in all this data. And then I think they realized, okay, I'm just throwing money and people at the problem. And this is actually a system problem, not a people problem. And it's a strategic problem, not an effort issue. So maybe that's where they're seeing the switch is that they're just going to make their money work harder. And instead of just investing in a lot of workhorses to go manually pull data from places, they hope they're going to be using systems and tools that are already in place that do that automatically. Then you can have fewer people more focused on frankly, the more fun aspects of analytics, which is analyzing the data and deciding what to do next. I mean, that's really the cool part of it. Managing data and copying and pasting data into Excel all day long? That's not anybody's dream job.

Is flipping the 80 percent of time we spend preparing data with the 20 percent of time we spend analyzing it just a matter of up-skilling our teams?

I think that's part of it too, for sure. It's the two pieces, right? It's investing in a solution that can get you all of the data together [in a way that's] consumable, make sure it's accurate, make sure it's updating automatically. Let's get our solution in place, and then let's make sure we have people who are either trained or that we can train that understand marketing, understand our campaigns, and can analyze it in a way that allows us to do something with that data. And I don't think that that's as easy as people maybe think it is. Not everybody is probably cut out for even looking at great, pre-prepared dashboards or reporting. Not everybody is of that analytical mindset to know what to do with that data. I think that's probably a gap in education and just making sure that we're identifying the right people that would be good at that, because it's certainly not for everyone.

How does Michelle propose we find people who are right for analytical roles?

I think that you'll find people who probably come from more of a background like mine, more of a media background, are more analytical in nature just because being media planners and buyers leads to that more analytical thinking. I've found that if we look for people who have that type of background, they fit nicely into an analytics role. People who are more marketing campaign managers or strategists, and certainly creative people, not that they can't have that aptitude, but they tend to not be as interested in the data pieces of it as they are kind of the more overall campaign objectives or the beautiful creative executions or in the branding messages.

Do clients trust their gut more than the data?

It's not as bad as it used to be, but we've had clients over the years specifically that we have shown them, analysis after analysis, that says "Stop doing direct mail." And they tell us, "I know that says to stop doing this, but my gut says it's working." ... And I'm not picking on direct mail at all. It could be, you know, Ad Words. It could be whatever, it's just this was one specific example where it wasn't working for them anymore. I mean, it works for other people, but we do have a lot of attribution models that our clients can use out of the box, and you have to try to put your feelings aside and trust the data. And that can be very difficult to do.

Walking back the Data Death March -- what happens when clients learn to trust the data:

My business partner, Matt, he speaks a lot in educating marketers and he has this slide that shows the Data Death March ... it's getting the data out of these systems and then putting it into Excel and then putting it in the PowerPoint and then you try to analyze it, and somebody has a question -- you have to go do the whole process over again. So this Data Death March is why almost all of our clients come to us; they're typically in that situation where they're manually working through data and doing exactly what you said, they're spending 80 percent of their time just getting data ready for an analysis and 20 percent of their time, if they're lucky, actually analyzing it.

So what we've seen with a lot of our clients is a complete turnaround and then being able to provide amazing insights. And a lot of our clients are ad agencies, so they're able to then take that great insight to their clients. So they see an overall improvement in the stickiness of their client base. They see an improvement in the amount of media spend and where that spend is going. They have a lot happier employees, because they're not just mucking around with data all day.

A real-life example of someone being freed from the Data Death March:

One of our clients is the NBA, and a typical year's playoff season is in a stadium for them. They have some big games throughout the year, and our contact would work all night, all weekends, pulling all of this data together -- a lot of it from Twitter to figure out how everything was performing. And once they brought us on board, that went away completely. She's like, 'For the first time, I have my nights and weekends back during the playoffs season! This is life-changing!' And, we heard that from another client too, that, 'Oh, my gosh, I'm actually able to go out and date now! I don't have to just sit home and work with data!' So it's not always just about the great marketing decisions that people can make, it's also about the time that people are getting back to their life, where they don't have to be spending all this effort manually working with data.

Is PowerPoint where data goes to die?

Over all these years, we've all [gotten] this great data together and [we've made] these amazing charts, and [we've] put it in PowerPoint and [we've presented] it. We never look at it again. It's just not sustainable. It's not as great as having something that updates for you every day that you can look at.

On adapting to the rapid pace of changing technology:

The ability to react quickly is huge. [And] not only is technology moving faster, but from a marketer's standpoint, there [are] more and more ways to get your messages to people than ever before. Every day, there's a new source, a new channel -- that's [a pain point] that we've heard [about] from people who try to manage all of this data themselves without relying on a partner.

Facebook changes their API once a week ... so unless you have a team of people who are a hundred percent dedicated to monitoring all of your connections to all of your data sources all of the time, it's going to break on you and you're not going to get that data. So that's why we're really dedicated to having a team -- all they do is manage our client's data, manage the connections to the sources. That's one way we're trying to keep up with technology from a very tactical standpoint.

We're always adding new connections to new sources or figuring out ways to get in the data. And then another way that we're keeping up with it is really the focus on modeling and attribution and forecasting. While not everyone is ready for that yet, we do have some clients who are ready to start investing in those models and making decisions based upon those models. Years ago, you just would never have imagined that you could look at your marketing data and predict how your spend is going to work and, over the next few months, be able to make changes based upon how much you want to spend, or how many more leads you need, or whatever it is. The ability to do that is constantly changing and improving, and it's just going to help our marketing get better and better.

New data channels Michelle is most excited about:

People are starting to be able to get more and more of their sales and CRM data in a good place, like a Salesforce or somewhere where they can easily access it. And even though nothing is more boring and old-school than sales data, but what is cool about it now is that we're able to pull it in and tie it to marketing performance data so that we can really see exactly how many leads [and] how many sales [can be traced back to it]. And while that's an old channel, that's one that we're seeing getting a lot of attention right now. Even if it's lead generation or e-commerce or traditional retail, they're very interested in making that connection. It's not just about how many views or impressions or clicks did I get? People are really starting to take that next step.

How the data proves that marketing is more of an investment than an expense:

Marketing has always been treated as an expense, so we tell people it's an investment. When you have analytics, marketing can be an investment -- it shouldn't be the first budget that gets cut. You should show them that it's actually working for the company.

Yesterday's data doesn't accurately predict the future when the present is unprecedented. So how do we adapt modelling and forecasting to be useful under the circumstances?

It's not as dramatic of a shift as we might expect -- at least what we've seen with our clients -- so we're not having to completely throw out old data. We can look at seasonalities or holidays and kind of model off of, okay, when we have different times, when we're in or out of market or changing up how we do things, can we look at what those models tell us and apply it to this time until we get enough data to have a model that we feel confident in?

How does Alight Analytics respect someone's privacy while also personalizing?

They would have had to opt it in and the data is kept at an anonymous level. [But] if that [data has] been captured anywhere, it's typically stripped out before any analysis has been done. Doing an analysis on an individual is not usually very helpful or relevant when you're trying to make buying decisions or planning decisions. You're really looking at how did this person behave as a part of a larger group of people? What was this person's customer journey and what was this other person's customer journey and how do they overlay? What's the same? What's different? but I don't need to know their name or anything.

What impact does the role of AI have on data professionals in the marketing space?

Really, we see it just in the modeling and forecasting kind of arena; we haven't seen too much of it come through from a media or marketing standpoint. We've heard of some new players out there that are starting to do more from a buying standpoint with AI that I think will be really interesting to figure out how that can be incorporated into their overall buy and how that can then translate into if they're meeting their strategic goals or not. But really, on some level, [we've] been doing AI for a long time. I think it's really hard to do much analytics or data work without it.

[But] it's kind of the new, exciting term right now that people are using. For a while, it was all about big data, and now it's all about AI. I think from a day-to-day marketer's world, they're like, 'Okay, yeah. Well, you know what I have, that's great. I'm going to listen and make sure I understand that that's going on, but I'm focused on my campaigns, my strategy, and what I need to do next.

Seeing the blessing within seemingly unsolvable challenges:

I tend to be a pretty blue-sky thinker, I guess. I mean, I'm kind of a glass-half-full person and tend to work my way through things. I think when things haven't gone my way actually have been blessings in disguise, because that's when things like starting the company happened. I had left my career at H&R Block and was just consulting. I was just taking a break from the corporate world, and that's when my friend asked me to help out with their analytics at their agency. And that friend became our first client. And here we are, 12 years later.

Recognizing the importance of partnership:

There's, there's no way I could have been successful in this area without him. We're completely opposite in about every way, and I think that makes a perfect partnership when it comes to trying to run a business.

As a woman in data who understand the importance of diversity in the field, what does Michelle do to promote this diversity?

We try to hire as many women as we can possibly find for any of our tech jobs, and the honest truth is: there's just not enough out there. We try to network and do other things to encourage women to know who we are and to apply.

We don't go, 'Oh, well, she's a woman; we have to hire her regardless of her credentials.' But we try to look for networking opportunities or other ways where we can be like, 'Hey, women coders, women developers, we're out here. Please take a look at us!' But there's just not as many women out there in technology fields, at least not in Kansas City. I think we're at a disadvantage where we are; it's probably better in other areas. But I think, overall, if we get 50 applications for a job, there's maybe a handful there that are women.

How might we entice more women to enter the field?

I think it's a pipeline problem. I think in general, women don't typically take their education in coding classes or development classes. Maybe, hopefully, that's changing, but I just think that it's a pipeline issue. I've had conversations with a lot of younger women who were going into college and trying to figure out what they want to do with their life. I tell them to learn how to code, and they don't want to. They're like, 'I don't want to ... I want to do this amazing fun thing, or this really beautiful thing, or this other really exciting thing.' And I'm like, 'Okay, well, that's great. But if you learn how to code, you're going to make a lot of money and then you can do this other amazing fun thing on the side!' So I try to encourage it, [but] I think it's an age old question of women tend to go more into the arts and marketing kind of careers and men tend to go more into the math and science tracks. And I think until we're able to really redirect that at a young level, we're going to continue to have this challenge.

As the industry moves toward codeless environments, would Michelle still advise students to learn coding? And if so, what coding languages would she suggest?

We're still coding, and that's the kind of knowledge, at least in my opinion, that can take you regardless of industry. We code in Python, but I don't know that it matters honestly. I think if you know any coding language, you are head and shoulders above most everybody else.

Advice Michelle would give to a new or aspiring data professional:

If I were starting over, I would be as hands-on as possible. I would build my own website. I would put analytics on it. I would buy some ads. I would spend a few dollars on Google, buy some ads, making sure my organic search is optimized.

I would use all of the free training that's out there from all of those sources -- the free stuff from Google, the free things from Google Analytics, the free things from Google Ad Words. I mean, there's so much free training just from them, just on those tools. ... And while that's really marketing specific, the more you understand about that underlying data and the strategy behind marketing, the much better you're going to be at your job and a lot easier it's going to be.

Technologies and innovations Michelle sees as being relevant two or three years in the future:

We've been using the cloud since we started and I think that's just growing and getting faster and better and easier. And I think the thing that we continue to see is that a lot of the systems and tools that marketers are using to advertise in are starting to understand that it's really important for people to be able to get data out of those systems. ... [A client] wants to see how millions of dollars that [they] spent on this campaign performed, not just how this one particular tactic did, and so we are seeing more and more cooperation from a lot of these companies. ... We're seeing that as a trend because ... people are hungry for this data; people are hungry to make better decisions.

I know we're going to see more and more consolidation in the space. We're seeing it already with Salesforce going and buying Tableau and buying Datorama and buying up all of these visualization tools or other marketing analytics tools. And I think that's going to continue. Salesforce is really trying to compete with Google, which is trying to compete with Microsoft, in this big stack of ... marketing and sales technology, and I don't think that's going to go away anytime soon. So I think people are going to feel like they have to pledge an allegiance to a particular stack in order to get what they want. I think that that's okay, but also dangerous for some; I think not everybody can get what they want out of a pre-baked stack. Some people are more sophisticated or have specialized needs, so these stacks are also going to have to think about how they can continue to cooperate with maybe other tools or systems or platforms so the people who are using their stacks can get the insights that they need.

The impact of 5G and data at the edge?

It's just going to require people to move at an even faster pace. Everything just gets faster and faster, [and] people want that. They want the insights more quickly. They want to be able to react, not just monthly or weekly, but daily. Intraday, right? It's almost like day trading at this point. People are having the data coming to them so fast that they're having to react so fast. And in order to do that, they have to have a solid platform in place in order to manage the data or else they're never going to be able to make the decisions that they need to make on it. You can't be manually downloading data out of Facebook every two minutes. It's not possible. So I think until people have reliable systems and reliable processes to make sure that the data is clean and accurate and reliable, it's going to push them to [new tools]. I've heard that the idea of Google Glass is coming back and you know, we're going to see a rebirth of something like that where, with COVID, it'll probably be a whole face mask full of it!

One of Michelle's biggest takeaways:

If you're a marketer and you're looking to make marketing decisions, get your marketing data in one place so that you can look at it holistically and understand how your entire ecosystem is performing.

Bio:

Michelle Jacobs is the president and co-founder of Alight Analytics, and she's on a personal mission to revolutionize how marketers use data.

Alight's marketing intelligence platform ChannelMix fuels a suite of next-generation solutions that enable any marketer to turn marketing from an expense to an investment. Alight's solutions combine the speed and precision of software with the flexibility and expertise of consulting, delivering an experience that's utterly unique in its industry.

Before co-founding Alight, Michelle drove marketing, advertising, and Web analytics strategies for leading companies such as H&R Block, American Century Investments, Saatchi & Saatchi, and Toyota. Michelle is a sought-after speaker and panelist with a unique perspective both on marketing analytics generally and being a woman in data and MarTech specifically.

 






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