Why Your Retail Data Strategy is Failing—And How to Fix It

Speakers:

• Megan Billingsley: Moderator
• Brendan Witcher: VP, Principal Analyst, Forrester Research
• Janet Jaiswal: VP of Marketing, BlueShift

Megan Billingsley: Hello and welcome to the webinar, "Why Your Retail Data Strategy is Failing... and How to Fix It." My name is Megan Billingsley, and I'm excited to moderate this discussion today.

Today, we'll be talking about how AI-driven tools can empower e-commerce and retail brand marketers to unify customer data, drive personalized experiences, and enhance omnichannel engagement. This discussion will cover strategies for seamless data integration across channels, utilizing AI to enhance one-to-one customer engagement, and real-world examples of successful omnichannel campaigns.

And with that, let's meet our speakers. We are thrilled to be joined today by two industry experts. We have Brendan Witcher, VP and Principal Strategy Analyst at Forrester. Brendan is a renowned expert in digital customer engagement and personalization, helping brands redefine their customer journeys.

And we're also joined by Janet Jaiswal, Global VP of Marketing at BlueShift. With a proven track record in driving growth, Janet brings expertise in AI-driven marketing strategies that deliver results.

In this webinar, Brendan and Janet will discuss integrating real-time insights to optimize marketing strategies and share practical insights and actionable strategies to elevate your e-commerce and retail marketing efforts and stay ahead in a competitive landscape.

I do have just a few housekeeping notes for our viewers. A copy of this presentation and session recording will be sent to all that have registered. You can submit questions through the Q&A box at any time. We will answer these at the end of the presentation. For feedback and comments, please use the chat box, and we will share helpful resources at the end of the session.

Before we get into our discussion, I'd like to invite Janet to share a bit more about BlueShift. Janet?

Janet Jaiswal: Hi. So, welcome. I wanted to give you a little bit of information about who BlueShift is. We were founded in 2014 and are headquartered in San Francisco, California. We've been recognized in the Gartner Magic Quadrant for customer data platforms, and we're also recognized by Deloitte Technology in the FAST 500 for three years running, 2020 through 2023.

BlueShift provides a customer engagement platform. What that is, is it's a customer data platform, cross-channel marketing hub with patented AI that helps activate customer profiles and scale personalized one-to-one engagements for brand marketers.

Megan Billingsley: Excellent. Thank you, Janet. Our agenda today is packed with useful information. Our experts will provide a brief overview of why cross-channel success matters. Then they'll discuss various key topics. Finally, we'll open it up to questions from attendees. And with that, let's get started. Brendan and Janet will now provide an overview of why cross-channel success matters. So, Janet, I'll turn it over to you.

Janet Jaiswal: Great. Thank you. Okay. So, let's talk about why cross-channel success matters. Consumers have certain expectations when they're interacting with a business. As you can see, 90% of customers expect a consistent interaction across all the different channels and platforms. And that's because they're using a lot of touchpoints through which they engage. On average, consumers use about six touchpoints when making a purchase.

So, what's the impact when a company has true cross-channel marketing? Well, several.

• Businesses that adopt cross-channel strategies see a 91% higher year-over-year customer retention rate compared to those that don't.
• Also worth noting is that cross-channel shoppers have a 30% higher lifetime value than those who only shop on one channel. So, it is important to meet the needs of those cross-channel marketers or cross-channel buyers.
• Finally, marketers that are using three or more channels in a campaign have earned on average a 494% higher order rate than those that use a single-channel campaign.

So, again, very important and why cross-channel marketing matters. BlueShift works with marketers from many brands, and we've seen that they face a few challenges when it comes to cross-channel success.

1. Fragmented data and silos: The amount of data collected and stored is growing, but not all companies are able to leverage it effectively when engaging their customers.
2. Inability to deliver personalized real-time engagement: Competition is a click away, and attention spans are tiny. It's important to meet them on their terms and where they are.
3. Difficulty measuring ROI across those channels: Insights are needed, not more data, to truly understand the impact of each channel, regardless of which one the customer converts through.

These are some things that we've seen quite a bit. Something you research quite a bit is consumer behavior. Can you share with us how has behavior shifted in the last few years that organizations still have trouble addressing and aligning with?

Brendan Witcher: Thank you, Janet. You know, what's really interesting about the way the consumer has shifted, it really comes from what happened three years ago and just before that. When we were going through the pandemic and all those sorts of things, consumers began to become more digitally savvy, and they also became more channel agnostic.

Since that time, we've also digitized more parts of our lives because we were forced to for a long time. It used to be that when you went to yoga class, you would just go to the class. When you went to go shopping, you would typically just go into the store. When you would go to a doctor's office, you would just go into the doctor's office. Now, in this day and age, consumers have digitized more parts of their lives, from doctor's visits, even things like dating, even hailing a cab. Everything has been digitized. We no longer send photos of our kids to our mom and dad; we put them up on Facebook for mom and dad to see.

So, what's happened is these digital channels have become ubiquitous, not just in commerce, but in all parts of our lives, which has made the marketer's challenge even more difficult because it's become more complex as the consumer is everywhere. What companies often talk about is, "Oh, everyone's on Facebook or everyone's on Instagram or they're on TikTok or whatever it is." What they forget is that we've really enabled people to always be digital, to be walking around digital or digitally connected, rather. So, this is really an important part of why marketers today have to understand that the opportunities are there, but they come with those challenges that you talked so well about.

Janet Jaiswal: Yeah, makes sense. All right, let's move to the next slide.

Janet Jaiswal: So now let's talk about the impact of data in cross-channel marketing. Studies from Workday show that companies that leverage customer data alongside omnichannel strategies see a 9.5% increase in annual revenue. And that impact is across the company. So, for example:

• Higher customer spending: Customers who use four or more channels spend, on average, 9% more in-store compared to single-channel customers.
• Contribution margin improvement: Retailers investing in customer data can generally see a 3% to 5% increase in contribution margins after accounting for that initial investment.
• Increased repeat purchases: Within six months after an omnichannel shopping experience, customers logged 23% more repeat shopping trips to a retailer's store.
• Enhanced store performance: Top-performing stores with high customer satisfaction saw a 28% higher sales growth year over year.

So, what does this data tell us that might not be so obvious? Well, without capturing the right data about customers, the engagement that a brand has with its customers might be limited, superficial at most, or at worst.

Brendan Witcher: Janet, if you don't mind me jumping in there on one thing. I think it's really important to note, I mean, that really needs to be underlined quite a bit. Really, what a lot of companies are working on as well is that idea of not only creating these cross-channel experiences, but personalizing those experiences, right? I was covering personalization at Forrester for many years. And what I started to realize is my clients didn't have personalization problems. They had data problems.

Now, that was a very unsexy topic to try to shift to, right? Everyone likes to talk about personalization. But at the end of the day, it's sort of ridiculous to say, "You know, we're going to create great experiences for customers we barely understand," right? That doesn't make any sense. And so, what companies need to do is they need to understand how to capture that data, how to learn about the customer, not just when they're in the store, not just when they're online, but no matter where they are, no matter where they're interacting, because that data can come from everywhere. And that should not just inform the channel in which that data is collected, but all the channels that the customer is engaging with.

Janet Jaiswal: Yeah. Next slide. So, here, Brendan, you wrote a report called "Conditional Love, What It Takes to Keep Customers Faithful." And you talk about the six-C strategy. Can you elaborate more on that?

Brendan Witcher: Yeah. You know, a little bit of history here, if nobody minds listening to it. It's actually a fun story. You know, I was going to all these conferences. I've been an analyst for about 11 years now. And so, I'd go to these conferences, and I'd listen to CEOs get up on stage, and they'd say, "You know, oh, we're delighting the customer, we're surprising the customer, we're making them happy." You hear it on the earnings calls all the time. And it just so happened that I was at one of these conferences when a brand that I do a lot of business with said those same words. And I'm sitting in the audience going, "No, you don't." You don't. I shop with you all the time, and you don't really deliver those experiences to me.

So, I said, "Wait a minute. This is kind of a light bulb moment for me as an analyst." And I said, "Let's dig into this." We spent about a year and a half interviewing consumers, interviewing companies, interviewing psychologists, teachers, educators, and what makes us us, what makes us feel understood sort of thing. And what I found was that we are just scratching the surface when it comes to really understanding what a customer needs and wants.

And so, what came out of it was this 6C strategy. What it means is that customers today are more than just their characteristics. They're more than their age, their gender, their zip code, the last product that they purchased. They're way more than that. When you start to ask companies, "What is your strategy for marketing or for commerce and how are you using data?" They're usually using those characteristics that I just talked about. What's really interesting is that almost everybody is using the same characteristics. So, if your competition is using gender and you're using gender, guess what? You have no competitive advantage because you're probably showing the customer the same things, because the algorithms tend to work the same way.

And so, what really creates differentiation for a brand isn't about using those things that everyone can go out and buy and do with third-party data, but combining with first-party data, not just losing the third-party data, but combining it with first-party data and creating journeys to learn about the customer and understand the customer. And what you get into is a deeper level.

So, for example, Considerations. I think it's very interesting how companies today build websites for the minority of their visitors on websites. And what I mean by that is that the vast majority of people coming to your site to buy or to shop or to do whatever, they're not there to actually convert. Anybody's conversion rates, I don't know anybody's conversion rates that are over 50%. They're usually in the low single digits, maybe in the low teens. That means the highest percentage of people coming to your site are there to say, "Should I be buying from you?" That's really what they're asking. That's what they're considering. They're considering you as a brand. And so, that experience that you create pre-purchase, during purchase, and even post-visit to the site if they don't buy, that matters a lot. It matters quite a bit on creating that relevancy for them because then they say, "Oh, I get it. Now I see why I'm buying from you."

Then we get into things like understanding Curiosities, like about a certain product or a service, like a leather jacket. Do I care that it's real or fake, black or red? Do I care that it's got buttons or zippers, that it's, you know, I can buy online, pick it up in store, or I can get it monogrammed? There's all sorts of things. Does it have a line or does it not? There's so many things about a particular item that a customer says, "That's the most important thing to me." And we really don't know those things. We say, "Oh, Brendan looked at a black leather jacket." You could probably guess that I would say that. But the reality is I live with two vegans. If I buy a real leather jacket, I'm sleeping on the couch for a year. So, the reality is that you didn't really know me. What you knew is that I looked at leather jackets, and you made an assumption that black was the most important thing, when in fact, there's something else that's way more important for me. So, if you're sending me emails and sending me marketing with real leather jackets that are black, you're missing the mark. That's the idea. We're going to get into a little bit of stats about personalization later, but the reality is we're missing that.

And the fourth, the fifth one on here, sorry, fourth one on here is Conditions. What are the conditions that I need to have that? Do I need to be shown things? Do I need to experience things? Do I need to talk to a sales rep before I buy? Do I need to have an email with a promotion on it? Maybe I don't need an email with a promotion on it. Maybe I'm a full price buyer, but you try to get me to convert. And so, you're sending me promotions when I'm on my mobile phone, when in fact, an hour later, I'd probably just go home and go to my laptop and pay full price for it. So, you're leaving money on the table. It's understanding the conditions of buying that each customer has. You can see how for many of you listening, you're going, "Yeah, we're only at level one," as a company. And so, if you're at level two, that's pretty good. But if you want to create differentiation, you've got to get into level four or three and four, curiosities and conditions.

Context, that's sometimes hard to do, sometimes easy. If you can do it, great. If you don't, then just do three and four. But if you get into Conceptions, there's ironically, what's interesting about conceptions is how does the customer feel about a certain experience? Again, I mentioned the whole surprise and delight. We're talking about surprise and delight a lot. I don't know if you have any idea if consumers are surprised and delighted or not. Really, if somebody goes to a site and you show them a sustainability message, you have no idea if that person is thinking, "Wow, that's really great. I really love you," or if the person is saying, "Well, you're just a hippie tree hugging liberal company and I'm not going to buy from you ever again." You have no idea what really the experience is for the customer. We're getting there. We're getting there a bit with natural language processing, pure vision, some things we're going to talk about a little bit later. But we're not there yet, which is why I don't encourage people to try to figure out people's emotional state quite yet. If you could just get to that third and fourth tier, that really becomes important.

Janet Jaiswal: No, no. You bring up a good point. It is difficult to collect all the right information, but that's step one. You've got to do that, right?

Brendan Witcher: Yeah, absolutely. But then you start to say, "Well, I love the word that you use there, which is the right information." So, left-hand side, talk about the depth and whether or not the stuff's going to be valuable. But it also has to align with your business, right? It also has to understand what can we use? What can we do? Does it align with who we are as a company?

So there's four questions on the right-hand side there:

1. Is the data reliable enough to trust it represents customers clearly?
2. Can it be used to directionally predict customer behaviors?
3. Is it actionable enough to improve our marketing engagement strategies?
4. Is the data relevant for supporting our company's priorities, goals, and objectives?

Now, if the answer is no to any of those questions, you shouldn't be going after that data. If it's not reliable, you can't use it, you can't do anything with it, then don't make your data teams go chase it. Don't build solutions that capture it. If you can't figure out a way to do something with that data that actually is going to create value for the consumer, then don't go chasing that. I remember a company was telling me one day that they were looking for how much consumers care about same-day delivery. This was a pure play retailer with a warehouse in the middle of Kansas City and no physical stores. I'm like, "There is no way you could do same-day delivery. Why are you learning this?" So, it would never have improved their marketing campaigns to learn that about consumers because they couldn't do anything about it anyway. So, you've got to be practical about this. Just because you can learn something doesn't mean you should. So there's depth and then there's correctness and action that you can take with the data. And that's really what this model is all about.

Janet Jaiswal: That makes a lot of sense. All right. So step one is collecting that data. Let's go to step two. So next slide. So talking about AI, right? So once you have the data, what do you do with it? And Brendan would love to hear what you recommend.

Brendan Witcher: Thanks. Yeah. AI is a really interesting space right now. Obviously, everybody's talking about it. You know, we just had a couple of big retail shows, and everyone was talking the talk about AI and how it's being used. The reality is about a year ago, prior to this 2024, beginning 2024, the reality is most companies were saying, "You know, we're learning about AI. We're trying, we're testing, we're experimenting with." And the people that I was talking to were typically CIOs or CMOs. And these people don't mince words. If they're doing something and they say, "We're doing this," they're doing it. But they weren't really doing that this year. They are saying more of that. They're saying, "We're doing this, we're doing that. We're actually executing," but it is a lot of back end stuff. It is a lot of using to create better knowledge workers in the organization, being able to take action on certain things, gain better insights, scale the production of content, looking for insights within data that we wouldn't have captured from a human being going over information.

So, those sorts of things are where companies are doing it. I would be cautious to say that consumers today are saying, "I expect Gen AI experiences." That's not really true. I don't know anybody that's left a website because there was no Gen AI on the website; that really hasn't happened yet. But what companies are doing is they're getting competitive advantage with that.

Now, why is that important? That's important because even things like personalization, which we all talk about, like everybody's doing personalization. In fact, you can see the stat here that 92% of companies say we've made significant investments in those personalization and related technologies. And yet only 33% of consumers say that companies do a good job of creating relevant experiences for me. So which number is right? It can't both be right. They're very opposites of each other, really. And the number on the right hand side, the 33%, is the number that's right. Why? Because consumers get to determine if it was personalized. You know, if you say we did personalization by giving someone a name on a homepage or in an email, the consumer may say, "Well, that's not really personalized because I know my name. I didn't need you to tell me it." Or saying happy birthday to me on a website. I mean, that's nice and all, but I didn't, I have friends and family to tell me happy birthday. I don't need you to tell me that. You didn't create any real value for me. And so, they don't really consider that personalization. Personalization is when you give me something that just feels right. It was like, that was easy, simple, convenient. That's what I was looking for. And so we're not getting our marketing right often enough.

So the scary part is when you look at the 92%, it means a lot of companies have probably checked the box on personalization. And yet the consumers aren't saying I'm getting great personalized experiences. So we need to go back to the drawing board and get deeper understanding of the consumers to get that number up out of the one in three and get it more towards three out of four people are saying I'm getting relevant experiences.

Now, to my point earlier about using data and AI and things to create those experiences, you can see on the bottom that knowledge workers are a big part of this, right?

1. Workplace productivity and efficiency.
2. Messaging. That's outbound. That's straight to the customer right there.
3. Market and buying research.
4. Content development, scaling that. You can't create personalization if you don't have the assets to do the personalization with.
5. Competitive analysis. What are other people doing and should we be doing it better?

So how do we use AI to create more relevance, more value? How do we get customers to feel like this company just gets me? Because that's why consumers are buying from companies today. There's nobody on a call today that I would say, "When you wake up tomorrow morning, if you need a toaster, are you going to panic about the ability to buy a toaster?" No, you're like, "I know I can get a toaster." I can get Q-tips, I get dog food, I get bananas, light bulbs, batteries, whatever. That's easy. You also probably can think, you're probably thinking to yourself, there are a hundred places I could buy this stuff from. In the old days of buying and doing things like banking, insurance, retail, we had to deal with the companies that were around us. And you said it earlier, "Everyone's a click away," Janet. And that, I love that statement because that's the reality. There is no proximity effect anymore in marketing, which used to be a big part of the equation. That's no longer the case. And so consumers say, "I can get this stuff anywhere. Why should I be getting it from you?" Which means you have to use AI to improve it. Improve that messaging, learn more about customers, get back to the knowledge workers through the market and buying research, and then content development, scaling that. You can't create personalization if you don't have the assets to do the personalization with. And then competitive analysis. What are other people doing and should we be doing it better? Sort of a cyclical, iterative thing, if you will, where you're learning and you're understanding, then going back to the things above that and saying, "How can we do these things better based on what others are doing?" So you can see, it's complicated. Retail and business is not that hard. Like in retail, it's like, let's buy some stuff over here, put a markup on it and give it to people over here. When you say it like that, it sounds like it should be very simple. But at the end of the day, consumers are what makes this hard. It's the customer. Most of our customers just be an easy business. So, this is what we're dealing with and this is what's going on right now. And I'm very excited about this time.

Janet Jaiswal: Yes. Yeah, definitely a challenge worth solving. Great. Let's keep going.

Megan Billingsley: Excellent. Thank you both for that helpful context. I would like to now move into our discussion. And the first topic that we'll be discussing is data overload. Now, retail and e-commerce brands collect massive amounts of customer data, but only a small percentage is ever activated. Why is that? Brendan, I'd like to go back to what you were discussing earlier about the Forrester report called "Conditional Love," what it really takes to keep customers faithful. And you really challenged marketers to rethink how well they know the customer. So could I ask you to expand a bit more on how that came about and what you learned in researching that report?

Brendan Witcher: Yeah. So when I got into the, I said where it started, where it incubated from, you know, it was that speech, that statement that somebody made. But in the course of the research, I found that there were certain variables or certain teeth of the cog, if you will, that were causing the company to not have the information they wanted. They were missing teeth that they needed to execute on things. And some of the things were accessibility to data. You know, the people that have the data don't necessarily need it. And the people that could use the data don't necessarily have it.

What's interesting about that is what that really comes down to is something that's a little bit further down, which is data strategy is not sufficiently defined. You know, that, of course, this is a survey. So the answers come back the way that they answer them. But as an analyst, I look at this and say, what you're really saying is that actually above here, if you had a true data strategy, accessibility would be with the right people. It's sort of like an overarching blanket statement, like not having a good strategy. Because a lot of people will say, "Well, we have a data strategy." And I'll ask them to define it. And they'll say, "Well, we have things in, you know, we put things in a data warehouse." I'm like, "Well, that's data storage. That's great." "Well, we make sure the data is safe and secure." I'm like, "Well, that's data security." "Oh, we share data with, you know, with our partners. We sell data. We commercialize it." I'm like, "That's data sharing." Or "We gather third-party data." "Well, that's data sourcing." You can tell I love alliterations. So it's all these DSs. Data strategy, data sourcing, all that. But data strategy, another alliteration, is really very rare, which is that ability to say, "Well, who's in the organization is in charge of learning about the customer?" And then who's in charge, maybe the same person's in charge of making sure the right teams have that access so they can do certain things with it.

Often the strategy is every person for themselves, you figure it out sort of thing. Marketing data and store people have to look at store data and operations people look at operational data, and customer service people look at customer service data. Social teams will get social data. But it's not really bringing it together to create that one view of the customer. And so that's what that big, that's why the first one's a big bucket of one in three people saying, "You know, it's not accessible."

Then we get down into some of the other layers here about data management resides in the wrong business area. Again, related to that, but again, I would say falling under that data strategy. Not being really well defined. Responsibility for data management is not clearly defined. Like who's responsible? That's what I said earlier is like, who in the organization can you point to and say, "There is a person that says, where should we be collecting data? And where should we be using data? Regardless of channel, regardless of team, who is that person?" If you can't name them, then you don't have them and you probably don't have a data strategy. I was working with a large retailer, one of the top 10 in the US here, just a couple months ago. And they basically had just hired that company. This is a like $12 or $15 billion company just in the US alone. And they simply just didn't even have that individual. So this just goes to show that this kind of stuff becomes really important. Suddenly making the unsexy very sexy, if you will. Something you need to deal with.

Megan Billingsley: Yeah. Excellent. I'd like to dig a little deeper into data strategy. I mean, we've been talking about the use of data and marketing for decades. So Brendan, what are marketers still missing from their toolbox? Or in the way that they think about a data strategy?

Brendan Witcher: Well, aside from the talent that I just spoke of, what they're missing from their toolbox is the right technologies to be able to do this. When you look at why, when people are buying their MarTech, I don't have the data on the slide here, but when people are buying their MarTech today, if I asked you, what is the primary reason people are buying MarTech today? Or what's determining which vendor they end up going with? You know what's really interesting? Is how much this has shifted. It used to be, and you're probably going to think this is going to be number one, is the features and functionality of that vendor. It's not. The cost of that solution. It's not. The number one thing is the ability for that system to connect into other systems for the usage of data. That's the number one thing. And it's been the number one thing now for three years.

So what does that tell you? Is that people see the value of creating these cross-channel experiences. You don't just connect data for yaya's, right? You do it to execute something. And the thing you're trying to execute is a better customer experience, as you can see. So, you know, when you're evaluating companies, you need to ask yourself, not only do they have the capabilities of doing this, but do they have the vision for the importance of this? This is why, you know, when Janet asked me to be a part of this, I was really excited about it because I don't think a lot of companies really appreciate the value of what this really means for the company, which is why I'm glad she brought that data in early into the conversation too, about how this creates great value. Business outcomes.

Megan Billingsley: Excellent. Thank you. You know, the impact of data can overwhelm and paralyze decision-making. So before we close out this topic, Janet, I'd like to ask you, how can AI driven platforms like BlueShift help simplify and prioritize?

Janet Jaiswal: Yeah. So to Brendan's point, I agree that, you know, a lot of data definitely can lead to inaction because oftentimes, you know, marketers are overwhelmed and they start to second guess themselves. Many years ago, BlueShift was named an innovator in the use of AI in a customer engagement platform, according to Gartner. But our company has even patented its predictive capabilities. And so, what our AI does is make sense of all of that data. We simplify and we streamline many key parts of our marketers' journey. So for example, it'll use the data that it has ingested into its customer data platform. So it'll use that data to predict which segments will best meet a marketer's goal, whatever that goal is, say, increase revenue or prevent churn. And it'll then provide a segment of contacts that, you know, with a prediction of how likely they are to meet that goal that the marketer has set out. And then it'll display the number of people that are likely to convert, say 90% are likely to help the marketer hit that goal. And the marketer can move that dial around to target just the right number of people. If 90% certainty means there are too few people targeted, the marketer can dial it to 80% and then it'll be a larger number and so forth. So no more segmenting the list on the basics of geography, demographics, behavior, et cetera. It's all incorporated into the BlueShift solution. We call it Customer AI, but that's the suite of AI capabilities that help marketers.

Megan Billingsley: Excellent. Thank you. All right. So let's move into our second topic, which is activating data for personalization. Data is only valuable when it's activated, yet marketers struggle to create personalized experiences despite having so much customer information. Brendan, what risks do companies face when data isn't activated or utilized in real time to win, serve, and or retain the customer?

Brendan Witcher: It's a, you know, that's a really good question because the reality is that lots of companies think, "Oh, you know what? I'm going to create a personalized email." We talked earlier about what that means when you just put a name in there, but let's say you actually are doing something to create personalization in there with some value. Okay, great. What companies tend to think is that we'll check the box. We sent out an email. It had personalization in it. And let's just create a sterile experience for the rest of the journey. It doesn't happen that way.

What ends up happening is you don't stay net positive. So I'm going to get kind of dorky and geeky here. Okay, just for a second. So very analyst-y thing. So if you start at zero, let's say you're starting at zero with the customer. They have no positive or negative feeling about you. And then you manage somehow to get a personalized email out to them. Great. You go up. And then let's just say by chance you happen to also do a personalized text message, and boom, you actually get it even a little better. But then, you know, maybe the customer is going to wait six times to hear from you, but then you miss the mark. Well, you don't stay positive. You start going down from there. Those messages start to be irrelevant. And by the fourth message, they're maybe not even looking at your messaging anymore because the last three of them weren't relevant to them.

And this is what people forget is you can't just do it once and be like, "Yay, you know, we won." You didn't win. You ended up over time, you ended up losing the customer, which is why consistency and personalization, the ability to understand the customer at different levels. Look at the screen you have in front of you here. I know that Janet's going to speak to it, but I also want to point something out is that these are different characteristics of that same customer. You could personalize on all of these things. As long as you knew them with some confidence, you could personalize on all of these through a course of maybe three, four, five, six different interactions and still be personalized, and still hit the mark, which means you're going up, up, up, up, up and adding positiveness to the journey rather than doing two, and then you're going to be in for wrong. Because then you end up in a negative space. And that's the way I like to describe it to companies. And that's what the risk is, is that the biggest risk is that you're checking the box and saying, "We did it. Now we don't need to do anything more." And then you're wondering why you only get 3%, 4% lifts out of doing some major personalization campaign, which means let's think about it this way. If you get a 9% conversion on an email, as an example, maybe last year you got 7%. Everyone's high-fiving each other. It's like, "We did better than last year." It's great. It's 2%. You were irrelevant to 91% of the consumers you sent that to. Is that a win? I don't think that's a win. I don't call it a win at all. And yet we've accepted it as a win. So we need to get that number up.

I think it's important to note, one last note on that. And this stat is the scariest stat of all, which is only 5% of consumers say that emails are relevant and well-timed to my needs. Now, here's the funny thing about that. That's abysmal, by the way. But on the other side of that, in 2019, that number was 6%. We're going in the wrong direction. We're not getting better at this. We're getting worse at it. So this is part of the problem that we need to solve today.

Janet Jaiswal: You know, what I like about the slide is, to Brendan's point, that personalization should happen throughout that life cycle. So in this example, you'll see that the message is not just for Gabby, who's a Gen Z person and female, but she's a new user. So that message is different than for Sean, who's already a champion, or Emily, who is an existing customer that has renewed their subscription. So it's possible to also speak to them where they are in their life cycle.

And so let me provide an example. Let's talk about Tradera. So Tradera is a leading e-commerce marketplace in Sweden. And I think they have something like 5 million plus visits a week. eBay acquired it and then PayPal, but it still operates under the Tradera name. And so Tradera needed a solution that could collect, unify, and activate all their data from their website, their mobile application, and their email campaigns all in one location. And so what BlueShift helped them do is give them the ability to surface the right actions to the appropriate users at the time when they're likely to engage. So they certainly wanted to provide that personalized recommendation, because obviously they wanted to drive user engagement and eventually sales, but they also wanted to optimize and track their ad dollars. And so they used BlueShift because it integrated nicely into their specialized MarTech. So to Brendan's point, integrating with the existing MarTech is important because then it utilizes the data you've collected through all those different solutions already. No point in having a standalone solution where you have to work hard to get it to work.

And so some of the results that Tradera had when they activated their data for personalization is:

• 131% increase in sales when using personalized recommendations.
• 40% increase in homepage click-through rates. So not just a visit, but they actually click through to an important action.
• And then two and a half times email pin rates. So still abysmal to Brendan's point, but that 5%, you know, suddenly became a lot more. So it's getting closer to where we want to be using personalization.

Megan Billingsley: Excellent. Love that example. Thank you so much. So let's shift into our third topic. So moving from insights to impact, optimizing cross-channel campaigns. Even with actionable insights, many marketers struggle to apply them effectively across multiple channels, leading to inconsistent customer experiences. Brendan, why do you think CDPs have become such a hot topic over the last five years?

Brendan Witcher: Yeah, this is a great question because of the fact that a lot of companies weren't iterating on their marketing. What do I mean by that? Is that when you're in business, what you never want to do is start from ground zero every single time you do a campaign or every single time you send out a message. If you do something, you should learn from that thing. And then you either reduce what you're doing, what didn't work, and you improve what did work. You learn something about it. This was, why does this happen? Because we ended up with this sort of world where we tried to automate things. This happened about 2017, 2018. It was all about automation. Let's automate everything. But what ended up happening was we kind of automated ourselves into stupidity, which is that we, you know, basically said, if this number is higher than this number, the machine will know it's higher and then they'll just go with this campaign or that campaign or whatever. If you ask the marketer, "Well, why did this campaign work versus that campaign work?" They wouldn't be able to tell you. My 16-year-old daughter can push a button because the number's higher. I mean, that's really easy. But telling you why, that takes some marketing chops, if you will.

And the reason you needed CDPs is because you start to understand why certain things got selected, why certain things happened. Why are we selecting this group? And how did the outcome affect this group versus that group? Measurement and analytics became a really big thing. Somewhere around that same time, period around 2018, 2019, I saw a huge shift from organizations where they were saying, "You know, we're not getting beat because of our assortment. We're getting beat because companies are being smarter about the customer, smarter about their operations, smarter about their business." And so what they started to say is we don't want vendors anymore who just provide black box tools. We want them to provide insights so we understand what's going on.

And why is this important in a cross-channel world? Or more importantly, back to what I said earlier about the channel agnostic consumer, if you will. It's because not all solutions are going to be utilized everywhere. So for example, you may learn something in your email channel or your social channel or wherever, and that may be useful for your clienteling tool and your store associates using that. If you use a solution that doesn't tell you why certain things happen or doesn't provide you with deep, rich insights about the customer, then you can't utilize those things in those channels, which makes it very, very difficult to create a consistent one brand experience for the customer because you're not sharing that data. You're not enabling that. But the other side of it is the reality is, and this is what I said earlier too, is the ubiquitous nature of digital and the channels that have just expanded and blown up. There's so many of them now. The reality is you just couldn't do it at scale. I'm going to seriously date myself here, but I used to run the catalog marketing for Harry and David way back in the day. That was a pretty easy channel. We had catalog, we had a few stores, we had call center. I didn't know how easy we had it to be quite honest. It was pretty simple, but nowadays it's crazy, and there's just no way you could do without the right and robust digital tools to manage all those channels and not overdo it with the customer. I remember when I was head of strategy at Guitar Center, we found out that there were some customers getting 14 emails a week from us because of the nature of how we were managing things. It was abysmal because we didn't have the right tools to make sure that we were being responsible.

Megan Billingsley: Yeah. Wow. Thank you. Janet, could you talk about the risks of inconsistent messaging and how poor execution can erode customer trust?

Janet Jaiswal: Yeah. And, you know, I like this example in that it shows personalization by channel. So Gabby has a different preference than Sean or Emily and also time, although you can't tell by this slide and the tone. So it's getting much closer to optimizing across channels. So to answer your question, you know, I've been marketing for 25 plus years, right? And so I can speak firsthand to how inconsistent messaging and poor execution, they confuse our customers. They erode trust. It also wastes resources, because customers, to Brendan's example, they're bombarded with way too many different messages. And they end up not remembering the brand for any one thing. And I think all marketers know this, but in reality, it's difficult to achieve. Most marketers have an email marketing platform, but they don't have it connected to their mobile app or SMS or the website or the store or paid media, et cetera. And this is where integrating your channel to the cross channel marketing platform can help. And then by incorporating all of the data that the marketer has collected or the company, from all the different sources, the messages then can be consistent, but tailored to each individual in a way that they can relate. That's when that magic starts to happen. And suddenly those goals start to become much more achievable.

Megan Billingsley: And could you provide a customer example of how insights are used to optimize cross channel campaigns?

Janet Jaiswal: Sure. Yes. Let me give a quick example. Five Below. They're a national retailer that they curate popular and trendy merchandise for kids, teenagers, and adults for $5 or less. This BlueShift customer had a challenge in that they were only prior to using the BlueShift platform, they were only really able to send batch and blast emails. And they were designed from scratch on every send. They didn't have the ability to include product recommendations or dynamic content, meaning that if a visitor is browsing at certain things, they didn't have the ability to then change that content to show merchandise that was related to what they're browsing. That's real-time personalization. So what they wanted to do was they wanted to drive traffic to their site, but they also wanted to drive foot traffic to the stores as well. So, two channels there. And so using the BlueShift customer engagement platform, they were able to harness all that data, apply the AI, such that they were able to message consistently at the right frequency to the customers' preferred channel. And at a time that they were most receptive to opening that message.

And so some of the results:

• Open rates were 41%. So much, much higher than the 5%.
• Click-through rates, 5.3%.
• And what I love the best is that 21% of those clicks turned into purchases. Amazing, right? That's much higher than standard marketing blast emails.
• And so using the recommendations from Customer AI and using those predictive capabilities, they were able to drive an overall 22% increase in sales.

Megan Billingsley: Excellent. Thank you both so much. So let's move into our final topic, which is staying ahead of the curve, trends shaping data-driven marketing. The volume of data isn't slowing down and the technology to analyze it continues to evolve. So how can marketers stay ahead? Brendan, when you hear the words technologies, which ones are you advising Forrester clients to pay attention to? And how do they impact the marketer's future ability to find success?

Brendan Witcher: Well, if you're visually oriented, I put it right there on the slide for you. To me, this is the big thing. I just did a session at NRF, National Retail Federation asked me to do a session. They're like, "Do a session on Gen AI, but what's next?" I'm like, "We're not there yet. We're not ready for what's next." Okay. But we are, but I said, I will do one on what else? Because there are other innovations going on that will actually enable AI and Gen AI and other things to become far better at what they do. Today, we're limited by the applications of some of the input devices in some ways.

For example, Computer Vision. So if I can start to use computer vision to identify that a customer picked something up from a shelf and didn't buy it or whatever, right in a store, maybe that's the email message and they've logged in with their loyalty program or whatever. Maybe that's the email that goes out next time to that customer is here's a coupon for that particular product or whatever it is. This is what computer vision helps with.

Natural Language Processing. I mean, you see so many people these days talking about how we're no longer going to be keyword jockeys anymore, putting in Google minus in parentheses this and, you know, whatever, that sort of idea is going away. And now we're going to be able to talk in just natural language. Well, as we enable these, you know, remember I said, digitally savvy and channel agnostic on the digitally savvy part of what the consumers have adopted or become, they are now willing to self-serve more than ever, but they want to self-serve in a way that works for them. They don't want to figure out your language to find the products and to learn about things. They want to find the language of themselves. They want to use their own language to do those sorts of things. And so this becomes a goldmine for marketers. Why? Because it brings voice of the customer into the experience. And so when marketers can use something down the list, their Unstructured Analytics to understand, you know, how people are talking about things is how we should be talking about things. During the Super Bowl last year, there was a commercial for some car company, I can't remember what it was, CarMax, I think, I believe it was. But they had a thing that said 10,000 of our customers said that the experience was smooth. Like, what a weird thing to say about a car buying experience, right? Smooth. Who says that? Well, they did that by learning that, you know, they scoured through all the reviews and the conversations and all that. And that's how they found that word smooth. And they wouldn't normally have done that. The marketers couldn't have done that, couldn't have created that ad without that insight about the customer. But that's the voice of the customer coming into how the brand approaches other consumers.

And the last one on this list here, or one of the last ones that I talked about this session was Biometrics. I think that there's definitely a future for biometrics. And you may say, "Well, you know, I'm not ready to show my face, use my face to check out at Target tomorrow," and I get that. I get that. I get that. But don't forget, you're already using your face to log into something very important, typically, which is your phone, or at least you're using a fingerprint very often. That sort of thing is happening in other parts of our lives. And as an analyst, what I've seen is that when we start to adopt other ways of doing things in other parts of our lives, we become more open to it in the general parts of our lives, like shopping and things of that nature. So if you go to an airport these days, about half the airports are going to take your picture to identify you. There are other companies using some of these automated checkout technologies, like Planet Fitness and others, where you use your palm or your fingerprint to identify yourself to go in. You don't need a membership card anymore. There's office buildings doing that as well. And so, as those things come to be seen by the consumer as, "This is for my benefit. This is for my security. This is so someone doesn't steal my identification." One of the most interesting things happening right now with biometrics is the credit card companies are all working on cards that have a little fingerprint icon on it. On your phone, when you have that fingerprint, at least if you have a Samsung, they have that fingerprint icon. And on the card, it has that. And what you do is you have to hold the card with your finger over that print while you use the chip or the Wi-Fi, because anybody could just take your card and use it, right? There's no personal identifier. The most secure way is to make sure, "Is it Brendan holding the card?" Now that, you may say, "Well, what does that have to do with retail?" It's training the consumer to think, "My biometrics are securing me in this transaction." And that's going to start to bleed into retail as well.

The last one I want to talk about that I think is really important for marketers to understand is AI Agents. About half the people I spoke to at the last big retail shows were like, "I don't even know what that is." I'm being honest with you. So what it is, is like the idea is that AI is acting on your behalf. It's not just enabling things. It's actually doing things for you that you approve with certain parameters that it takes action on. So it's not just providing prescriptive sort of actions you could take, but it's actually doing some of those steps for you as well. And so this is where it's heading. It's creating a lot of efficiencies and things. And when marketers are looking at, you know, using AI in their business, they should be asking themselves, "What are the other parts of the organization working on that may be inputs for my own marketing campaigns and capabilities?"

Megan Billingsley: Excellent. Thank you. Janet, you're an experienced marketer. So what can you share about how AI and machine learning will transform marketing in the next five years?

Janet Jaiswal: You know, Brendan summarized my thoughts exactly. So I think I'm good.

Megan Billingsley: Wonderful. Well, this has been an incredible discussion with fantastic insight shared. So thank you so much. Just to recap with a few key takeaways:

• Integrate your data: Everyone has data. Those that collect the right information can successfully unify it, will have a competitive advantage.
• Enhance one-to-one engagement: Customers that receive personalized or one-to-one communication will spend more time and will likely become repeat customers.
• Optimize cross-channel strategies: Consumers are everywhere. You need to meet them on the channel of their preference at a time that's convenient for them with relevant product info and tone and style.
• Stay ahead of trends: Generative AI is hot and still evolving. However, there's enough evidence to show that it will help marketers scale their efforts and speed up repetitive tasks.

Before we move on to our Q&A session, I do want to remind everyone that this session recording will be sent shortly. And we also want to encourage you to connect with BlueShift for additional resources or a demo of their platform. A special highlight is their recently released digital retail playbook packed full of helpful campaign examples you can use to boost engagement and revenue.

All right. So with the time that we have left, I'd like to get through some Q&A. We have had a number of questions submitted, so we'll just get through what we can. We'll start with this one for Brendan.

Megan Billingsley: What are best practices for integrating third-party data with our existing first-party data?

Brendan Witcher: Well, that's a good question. For the person that asked that, I would go look at what are you gathering already? What are you getting from third-party data? What do you have? What are the gaps in that first? That sort of thing. Because that's easier to get. The second question should be, "Okay, with what we know, what can we build on top of that? What can we learn about that?" And a lot of times it's a good idea to go out and find data that says, "Okay," or work with a vendor that can help you understand what's going on. To store large volumes of structured and semi-structured data, typically used for reporting and analysis. And it is typically used to aggregate data for long-term storage, for BI, for reporting, historical analysis, et cetera. But it does not make sense for real-time customer engagement or for marketing-focused data needs. For that, you'd want a customer data platform or a CDP. A CDP is ideal when you need to unify customer data from multiple sources for personalized marketing and engagement. It's purpose built just for marketing use cases, whereas a data warehouse really serves or can serve the entire company. And then, you know, that, it also means that, you know, just buying the technology is enough. It's not. The power really comes from integrating that customer data and activating it through a cross-channel marketing platform. And so marketing automation platforms, they're ideal when you want to reach out to people in a single channel, email. But it's not truly cross-channel, meaning you can deliver your message and run your campaigns across, you know, mobile, SMS, website, and social. And so they both have a time and a place, but ideally you'd want a CDP integrated with a cross-channel marketing platform.

Megan Billingsley: Excellent. Thank you. I think we've got time for one more question. So we'll end with this one for Brendan. How should we think about the customer journey today and what should we be doing?

Brendan Witcher: Yeah, you should stop thinking about the customer journey as linear, first of all. It's very dynamic. And some consumers will shop certain ways for certain products with certain brands, and they'll shop totally different ways with other products. What we're really talking about here is, again, I don't mean to get totally geeky with everyone, but this is really a three-body problem. If you understand, you know, there was a television show about it, made me look it up. Now I'm just going to explain what three-body problem is. Three-body problem is when you have three things in motion against each other. It's very hard to predict the cause and effect of the impact on those things on each other. They're talking about gravity when they're talking about it typically. But in our world, the three-body problem would be more of the customer's relationship to the product and the relationship between the product and the brand and the customer and the brand.

So I'm a very visual person, as you can see. So the idea is that, you know, when I want to buy Q-tips from retailer A, my expectations will be different than when I want to buy Q-tips from retailer B because of my relationship with that brand. But if I want to buy a smart refrigerator, which is a much different product, I do not want the same experience for Q-tips and smart refrigerators. When I want to buy a smart refrigerator from retailer A, what I want is going to be different than when I buy that smart refrigerator from retailer B. You can start to see that. Now, the third variable is every consumer that engages with you. Is that bubble here? Every individual, because there is no "the consumer." There are individual people with individual needs, me, my wife, my neighbor, my kids, my mom, my dad, my everything, my high school teachers, whatever. The idea is that each person is going to want their individual thing. So that's the three-body problem that you have. And you need to understand the customer at a deeper level to be able to address that. Because the danger that you have is you start saying "the customer" as if every customer wants the same thing at the same time. If you don't treat people as individuals at the end of the day, you're not going to get to that level of personalization that gets it greater than one in three or 33% saying things are relevant to me. So when you start to think of it that way, when you start to think about your problem in customer engagement as not just sending out coupons, but rather this three-body problem, you start to understand how your marketing strategy really gets molded and shaped by the variables of those moments or those three things impacting the customer journey.

Janet Jaiswal: Good point. Excellent. Brendan, let me add to that. A lot of marketers, and I'm not an exception, but a lot of marketers are heavily focused on acquiring new customers. But that old adage where it's cheaper to market to an existing customer than it is to acquire a new one still stands. And if you can solve that problem, you want to make sure that you focus on that entire journey. It's complex, as Brendan has described it, because you want to keep them happy and you want to keep engaging them. So collecting that data is important, not just for acquisition, but it's also to continue to engage them and earn their loyalty such that hopefully one day they become raving lunatics. They love your brand so much that they refuse to, you know, if you take it away, that they will riot in the street. That's the ultimate goal. I'm not saying we can all get there, but that's where paying attention to that entire journey does make a difference. And data is the heart of it. If you don't have good data, you're not going to be able to engage them effectively throughout their journey.

Megan Billingsley: Excellent point and a perfect one to end on. So thank you so much, Brendan and Janet, for that insightful presentation. We are all out of time for today, but before we sign off, I just wanted to thank you, our audience, for joining us today as well. This webinar will remain available on demand if you want to review anything that we talked about. Thanks again for watching.