Podcast: Deploying AI personalization at scale (with Christina Augustine)

March 17, 2026

On this week’s episode of the podcast, I am joined by Christina Augustine, the COO of Bloomreach, to discuss the rapidly evolving landscape of AI-enabled personalization in digital marketing and e-commerce. We explore how the shift from predictive models to generative agents is fundamentally changing how brands interact with consumers across multiple touchpoints. Among other things, we discuss:

How agentic commerce tools will redefine the traditional customer journey beyond simple search and browse functions
Whether real-time behavioral signals can replace static cohort-based segmentation for truly individualized marketing
What role Answer Engine Optimization will play in the future of organic discovery as search habits shift
Why data quality remains the primary bottleneck for brands attempting to deploy sophisticated AI personalization at scale
If conversational shopping interfaces can significantly reduce product return rates by improving consumer purchase confidence
How marketers should balance the high cost of personalized SMS with the broader reach of email campaigns
When the industry will transition from defensive data siloing to a more integrated cross-channel signal environment

Thanks to the sponsors of this week’s episode of the Mobile Dev Memo podcast:

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Transcript

Eric Seufert

Welcome to the Mobile Dev Memo podcast. I am your host, Eric Seufert, and I am joined today by Christina Augustine. Christina, welcome to the podcast.

Christina Augustine

Thank you. Thanks for having me.

Well, thanks for being here. So today we are going to be talking all about AI-enabled personalization and I am happy to have that conversation. Before we get there, can you introduce yourself to the audience and provide some background on yourself?

Yeah, sure. I am currently the Chief Operating Officer at Bloomreach. We are an agentic commerce personalization platform. So really looking at combining customer data, product data across all the marketing and onsite channels to make sure that we can deliver personalization. My background is as a software engineer as well as a management consultant at Bain.

Just out of curiosity, how did you get into this particular space? Because Bloomreach has been operating for some time, right? So it is not necessarily seizing on the current agentic personalization trend. How did you move into this area?

Yeah, I have been at Bloomreach for about 15 years now, so since we were an early stage startup company focused in SEO. We were leveraging AI for SEO optimization to make sure that people got discovered in Google and in Bing at the time. What we did was we were taking these algorithms that we were building to identify relevance and bringing it onsite. So our next product was site search and we were really optimizing the site experience.

With all of those predictive AI algorithms and machine learning, as generative AI came about, it was just a natural place for us to start building agents. So we have conversational agents for shopping, we have campaign agents for email marketing types of activities. So it was a natural evolution to go from really using as much data and signal as possible to personalize to moving into more of the agent-to-agent space.

So Bloomreach is an established company. When did that transition start? To my mind, there are two distinct waves. There is the very recent adoption with things like Open Claude and people bringing agents into their computer and having them operate across everything. But there has also been this slower, maybe more subtle adaptation of agents starting with the introduction of some of these tools from the bigger model providers, but then even more discrete adaptations before that going back years and years. Talk to me about how you have seen that timeline evolve.

It kind of happened overnight and then happened so quickly in a way for most people. But as you mentioned, AI has been around for a really long time. It has been around since the 1950s. From a development standpoint and what we have always wanted to do with software, I think this notion of personalization and AI has been around for a very long time. But there has not really been the interface or the connectivity that we are seeing now.

So certainly generative AI was around for a while. Bloomreach was playing with things like vector search before people were seeing AI really from ChatGPT and this interface where now as a person I can interact with it. Once that happened and the environment started opening up, now you are seeing a rate of development that is really quick, but you are also seeing all of these channels that could not talk to each other before. Now they are able to talk to each other and people have even more signal.

So it is like we went from predictive AI with a few signals and you kind of always wondered why are you recommending this product to me when it is a terrible idea, this is not what I wanted, to much more accurate predictions because now I can see data from not just email and SMS, but I can start to see it from other channels that maybe were closed off before.

I think that is really helpful context. Maybe as a broad kickoff question, what does AI-empowered personalization mean in the marketing context? What are we talking about when we talk about AI-empowered personalization?

It is such a good question because I feel like 10 years ago we would have said something very different than what we would say today. 10 years ago we would have said AI-powered personalization means I am sending an email that says, ‘Hello Eric.’ Okay, cool. I know your name. And that is supposed to be awesome and really cool that I could put your name in the email. And then everyone is getting the same products, the same email subject line, the same everything but I said your name. So that was personalized.

To now, I think AI-empowered personalization in the marketing context recognizes that marketing is a whole host of channels. It is not just that I am sending you email, but maybe I am sending you an SMS, maybe you are on Instagram, maybe you are looking on Google. But now maybe you are on OpenAI and you are looking in the AEO answer engine space. Now I have a proliferation of channels and I have really complicated customers and so many more signals than I had before.

For me, AI-empowered personalization now in marketing means that I can focus on the customer and how they want to behave and how they want to be treated, no matter what channel they are coming from. Whether they are coming from email or SMS or they are already on my site, I can start making really smart decisions about what message they need, what products they are looking for, what their intent is, maybe where they are in the journey, and I can trigger based on that.

One of my favorite examples of a customer I am working with leverages data across the customer behavior across many different ways. So they will know you actually as a customer, if you spend more than two minutes on my website, I should probably pop up an AI agent. I can tell that for you, this means you are starting to struggle and you are not sure what you are looking for. Whereas for me it might be 10 minutes. I love to shop and scroll, spending time on a website is not an indicator that I am not going to buy. Maybe for someone who is more impatient, you need to know at two minutes I need to start helping them. Versus someone who maybe responds better over text with a discount offer and knowing what kind of shopper you are or what persona you are as well as those signals across all the channels, I think is where AI personalization is just taking it to another level.

As a corollary to that, how much needs to be known about that individual and how much can be inferred from the similarity of their early behavioral patterns relative to historical users? Do you need to know very specific things about that person or is it really pattern matching across groups of people that have behaved the same?

It is a great question because to date we have really been pattern matching, creating these customer segments. So it is not true one-to-one personalization, you are identifying me as a cohort of someone who acts in a certain way. Maybe I am the cohort that is in your loyal customer base and I am a high spender. Or maybe I am in your cohort that you have never seen before, a new shopper, and I like a certain category or I like to be communicated a certain way on email or SMS.

That is really where we were before. Now I would say AI has opened up this complexity that you can see where not only do I need to know a bit about you and I can know you on a customer level and I can start to do this very specific one-to-one personalization, but I can also treat you like the complex person you are. I can treat you like a human and not this fortune-telling pixel gathering pattern matching. But I can tell that you in this visit, you are actually buying a gift for someone.

So all those signals, maybe I used to have a quiz on the site and I would ask you your size and your color preferences and I would ask you that you are really only interested in shoes instead of tops. But people are pretty complex. We buy gifts. We purchase on behalf of someone else. And then we have got different types of purchases. We have got those impulse purchases because I was on Instagram last night and they just got me with the feed and I clicked through and I made an impulse purchase and that is very different than what to me is a high consideration purchase which is very different for everyone. It is based on their budget, it is based on what they do.

For someone who is a runner, their consideration is high consideration for those shoes. They are going to be running in them day and night. For me, tennis shoes get me to work. Not a super high consideration purchase. I do not need to know the wear on the heel and what kind of stride I have because I do not have a stride. But I need to know and so in that I would say, how much do I need to know? Ideally I need to know about you but I need to know what you are doing in the moment. What do you want in the moment? So no amount of quizzing is going to get you into that, it is going to be really on these real-time behavioral signals that you are picking up on. And so I think what we are able to do with machine learning now is really interesting to treat people like people and not clicks. And I think it just unlocks a whole new level of types of purchase and conversation you can have with customers.

Where do you get the most leverage? So there are these different customer interaction points that you could apply this to but my sense is, in a lot of my experience comes from mobile gaming space where the retention curve is just very severe, it is very punishing. And so all the leverage comes from day one, hour one, minute up to ten. That is it. That is if you can bend the curve a little bit there, that is where all the leverage is versus in 30 days well that person is going to be retained forever but the vast majority of people have churned.

But I mean in more in the e-commerce sphere, there are more win-back opportunities and there are more upsell opportunities and you are thinking more about different AOV opportunities over time. What are the different interaction points where personalization can be applied and which are the ones that produce the most leverage?

I love the way you framed it because I honestly feel like every industry should treat customers that way. Really that first impression that you are going to give someone, that should always be your best moment is that first impression. And then definitely you want to treat people great along the way but if you are not starting off on the right foot you are not in any industry, it is not going to be a great experience.

But when I am talking to customers in e-commerce, usually the first thing I try to understand is your leverage point. What leverage are you trying to get today? Are you trying to get top-line or bottom-line leverage? So for example on bottom-line leverage cost piece, SMS is really expensive to send compared to email. And so if I am trying to send out more and more SMS and I have identified this customer pool that really does respond to SMS, I want to leverage that AI where I know I am only sending SMS to the people who matter for that and I am not just sending it out and wasting a lot of money since it is really not a great margin thing for me to be doing.

Whereas email is maybe a top-line impact or onsite is a top-line impact where the more personalized my email can be or my landing page can be, the more benefit I am going to get from a revenue perspective. It is not that expensive for me to be doing relatively speaking. Those people are showing higher intent so the more I can get them to that purchase intent or that activity that we are looking for for them to take then the better off I am going to be.

So for me it is looking at each of those channels and saying how much personalization benefit can drive from a revenue or activity perspective versus what is super costly and can I leverage AI to make that more efficient? And then you have got AEO blowing the whole thing up right now as an emerging channel with traffic increasing but conversion iffy at best. No one is really seeing the dollars come from it and I think that is throwing an experiment wrench into everyone’s plans right now.

With AEO it is interesting because maybe just for people who are not familiar with the acronym, AEO I think you are using that synonymous with GEO I have heard it be called.

GEO, AEO, we can not decide as an industry what we want to call it, generative or Answer Engine Optimization.

The idea here is that it is publishing content to be discovered by these chatbots to then be picked up and served in an answer. It is essentially equivalent to SEO for chatbots.

Exactly. But for Perplexity and ChatGPT, how do I get my content discovered? How do I get it to convert in the conversation or on my website later? How do I get all that directed over to me? And that is just a big open unknown. I mean a couple weeks ago there was instant checkout on ChatGPT, now there is not. Maybe there will be again in the future, we do not know. So it is this big concern as people are seeing the SEO drop off and they are assuming that it is cannibalizing for AEO. In some cases there is just traffic drop as people spend less right now. But if you assume the one-to-one, you are seeing this drop in SEO, this increase in AEO, but I do not know how to get discovered in AEO and I do not know how to get it to convert yet. And so there will be a big place to play there I think with driving more revenue and traffic once people have it figured out.

It is kind of redolent to me of the ASO approach that emerged in the earlier days of the app stores, so this app search optimization. It was just being discoverable from search and using whatever tactics with manipulating your keywords, your app keywords, your app title, even that improved your chances of being at the top of the search rankings. But the problem with that, there is a couple problems with that. One is that it is not systematic. I mean you can test stuff and see how it works but you are very much at the mercy of what people are searching for. And so if that is cyclical or there was just a short-term trend or something that you benefited from, well that is going to go away.

And then the other piece of that is ultimately these platforms want to monetize with ads. And so if they see an opportunity to do that, it is almost like you are a victim of your own success. If they see that people are actually deriving a lot of value from ASO or SEO or GEO or AEO, they are going to say well I should actually just be selling ads and monetizing that value myself. If you are getting a lot of value from being discovered organically, I should claw that back in the form of ads. And you see that with Google Search, the entire above the fold is ads and now they have replaced mostly with AI overviews and AI mode. You saw that in the app stores themselves, Apple has ads in the search results and they just inserted another second placement. And ChatGPT has ads. So the thing is AEO, SEO, ASO, all those approaches to drive more organic discovery ultimately get co-opted by the platforms themselves because they want to monetize that value.

I totally agree. And you are right. I think we definitely have been talking a lot about using SEO as an indicator of where AEO is going to go. I do not know if that is super accurate. I think that on some dimension absolutely there were early days of SEO, everyone was guessing how do I get crawled, how do I flatten my link graph so that I get crawled for more content, how do I get recognized for that content, let me enrich the content.

And certainly one of the biggest topics I hear from everyone right now is my data is not good enough. Is the blocker I think to most people trying to optimize advertising, any of their channels, is this conversation that with AI, I think we have done a very good job educating everyone that AI is only as good as the data you give it. And now everyone’s turned to, well my data is not good enough. And as you look at any of the channels, I think the more that the native platforms are retaining the data and hiding that data, the more blind you are to what are people searching for, how do I optimize for that, what data do I need to enrich?

And so part of this is really what channels do you control and how do you bring them all together? AI lets you bring a lot of those data signals together instead of before where it was kind of all separate. Maybe an SMS channel I had an SMS vendor and I could just see that but I could not really tie it anywhere else. I had separate customers that were SMS customers, I had separate customers who were email customers, I had separate customers on the app store and I could not bring them together. And so I think as we really start to develop more of this agent-to-agent profile, more MCP options, we are tying more of this data together. At the end I just think we will be better for all of us and our experience than we had before.

When you are thinking about the AI-empowered personalization regime, how much of the success that you see with that with the personalization efforts is determined by the availability of that data? And then how much does that exercise become a function of surfacing more data, of actually building the mechanisms to surface more data? So like a product development or a product management discipline of saying, look, when the person hits the landing page, we need to be able to find out these things about them. We have got to create ways to find things. So our product management approach should be to try to build mechanisms that they can interface with that actually give us those signals, that data to then personalize against. How much of this is driven by product?

Great question. I think in this agentic world, 50% of it is going to be driven by product and 50% is going to be impacted by the customers, which is kind of cool. The customer has never been in the driver’s seat before. And what I mean by that is we have talked a lot about needing great data before. And the great data was great, put a pixel on your website, track all the user clicks. Let me guess if that click is a good or bad click. A bounce is a bad click. I do not know, maybe I am exploring, maybe I liked that product but just not today, I am filing it away. But we are reading the tea leaves.

And now that we have agents and we can have conversations with our customers, the customer gets to tell us what they want even more explicitly. But also we are getting all kind of signals that we could not accommodate before because we did not have the algorithms that let us handle this unstructured data in as efficient or constructive of a way as we could before. We did not know what to do with the conversations, we could not really structure the data and read the patterns like we can now.

So now customers can tell us so much more. I could not other than maybe putting a search query and two or three words into a search box, I could not have a conversation back and forth and tell it, no that is not what I meant, please give me better results than you just gave me. So I can actually feed in and give you feedback and tell you, no I really was interested in this pair of shoes but you did not tell me how much they weigh and I really need to know how much they weigh before I am going to buy them because I am going on a hike. I do not want five pound boots. Now I can tell you that, that is a data signal I maybe did not know I needed before, a data attribute that maybe I did not know I needed before because I never gave it to the customer.

So I feel like part of this is being very thoughtful with your product on what data signals you get, how do you acquire them, how do you open it up for more feedback, and now more of it gets to go into the customer’s hands and tell us what they really want, what they were interested in.

It sounds like what you are saying is there are these mechanisms that we can give to the customers to let them express themselves. So I am thinking more in terms of the customer journey. You kind of brought up the example before of someone is on the landing page and just a prompt to expose a chatbot to them. That is just a way to collect signal at that point, they are describing what they want and then in that way that is something that I can take, I can tether to to personalize the experience downstream for them.

Exactly. Maybe before you just see them clicking all over your website looking at different skirts or dresses or whatever and now if I can surface up that conversational agent or I can have ask AI ask me anything available at the time, then they can tell me well I am looking for something to wear to Wimbledon. That is very different. And now I know not only types of products but I know the style and what I should be recommending to you where before I was just kind of guessing. Looks like you are looking at a lot of white skirts, do you want more white skirts? What do I need to, now I can tell you as a customer.

Who’s got on their website a specific category I haven’t seen it that was like going to the Taylor Swift concert, need an outfit, maybe I need a sparkly blazer for my husband. That’s not a category. But now I can have a conversation and be more explicit and have the site help me find what I am looking for.

And not to mention a Wimbledon attendee is probably like a dream scenario for an e-commerce retailer. High propensity to spend there.

They are probably working with a personal shopper and they are not on the website, but you know, we can all aspire.

Right. That’s not just a potential customer just hit the landing page proposition, that’s kind of throughout the lifecycle that can be the case, right?

Absolutely. I mean maybe I was looking at Wimbledon outfits before but now maybe we are getting closer to the holidays and I am looking for gifting items and I just really do not know what to get my nephew. We saw a lot of that actually this holiday where engagement would start to spike with conversational because people would come to different websites and they are not searching for themselves anymore and they are searching for someone they really care about but maybe they do not know, right? Little kid’s preferences change all the time. So you are like tell me what should I be buying for my seven year old nephew? He likes video games. Maybe as a shopper I do not know much about video games and now I can be more thoughtful about what I am giving. So much more, those journeys and those purchase cycles totally change just because I nailed you in the first five minutes and knew what you were looking for doesn’t mean you are coming back looking for the same stuff.

Have you seen cases where customers reacted poorly to personalization?

I think that’s a really interesting one because I mean I think we all see it right now where we think it’s personalization and we think it’s awful. Just this morning I got an email from a e-commerce company saying, ‘still interested in this’ and it wasn’t anything I had ever looked at before. And then there were like a whole bunch of other products underneath of it and I was like, I am not a six foot man. What are these products? I have never bought anything bright red in my life. Why are all these kind of wrong? At the same time, where’s the harm? Not really going to be bothered me but I was like, this is weird.

I think that most people are so shy about personalization that we haven’t really gone to the explicit version of personalization. We haven’t gone really deep. In fact, the balance I see right now is people are so cautious to be overly personalized that I think a lot of money is being left on the table because you are reading the signals and saying I am just going to make a really small change for a really small amount of people. You end up in this AI is meant for us to scale, do personalization at scale, and I am going to hold it back to a very small amount of scale and see if it is okay, see if everyone reacts okay to it. I think the consumer behavior is changing. You can see people having more conversations, being okay with their data, conversations being used to inform further conversations.

So I am curious where we are going to go here on if we are going to become more and more explicit and that is when I think personalization has a chance of going wrong is when you start to get really explicit and you try to be very definitive about an answer and you get it wrong. Which is why I think AI is so good when it is there is no one right answer. So I can kind of get it right. So we’ll see. I think people have been pretty cautious to date on personalization so even when it is wrong, not that bad.

I mean my position on this is that first of all, there is a lot of cases like the one you just cited where you get the email and it is for something that you never engaged with or interacted with and you just dismiss it and don’t think about it again. But then you might get the email for something that you did browse or whatever and that feels like personalization but again it could just be total random because the things that are irrelevant you just ignore. That is where I think a lot of the confusion arises around people thinking their phone is listening to them. That truly is just ignoring the 97% of ads that you see that are totally irrelevant and then anchoring to, because you recently talked about taking a cruise on Disney, anchoring to the Disney cruise ad, ‘they must be listening because I was talking about that today.’ Well that only resonates because A) you were talking about it today so it is top of mind and B) because it is not noteworthy for all the ads you saw today and there were thousands that just were totally irrelevant. So you are just ignoring the totality here.

The other thing is I do think on net, people they truly do appreciate a personalized experience that is more relevant to them and it is really more about the context being appropriate or not. I mean personalize some e-commerce product that I would like to buy on Instagram and people love it but if you chose the wrong context, if you chose an unfortunate context to do that then yes people would bristle at it.

You are totally right. I mean context is super important. If this were my bank, they better get it right. If this were healthcare and I am going into my doctor, don’t give me the wrong medication or the wrong diagnosis. You have to be absolutely precise. In this area, the harm of personalization or I got it wrong, the risk of getting it wrong is less so. It is going to be harder for me to have an adverse outcome or offend someone. It is possible, but I think we try to be really thoughtful and not as explicit in those areas. But there are industries where it 100% matters and you can’t just have no one right answer or it kind of got it wrong, you have to be perfect.

And that is nuance that needs to be recognized. There are circumstances where there is no room for regret and yeah that just requires a lot of thoughtfulness and care. And then there are circumstances where you can be experimental and actually there is just very little downside to getting something wrong and in those circumstances or in those contexts people are just they are very forgiving of a misfire. I don’t want a baseball cap, I don’t wear baseball caps, you shouldn’t have recommended that to me, but at the end of the day that didn’t really derail my life to see that.

Exactly. You can recommend baseball hats to me all day, that is typically what I live in.

I want to switch gears to the advertising use case. So how can advertisers utilize AI-based personalization tools outside of what is available on the ad platforms themselves? What do they have available to them to improve their advertising outcomes?

The hardest part about these large platforms really is how do I get the best return but how do I deal with the fact that on any of these platforms they may or may not share data with me? It is siloed data. And so as much as I can get data from these different platforms and channels, at least what I can do is be leveraging the AI to create better audience strategies. Whether that is unique to particular platforms and recognize that certain platforms have different audiences. But creating that better audience strategy per platform, per personalization, personalizing that actual destination from those platforms.

Making sure that my messaging and targeting and creative are actually personalized and correct for that audience or that person and making sure that I have got a really strong feedback loop on that. So now I can do this more in real-time behavior or real-time testing and really get quickly to which audience strategy, which content strategy is actually winning on which channels and optimize much quicker. Where I think before we would spend maybe a month of data and analysis and then say, okay turns out the AB tests that we just ran on personalization B won. Now we can know within a day and be optimizing. That is a lot of money that you are leaving on the table if you are running these AB tests or control tests for a long period of time.

Is there a risk that any approach like that where you are analyzing the data set on your own and then using that to adjust the platform levers, the platform settings, is there any risk that those sort of are incongruent or because well we see now this bifurcation of the very large platforms that all run these totally automated systems, people call them black boxes, right? So you have got Facebook and Google were kind of early leaders there. You have now got Reddit, Pinterest, TikTok, they have all got essentially like end-to-end automation. I think with most platforms following suit, is there any risk that what an advertiser is doing on their own then kind of works against or undermines what the platform is doing in this kind of black box environment?

I think there are two big risks there. You bring up a great point because your native platform does have configurations, optimizations, different data, very mismatched audience logic, maybe audience definition, different goals that they are trying to accomplish and optimize for. All of these algorithms are giant math formulas with different coefficients or different goal setting that they have built in. And so I think this is the world we live in today which is why customers feel like it is such a discongruent experience based on whether they are on your site and went to your brand in particular or whether they are on one of these native platforms.

I think you have got a lot of that off-platform personalization not necessarily being complemented by the native tools. And then I think the other thing with these native platforms is you have got user-generated content. The user-generated content is opinion-based, not always factual. At the moment it is what someone thought the answer was, but it wasn’t. No ill intent, they just got it wrong. Or it was their experience and that experience isn’t relevant for me, it is relevant for someone else. And so you do get these conflicting signals. I think right now people generally understand that but you are right if you are building these strategies you need to be very thoughtful about how much weight you give a signal that you are not confident in. Or if you use it at all. And what do you want to use from these platforms?

What are some cutting-edge applications of AI-based personalization? So what is Bloomreach doing? What are your clients asking you for? What are your clients having the most success with? What is coming down the pike? What is kind of like the most exciting frontier here in AI-based personalization?

I think we are starting to see some of them, they are just not as prevalent. I am super excited about the conversational shopping agents. You see it with Rufus, I love the magic apron at Home Depot because it is just not my, I am not an expert there so it is very helpful. But I think what next really I am very excited about the agent-to-agent experience. The fact that we can really start scaling I think in marketing in particular right now you are kind of limited by how many campaigns you can come up with and run.

But the fact that we are moving to agentic campaigns, the fact that I can do not only more campaigns than I could do before at micro segments instead of broad segments, but I can also feed in signals that I could not feed in before and the AI will optimize for those outcomes, just means that our customers are getting a better experience with those campaigns but I am going to have way better outcomes than I could have with the fact that I do not think anyone ever feels like their marketing budget is enough. We never sit here and say you know I have enough people and enough time. And now there is going to be more throughput on that there. So I think the agentic automated marketing, the campaigns that we are going to be able to come up with is one of the things I am most excited about.

Have you seen anything that surprised you? Like things that your clients are doing or just applications of this that had dramatically better results than you would have expected? Just something that happened that kind of took you by surprise.

I guess I thought I have got one customer I work with who does a lot of the Funko Pop products, they do a lot of gaming products, and really started experimenting with these agentic campaigns. And I thought they would get a little bit of a pop from it. I hoped that we would get a little bit of a pop from it, but the thing that I did not factor in, the human element of it, is when you get into certain of these categories like gaming or like pop culture, your fanbase is so important and so bought in.

That getting that right drives so much more of an impact than if you kind of got it right or if you threw them into the population of general. I guess in there are certain universes that do not play well together. I mean if you like DC but then you, I do not even know the superheroes so I do not even want to attempt this analogy but I guess they are not all the same. And you do not want to get that wrong with your customer base and so they really leveraged AI, got it right, and the pop was a lot bigger than I thought it was going to be.

That’s actually a really interesting thought. It is kind of like AI, a lot of these AI tools allow you to go beyond the Pareto rule. You can actually say, no I have got, I mean it’s relatively costless for me to actually take this to 100% versus before I was like look, the tradeoff is you kind of run into a cliff at the 80/20 threshold, it’s not really worth going the extra mile. But when you have got kind of a fanbase of certain categories that’s fanatical, going to take that to the extreme actually does sort of really unlock all the value and it’s almost like an inverse Pareto rule for some of these groups and unlocks all the value for those fanatical fans but who are probably have like a higher willingness to pay and that creates a pretty compelling opportunity. I’d never really thought about that. That’s a really interesting idea.

It is the long tail. There has always been this holy grail of oh I wish I could optimize the long tail but as you mentioned that extra 10% it is going to be monumental effort. So forget about the long tail, I am just going to 80/20 it and focus on the top 20, get 80% of the value. But now I can go after the long tail and even though each one of those is maybe pennies, in aggregate it is a huge impact and unlocks all the value for those fanatical fans who are probably have a higher willingness to pay. That’s a huge shift in the way we’ve thought about e-commerce for 20 years.

Christina, this was great. Appreciate you coming on the podcast. How can people learn more about Bloomreach? How can people engage with your company?

Yeah, absolutely. I mean we are at Bloomreach.com. So follow us on LinkedIn, come visit our website, be happy to chat with you.

Cheers, well thank you so much for your time today.

Thanks for having me.

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