Effective AI Adoption Case Studies

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The adoption of AI in marketing can be challenging, but when done right, it can lead to significant success.

However, understanding how to effectively leverage AI can often seem daunting. If you’re curious about how real businesses are transforming their marketing strategies with AI, this episode is for you.

Today, we’re joined by Eva Dong, a Senior Expert Manager at McKinsey & Company and an AI marketer with extensive experience in digital transformation across various industries. Eva will share inspiring case studies and success stories of businesses that have effectively adopted AI in their marketing strategies, highlighting key takeaways and lessons learned.

AI in Marketing: Unpacked host Mike Allton asked Eva Dong about:

✨ Overcoming Challenges: Learn about common obstacles in AI adoption for marketing and how to overcome them.

✨ Real-world Success Stories: Discover inspiring case studies of businesses that have effectively leveraged AI in their marketing strategies.

✨ Best Practices: Gain insights into best practices and strategies for measuring success and ROI in AI marketing projects.

Learn more about Eva Dong

Resources & Brands mentioned in this episode

Full Transcript

(lightly edited)

Marketing Wins Effective AI Adoption Case Studies with Eva Dong

[00:00:00] Eva Dong: Apple has the WWDC conference and they released Apple Intelligent. Google I O conference released AI overview. Uh, Microsoft is launching co pilot in the PC. There’s like so many things going on. Sometimes it’s like for us, like, it’s even hard to like keep up with like, what is everybody doing? I think in this kind of atmosphere, what I will suggest business to do is really stay focused on your use case and objective, really looping back to the first question you asked me.

I think that’s so important because there’s so much technology advancement. Some are ready for implementation, some are not. Everything is very interesting to test, but you as a business, especially if you’re a small business, you can’t test everything. You can’t leverage every single latest technology on the earth.

Like you just have to stay focused on your use case and your objective. And then once that is clear, you pick and choose.

[00:00:56] Mike Allton: Welcome to AI in marketing unpacked, where we simplify AI for impactful marketing. I’m your host, Mike Allton here to guide you through the world of artificial intelligence and its transformative impact on marketing strategies. Each episode, we’ll break down AI concepts into manageable insights and explore practical applications that can supercharge your marketing efforts.

Whether you’re an experienced marketer just starting to explore the potential of AI, this podcast will equip you with the knowledge and tools you need to succeed. So tune in and let’s unlock the power of AI together.

Greetings program. Welcome back to AI in marketing unpacked where I selfishly used this time to pick the brains of experts at keeping up with and integrating or layering artificial intelligence into social media, content, advertising, search, and other areas. And you get to learn to subscribe to be shown how to prepare yourself and your brand for this AI revolution and come out ahead.

Now, the adoption of AI marketing, that can be challenging, but when done right, it can lead to significant success. However, understanding how to effectively leverage AI can often seem daunting. If you’re curious about how real businesses are transforming their marketing strategies with AI. This episode is for you today.

We’re joined by Eva Dong, a senior expert manager at McKinsey and company and AI marketer with extensive experience in digital transformation across various industries. Eva’s going to share inspiring case studies and success stories of businesses that have effectively adopted AI in their marketing strategies, highlighting key takeaways and lessons learned.

Hey, Eva, welcome to the show.

[00:02:30] Eva Dong: Hi, Mike. Thank you for having me.[00:02:33] Mike Allton: So glad you’re here. Could you start by just sharing your journey in the world of AI and digital marketing and what led you to focus on AI and adoption?[00:02:43] Eva Dong: Yeah, for sure. So for myself, I started as a data scientist. And my first job was a cyber security data scientist at Visa back in 2000.

15 2015 was the year when cyber security just blow up and so you want to be the coolest kid in the class. You got to choose cyber security.

So that’s what I did during the days as a data scientist. I truly enjoyed a privilege to work with the best cyber security analytics team. We were one of the best and the first in the world to adopt machine learning to use in cyber security. Two years after that, I realized my all my passion relies in machine learning and data specifically.

Then I joined McKinsey with the marketing sales practice as a senior data scientist. That is the time I start to come to the cross domain between digital marketing and AI. So back in the days of 2017, that was the early days of AI implementation in digital marketing. I remember in my first year as a consultant at McKinsey Google acquired double click as their like search enablement team.

And then at one of my tech clients, they are pioneering with a lot of the new techniques of Google search and Google search ads. So I remember for the, for there were six months, every morning, It was a McKinsey team, including myself, the double click came from Google and my client will always sit together analyzing.

Did the algorithm work yesterday? Did it work as we expected? So things goes on and on for 6 months and there’s like. A lot of things we didn’t expect. And at one point I was like very frustrated with the process. So I decided, okay, I’m going to write a Python to compete with Google search.

Obviously that sounds really funny right now because Google is so mature and so comprehensive, but at that time for me as a young data scientist, I believe my Python skills are much better than the Google algorithm. So I just say it’s like how early. That day was and how much progress Google has made until this day.

So fast forward, I had been with McKinsey for eight years and I’ve always been in the cross field of AI and digital marketing.

[00:04:53] Mike Allton: That’s, that’s fascinating. And I love that gumption you showed by deciding, you know what, I’m going to try and do it better. That’s a fantastic entrepreneurial attitude. A lot of the folks listening can, I’m sure appreciate and relate to that.

What are some common challenges then that you’ve seen businesses facing when they’re first trying to integrate AI into their various marketing efforts?

[00:05:16] Eva Dong: Yeah, I think there’s like one common thing I see in a lot of my clients, regardless big or small, is they want to join the hype, especially last year and this year.

Specifically, Gen AI is such a big topic. Everybody’s talking about Gen AI. So a lot of our clients come to us say, Oh, my God, I heard about this thing. Gen AI, like, I want to do it. Please do it for me. And then we asked, like, doing on what? And it’s like, you figured it out. So that was one of the common sense we achieved.

And but that’s kind of alarming, because I think in order to use AI as a tool, well, for your business strategy, you should start with the objective, like start with the use case, know what you’re trying to solve. And then we can try to solve it. Maybe with AI, like there’s many forms in AI, like there’s machine learning, there’s deep learning, there’s generative AI, like there’s many forms of AI.

We can choose the one that’s best for you, but we should always start with the objective and the use case.

[00:06:20] Mike Allton: That’s terrific advice. In fact, I’m going to link to our previous episode with Danchez, where we talked about five very distinct and different applications of AI specifically in marketing. We’re like, you know, creating content.

That’s just one kind of use that people can use generative AI for this. There’s many others. And I definitely appreciate the idea of coming up with the outcome in mind. I like that. A lot of people are. Looking at the processes that they’re repeating, you know, if they’re doing something, you know, three or four times in their business, and they know they’re probably gonna do it 10, 12 more times in their business.

Well, how can AI helps this particular process? Make it faster, make it more efficient, make it more effective. Those kinds of things. So could you share some examples, maybe a case study of a business that’s successfully adopted AI into its marketing strategy? What was the objective, right? What was that, that end goal and how was AI used to get there?

[00:07:14] Eva Dong: Yeah. So there is one company and they, they run a website. They have shops offline and they have websites and they’re like omni channel typical retail. And then they’re. Objective, like many other retailers in the same shoes, is their customer decision journey is very long. This is very common across the retail, especially if you sell anything that’s higher ticket item, let’s say like 1, 000 more per, per item.

Usually the. The customer need take the time to think about it. So if you think about a customer decision journey, start right from customer awareness, like they get to know your brand and then into consideration, they start considering, or maybe they will buy a product from you eventually to conversion.

Usually this loop is quite long and it involves a lot of distractions, including your competitors and supplements. They might go to offline to look at the product, and they might eventually purchase it online, or they might jump back and forth many times. It’s a, it’s a common objective. It’s, it’s a, like, a common thing, like, retailer space.

And this company want to shorten that journey. So, what they developed is a virtual shopping advisor. How is it different back in the days versus now is they try to really mimic that In store experience into online, which was not very possible many years ago, like before gen AI become such more mature.

So how the virtual shopping advisor works is once you come into the website, it’s basically like a RAG process and RAG stands for retrieval, augmented generation. So when the customer come to the website, bot with natural language. So, for example, say I’m going to attend my best friend’s wedding in Los Angeles it’s an outdoor ceremony, and then it will be followed by a reception.

I need a dress. for myself. And then the chatbot will understand, extract information, infer the information, and then at the back end, all the product description and the color and the info and the tag will be ready for you. And then the bot will match the customer’s query with the back end information.

And return the product with a storytelling. So for example, maybe the bot will say, Oh, for your outdoor wedding in Los Angeles, in a warm weather in the summer, probably light color, longer dress was this kind of shoes will be the best. So it’s, it’s kind of like you coming to Neiman Marcus and you talk to this shopping advisor and they talk back to you, you never said anything about, Oh, there’s.

This I need a long dress or a short dress. You never said anything about I want pink or blue, but the person can infer what you’re trying to do and give you suggestions like a human. So this one turned out to be really successful. I think eventually they increased a full funnel conversion rate by 15%.

[00:10:11] Mike Allton: 15%. Holy cow. Yeah, this is, this is one of those use cases. We talked about in that previous episode with Danchez, this is conversational marketing, and this is such a difference from the chatbots of old. And I say that kind of with a smirk because old was just a couple of years ago. But for those who aren’t familiar with what, what Eva are talking about, if you’d ever used a chatbot before on a website, Every single question and every single response had to be pre programmed.

It wasn’t conversational. There was no AI involved. It knew that it could prompt you as the website user or viewer to ask what kind of garment or apparel you might want. And you could have a very specific limited set of responses, men’s apparel, women’s apparel, children’s apparel, whatever. And you’d click one or select one.

And it would say, great, would you like a dress or a blouse or shoes? And then it might direct you to that web page because somebody had to think in advance. These are the questions we want to be able to answer. And these are the potential answers. And it was just a complicated workflow, nothing more. Now with the AI, as you said, even you can have an entire conversation with it.

And it will ask you questions, which is one of the really powerful things about AI. And, and the users can. Kind of input, whatever they want in the air. We’ll try to help them in any way that it can. So what were some of the key factors that contributed to that success, to that huge uptick in conversion, do you think?

[00:11:37] Eva Dong: Yeah, so like a lot of the points like you, you were just implying just now, I think there is probably by summarizing to 3 points. The 1st 1 on the technical side is the translation of the client’s query. So this was not possible before the AI was so mature because. Just like we’re just talking about. I didn’t, the client didn’t give very specific instruction.

I never say long or short or pink or blue. I said, I’m going to a wedding in Los Angeles. And then the AI will try to infer, okay, you are a wedding guest. So it’s a cocktail attire or a formal attire. You said Los Angeles. So I know what’s the weather like. And you said there’s it’s outdoor ceremony. So I can imagine like what you need for this outdoor to match the, Did like the atmosphere of Los Angeles.

So there’s, there’s like one is like, take what it is. And, but we’re so beyond that right now we’re imagining we’re following the train of thoughts of a normal human being. And then infer a lot of new information out of what this customer told you. So that was like one of like the biggest technical unlock.

Besides that, I think second delay is like. Keep the human touch. So when I, when I was like saying, when a return was the query, don’t just say, Oh, here’s five products, ABCD, like it should always be a storytelling or like, Oh, for your best friend’s wedding. I think this like long pink dress with like a, a size like skirt type of skirt will fit the atmosphere really well, like reply back, like an actual salesperson, reply back like a.

Like a friend who are going shopping with their besties. I think that’s very important and AI is capable of mimicking that. Not perfectly, but mimicking that at least. I think last one is always keep the brand image. Like AI is capable of train, being trained to mimic your tone. So if you’re selling, Luxury apparel for women versus you’re selling hardcore outdoor gears for outdoor lovers.

Maybe your tone isn’t very different. Maybe for outdoor lovers, you want to be technical. You want to be concise. You want to sound like you’re their bro that will go hiking with them this weekend. Versus for luxury apparel, maybe you want to sound they’re their best friend. You want to sounds like you have fashion sense.

So that tone is also important. But it’s that tone can also be done by training your AI and fine training your large language models. So summarizing that just translation of the query with AI, keep the human touch and then train the model with your brand tone probably contribute to the huge success.

Thanks.

[00:14:17] Mike Allton: That human touch combined with the brand tone is phenomenal because I imagine that people who are interacting with these kinds of AI chatbots and having these kinds of conversations, they’re not feeling pressured to buy. So it’s actually more enjoyable experience for The customer for the prospect to actually have this kind of a conversation to your point.

It’s almost like just talking with a friend and we all know word of mouth sells the best, but it’s hard to scale and replicate. We may have just found this way to scale that aspect of word of mouth where we can actually trust that this particular Conversation is not trying to pound in a sale to us.

They actually have our best interest in mind simply by using those natural cues and those human touches that you mentioned, folks, we’re talking with Eva dog about how businesses are adopting AI today, and I’ve got several more questions for her. But first I’d like to share with you the tool that I’ve implemented to adopt AI into every aspect of my business.

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With your audience. Don’t just market, market smarter with Magai. Tap the link in the show notes. So Eva, what role do you think data analytics is going to continue to play into this new era of AI driven marketing?

[00:16:34] Eva Dong: I think very important roles. And personally, I want it to be a very important role too as a, as a data scientist.

So, So I think when, like you and I both in the industry for a long time, and probably a lot of our listeners are, if you think about marketing two decades back, it’s very brand focused we call it like brand focused marketing. And then the last 20 years has been very digital marketing. So everything needs to be digitized.

We play with algorithm across different platforms. I think we’re in the new era of marketing, which is AI for marketing which Mike talk a lot on this podcast.

I think data and analytics will continue to be very important and probably more and more important, but maybe it’s in a different format than we already know it in the last 20 years. So, personally, I make. Three predictions about how it will play out. I think the 1st 1 will be envisioning a conversational interface.

So it’s just like what we just talked about with the virtual shopping advisor. I think for data and analytics, eventually it will be natural language too. So, for example, maybe I see a spike on my graph. And I’m curious, like, I don’t know what’s going on. So right now, maybe I asked my data scientist to go into the database and look it up and tell me what is happening.

But maybe right now, I can just, instead of talking to my data scientist, I can just ask the tool, like, what is going on? Like, why do you have a spike right now? Is this good? Is this bad? Should I be nervous? So that’s the first one. I think the second one will be continuing that proactive phase of AI, which is fundamental, is, is the, is the beauty of AI from the very beginning.

So when you think about traditional analytics, A lot of part of it is looking back, looking into the history. What happened in the last year? What happened in the last quarter? And you try to use that to inform you for future decisions. But AI changed that dramatically, even from the early days of machine learning, which is probably the most mature part of AI so far.

A lot of time is spent is very predictive. And that’s the beauty of AI. Like we have to use this predictive tool always guide us to make predictions to to, to guide us into future decisions. So we don’t need to use don’t always have to look at historical data to make future decisions. We can look at future data to look at future decisions that, which is much easier and straightforward.

And I think the last thing will be ideally, It should be following us everywhere. What I mean by that is a lot of times we have to use our experience and domain knowledge to find an opportunity, find the issue. So, for example, I see a spike on my graph. I was like. Why there’s a spike. Maybe my experience will tell me, Oh, during the weekend, the traffic will usually spike.

Then that’s normal. I don’t need to be alerted or is telling me like this. Wednesday, there’s a spike. Why is that? Is something happening? This is not a weekend. This is not normal, but all of that is process in my brain with my experience with my domain knowledge, hopefully in the future, I will have that domain knowledge and that experience.

experience to an AI can tell me, oh, there is a normal spike this weekend as. Just our seasonality, or you can tell me, oh, there’s a spike on Wednesday, which is not normal because this and that. So that’s my 3 hope. And that’s like, what I’m working on and what I’m seeing analytics field is working on is really have conversational interface, like, always embrace the predictive future.

The predictive feature of A. I. The beauty of A. I. And lastly, like always tell us everywhere instead of using our domain knowledge has a domain on it and tell us everything. So our life will become easier and easier.

[00:20:27] Mike Allton: I absolutely agree. I’m seeing these trends as well, and I feel like we’re almost there, right?

Today, you can you can open up chat GPT or any of the large language models, and you can feed it all kinds of data, and you can have it analyze that data. You can show it a screenshot of your Google Analytics, and it will know that it’s Google Analytics, and it will help you understand what it is that you’re looking at.

But there are limitations. There’s limitations in how much data you can feed the AI today. And there’s certainly limitations in terms of how it’s connected or not to all of your different tools. You kind of still have to go through this process of telling you this is what you should look at and look at this didn’t over here or combine them in some way and give you some insights.

I agree. I think within really just the next 6 to 12 months, we’ll see increasingly tools like HubSpot and Salesforce and so on have so much a I layered into their abilities and so many other integration with the tools that You’ll be able to have that conversation with the A. I. S. A. Hey, look, let’s take a look at our last 18 months of sales trends and traffic trends and tell me what you’re seeing there quite there, but it’s coming.

So I appreciate that. What about what we can do today? Can you share an example of how A. I. And data have already significantly improved marketing and decision making for the business?

[00:21:44] Eva Dong: Yeah. So, you know, like for marketers that this won’t be like new. So like agile marketing has been a concept for us for a long time.

So I job marketing, we usually have something called agile marketing war room, which is a cross functioning war room with. All the channel managers, the data team there will usually be a scrum master who is like orchestrating all of this. There might be legal involving, might be like copywriters and all, all of that.

So usually we have something called a routine called a daily stand up, which every morning we come together and discuss for the testing we’re doing, which one is doing well, which one’s not doing as we expected and what should we do? So like. That process, like for someone familiar with marketing and agile marketing, they’re probably like not new at all.

So in that process, I feel like data is like, always has been guiding us. So a lot of times we’ll look at a dashboard and say, Oh, let’s say, Oh, traffic decreased yesterday. And then someone will ask, Why is it? And then someone will say, Oh, because the traffic from Meta is decreased. And then why is the traffic from Meta decreased?

And someone will tell us, Oh, because the CPM increased yesterday out of blue, because one of our competitor launched a new product and, and they purchased a lot of impression. And like, take a lot, most of the market share to make that CPN very high expensive. So we get less traffic from Metta. So in that process is like one of the ways, like data is guiding us from the beginning to the end, from the root cause to the surface to like, what is happening, why is it happening?

And also, and from there we can have like a data driven discussion and data driven conclusion, like what we should be doing.

[00:23:27] Mike Allton: Got it. That makes a lot of sense. And to your point, these are the areas where increasingly AI is going to be injected into those conversations. You might not necessarily have somebody in the room who has that one piece of information that you need today to solve the little mystery of why C X, Y, Z is happening but pretty soon AI is gonna be able to fill in all those blanks for us.

I know one of the questions that a lot of folks listening and business owners in general have is they’re they’re facing This idea this prospect this challenge of adding AI to their businesses to their marketing strategy and there’s a cost That has to be incurred by doing that. A cost to learn it, a cost to use the tools.

Most of that’s pretty negligible these days, unless you’re enterprise level, but still there’s a cost there. How can businesses kind of measure the success and the ROI of using AI and injecting it into their marketing projects in business?

[00:24:20] Eva Dong: Yeah, good question. And that’s a very marketing question. Like, all right, like, we love a lot of asking.

Oh, what’s the return of investment? What’s the, like, the return on spend? Like, we, we, we marketers love that kind of question. So I think that will be at least my perspective is, I would adopt a similar perspective, like, how we measure. The things we have been measuring. So a lot of times we run AB testing.

A lot of times we run like like a different campaign and compare with the previous campaign. Like we launched new images to pre compare with previous images. Like we have, we have like, we marketers have a history of measuring things and I will continue that methodology to measure this AI part. I will break it down to primary KPI and secondary KPI.

And probably I will choose one. Primary KPI as my main goal, like on whatever I’m trying to achieve, and I’ll choose several KPIs as my secondary KPI. And just to remind everybody, like nothing is perfect for everything. So when something increase, something might decrease at the same time. So nothing is perfect all at once.

So we probably have to pick and choose, like, what do I care about the most? What, what I’m willing to sacrifice. So just make example, let’s say We adopt this new AI strategy that make, instead of two image per campaign, we can have 25 image per campaign with the same cost. So maybe with that, you, your primary KPI is like revenue.

I just want to sell more things. Okay. So the primary KPI is revenue. You, you, you eventually only cares whether you have more sales or not. And then you have a bunch of several. Secondary KPI that might be traffic, maybe conversion rate, might be clicks through rate, might be impression market share. And what you might see is if this AI initiative really works, your revenue will be increasing.

Your traffic is increasing. At the same time, your conversion rate will be decreasing. Your clicks through rate will be decreasing because you have a larger pool of audience. You might be reaching to new audience who’s not familiar with your brand. However, with this new image and lots of image that get attracted, but they’re not, not really like they just know you, they are not going to convert.

They’re not going to click. So you have to sacrifice something for your primary KPI. So eventually I think everything will turn up okay, but just don’t panic when things are decreasing.

[00:26:45] Mike Allton: That makes a lot of sense. Eva, I’ve got just one more question for you. And it’s the kind of question I love asking folks, cause it’s kind of putting them on the spot because a lot of things that we’re talking about is how quickly AI is changing, which means it’s really hard to predict where AI is going and how it’s going to be integrated.

And yet that’s exactly what I’m going to ask you to do. Look ahead. What are some of the future trends that you foresee in AI marketing and how can businesses prepare to leverage these kinds of advancements that you think are happening?

[00:27:13] Eva Dong: Yeah. Yeah. Good, good question. I think like there’s a lot of things happening, just like you said, and then today’s like June 21st as we recorded especially in the last two or three weeks, there’s like tremendous amount of things going on.

Apple has the WWDC conference and they released Apple intelligence. Google I O conference release AI overview Microsoft is launching copilot in the PC. There’s like so many things going on. Sometimes it’s like. For us, like, it is even hard to like, keep up with like, what is everybody doing? I think in this kind of atmosphere, what I will suggest business to do is really stay focused on your use case and objective.

We’re like, looping back to the 1st question you asked me. Me. I think that’s so important because there’s so much technology advancement. Some are ready for implication implementation. Some are not. Everything is very interesting to test, but you as a business, especially if you are a small business, you can’t test everything.

You can’t leverage every single latest technology on the earth. Like you just have to stay focused on your use case and your objective. And then once that is clear, you pick and choose, like, maybe you test three vendors at a time. Maybe you choose three different technologies all at once to test it out.

But what you should always remember, like, what are you trying to solve? Like, maybe you have a roadblock on, like, generating enough, like, maybe you have a problem of, like, Attracting new customers, and then maybe you, you should work on your creative side. But if your problem is like, you’re like, your algorithm is not working as you expected.

Like, I think, I don’t think you need, like, more advanced AI. You’re probably more in the, in the early stage to, like, work with the Google algorithm. So that’s what I, that’s what I suggest. So the second thing I want to suggest, like, once you decide to use the technology or a vendor, actually use it.

There is a stats I read, like, for marketing technology, 2024 is the second year that utilization rate has been dropping. And so far, the annual utilization rate is 33%. That means out of like, A hundred days you could utilize this technology on average is only utilized for 33 days. That is usually not a good thing if, especially a lot of AI technology and vendors are very expensive, especially for like smaller business.

So if you truly bought something, you pay for the subscription, actually use it. Don’t just buy it because it sounds good for the pr for you to use it, actually use it, make the utilization hide. Like I think. Being clear with the objective and actually make the full use of the tools you have is much more important than chasing after 10, 000 different technologies and vendors.

[00:30:05] Mike Allton: That is a huge point. I had an entire conversation with Jim Williams to see the chief operating officer for up tempo on one of my other shows, the Martech show, and the entire conversation was about this problem that as marketers and CMOs, we have where we go out and we purchase subscriptions, sometimes extremely expensive subscriptions to tools and services, and we underutilize them or don’t utilize them at all.

And not only is that wasting the money, it’s wasting the capital of relationship and trust that we’ve been trying to build with our financial officers and our chief executive officers to get approval to buy the next thing that we want to buy and the next thing. So definitely a cautionary tale, though.

Thanks for bringing that up, Eva, that those of us who are looking at investing in. Tools specifically because of their AI capabilities, we need to have that in mind. We need to make sure that we have a very solid use case planned out, and we’re going to use this particular tool. We vetted it. And if you’re not sure, start with some of the free tools, start with, or the basic, just large language models.

Claude just came out with a 3. 5 of, of their sonnet. So there’s, there’s a brand new, Supermodel on the board. We have open a eyes chat GPT four. Oh, and one of the cool things that everyone needs to be aware of is that and I’m not saying this. This is what other people have said. Sam Altman in particular today’s Language models are the dumbest they’ll ever be.

They’re only going to get better and better and better. So as you start to integrate AI into the things that you’re doing today, it’s only going to improve and become more efficient or more cost effective. So thanks again for sharing those points. Eva, you’ve been fantastic. This has been such an interesting and wide ranging conversation for folks who want to know more.

About you? They want to follow or connect with you. Where should they go?

[00:31:54] Eva Dong: Yeah. So I’m very active on LinkedIn, so you can always find me on LinkedIn. I’m Eva Dong and if it doesn’t pop up, say Eva Dong McKenzie. And then at the same time I run a newsletter called smart AI marketing. In the newsletter, I publish weekly articles on how to use AI.

What’s the latest of AI, specifically how to use it in the field of digital marketing. So some of the things we talked about today, such as what is my projection for the future marketing analytics? I break it down more in detail in my in my newsletters. So if you’re interested feel free to subscribe me and find me on LinkedIn.

[00:32:31] Mike Allton: Terrific. Thank you, Eva. Thank you all of you for listening. We’ll have all the links to everything that Eva just mentioned and other resources we talked about during the show in the show notes, that’s all we’ve got for today, but don’t forget to find us on Apple, the AI in marketing impact podcast, and give us a review.

We’d love to know what you think until next time. Welcome. Thanks for joining us on AI and marketing unpacked. I hope today’s episode has inspired you and given you actionable insights to integrate AI into your marketing strategies. If you enjoyed the show, please subscribe on your favorite podcast platform and consider leaving a review.

We’d love to hear your thoughts and answer any questions you might have. Don’t forget to join us next time as we continue to simplify AI and help you make a real impact in your marketing efforts until then keep innovating and see just how far AI can take your marketing. Thank you for listening and have a fantastic day.

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