Hello, You: Crafting Experiences That Remember (and Reward)

NOTE: This is a raw transcript and contains grammatical errors. The curated transcript will be uploaded soon.

Okay.

Hi, everyone. My name is Makram Bansour. I’m a product manager at Intuit.

Before Intuit, I was with LinkedIn. I was managing LinkedIn’s experimentation platform. I was also working on LinkedIn premium.

And before that, I was with Texas Instruments. I was managing all of their online design tools suite for Texas Instruments.

I also did a startup after finishing my PhD. I will reference some example of the experience I’ve done there relative to this today’s topic.

At Intuit, I manage experimentation platform called iXE Intuit’s experimentation platform. I also manage the personalization platform capabilities and the web platform.

So all of them together, we are called the customer experience management. That’s how we deliver the experience and the go to market for our customers, whether it’s for QuickBooks, TurboTax, Credit Karma, or merchant.

For today’s talk, I thought well about, you know, what we wanna talk. And the customer is always right, came to me as a key theme of what we are trying to do in terms of growth and in terms of whom are we trying to do to target for experimentation.

So, you know, we all heard about, you know, customer is always right from the hospitality industry. This has been like since the 1980s, the famous quote from the Swiss hotel, The customer is never wrong. And it’s not intended to be taken literally. Like, obviously the customer will be wrong sometimes, but it’s all about going the extra mile to understand the underlying needs and points of friction for our customer and make sure that they have this delightful experience while engaging with us.

And I learned it the hard way when I was doing the first startup after finishing my PhD.

So obviously we have a software, you know, an early stage software product. And one time our VP of sales scheduled a meeting with a prospective client. So I go with my CTO to that meeting and we meet prospective client. And we were in meeting room talking about the technology, why, you know, how exciting, you know, the product that we’re building.

And then suddenly we started to see our CTO having this kind of early argument with our prospective client.

And they started, you know, it became like a, you know, a theoretical PhD exercise between both of them.

And they started to become like a heated discussion of, you know, who’s right here?

And the CTO wanted to prove the client is wrong. The client was saying them, no. My point is right. And you can imagine, you know, the how the meeting dynamic in that conference room started to become more like a weird.

I’m looking at our VP of sales and what’s going on here? And eventually, to my surprise, the the prospect client just stood up and said, okay. You’re right. I’m wrong.

I’m done here. I’m not interested in your product. And he walked away.

So what happened here?

You know, you know, it was really amazing learning for me to just see this thing happen in front of us.

So is it truly about winning the argument even at the expense of losing the business?

So what I learned that day, it doesn’t matter how right you are. If you fail to acknowledge your customer’s perspective and needs, you will lose.

You know? And so it’s yeah. You want the argument, but you lost the business. But even if you know so what’s important is the ultimate failure is the customer centricity environment.

So customer centricity should be a core value and not something that we say and we hang on the wall and a reality how we operate. It’s it’s not main happening on the ground.

So then I joined, of course, you know, I worked at LinkedIn and then at Intuit. And I saw all these customer centricity as a core operating in the DNA of the company.

So for example, you know, at LinkedIn, LinkedIn is a mission driven growth company.

And the mission for LinkedIn is create economic opportunity for every member of the global workforce. So the mission of the company is all about the members and creating that economic opportunity.

And the way I’ve seen it happen throughout my work at LinkedIn was members come first. So every time anybody has any initiative, any new idea, any new project that they wanna kick in, the first question that leadership, that everybody is asking, what’s the member value? Before we talk about, oh, this is gonna increase our sessions, or this is gonna drive growth or anything like that, don’t talk to me about business metrics. Talk to me first, what’s the benefit for the members? So this really tells you that, you know, it’s part of the core value, you know that this is customer centricity.

Similarly, add Intuit, Intuit’s similar mission, mission driven growth company, power prosperity around the world. It’s all about powering the prosperity of our customers through our fintech tools.

And one of the core values on how we operate at Intuit is the key theme, which is design for delight.

And this is how we do things at Intuit. And the framework that we have into it is called customer driven innovation and design for delight. So it’s all about the customer. Customer driven innovation starts with deep customer empathy. You know, so that we are really empathizing, we are observing our customers, we’re savoring these surprises and we’re asking a lot of why to really understand what they need and why do they need it. Then we go broad to go narrow. This really helps us to pour many ideas and we’re not like locked down with very specific ideas.

And rapid experimentation is the core loop that’s helping us quickly come up with ideas, test them with the customers to really get their feedback on it, and then iterate. So our goal is targeted towards this customer because we know that if we are able to delight this customer, then we’re gonna have big word-of-mouth. We’re gonna have more customers. We’re not only willing this customer so they’re becoming a loyal customer, but we’re gonna be talking about, you know, how awesome of an experience they’re getting with Intuit. So to to their friends and family, and that’s how eventually the revenue will go. So the revenue and growth will all come if you strongly, if the company strongly believes that customer delight is the key pillar of that growth.

So when you have customer centricity as a core value, it guarantees sustainable, non disruptable, compounding growth. And you can see it with many of the hyper growth companies that leverage experimentation and that customer centricity as far as as as their core billing.

Now customer centricity is easier said than bold.

Everybody would say, hey. You know, we are customer centric and everything like that. But when you come to reality on the ground and when you see things, how things happen in the ground, I have seen three main situations happen.

So and this I put down in terms of who are we prioritizing for, the reality check.

And this is where you truly test the experimentation maturity of the company. In my opinion, experimentation maturity is not about the number of experiments that are being run. It’s not about the number. It’s about these kinds of friction points and decision points. So the first one is leadership opinion. We all heard in the experimentation copy community about the HEGO, highest paid person’s opinion. So how does that reflect in reality?

Leaders are sometimes, you know, hyper focused on short term pressure.

Like, sure, you know, obviously public companies, quarterly earnings, you know, we need to meet our revenue goals. So they ignore the longer term customer value. We need this revenue now. Let’s ship, ship, ship, and let’s you know, we are we don’t have time to test kind of thing.

So just, you know, trust me, just go ahead and release that roller. And we’ve seen some of the bad examples. For example, Solaris, I think, you know, where we’ve seen that the mad behavior, how much that could have a negative impact. So leadership could be, you know, and that’s where the one of the pillars of cultural experimentation maturity is about leadership, truly embracing experimentation and embracing them at the difficult moments and believing in that philosophy that customers go first.

The second one is about employees. We really need to acknowledge and recognize what’s happening with the employees.

And employees are, you know, sometimes subject matter experts, and this is where the ego or their personal benefit of promotion comes into place.

Are we doing this project and we’re pushing it forward without putting a lot of emphasis on the customer’s needs and the customer value? Are we just pushing it forward just so that we can announce on Slack, all of this is released so that I can focus on my performance review or my my getting, you know, the promotion that I’m looking for. So these are sometimes I have seen in in many places and of when I talk to people on the ground, are key pillars that are not really discussed in companies, and they need to be discussed in the experimentation maturity as well. And last but not least is the competitive pressure, the me too syndrome I cause.

Building features just because our competitors have them, not because they solve real customers’ problems. So this is when our company now has, oh, we have an AI pro, and now we need to have an AI pro. What does it bring to the customer value? Have we tested that? No, no, we just need to be copy, copy guts here. And we’re releasing features just for the sake of, you know, being, you know, it’s not, it’s a no, it’s a feature sloppers game because. So these are sometimes, you know, very big loopholes that companies need to be very much aware that could distract them from solving for the customer.

So what happens when we start doing that? This is where the silent killer starts to come in. The feature creep. You know, if you’ve seen many products, we have this you know, they start with a very clean interface with a very clean value proposition.

They’re focused on this core value. And this is where we love these products. But what happens over time? Well, what happens over time, we have all of these teams who are working on different pieces of the product.

Imagine LinkedIn, we have the LinkedIn Messenger team, we have the LinkedIn feeds team, we have the LinkedIn profile team. And now every team is building new features. If they’re not being very careful on how they do it, eventually, LinkedIn is gonna be, you know, like this crazy experience over here with so many different products and functions and all of those. Many of them are not even users.

I rarely click on them or use them.

So what I call here, what is needed is a spring cleaning mindset. Just like in our house, you know, we buy a new house. It’s nice and empty at the beginning, and then we start keeping buying furniture and buying stuff and putting them in the house. Eventually, the house is gonna outgrow us. It’s gonna be so crowded.

Spring cleaning is very important. If a feature that you released is not heavily used, why is it there? It should be killed.

It should be taken down. This is distraction. This is causing new customers, causing them more cold fusion. So there should be a solid reason why a product feature is there. How does it align to your strategy? How does it align to bringing value to your customers?

And this is where testing of new features. So existing features should be always be monitored over time. If they’re not being used, then we need to be questioned why they’re there. Testing is also gonna be a critical guardrail on how we know and launch your Are they meeting their intended hypothesis, the customer benefit? If not, why are we still launching them? But there’s always a catch here.

These features like we discussed that are being launched, that they they these features, there’s a team behind them. There is a leader who’s sponsoring them. And this is goes back to the experimentation maturity.

Sometimes they just wanna keep them there for the promotion or for their own job security.

So this is where it’s very important on their organization and maturity levels to realize that, you know, it’s not easy to kill the future.

They need to create that safety, job safety heaven, that failure is indeed acceptable and is not gonna be detained these employees or these leaders because of that. They can move them into the next exciting opportunity. So I’m emphasizing a lot that there is a big burden on the organization, on the leadership to to to be able to focus on that aspect.

So let’s talk about some examples here. So the first example is LinkedIn stories.

When I was at LinkedIn, I remember we hired the director of product from Instagram. And if you guys all remember, Instagram stories was very big, a hot topic.

And it was neural and all of those. So obviously, you know, that director of fraud came in and the great idea was, hey, we should do LinkedIn stories.

But obviously, there is some logic behind this hypothesis statement. LinkedIn, you know, with the LinkedIn feed, social platform, just like LinkedIn, Instagram. Instagram stories is gaining a lot of growl and tech attraction, so we should test we should test that out. Again, that’s the experimentation wins.

So quickly, a team got assimilated, and they lost the Insta the that LinkedIn stories.

And, you know, and with the opportunity, the member gives our members the opportunity to speak about what’s happening in their real life.

But it didn’t work. The data was not showing. It became viral just like with Instagram story. So leadership was at a critical point, and I think it’s a critical testing point on how mature experimentation mindset is at little.

They could have decided, hey, maybe let’s wait it out.

Maybe in the future people will start using it better.

But this will cope at a clutter. Imagine, you know, you open your LinkedIn app, the big horizontal real estate on your LinkedIn app will be the LinkedIn story, but nobody’s engaging with them. And this is a very precious real estate. So LinkedIn took the bite and decided, no. We’re gonna kill it. Members come first.

If users don’t want it, we’re gonna listen. We’re gonna remove it. But what happened to that team who spent the the quarter of time building this feature? Well, they are working on the next project. It was a great learning opportunity.

Well, I can tell you another story. For example, during COVID, we had Clubhouse. You remember Clubhouse? People were locked down in their houses and Clubhouse quickly became very popular. Similarly, another person came up with the idea, hey. We should introduce inside the LinkedIn Messenger app an ability for to do a globe house style functionality.

Again, this was quickly built and tested, and it wasn’t as viral as we were expecting to. So imagine all of these ideas coming in, and let’s just keep them. Let’s clutter this LinkedIn app with Clubhouse feature and with a LinkedIn stories feature.

I can tell you all other idea. Business cards, I was also supporting the international team, and one team from Japan also came up with the idea, hey. In Japan market, you know, we there there’s a lot of near emphasis on business cards. So we should do the business cards.

Again, that got ramped and tested, and it wasn’t showing that growth in that particular market. Imagine in the US where we don’t use business card scanners. So again, you know, you you don’t see all the hard work that companies do to run experiments, to develop and test them, and then they quickly kill them. Don’t expect every experiment or every idea to win.

The the opportunity here is to stay true on your customer come first mentality.

And if the customers are telling you, I don’t like it, you listen and you pull it down to keep your product clean and focused on developers and what they need for it.

And last example from LinkedIn, in terms of this is about the subject matter experts. So designers working on them looked at the LinkedIn banner ad and said, okay, you know, this is too big here. Maybe we can just reduce it by five pixels. And they did that without running an experiment.

And after a week, we saw drop in the click through rate, and that accumulated to almost a million or two million dollars of revenue loss because of just, hey. We can just reduce this by five pixels without the test.

So these are some examples of why experimentation is very important, not only for growth and meeting customer experience, but also in terms of guardrails of everything that needs changes in the platform needs to be run through an experiment.

So how do we do it right? How do we really stay true to the customer centricity and do it right?

We need to be customer obsessed.

Our customers are like the iceberg here. And we only know this much of our customers.

So we know how from how they ask us the feature request that they submit to us from the survey results that they give us feedback on, and even from how we observe them using our product, whether it’s in one on one sessions, or if you’re doing in session, like session recordings, for example, we use a lot full story. We are able to understand how they’re using our product or things like that. But there is a lot to learn about our customer. This is like how much we still don’t know about our customers.

And I’m gonna talk about spoken needs and unspoken needs. This is these are the deep trenches that we ideally wanna go into to really be obsessed and know about our customer.

So we start with doing interviews to really understand what what they’re looking for and why. We do diary studies as well. An interview could be a one hour discussion. You’re not gonna get a lot of information with a one on one hour discussion with your customer.

If you go even deeper with a diary study, maybe you can do it over a week or over a period of week or multiple weeks where you’re gonna fly on the wall. We call it attenuate follow me walls, where we’re just observing them, how they go about doing their work, the workflow, the friction points. We really understand what makes them tick. Now this is where experimentation is very important.

And when I talk about experimentation, it’s not only online AB test like the way, you know, we typically skew. It’s all the spectra of testing. Well, let’s just try it out, do a fair, fake, you know, fake experiment and just show it to someone and get their feedback. It doesn’t have to be statistically significant at that point.

But these are still, you know, iteration books with our customers that we’re learning.

And ideally, we wanna reach a point where we understand the psychology of our customer, what makes them do that work, what makes them win, what’s their anxieties at bay, what’s their desire to feel confident, So their intrinsic motivations. And this is where we start going into the unspoken need. The customer is not speaking about that, but we are starting to interpret them and and and be able to drive towards them.

So we hear a lot about product market fit. So we talked a lot about now the customer and and what we want in our product and the features that we are building, we wanna really match them to our customer needs. And here I think about them as table stake functionalities. Every product needs to have a core table stake functionalities.

You know, these are non negotiables. For example, like security, like login, being the frictionless login and things like that. These are like table stake. We need to have them.

But we don’t stop there. We just talked about the pain that the customer is having. So we need to really see how we are building painkillers and how good are these painkillers in solving our customer pain. But we do not stop there.

It’s not only about, you know, solving that pain. Our goal is higher than that. Our goal is to make to have delighted customers.

And we just talked about that unspoken pain and the motivation. So the way I think about it, we need to also have these energy drinks that are targeting the unspoken pain, the unspoken needs. That’s we’re gonna make our customers superhero. So we’re gonna be building and bringing new features and capabilities that’s gonna make our customers superhero.

And that’s where the customer is always right as a vote. We’re gonna solve their pain. We’re gonna make them superheroes. They’re gonna love us.

They’re not gonna leave us.

So we’re gonna score them, and we’re gonna have our customers score them. We’re gonna do it through customer satisfaction score. That’s how our customer is rating our solution. But we are also scoring our customer.

Because sometimes our the customer would say something but do something else. So we really need to get what they’re saying and how they’re behaving. And that’s where the customer engagement score is gonna be very important. For example, you go and meet a customer, You show them the product. They tell you, hey. I love it. It looks great.

Sounds great. Here’s the product. Go and play with it. But they don’t use it. So what’s going on here?

They said they loved it, but they’re not using it. How can we know? Is that truly, you know, many of being very nice with us? So this is where you can start to do the equilibrium of what they’re saying and what they’re doing.

So now that we understand our customers, their needs, one thing that I have seen everybody do is wrong. Honestly, many people are doing it wrong, is that product feature comparison matrix where they compare their solution versus the competition. And what you quickly see is feature one, feature two, feature three as the rose. And I think this was the trap that everybody is like really very popular.

They they got stuck.

We need to move away from that completely. It is not again about features.

We need to be looking when we are looking at our product, our offering, and we do a competitive analysis versus the competition, it is all about the users. And we we should bring them into table stay features.

How are you? How is your product offering in terms of table stakes compared to the competition? The painkillers. We know our customers. We know what’s their pain.

How good are how good are your painkillers who pain one, pain two compared to the competition. And the superpower energy drinks.

You know, this is what makes you unique and stand out. You are the competition. Are you clear on those? And this is where this is how it should be looking at.

So try to avoid feature comparisons and focus it on the user.

So now how do we go about building this product?

Well, this is what we are used to in this new agile environment that we’ve been doing throughout these years, which I’m now questioning.

We always start with an MVP, minimum viable product. I think of it as one scoop of ice cream. And then we go and continue to build on it for a minimum marketable product. And then ideally, we wanna reach a minimum lovable product where we even have the cherry on top. This is where ideally we, you know, we wanna do that. So how do we go about building this product?

Typically, what I have seen in, again, in agile, we have these sprints and we have user research and we’re capturing product requirements. We have the UI design if it’s a flatbed experience, and we have the product development. Sprint one, we do research, we do lo fi design, we might interact with the customers, Then we capture the user stories, put a PRD, we start building the UI functionality, the high fidelity UI. Our engineering team are doing, preparing technical planning. If there is any core infrastructure, we start putting the so this will take us multiple sprints with minimal lightweight testing for us to get towards an MVP. And then we might take us even more sprints to get towards an MMP and then eventually towards the minimal level product.

So it’s long feedback cycle, late discovery of critical flows, rigid sequential orders. And this is where AI is really disrupting this whole workflow.

And what I propose to do are two things. Number one, we need to introduce minimum viable test.

I have seen it in many, many occasions where teams tell me, hey. You know, we should be we we don’t have time to test right now. We’re still building MVP.

Well well, okay. Well, if it’s gonna take you six months to build an MVP, that’s a lot of risk without any customer feedback.

So you should be thinking about MVP before an MVP. MVP means minimal viable test.

And the other thing so let’s me double click on the MVP first.

Any big idea is a collection of leap of faith assumptions.

So these leap of faith assumptions you should be testing before you go and build an MVP product.

So these assumptions are gonna be critical in terms of you’re gonna it’s gonna break make it or break it for your idea. Let’s go back to the LinkedIn stories, for example. We need to list down what are the assumptions.

I mean, what LinkedIn did, they put together a big team and went out full stream, building the whole experience and launching it as an online controlled AB test.

Now what we need to understand, did they do that all in one shot? Did they do any leap of faith assumptions? I don’t think so. So this is where, you know, if they are unable to quickly list down these are the key leap of faith assumptions, and this is the minimum viable test that I can do to test them. Before I go and build the whole experience and launch it, then you will be able to de risk that major investment, and you’ll be able to have higher confidence on testing the big thing later. So the moral of the story, don’t build a product to see if it works. Run experiments to see if you’re right.

And how can we do that? Well, we are at an amazing era right now with AI.

The job functions are all merged together. We are able to run with with vibe coding, with rapid UI prototyping. We are at an amazing era to be able to use AI all over our product development life cycle. AI assisted research, following along with AI questions and recordings that we gain insights quickly.

As I mentioned, rapid UI prototyping, so we can skip that lo fi design kind of thing. We can just directly build experiences. For example, one thing I’m using Intuit using a platform called PreZero, where we brought our Intuit design systems into the platform. So when I’m building experiences, they come directly with our UI components and experiences right out of the box.

So this is allowing us to break loose of the role of the previous sprint development life cycles.

So let’s go back to lectin.

Here’s another example from the learnings I got from LinkedIn, which is called the network effect, which is basically the butterfly effect.

Many teams are building different things on the LinkedIn platform. For example, you know, we have the AI team who’s building the people you may know, PYMK algorithm.

So that team, one time they changed that algorithm and released it.

And because of this butterfly effect, we started seeing drop a four percent drop in the LinkedIn sessions.

And how can you know, it’s not like a one to one easy to map, you know, so that you can connect the dots and know, oh, this is the PYRK team that caused this drop in leading sessions. So one thing we learned over the years is that even small localized changes can have massive impact on the blood.

So this helped this made the company double down that we need to have a strong discipline around experimentation like that.

So the moral of the story at LinkedIn, everything we they’re testing everything.

They test the search algorithm, the front end experiences, the back end experiences, the ranking experiences, everything is being tested. And there are hundreds of tests that are new tests that are launched every day, And they involve like AI, ML models as well that are being tested across the platform. All of these teams are running AB testing that maybe you’re not aware of, but this is how it’s happening.

So what happens now that everybody is running test, everybody’s launching?

When I was at PM with the LinkedIn premium team, this incident happened to me.

One day we go and we see a drop in LinkedIn premium sign ups by five percent.

And then we started to question, okay, what’s going on here? Why did LinkedIn premium sign ups go down by five percent?

So I asked my engineering team, did we have any, like, issues in our code or anything? No. Knocked out the top of the funnel, did we have any breakdowns in the funnel activity? No. Everything is good.

Then we started looking international. Did we have any impact in one of the key international markets? Again, the answer is no.

So it’s very hard to see sometimes what is causing it. And you can see you can imagine now we have all of these teams of the company in the company running experiments. And there’s their focus so every team is running their own experiment, and they’re focused on their own KPIs. They have their own metric. So so the the the network effect will start to bubble up. So one experiment might be affecting different metrics. So this is where the experimentation platform is gonna become the checks and balances of the corporation.

As many teams as these growth teams, the experiment owners are running their experiments to grow their individual target metrics, we need to be also introducing metric owners in the company.

And every metric owner’s job is to ensure our customer and our key metrics, especially, you know, the customer metrics like sessions, customer happiness, revenue.

And this is their job is to guardrail that grow. For example and the way we do it, we did it at LinkedIn is through the most impactful experimentation dashboard.

So so now the experimentation platform is not helping only the growth teams in terms of running experiments. It is also helping the metric owners in keeping check on on those experiments. For example, for my LinkedIn premium case, I could come to the most impactful experiments. I would put my LinkedIn premium sign up rate, and I would see who is impacting me, my expert my metric the most.

And I can go and reach out to this team. Hey. What are you doing here? Do you know that, you know, your experiment that you are running is a is a part of the premium signups?

Let’s go and investigate what’s going on.

On top of that, we built ramp alerts. So this way, you know, people can subscribe anyone who’s ramping or increasing their percentage now. We got the metric owners will get notified about it. And then Wrap Assistant. And Wrap Assistant is an autopilot. This way, you know, I’m introducing a new feature and I’m gonna start roll it out and I will start the testing phase, I can do it in a safe manner, which balances speed, quality, and risk.

So let’s switch gear and talk about another use case, which is QuickBooks website for personalization.

So here’s another scenario.

For example, at QuickBooks, we have many different products of QuickBooks. We have QuickBooks Online, which is the core base product, but we actually have nineteen other products. We have QuickBooks Payer, Buy. We have enterprise feed, accountants, solopreneurs, HR services, so many different products of QuickBooks. But we only have one quickbooks dot com website, and we have only one hero section.

So you can imagine, you know, everybody wants a piece of this real estate.

So do we just, hey, rotate?

One day we have QuickBooks Payroll and then another day we have QuickBooks Time on it. Is that the best way to prioritize? Or is it the loudest team who screams loudest we just give them that real estate? Obviously, this is the wrong approach to do it. We go back to the customer is always right. What is the customer centricity? What is the best benefit for this customer?

Now this is where personalization is key. And we use personalization with experimentation to serve that. So for example, if I’m visiting quickbooks dot com, and I’m already a QuickBooks Online customer.

So obviously, if you’re gonna show me a QuickBooks Online, if you’re trying to sell me for QuickBooks Online, that’s a bad idea.

Well, we should know you should know me. I’m a QuickBooks Online customer. So from my end product experience, you know that I’m eligible for QuickBooks payroll. I should be most likely be seeing QuickBooks payroll when I visit that. So this is how we leverage data. And AI, we have a lot of ML models like customer propensity model. So we have even a supermodel, QRS supermodel, which really understands our customer needs and is able to give us a best recommendation or next best action for that particular customer.

So this is very good if we know this customer.

But the problem is when you have pre authenticated experiences like the marketing pages where people are not even signed in, so we don’t even know who they are. That’s where it becomes very challenging. How are you gonna know this visitor whether they are even an existing customer or not? Because they’re not even signed in. See, this is even signed in yet.

So but there’s a lot of ways to know about that. For example, they could have clicked on a Google ad. And in that Google Ad, it was about payroll. So, again, when they come to this page or a particular landing page, we should not be showing them a QuickBooks fine.

We should be showing them QuickBooks payroll. So that’s a simple use case of how we can take the information of where they’re coming from and be able to personalize that page. But that page should not be personalized by itself. And then if they navigate to another page, it just be static.

So we call that in session personalization. It continues at the same session.

So now we have offline data that we remember the last visit of this visitor and be able to pull pull out the personalization based on that.

We also have in session personalization, which sticks as as long as they are navigating. And where we are striving towards is same page personalization.

Imagine this same page, they scroll down and they interact with a particular plan. Maybe they want the as a high tier plan, and they they interacted with it. Ideally, when they scroll back up, we have this extra piece of information. And without a page refresh, we are able to provide them more context about this plan because they expressed interest in that. So we call that same page personalization.

And if you visit today your QuickBooks the QuickBooks dot com page, you’re gonna see a live scenario what’s happening.

So when you visit, it’s gonna come up up for you and tell you, help us customize your experience. Again, these are unknown visitors. So if we detect they’re unknown, then we just pop up. You know, provide them a pop up.

Tell me about your employees and what are your business needs. And you click customize. It’s gonna customize the whole page to what you’re looking for. So it could be as simple as that.

So again, this is where you for customer centricity is very critical.

Now your question you ask me, how do we do that?

This is gonna become very complex with with granular personalization. Granular personalization means we’re going from macro audiences to micro audiences and to even nano audiences, ideally towards the one on one as we mentioned.

And this require this comes back to the customer experience management of what I told you about. You need to have a web platform that is hyper connected with experimentation and personalization platform, and all the underlying data platforms and the pricing platforms and all. And that’s the platform we’ve built out into it. So you can go granular as much as you would like in terms of personalization. You can personalize the menu dropdown, the banner, the images, the buttons, all of those are opportunities to personalize.

If you don’t have that, what happens? Well, you have to make copies of these pages and you have to do customizations on these. So imagine the number of permutations that’s gonna take you to do that. It’s gonna be crazy number of quantities and permutation and the QA to ensure that the quality of those experience is gonna be alive.

So this is where the critical platform that powers these experiences is very critical to be able to deliver that experience to your customers.

Thank you very much. I hope that you found this useful.