Reading Time: 29 minutesAre you struggling to keep up with the growing demand for personalized customer experiences? As consumers expect more tailored interactions, the pressure on marketers to deliver can be overwhelming. Traditional methods of personalization might no longer cut it, leaving many wondering how to meet these high expectations efficiently.
The frustration is real—how do you leverage masses of data to create genuinely personalized marketing without drowning in logistics and technical complexities? The answer lies in the power of artificial intelligence, which can transform how we approach personalization, making it more effective and manageable.
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That’s where our guest today, Zontee Hou, comes in. Zontee is a distinguished marketing expert and author of the groundbreaking book “Data-Driven Personalization.” With extensive experience in social media marketing, content marketing, and lead generation, Zontee has helped countless companies achieve their goals through innovative marketing strategies. Her work at Convince & Convert and her own agency, Media Volery, has earned her recognition as a thought leader in the industry. Today, she will share her insights on how AI is revolutionizing data-driven personalization in marketing and provide practical strategies to help you master this essential component of modern marketing.
AI in Marketing: Unpacked host Mike Allton asked Zontee Hou about:
✨ AI-Driven Personalization Advancements: Discover how AI is revolutionizing the approach to personalization in marketing.
✨ Real-World Success Stories: Learn from real-world examples of successful AI-powered personalization campaigns.
✨ Future Trends in AI and Personalization: Gain insights into future advancements in AI and how they will further transform data-driven personalization in marketing.
Learn more about Zontee Hou
Resources & Brands mentioned in this episode
Full Transcript(lightly edited)
Mastering AI: The Future of Data-Driven Personalization in Marketing with Zontee Hou[00:00:00] Zontee Hou: What they’ve actually seen is not only 150 percent increase in the open rates and the engagement with the actual content, but they’ve also seen a 40 percent lift on their click through rates. So you’re not only seeing that people are actually consuming the content that they’re sharing, but they’re actually clicking through more to engage further.
So I think that that’s really demonstrative of the impact that you can have when you’re offering these highly tailored, highly relevant content messages. And I also think it demonstrates that you can do this at scale with technology. It used to be very conceptual for us to say, okay, we’re going to have different campaigns for different audience segments.
But what’s the return on investment to create eight different campaigns versus 32 campaigns. There’s a point where you just say, we don’t have the marketing bandwidth to be able to run and effectively optimize each of these campaigns.
You can create a lot of value for an organization by being strategic about implementing AI in the right places.
[00:00:59] 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 will 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 use 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 of digital marketing. 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, are you struggling to keep up with the growing demand for personalized customer experiences? As consumers expect more tailored interactions, the pressure on marketers to deliver can be overwhelming. Traditional methods of personalization might no longer cut it, leaving many wondering what to do. How to meet these high expectations efficiently.
The frustration is real. How you leverage masses of data to create genuinely personalized marketing without drowning in logistics and technical complexities. The answer lies in the power of artificial intelligence, which can transform how we approach personalization, making it more effective. And that’s where our guest today, Zontee Hou, comes in.
Zontee is a distinguished marketing expert and author of the groundbreaking book, Data Driven Personalization. With extensive experience in social media marketing, content marketing, and lead generation, Zontee has helped countless companies achieve their goals through innovative marketing strategies. Her work at Convince Convert and her own agency Media Volery has earned her recognition as a thought leader in the industry. And today she’s going to share her insights on how AI is revolutionizing data driven personalization in marketing and provide practical strategies to help you master this essential component of modern marketing.
Hey, Zontee, welcome to the show.
[00:03:07] Zontee Hou: Mike, it is my pleasure to be here. Always happy to have a chat with you.[00:03:12] Mike Allton: Thanks so much. I would love if you could just start by sharing a bit about your background and the journey that led you to write this book, Data Driven Personalization.[00:03:19] Zontee Hou: Yeah, absolutely. I’ve been in digital marketing my entire career.
So I’ve spent last almost 20 years really digging into how organizations can and should reach their ideal customers through digital channels and how they can effectively tell their stories and provide the right resources to build relationships with their customers. And in so doing the questions that I often have gotten from clients is how do we measure against.
This, how do we do something with the data in an impactful way? And how do we stand out from our competitive set through a more strategic, thoughtful, and personalized approach to our marketing. And because of these questions, I started to really synthesize my ideas around the importance of having a marketing data strategy to not only help you make the most of your owned data, but also to really think from a marketing lens, how should we best collect our knowledge about our customers in order to build that thoughtful, effective marketing strategy. And this book is really the culmination of that work. You know, I’ve worked with organizations, including Cisco, Oracle, SAP Hilton’s, sam’s club, all kinds of organizations on their digital marketing strategies. But ultimately all of us in the modern age have been tasked with measuring and being more deliberate in our marketing. That’s essentially been the promise of the last 15, 20 years of marketing, right? We’ve got all these digital tools.
We can do better. The internet has empowered us. And yet when we actually speak to marketers, one of the things that we find is that most organizations are actually not collecting enough data, and the data that they’re collecting is not the right data to be strategic. So I’m hoping that as folks read the book, one of the things that they recognize is what are the opportunities, For marketing leaders to really set the pace for the organization in terms of this approach.
[00:05:23] Mike Allton: So that’s fantastic. And I’m wondering how AI is kind of forcing itself into the situation of personalization and data personalization.
How have you started to think about that in your own work?
[00:05:37] Zontee Hou: Absolutely. I was actually really excited to be writing this book at this moment because not only are AI tools evolving more quickly than ever, but also the truth is that marketing has actually been able to take advantage of a variety of AI tools for a little while now.
So it was not new to our industry in the same way that it might be new to some other spaces. Right? So within the book, I talk a lot about some of our opportunities with AI. One of the examples that I share is about how target many years ago, around 2002 or so they were trying to do analyses of a desired audience and it took their Data analytics team, something like six to eight months to answer this hypothesis that the marketing team had.
The marketing team came to the table and said, hey, what if we could identify people who are in their first trimester and have not announced that they are pregnant yet, and so we could win them over before the competition, right? Hand done analysis by an entire data marketing team. Data analytics team.
It took them about six or eight months to develop the rules to actually parse out these people this same kind of exercise now with a data layer that is taking advantage of AI to use natural language queries could be done in a couple of queries. You could see a team go in and ask, give us a segment of people who have announced that they are pregnant.
And share, find all of their common traits during those first three months of pregnancy based on the due date that they’ve given us now, based on those particular traits develop us a lookalike segment that we could market against now, that’s three questions and you can achieve the same thing. That is the kind of power that I’m really excited about when it comes to AI within a marketing team for this kind of strategic, thoughtful, competitively driven strategic thinking, it’s also something that we’re seeing make it easier for teams to deliver on personalization, right? In the book, I share the example of fresh direct, which is a New York City based, um, direct to consumer grocery delivery company.
So imagine a supermarket with no physical stores where they deliver everything to you and they have brought together all of their data into a great warehouse in order to better deliver a personalized recommendations for products and sales and offers. Whether you’re on their app in email through SMS, et cetera, based on all of your different shopping habits.
Now, they’re able to offer that personalization because not only do they have this data layer, but again, they’re using artificial intelligence to generate some of that messaging. We’ve seen that with a variety of different companies, because companies like sales for. Force and optimized, et cetera, have developed tools that allow the messaging to be more tailored based on the shared history that exists, right?
The more data you have on how your customers have behaved in the past, the more you can predict what they’re going to do in the future and then offer them messaging that is more highly relevant. So those are some of the things that I’m really excited about and that I see as big opportunities for AI.
And as we are working with clients across the board. across many different industries, we’re identifying places where AI can also help to deliver content more effectively to customers on a more one to one personalized level through different channels. You know, whether that’s a chat bot on your website so that people can find the exact right resource.
And then that data layer is Again, saved so that you can deliver them better resources the next time around. Or again, it’s something like email or advertising where they’re getting better messaging and more customized language offered to them based on their behaviors.
[00:09:30] Mike Allton: The speed at which we’re able to move forward with campaigns like the example that you gave is, is absolutely exciting.
And I love all these different examples you’re sharing of ways that companies innovative ways that you, Are able to come up with that AI is just revolutionizing every aspect of marketing, particularly personalization. Are there other ways top of mind where you think AI is really changing how we do personalization today?
[00:09:55] Zontee Hou: Yeah. I mean, one of the areas that I’m actually really excited about is really thinking about customers experience in a holistic way, right? So let’s imagine from a customer service perspective You and I, we’ve all had to deal with a customer service agent at some point. Now, many organizations are able to implement AI driven systems that take your shared history, that past experience with the company into consideration so that they can give you more personalized, more relevant service, but then let’s extend that further and maybe even treat it as an opportunity for agents, right?
So imagine you are on a platform like. Travelocity or Expedia, and you need support to help you select the right flights. By using the integrated saved data that exists in the system about your past search history, as well as your preferences, your family makeup, et cetera, that agent that’s helping you pick a solution for the future is also able to deliver a more relevant customer experience.
Those are some of the things that I’m really excited about. But it goes beyond this kind of service layer. It’s also about the self serve layer, right? So think about a store like Sephora which is a makeup store. They’re one of the case studies that I include in my book. And what you see is that they have a wonderful app that allows people to better keep track of their own preferences.
Now, as you can imagine, Skin care and makeup are very personalized. We all have different skin textures. We all have different skin tones. We all have different color preferences, etc. So it really makes a difference, right, to actually have information at the tips of your fingers. They realized very quickly, once smartphones were rolling out, that people actually really,
And so they developed an app that delivers on that need from the customers. Now, they are able to then, again, deliver more personalized, tailored recommendations, whether you’re on their website, in the app, or in a physical store, based on What you prefer, what you’ve bought before, what they think you might like, and even give you a discovery opportunity.
And that’s a self service experience. It doesn’t require anybody to be on the other side of it. Even in a bot driven experience, it, it is self serve, but that self serve experience is personalized each time you log into the app to the needs of the particular person who’s looking at it. And I think that that’s very powerful.
So that’s on the B2C side, again, on the B2B side, we’re seeing some great opportunities as well. I’m really excited about being in this moment where it’s more cost effective than ever before to offer a more tailored experience. It’s really just about having that wherewithal and that strategy within your organization to move it forward.
[00:12:47] Mike Allton: Love it. And I love all these examples. That’s something I talked about a lot on this show, because unlike other. Tools that are designed for very specific use cases. And they tell you on the website, this is how you use this. AI is very different, right? It’s, it’s, it’s a technology that’s an underpinning of everything else that we’re doing.
And so I think that’s one of the struggles that a lot of marketers have when they first start to think about how can I use chat GPT or generative AI. They just don’t know they have to be shown. Here’s some examples of how you can do it. So that’s why I love that. You’re just sharing so many examples.
It’s very illustrative, I think, to the listeners. Could you share one or two more? And I’m looking, I think, right now, specifically for any examples you might have where They achieved a lot of success with the campaign and success that could be measured. It’s great to say that, you know, this was, this was a more powerful experience and that sort of thing, but how do we really know, right.
That there was an impact on that. Do you have anything like that?
[00:13:44] Zontee Hou: Yeah, absolutely. An example I will share with you is general mills. So you may know that general mills is a portfolio brand, right? They sell many different kinds of food products. You might know Pillsbury, you know, their cereals, et cetera.
Now General Mills has implemented an AI technology through Salesforce to make their emails and their advertising offers much more personalized to the shopping behaviors of the people on their list. Now, that means that they are recommending specific kinds of, let’s say, breakfast products.
If you are. family versus you are a single person. If you are somebody who’s buying more high end items versus somebody who’s buying more budget items, it’s making a whole bunch of recommendations, not only in terms of those, those foods and offers, it’s even giving content to you based on your specific behaviors, which might include things like recipes, which as you can imagine, as a food company is a very big part of it.
What they’ve actually seen is not only 150% increase in the open rates and the engagement with the actual content. But they’ve also seen a 40 percent lift on their click through rates. So you’re not only seeing that people are actually consuming the content that they’re sharing, but they’re actually clicking through more to engage further.
So I think that that’s really demonstrative of the impact that you can have when you’re offering these highly tailored, highly relevant messages to people. And I also think, again, it demonstrates that you can do this at scale with technology, right? It used to be very conceptual for us to say, okay, we’re going to have different campaigns for you.
For different audience segments. Now, how fine do we cut those segments? What’s the return on investment to create, you know, eight different campaigns versus 32 campaigns versus, you know, 2000 campaigns, right? There’s a, there’s a point where you just say, we don’t have the marketing bandwidth to be able to run and effectively optimize each of these campaigns.
But with technology like this, you are really leaving it up to the computer to make some decisions based on the inputs, based on the Based on the parameters that the marketing team has strategically placed. But again, the individual tailoring that’s being given to each customer is done by the tool itself.
So you are really bringing this idea of personalization to scale. And again, it may not sound like Groundbreaking idea. But you know, when you can achieve that kind of 40 percent lift in click through rate, you’re really talking about a lot more people actually clicking through. Purchasing your products, right?
Because they’re consuming those offers. They’re placing the orders. They’re downloading the coupons. They’re going to do something about that. That’s a high value behavior in that particular industry. And so I think it, it really shows that you can create a lot of value for an organization by being strategic about implementing AI in the right places.
[00:16:43] Mike Allton: Yeah, 40 percent is huge, particularly when we’re talking about a major, major brand like General Mills, their subscriber size is not insignificant. So just to clarify, this was, this was an email campaign. This is email technology. Was there a particular off the shelf platform that they were using for this, that had a lot of this capability built in or was it homegrown?[00:17:02] Zontee Hou: Yeah. So, so they were using Salesforce and implementing the Salesforce AI tools in there. Now, what’s interesting about Salesforce is they also offer some tools on the B2B side that can also help for B2B prospecting and personalization as well. So they’re actually using their Einstein technology to deliver more personalized, individualized messages to go into those inboxes, which I think is also very interesting, right?
Because we, again, talk about prospecting, but. I’m sure you might have also experienced the really heinous emails that come into your inbox where you’re like, this is just a copy paste of, you know, do you have this problem? Let us solve this problem. You’re like, I don’t have this problem, and this title is incorrect, and it’s not appropriate for me at all, and it immediately makes you discount it, right?
I think it’s even more impactful on the B2B side.
[00:17:51] Mike Allton: I love that because you’re right. There’s, there’s best practices, but to your earlier point, there’s also a very real question of bandwidth. Your team probably can’t create 2000 or even a fraction of that. If you could do 200 campaigns, I’d still be impressed.
Right? Most of us were lucky if you’re able to. You know, if we’re having to do it manually, a couple segmentations, but that’s really interesting that Salesforce has that capability. That’s something we’re going to have to dive into more, but I’m also wondering, you know, we’ve talked through and around some of the benefits and now that you’ve shared some examples, I’d love if you could just kind of boil it down, what are the key benefits?
Do you think bringing AI into data driven personalization and marketing?
[00:18:30] Zontee Hou: Yeah, I think that a A. I makes it easier to access and make sense of the data. Most of our organizations do not have large data analytics teams. Now, of course, there are organizations that have a good data analytics team, but then it still adds a layer of complexity between your marketer who is making strategic decisions versus the person who’s actually analyzing and pulling out the data.
So I think that A. I gives us the ability to give access To the data more directly within our organizations, and that availability of data actually makes it more likely that your team is going to ask better questions. Part of this exercise is about building a culture of curiosity, so that your team is empowered to ask the right questions to help you build that competitive advantage.
Right. I spoke to my Jen, my friend, Jen Chase who is the CMO at SAS, SAS, and she. Runs a team where marketing is really a profit center, not a cost center, and they are driving the strategy behind their data usage within the organization. And one of the things that she has empowered her team to do is really be directly engaged Accessing the data.
This makes a really big difference because again, you can start to be more thoughtful, more experimental, and find those incremental growth opportunities when you have that access. So that’s one. The second is, of course, scaling the personalization piece, right? Again, personalization is really about making sure that we are offering messages, content, Resources offers that are really specific to the person on the other side.
Now I’m not necessarily saying every single person, right? Not every single customer is your ideal customer, but if you could offer high quality personalization to your top 10, 20 percent of customers, you would immediately improve your business because those are the people who are going to spend more money with you, work for more business to you and spend more Money longer with you.
Right? So the more we can improve the experience of the people who are in that top percentage of our audience, the more we’re growing our business to do that again, we have to make sure that we’re giving them the information that helps them to spend that money with us. So the more personalized, the more relevant that we can be the better.
And again, yeah. The technology is really about speeding up that process, right? Because, you know, listen, I’m, I’m in a consultancy. When we give a personalized, tailored set of recommendations to our client, it takes us a long time, right? But that isn’t necessarily a set up. Scalable model and not one that most businesses can necessarily spend the time on.
And quite frankly, whereas a consulting client is willing to wait for the recommendations, the vast majority of our clients are extremely impatient, right? So the more we can use technologies to speed up the process and make it more accessible. Easier for them to find the right resources, the better. I gave that example of the self serve process with Sephora.
I think that’s a good example of empowering your customers so that they feel confident so they can spend more money with you.
[00:21:46] Mike Allton: These are incredible examples of the benefits, not only of data personalization, but how AI is making those benefits accessible and affordable for everyone. So thank you for sharing those folks.
We’re talking with Zontee Hou about how AI can help your marketing be even more effective through personalization. And we’re going to dive even deeper in just a moment, but first, let me share with you the tool that I’m using every day for podcast interviews, analyzing reports, and more. This episode of AI in Marketing: Unpacked is brought to you by Magai your gateway to making generative AI, incredibly simple and And accessible wondering how to seamlessly integrate AI into your marketing strategy without getting bogged down by complexities.
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Ready to simplify your AI journey? Visit Magai today to learn how their solutions can revolutionize the way you engage with your audience. With your audience, don’t just market market smarter with Magai tap the link in the show notes. So Zontee, obviously one of the most important aspects of data driven personalization is the data part.
How can marketers better leverage data when it comes to personalization and AI?
[00:23:29] Zontee Hou: Yeah, I think that one of the things that I emphasize most in the book is that you need to be collecting more data, but you need to be collecting the right data. Now, I think that again, AI allows us to better parse the data.
better make sense of it, better understand what resonates with our audience. It can also be a great partner, you mentioned earlier, generative AI, it can be a great partner in developing the messaging hypotheses to actually test what resonates, right? Because ultimately this is a feedback loop, it’s not just putting stuff out there in front of the customer, it’s seeing, are our assumptions correct about what resonates?
And therefore, what should we do more of the next time around? So I think that we have an opportunity when it comes to generative AI, as well as other kinds of AI analysis tools, to say, this is what seems to be working. How do we continue to tweak the algorithm, so to speak, of the content that we develop and deliver?
And do our assumptions then, merit further exploration? Is it working? If we go further in this direction, are we seeing that it’s more effective or are there other consequences? It’s a little bit like we’ve been seeing the algorithms in YouTube or TikTok where people say, Ooh, it actually creates this a little bit of an echo chamber effect, right?
It’s almost too effective in driving you down the rabbit hole. Now, we don’t necessarily want to send our customers down a rabbit hole that’s negative, but we do want them to have that kind of feedback mechanism where they go, Ooh, this is so useful. I’m going to keep coming back. And because I come back and the content that I’m being served and delivered and has been developed for me is even more relevant.
I’m going to continue to come back even more. Right. So I think that where AI can be really helpful for us is in scaling the development of some of that content, some of that messaging. Another platform that I really like for that kind of experimentation is Optimizely. Optimizely is also using their personalization driven AI to do some really interesting things where they are making it easier for you to put different kinds of messaging.
directly in front of people based on their behaviors, not only leveraging your data, but also third party data that indicates what kinds of motivations or messaging might resonate with that particular audience. Another tool that’s doing some interesting things there is called Persado and Persado has what they literally call motivation AI, which is using emotional Archetypes and attitudinal archetypes to help you put messaging in front of people.
That’s really highly tailored. So those are places where, again, we’re actually using the AI just to make it easier for us to scale the kinds of marketing that we’re putting in front of our customers. And then again, giving us the feedback of which ones of these things are actually. Because you might find that there’s a whole bunch of people who, you know, you put like a hype kind of message in front of them and it does make them consume more content, but those people never spend any money.
Well, that tells you something different than if they were spending money, right? So if that audience, we say, okay, they’re high consumption, but they’re less likely to convert. Well, then we as marketers may actually say, let’s. Do less content in that space, just because they’re consuming content doesn’t mean they’re our best customers.
Let’s then focus more of our energies on the people who don’t consume a lot of content, but are more likely to buy because they exhibit this other set of qualities, right? And again, I think that. Right now, one of the things that I’m most excited about is the ability to query our data in more natural language so that we can speed up this process and ask these questions and get answers in a much more effective way.
Oftentimes, I as a consultant will come into a team and I’ll ask them a whole bunch of questions about what data they do and don’t have. And you, you know, as well as I do, Mike’s marketers are excited about the questions, but then they go, we can’t answer those things for you. We don’t have the ability to get that information out of our system.
And that’s super frustrating. So I think we have to solve for that disconnect. But then we have to put in place these tools that make it a little bit easier for us to pull that information out.
[00:27:57] Mike Allton: I agree that that is exciting. And the point about the feedback loop was, was brilliant being able to see what’s happening and then ask additional questions and drill down and say, okay, well, maybe some of the top line results were great, but those aren’t leading to actual business impacting results, so we need to re evaluate that. Now, a lot of folks listening are hearing some things that we’ve said, and they’re, they’re resonating because maybe they’ve done data personalization to some extent. Maybe they’ve been limited by their tools. Maybe they’ve got, you know, an out of the box email solution that doesn’t really have a lot of personalization options.
They can pop in my first name and probably that’s about it. Right. But for those who want to dig deeper and they want to start to employ AI, what do you think might be some of the unique challenges related to bringing AI into data personalization beyond just the normal personalization?
[00:28:50] Zontee Hou: Yeah. I mean, I think that the balance is between using AI to make better decisions and delivering content that still feels highly relevant and quickly naturalistic.
Now, of course, You are already seeing that AI is getting better at writing natural language content, right? We, at the very beginning of the experimentations with ChatGPT 3, the public release, you know, we all had that experience where you would read a paragraph and go, Yeah, you can tell that AI wrote this because it kind of sounds like an over eager high school student writing an essay, right?
Every sentence is a run on sentence. Every, every paragraph has The same exact structure. There’s kind of an an oddity to its rhythm, right? My friend Chris, Chris Penn has pointed out that The machine learning has already gotten better in terms of both perplexity and burstiness. So the rhythm of the writing is more naturalistic.
The language that it is using is more naturalistic. So those are exciting things because that means that the content that you’re Audience is going to see is going to feel more natural. But I think we’re right now in this uncanny Valley where when I ask AI to write copy, it’s not the best copy. It always feels a little bit, let’s call it tortured, right?
Because of that, I think that we as marketers cannot rely on it to do the final draft of anything. It’s not the ideal use case for it. I think it can help you again, tweak and tailor the, the parameters for what does our audience want to see? But I do think that there has to be human layer of, okay, how do we want it to actually sound?
How do we maintain our voice and tone? And how do we deliver the right offers within our parameters that feel like They make the most sense. It’s not good at making decisions. It is good at delivering, I think, within specific parameters. Again, where I think it’s most valuable is you design the strategy, you design the parameters, and it goes and executes and brings that to scale.
I think that that’s really valuable. And then again, giving it the, the inputs of here are all of the data points. What are the trends in here? What are the groupings that should exist and using it to identify feedback that you can then build upon? I think those are the exciting things for me.
[00:31:26] Mike Allton: I couldn’t agree more.
I love having these kinds of conversations with the AI where I tell it the kinds of things that I want to accomplish. And the first thing I often do is tell it, well, ask me what You need to know to help me with this, right? I tell the AI to ask me questions and it always comes back with five to 10 things that it wants to know more about that.
Maybe I would have thought to tell it, maybe I wouldn’t have, but then the conversation becomes so much richer and I’m asking it for ideas and different directions to go, but it’s given me really, really interesting options that often I would not have thought of on my own, at least not in a swift amount of time.
So I love that. I’d like to switch gears a little bit because. Some of the personalization we’re talking about sounds really, really personal, like hyper personalization. And I know some folks are going to be concerned from a privacy perspective. And I’m wondering what role do you think data privacy plays when it comes to AI driven personalization?
And probably more importantly, how do you think businesses can ensure that they remain compliant? With whatever the law of the land is at the time.
[00:32:31] Zontee Hou: Absolutely. I think that having governance and knowing what are the ethical boundaries of your both personalization program and the AI in your personalization program is extremely important.
I have a whole section of my book that really talks about this. I think that there are a couple of really key guidelines, right? One is really training your team to have that Framework of governance and really understanding that there’s a major human level that needs to be dealt with when it comes to not only creating those rules of the road, but actually doing the training with your team so that people are really thoughtful about how they approach not only the marketing piece of it, but the data management piece of it.
The second piece I think is really important is transparency, right? Allowing your audience to control what data is used for personalization, but also being clear about what data is being used for personalization. I give the example of how a couple of years ago you may remember that Meta bit Facebook at the time had to allow you to see and control what were the targeting parameters that they have for advertising on your individual account You can do it to this day.
I’ve done it in the last couple of months You go into your account you go into your your privacy settings And you can actually see what it associates with your account based on your behaviors And now some of them will be highly relevant. I’ll look at it and go into Yes, I’m the mom of a toddler. That makes sense.
But sometimes, you know, you will have looked at something in my case again, as a consultant, I look at a lot of things that are related to my clients. So recently I’ve been getting YouTube ads for for health care professionals, and I am not a health care professional. So I’ve been thinking to myself, oof, like that’s something that I need to tell it.
Like, don’t give me that content. It’s not relevant to me, right? So you need to make it easy for your audience to actually Access that and to be able to control that. And you should use that as part of your promise, your storytelling to your customers. In the book, I give the example that brands like Kay’s Jewelers, Levi’s and several others actually give people the ability to opt out of Mother’s Day Marketing because they know that that can be a sensitive time for some people.
And I think that that’s a great approach to say, we respect our relationship with you enough to recognize that there are some times when you don’t want to receive this marketing and we’re going to give you that, that option to opt out. Similarly, I give the example of lemonade insurance. Which has taken a very natural language, very human approach to their disclosures and how they describe how they use their data, because they know that that’s part of their overall promise.
That the organization as a whole is very human and, Wants people to really understand all of their disclosures. And so they’ve taken this entire kind of marketing approach around that, but I think it’s smart because it builds trust with their brand when they actually share this information with customers, I think.
So number two is that transparency. And then number three, I think it’s really about making sure that the data that you are collecting From your customers has a genuine, valuable purpose. What I think is uncomfortable for most of us is thinking that organizations of any kind have data about us that goes well beyond the scope of our relationship.
You know, it’s one thing for. I don’t know my partner, my husband to know everything about me. Right. But if I go into my local sandwich shop and they go, Hey, you know, I saw that you slept pretty poorly last night. You know, like, were you tossing and turning about something? I would find that very uncomfortable.
Right. I would go, I’m sorry. Why do you know how I slept last night? And why are you asking me about it? Right. I mean, the thing is we’re all wearing like wearables now and that data is being collected somewhere. And. You know, if, if an organization that I did not want to have that data had that data for no good reason, I would be deeply uncomfortable in the same way.
I think organizations need to be thoughtful about what do we ask for and how do we demonstrate value when we ask for it? Sephora asking you, what are your color preferences? What kind of skin texture you have? What are your, your preferences for SPF? That is reasonable. Again, if Sephora starts asking me questions about.
I don’t know my my shoe habits. I’d be like, I’m sorry. I don’t see how which shoes I like to wear has to do with the products that you sell. And I would find that uncanny. But again, there’s there’s this attitude right now where people think data should have a more is more approach. And I think we actually have to be thoughtful, both as marketers and as brands about how much do we collect?
And how do we demonstrate to you we’re collecting it because there’s a good reason to collect it
[00:37:28] Mike Allton: That is a really really valid concern and I think folks listening don’t quite understand the connection because you mentioned earlier how a brand might find that if they’re sharing very Highly energetic motivational language and they’re getting people hyped up that those people might click through But not actually purchase.
And if they’re doing a lot of research, they may find that those people are receptive to that kind of language because of certain personality traits, and they might start to mine their customers and their prospects for personality traits to help target or exclude people. Based on that information. And that’s the kind of thing I think you’re right.
We’d find very uncomfortable as consumers. You wait, you know, my Enneagram and that’s going to determine whether you advertise television to me,
[00:38:12] Zontee Hou: right?[00:38:12] Mike Allton: That there, there might be psychological sales valid data for that at the same time as consumers. It’s probably going a bit too far.[00:38:21] Zontee Hou: Absolutely. Absolutely.[00:38:24] Mike Allton: Be mindful of[00:38:24] Zontee Hou: in the book. I actually share an example of How some organizations and again, this is a, this is a human driven error. I want to be clear about that, but that when you collect too much data, there’s. There’s the opportunity to make these kinds of mistakes, right? I share a story about somebody purchased a list from a data broker, used it to send out direct mail, but unfortunately, because of a human error, there were pieces of information about why these people were in this, this The list sent to the people who received it.
So it doesn’t just say Mike Allton on it, right? It actually said the reason that you were on the list. And some of the people, the reasons were extremely personal, painful reasons. And so you don’t want somebody to get a piece of mail that says, you know, this person is on the list for drug problems, health issues, family issues, etc.
Like, that’s shocking. It’s terrible. It’s terrible. Right. And again, that’s, that’s a human error, but the data there that is being collected needs to be thoughtfully processed. We have to not only respect our customers, but understand, as you said, that there is a line here, right? Should our personality traits be the reason that we’re marketed to?
I think that there’s an, there’s an ethical question here.
[00:39:43] Mike Allton: Yeah. Yeah. That’s very powerful. That’s obviously a deeper conversation. We’re going to continue to have on this show, but for those who want to get started with actually applying AI to their personalization efforts, what are some practical steps that they could take?
I imagine it’d be maybe even just to start looking at the tools that they’re using today and see what AI options are there. But, but beyond that, what else could you share? What would you recommend?
[00:40:05] Zontee Hou: Yeah, absolutely. I mean, I definitely think taking a look at your existing data stack is a good place to start because there are so many more tools than ever before that have integrated different AI assistance into their programs.
You know in my small business agency, we work with clients where everything falls into place. from their scheduling tools to their content creation tools have already gotten AI assistance built into them. So that’s definitely a place to start. The other thing that I think is extremely important is to look at your data layers.
What are the different places that you have data repositories and how can you bring those together into one cohesive system? That you can use to feed a lot of decision making for not only, again, the content creation, but the actual personalization delivery mechanisms, right? If you currently have your data in many different places, that is something that you must tackle as a project.
And it is not a small project, I want to be very clear. To do that will require you to not only bring together marketing leaders, but your IT leaders within your organization, your customer experience, leaders, sales, customer support, etc. And so recognizing that that is a need is important. Something that can feel daunting, but ultimately will take your marketing to the next level if you can present a cohesive strategy around why you should do this in order to power not only the use of AI to get more out of your data, but the use of your AI and data to power the personalization that gives you that competitive advantage.
[00:41:52] Mike Allton: That’s fantastic. Now, for my last question, I’m wondering if you could just kind of look ahead, pull out your crystal ball and think about some of the future trends that you see in the realm of A. I. In data driven personalization and marketing.[00:42:05] Zontee Hou: Yeah, I mean, I think that what we’re going to see, hopefully in the relatively short term, I’m thinking in the next couple of years is not only is A I going to help you. Make sense of your data and make sense of the opportunities for personalization when you go and query it, but that in the background, your models will actually be consistently looking for some of the opportunities and making suggestions to you. We’ve already seen that companies like Google are trying to make their analytics suite smarter by saying, Hey, have you Considered these queries, right?
But a lot of that is currently very driven by human beings making suggestions and then them serving them up to you saying like, these are some different ways that you could use our tool. And then it goes and runs the query. I would love to see the tools, your tools that you are using within your organization.
Every time you open it, say like, here’s some different affinities, associations, affiliations that we think are worth taking a deeper look at. Do you want to run this query and look at it further or find the opportunity or deliver messaging around this? Because I think that oftentimes we as human beings by ourselves, we have a very specific point of view or a specific worldview, right?
Yeah. That’s one of the great things about working with a team, is you’re bringing together lots and lots of different viewpoints, but I think that what AI has an opportunity to be is a partner to us, right? To expand our mind, to expand our capabilities, to be a somebody to bounce ideas off of, and I think that if the AI is also coming to the table saying, here are some things that That seemed like there might be an opportunity there, then you can explore those topics further, or you can discount them if you go, you know what, like, that’s not relevant at all.
That’s fine. Right. But it’s it’s helped you to do some of that thinking. And I’d love to see a little bit more proactiveness in the AI tools. And I think that we’re very close to that kind of experience.
[00:44:11] Mike Allton: Love that perspective. Thanks so much. And thanks for everything you’ve shared today. We’ve gotten a lot of really interesting deep dives.
Appreciate it You’ve been amazing for folks who want to learn more. They want to reach out. Where could they go to connect with you?
[00:44:23] Zontee Hou: Yeah, absolutely. Well, please check out the book data driven personalization I have a website for a data driven personalization dot com You can also learn more about me at Zontee Hou dot com And of course, we have a fantastic newsletter and a companion podcast to the book data driven You Decisions on our website, convince and convert com. And I really encourage you to listen to that show. If you’re interested in hearing more stories about how people are actually using data and AI tools in their organizations today.[00:44:55] Mike Allton: Love it. Thanks so much folks. We’ll have all the links in the show notes. Be sure to check out that book. It’s fantastic.
And that’s all we’ve got for today friends, but don’t forget to find the AI in Marketing: Unpacked podcast on Apple and leave us a review. We’d love to know what you think. Until next time, welcome to the grid. Thanks for joining us on AI in 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. 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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