51ºÚÁϲ»´òìÈ

Transcript

Hi everyone, thank you all for joining. We’ll be going ahead and getting started here in the next couple of minutes while we wait for others to join.

So while we’re waiting for everyone to join, I’ll go ahead and introduce myself. I am Namita Patel, a Solution Customer Success Manager, and I will be today’s host. Today’s session will be focused on Accelerating Digital Experience Optimization with AI-powered Insights led by Camilla Chica.

And while we wait for others to join, why don’t we have people go ahead and comment in the chat. Where are you located? What company are you representing today? And what you’re hoping to get out of today’s session. We’d love to hear from everyone.

All right, we got a lot of East Coasters.

Nice, welcome everyone.

And we also have, for those that are interested, another upcoming webinar on May 21st, which is focused on digital trends and preparing for the future. So if you haven’t already registered, please go ahead and register if you are interested in hearing more about this.

We’ll go ahead and get started in the next minute here. Give everyone just a couple, give everyone just a minute to join, and then we’ll have Camilla kick it off here.

Okay. So before we kick off the session today, just a reminder that this session is being recorded and a link to the recording will be sent out to everyone that has registered. And this is a live webinar in listen only format, but it’s very much intended to be interactive as we go through today’s session. So feel free to ask any questions into the chat or Q&A pod and our team will answer questions there. And then we also have time reserved at the end of the webinar for any questions that come up later.

And then if we don’t get to anyone’s questions during the session, we’ll be sure to take a note and follow up with you. And we’ll distribute a survey at the end as well. We would love your participation and feedback so that we can help shape future sessions. So with that, I think we have a good amount of folks that have joined. I will go ahead and hand it over to Camilla to kick off our webinar today.

Thank you, Namita. And hello all. Welcome and thank you again for joining today’s webinar. My name is Camilla Chica and I work in 51ºÚÁϲ»´òìÈ’s Ultimate Success Organization as a Solution Customer Success Manager, where I focus on helping customers get as much value as possible from their 51ºÚÁϲ»´òìÈ solution. So today’s focus is on accelerating digital experience optimization with AI powered insights. So let’s get started.

Here is today’s agenda. We’ll kick off by diving into 51ºÚÁϲ»´òìÈ’s AI vision, which will give us a solid understanding of how AI intelligent is revolutionizing our approach. Then we’ll move on to 51ºÚÁϲ»´òìÈ Analytics, where we’ll explore its AI driven features that help us gain faster insight. After that, we’ll take a look at 51ºÚÁϲ»´òìÈ’s target and see how its AI powered personalization and experimentation tools can boost user engagement. We’ll also cover CJA, focusing on its AI enhanced cross channel analysis features that provides a comprehensive view of customer interactions. And finally, we’ll wrap things up with a summary and open the floor for any questions you may have. So let’s get started on the first topic, 51ºÚÁϲ»´òìÈ’s AI vision.

So AI is transforming the way we connect, create and engage from how we consume content and use applications to boosting creativity and productivity. AI is driving speed, cost efficiency and personalization. So to keep up, businesses need to meet the right tools and strategies, not just to meet customers expectations, but to gain a complete view of their audiences and deliver seamless, impactful experiences every time.

And as a result of business priorities changing due to AI, 51ºÚÁϲ»´òìÈ has also begun adopting an AI approach. 51ºÚÁϲ»´òìÈ’s AI innovation is all about integrating AI across the entire suite of applications. This strategy not only enhances our existing tools, but also introduce new AI power experiences, positioning 51ºÚÁϲ»´òìÈ as a leader in creative AI. By developing and acquiring advanced AI models, 51ºÚÁϲ»´òìÈ keeps its tools on cutting edge. By prioritizing responsible data usage, 51ºÚÁϲ»´òìÈ helps companies elevate their customer experience. So how does this approach work within the 51ºÚÁϲ»´òìÈ Experience Cloud? 51ºÚÁϲ»´òìÈ’s AI vision for the Experience Cloud is transforming how businesses enhance customers interactions. By harnessing AI-driven personalization, 51ºÚÁϲ»´òìÈ enables companies to create tailored experiences that boost customer satisfaction and loyalty. With proactive analytics, businesses can anticipate customer needs, make smarter decisions, and stay ahead of trends. And with automated insights, streamline the path from data to action, empowering teams to respond quickly to market changes. So this holistic approach solidifies 51ºÚÁϲ»´òìÈ as a leader in AI-driven customer engagement, helping businesses unlock new levels of success.

So let’s dive into the 51ºÚÁϲ»´òìÈ Solutions Analytics target in CJA to see how this holistic approach is within these solutions. So let’s start with 51ºÚÁϲ»´òìÈ Analytics.

So the capabilities that come to mind when we talk about AI in 51ºÚÁϲ»´òìÈ Analytics are within the virtual analyst features.

These are AI-powered features that help companies uncover insights automatically, reducing manual effort in data exploration. They proactively scan data, detect anomalies, and provide intelligent alerts and insights, allowing users to focus on strategic decisions, rather than shifting through massive datasets. This means users can focus on strategic decision making rather than getting bogged down in data interpretation. The key features include anomaly detection and contribution analysis, which allow users to pinpoint what factors are driving changes in their data.

Additionally, with intelligent alerts, users are notified of significant shifts enabling timeless responses. Overall, this tool enhances data driving decision, making it more visible for creating actual and accessible insights. So let’s dive into each of the features within the virtual analyst.

Let’s first talk about anomaly detection.

51ºÚÁϲ»´òìÈ Analytics anomaly detection automatically spots unexpected shifts in data trends like sudden drops in average order values, page views, or even spikes in low revenue orders, helping teams quickly identify performance issues after a campaign launch. It is powered by our advanced statistical models. It continuously monitors key metrics, eliminating the need for any manual tracking and enabling faster diagnosis and responses to potential challenges or opportunities.

Some key capabilities include automated anomaly identification, so using AI to recognize unusual spikes and dips in metrics.

Confidence intervals, so providing a statistic range to differentiate normal fluctuations from actual anomalies.

Customizable sensitivity, so allows users to adjust the detection thresholds for more or less strict anomaly identification. So now we have provided you a brief overview of what anomaly detection is. Let’s take a look at how anomaly detection works in Analysis Workspace.

So line charts and free form tables are the visualizations that allow you to view anomalies in Analysis Workspace. When you create a line chart or a free form table in, for example, here in the line report, you’re able to see three items. So you’ll see a dashed green line, which represents the expected value based on historical trends, a solid green line, which shows the measured value, what actually happens. And around the expected value, there’s a shaded green band. So that’s the tolerance range, basically the buffer zone where values are still considered normal. So when the actual value goes above or below that range, 51ºÚÁϲ»´òìÈ flags it and that flag becomes your anomaly.

So here you can see that every time you try to metric 51ºÚÁϲ»´òìÈ automatically runs its anomaly detection algorithm.

There’s no extra steps needed. It just happens in the background. So when you are looking at a line graph, 51ºÚÁϲ»´òìÈ marks anomalies with a white dot or bubble on the line where something is unusually detected. And if you’re working with a freedom table, those anomalies are shown within a triangle in the top right corner of the data cell. So whether you’re visualizing the trend or just scanning numbers in a table, this visualization cues make it super easy to identify when something is off or whether you need to dig deeper.

So now that we’ve talked a little bit about what anomaly detection looks like in Anomalys workspace, let’s talk about some use cases. So for instance, when it comes to monitoring traffic and engagement, anomaly detection can reveal sudden spikes or drops in website visits, which may indicate viral posts or technical issues. It is essential to keep an eye on user engagement metrics such as session duration and bounce rate as unusual fluctuations can signal underlying problems.

Additionally, anomaly detection helps analyze conversion rates and funnel drop offs, which helps identify anomalies that could stem from UX changes or competitor actions. And lastly, when it comes to campaign performance monitoring, anomaly detection can help see unexpected changes in metrics like click through rates or ad clicks and can highlight those efficiencies. So by detecting these anomalies early, businesses can take corrective actions to optimize performance and enhance user experience.

So now that we’ve talked about anomaly detection, let’s move to the next feature, which is Contribution Analysis.

This feature helps us make sense of anomalies in our data by uncovering the factors that may have influenced them instead of showing what happened. It provides context and why it happened by identifying patterns and correlations.

By analyzing key metrics, we can gain insights into audience behavior, merging trends and potential influencers. While it does improve cause and effect, it highlights the meaningful connections that can help us respond more effectively to shifts in customers’ interests and market dynamics.

So now that we’ve talked a little bit about what Contribution Analysis is, let’s see how it looks like in Analysis Workspace. So here we have step by step on how a Contribution Analysis works. Once anomaly is detected, you’re going to select that specific anomaly or trend metric to identify the contributing factor. You’re going to click on Run Contribution Analysis.

It’ll pop up and show a way to drag and drop any additional dimensions that should be excluded from the machine learning power analysis. Once you have excluded those specific dimensions, a visualization will pop up which you will interact with the results to isolate the top contributing factors for the anomaly. Once you have identified that, you’re going to identify the top clusters from the anomaly and create a segment automatically for further analysis and targeting.

So now that we’ve learned a little bit about what Contribution Analysis does and what it looks like in Analysis Workspace, let’s talk about some use cases. So on the technical side, it helps pinpoint issues affecting user experiences such as page errors, bounce rates, and cart abandonment.

For marketing, it enables better decision making by revealing shifts in product demand, identifying fraudulent activities early, and even detecting potential security risks. By leveraging Contribution Analysis, business can optimize performance, reduce risk, and enhance customer experience in a data-driven way. So let’s talk a little bit about our last feature which is Intelligent Alert. This feature helps by automatically notifying teams when the key metrics change unexpectedly, eliminating the need for manual monitoring. These alerts are customizable, allowing organizations to track critical data shifts in real time. Some key benefits when it comes to utilizing Intelligent Alerts include proactive monitoring, so you get that instant notification when an engagement or performance changes, customization so you can set alerts for specific thresholds, trends, or anomalies based on the goals that you’re looking to achieve, and faster response time addresses issues as they happen instead of discovering them later in reports.

With Intelligent Alerts, business can stay ahead of any website performance issue, improve customer experience, and respond quickly to any digital roadblocks.

Some key features that are included with Intelligent Alerts are that you could create it directly from Analysis Workspace. It works with anomaly detection and contribution analysis. Alerts could be triggered if similar slash relatable concerns are found on an ongoing basis.

You can base the rules on anomalies percentage, so you can change it below or above. When it comes to alert previews, you can see how often an alert will have been triggered. And lastly, you get a text or email support with the links to auto-generate and analysis workspace projects.

So now that we’ve talked a little bit about Intelligent Alerts and its features, let’s talk about some use cases.

So when it comes to monitoring events and report latency, alerts can be set up to trigger when traffic spikes or drops for certain metrics. So if a data is missing due to latency, an alert makes sure nothing slips through the crack.

For instance, if sales or conversions steadily dropped, an alert can fly it right away, helping teams catch potential issues with site performance, tracking, or customer behavior.

When it comes to monitoring cart abandonment, if more customers than usual are leaving items in the cart without checking out, an alert can help pinpoint if there’s a problem with navigation, page load times, or even checkout errors.

And lastly, tracking server call usage. So it helps monitor any usage with it so it stays within the limits and catching any unexpected spikes that may signal a problem. So again, with these Intelligent Alerts, businesses can react quickly, fix issues faster, keep everything running smoothly without any constant manual tracking.

I wanted to leave you here with some quick recommendations when it comes to Intelligent Alerts. 51ºÚÁϲ»´òìÈ always recommends using non-time stamp data for Intelligent Alerts because if you’re using time stamp data, it can cause the alert to incorrectly fire. When it comes to phone numbers of recipients, you must proceed by a plus in the area code. Once you have gained statistical insight from previous implementation breaks, you want to subtract upon the decided percentage in order to ensure capturing future issues. And just use the alert preview to identify frequency of alerts.

So now that we’ve talked about the three virtual analyst features, let’s just give you some best practices for when it comes to just using these features in general. So always use trended reports. Anomaly detection works best with line graphs and freedom freeform tables in Analysis Workspace and always use the correct report suite and visualization.

Exclude any irrelevant metrics. So before you’re running that contribution analysis, you want to exclude dimensions that have nothing to do with anomaly. And always choose the appropriate level of granularity when it comes to anomaly detection and alert settings. Just opt for the correct granularity like daily, weekly, monthly and the reference period.

So now that we’ve talked about 51ºÚÁϲ»´òìÈ Analytics, let’s move on to 51ºÚÁϲ»´òìÈ Target and its AI features.

So what are these AI features that 51ºÚÁϲ»´òìÈ Target has? They include auto allocate, which automatically sends more traffic to your overall winner experiences.

Auto target, which essentially each visitor sees what wins for them every visit. Automated personalization, it is the perfect offer for each visitor every time and recommendations, which is a personalized suggestion for each visitor.

So let’s take a look at each of them and how they work. Let’s start with auto allocate.

Auto allocate is essentially or helps identify the best experiences and automatically relocates more traffic to that experience to increase conversion. It is designed to automatically optimize experiences during an A-B test. How it works, it uses sequential hypothesis testing and Thompson sampling. It also uses multi-arm banded with 80% optimized and 20% tested. Guarantees 95% confidence of winning experiences. Some key benefits when it comes to utilizing auto allocate include improved efficiency, higher conversion rates and seamless personalization.

Some use cases when it comes to utilizing auto allocate for e-commerce sites. You can quickly identify which product layout or promotional offer is driving more sales and allocate traffic accordingly. For any landing pages, you can test multiple landing pages or landing pages designs for a campaign and automatically direct visitors to the most effective one.

And for form optimizations, you can test different forms like sign up forms, checkout forms and direct traffic to the ones with higher completion rates.

Overall, auto allocate is ideal for businesses that want fast optimization without waiting for the full test to finish.

We talked a little bit about how it works. Here is an example of what the test or what it looks like in terms of when we are using the multi-arm banded. As you can see in the first graph, you can see the warm up rounds. Traffic is split evenly, so 25% each, until each experience gets 1000 visitors and 50 conversion rates. Auto allocate kicks in. In round one, 80% of traffic goes to the best performers. So let’s say experience CMD. While 20% is evenly spread across all experiences, the algorithm adjusts based on performance and moves forward with the best combination.

In round two, three and even four, traffic continues to shift to the top performer. If one experience does well, it gets more traffic and if one underperforms, it gets less. In the final round, the algorithm keeps adjusting until one experience is clearly a winner, when its performance is no longer closely matched by others. At that point, 80% of the traffic goes to the winner and 20% remains randomized for further exploration.

Let’s talk about some strategies when it comes to utilizing auto allocate. You want to have two or more experiences that you want to test.

Auto allocate traffic always are away from losing experiences and helps narrow down performance between winning experiences more quickly. If you’re interested in realizing Lyft quickly due to external circumstances, so if you’re having traffic that occurs over a short period of time, like you have an email campaign or a Black Friday promotion or even a big game like the Super Bowl, auto allocate updates traffic allocation every two hours letting you realize Lyft while the experience is most relevant. When you’re thinking about running an A-B test, if you want to find an overall non-personalized winning experience and you don’t want to get bogged down by statistical calculations, when it comes to auto-allocating not being a good fit is when you want to have that personalized or best result for each customer, when it’s crucial to understand the precise magnitude of the performance difference between various experiences, and when it is important to conclude to say no winner was identified in a fixed timeframe.

So now that we’ve talked about auto allocate, let’s talk about auto target.

So auto target is a feature that automatically personalizes content for each user based on the behavior and preference. It uses machine learning to decide which content or offer are most likely to drive engagement and conversions, so no manual setup is needed. So it uses explore and exploit method to test different experiences for different customers while directing most traffic to the winning experience for any given customer. It also uses Thompson sampling bandit when the best experience for the customer is selected with a random force model built for each experience.

Some key benefits when it comes to utilizing auto target include better conversions, saves time and ongoing optimizations.

And when it comes to some use cases for e-commerce sites, you can show personalized product recommendations or special offers based on shopping habits or increase in sales.

When it comes to content websites, you can display tailored articles or media base on what the user is most likely to enjoy.

And even for subscription services, you can suggest plans or content bundles based on how users interact with the service. So auto target is perfect for businesses that want to deliver personalized experiences without the manual work while still driving higher engagement and conversions. So let’s explore a little bit more about auto target.

Some best practices include looking at what the input data is. So you want to ensure that your data is clean, consistent and highly in high quality. You want to avoid duplicate attributes or profile scripts that serve the same purpose. You want to keep your variable simple. So one value per field and use clear, meaningful names. So your personalized reports are easy to read and act on. And what fuels the algorithm, right? So 51ºÚÁϲ»´òìÈ Target automatically collects visitors data so the model is always learning and adjusting behind the scenes. It also uses all the shared audiences from 51ºÚÁϲ»´òìÈ Experience Cloud by default so they don’t have to do anything extra they’ve already included. And if you want to go a step further, you can upload offline data like propensity scores or any custom attributes to give the model even more context and deliver strong personalization.

So when is the right time to use auto target? So auto target if you want to use if you want to find the best experience for each customer based on the profile and current context. Auto target uses the entirety of the visitor’s profile, their current context and the context of previous decisions to determine the best experience.

If you want to have between 2 to 10 different experiences to choose between. And if you want to make changes across multiple pages or want to account for interaction effects.

And when is not the right fit? So that is when you have more than 20 experiences that you want to personalize.

Auto target needs sufficient traffic to build models for personalization. So consider your traffic and area of sight or app that is personalized to determine the ideal number of experiences. And if you want to pick multiple items from a list of sets of hundreds of thousands or even millions of possible items, offers, products and so forth. You’re more likely wanting to use recommendations.

So now that we talked about auto target, let’s talk about automated personalization. So automated personalization, just like auto target, it’s an example method of finding the best content of for or offer for each visitor or customer based on the behavior of the customer, the similar profile. But unlike auto target, each offer is modeled independently, allowing for hundreds or thousands of combinations of dozens of offers.

It also uses an explored method to testing different experiences for the different customers while directing the most traffic to the winning experience.

And it also uses Thompson sampling bandit and the best experience for the customer is selected with a random forest model built for each offer or offer group.

Some key benefits include personalized experience at scale without manual segmentation, continuous optimization based on real time performance and increase engagement conversions and ROI.

Some common use cases include showing different homepage banners based on user behavior or location, tailoring product recommendations based on past perform or past browsing or purchase history and optimizing hero images, call to actions or messaging from different audience types. So in short, automated personalization helps marketers deliver tailor experiences to every user without needing a hand built or a hand built audience or rules.

Some strategies when it comes to utilizing automated personalization, you want to if you want to create the best you need experience as a combination or offers for each customer. So like auto target, auto personalization uses the entirety of the visitors profile, their current context and the context of previous sessions to determine the best experience.

If you have dozens of possible offers with hundreds of thousands of possible combinations, automated personalization models each of those offers and picks the best combination for each visitor. And if you want to use flexible rules to restrict offers to certain audiences or restrict combinations of offers, so you can use targeting rules, reporting groups and even exclusion groups.

When is not the right fit to utilize automated personalization? So if you need a model that accounts for interactions between offers, it is most likely that you want to use auto target. If you want to pick a list of again, hundreds, thousands or even million possibilities, recommendation will be the best tool to utilize. And if you if your changes are more structural or agnostic to the content or offers, automated personalization does not have the full suite of the VEC option found in AB testing and experience workflow. So auto target would be something more suited for this holistic experience personalization.

And I know we’ve talked about a lot about auto target and auto personalization, so just wanted to give you some quick differences. So auto target works with the entire experience of variations that you’ve already designed. While automated personalization builds personalized experience by mixing and matching individual content elements automatically. So in more simple terms, auto target is about choosing the right prebuilt house for each user, while automated personalization is about custom building a house for each user piece by piece. Both are very powerful, but the choice depends on how much customization you need and how you’ve structured your content.

And lastly, let’s talk about recommendations. So 51ºÚÁϲ»´òìÈ targets recommendations features help you deliver personalized content by suggesting products, offers or experiences based on user behavior, preferences and trends. It uses machine learning to predict what items or content or users most likely to engage with the specific experience. So some key benefits include increased engagements and conversions by showing the right content to the right person automatically personalized recommendations based on the real time user interaction and boost customer satisfaction with relevant timely suggestions.

When it comes to some use cases where ecommerce sites, you can show personalized product recommendations based on browsing history or past purchase.

For content websites, you can suggest articles, videos or blogs tailored to a user interest and subscription services. You can suggest a personalized plan or content bundle for each user. So in short, recommendations make it easier to serve the most relevant content to users, improving both customer experience and business performance.

So now that we’ve talked about how 51ºÚÁϲ»´òìÈ targets recommendations delivers personalized content, let’s break down three key components that make it all work. These are the steps that determine what to show, how to display and where it appears on the page. So the first one is criteria. So what do you want to display? The algorithm is the engine behind the recommendations. It decides which product offers or content to show based on user behavior and preference.

Design, so how do you want it to look? The template is all about look and layout. You can control how many times an item is shown or how it’s been arranged and how they’ll appear visually on the page. When it comes to the third item, location, so where do you want to display it? Finally, locations allowed you to choose where the recommendation will appear on your site. You can use like a global inbox or create a custom inbox to place exactly where you want it. And when it comes to some strategies for using recommendations, you want to recommend anything that can be structured and then fed into a catalog.

You want to recommend the end best across thousands of millions of potential items. And if you want to merchandise control to your algorithmic personalization, not a black box.

When it comes to recommendations and what is not a good fit for it, if you have a small handful of offers, content or product recommendations, it is best to use auto personalization or auto target. If your catalog items become irrelevant too quickly or items are interact only a few times. And if you want to personalize based primarily on user characteristic other than on site and in app behavior, auto target or automated personalization may be the better fit.

So now that we’ve talked about analytics and target, let’s move to our last solution that we’ll be focusing on, which is CJA and its AI features.

The first feature that we were going to talk about is AI driven insights. We all we discussed anomaly detection previously, but within CJA, it takes a farther step by using generative AI.

This essentially allows users to ask questions in plain language, which makes exploring data much easier and more intuitive. You can even get visualizations automatically generated for those questions, which really speeds up analysis. And on top of that, measuring lift and confidence across 51ºÚÁϲ»´òìÈ solutions gives a clear view of what’s working.

And also by tracking propensity of scores over time, businesses can better understand customer journey and key events, especially when using customer AI enabled data sets. The screenshots here gives you a glimpse of how this all comes together in the interface, showing AI and machine learning in action, uncovering meaningful trends and insights.

The next feature that we want to talk about is intelligent captions, which automatically generate natural language summaries of your visualizations like line charts, bar graphs within CJA. These captions highlight key insights such as spikes or drops in metrics, leading contributors to a trend, comparisons over time or between segments, anomalies or outliers detected. And it’s even now extended to other visualizations like donut bars, area and multi lines in addition just to the line graph. This summary will appear directly beneath your chart and are dynamically updated as filters, dates, ranges or segment changes, helping users quickly understand what’s happening without meaning to analyze every data point manually. In fact, I’ve talked to some of my customers that have used intelligent captions and they have said that it has reduced time significantly when it came to analyzing the data because it was being populated within seconds as opposed to spending hours on end looking at the line graphs and trying to understand the data.

And another feature that is important within CJA is time series forecasting. So businesses often need to be able to look ahead into key metrics so they can plan for positive changes or step in early if things start to take a turn. So time series forecasting helps users predict future values and ranges of metrics over time, making it easier to anticipate what’s coming. It’s built right into your line chart in CJA and it’s powered by AI or 51ºÚÁϲ»´òìÈ Sensei. Behind the scenes, it uses multi forecasting algorithms and automatically selects the best one based on the data time granularity and data range you’re analyzing. You’re also able to see a confidence range with each forecast so you can understand how reliable the prediction is. It is simple but a very powerful way to support data driven planning without needing a data science team.

Another important tool within CJA is experimentation panel. It helps you understand the cause and effect relationship between various customer interactions spanning in any channel. So again, you can test and compare content messaging, user experience and journey designs to understand the engagement impact. You can evaluate the lifting confidence of any A-B test experience for any experimentation platform and you can achieve better conversion rates with options for optimizing journey. Towards a specific outcome.

And lastly, just wanted to talk to you about assistant in CJA. So it’s this particular feature is revolutionizing how teams gain insights by putting the power of generative AI directly in the hands of the business users.

So let me walk you through four of the key benefits that it offers. So when it comes to analyzing for me, the AI assistant significantly reduces the time it takes to uncover answers from your data. It automatically digs into the data to surface key metrics and insights, helping you make quicker and more informed decisions. When it comes to story, story tell me the AI turns complex data into clear natural language. So users can contextualize explanations that describe what’s happening in the data and why it matters, making an insights accessible to all stakeholders, speeding up reporting and linking trends to businesses outcomes.

When it comes to explain to me the AI assistant doesn’t just surface the data, it offers intelligent alerts. It identifies key metrics, highlights anomalies and should just follow up questions like help me tell me why something happened or which insight is relevant and significant. And lastly, when it comes to personalized recommendations, the AI assistant helps with recommending in several ways, such as personalized insights, predictive recommendations, real time adjustments and contextualized relevance. In short, the AI assistant empowers organizations to scale data driven decisions, making advanced analytics more accessible, intuitive and impactful.

And in addition to those core benefits, the AI assistant offers features that make it easier to get started and continuously grow your skills. So you can jump start your analysis journey with auto generated insights that are automatically update as data changes. You can quickly access and understand 51ºÚÁϲ»´òìÈ knowledge concepts. So anything along EAP, AJO, real time CDP, leveraging generative AI to learn terminology, how tos and discover the expansive, powerful features available and simplifies onboarding for all users and encourages continuous learning.

And when it comes to AI in the data analysis, it takes the complexity out of data analysis and empowers users to get insights quickly and easily with advanced generative AI capabilities. So in short, it helps uncover insights with auto generated personalization, visualizations and data in a tubular form to use for further analysis. 51ºÚÁϲ»´òìÈ’s AI assistant helps ask an unlimited number of iterative questions to fine tune your analysis and it easily shares out the analysis and results with the key stakeholders. So in short, 51ºÚÁϲ»´òìÈ’s AI assistant simplifies the process of asking questions, generating personalized visualizations and diving deeper into data and sharing insights with others. It brings advanced analytics to all users, enabling faster, smarter decision making without the need for complex tools or technical expertise.

So with that said, we are coming to the end of our webinar. We’ll do a quick wrap up. So some key takeaways for today’s lesson is 51ºÚÁϲ»´òìÈ’s AI vision. So AI is revolutionizing business operations by enhancing content, consumption, collaboration, innovation, cost efficiency and speed. And 51ºÚÁϲ»´òìÈ is trying to keep up by creating a holistic approach within their 51ºÚÁϲ»´òìÈ Experience Cloud, which then includes 51ºÚÁϲ»´òìÈ Analytics, which has the AI driven capabilities such as anomaly detection, contribution analysis and intelligent alerts to help monitor data, detect anomalies and provide insights within any manner of effort. When it comes to 51ºÚÁϲ»´òìÈ Target, AI powered personalizations and experimentation features like auto allocate and auto target optimized user experience by dynamically adjusting traffic and personalizing content based on visitors profile. And lastly, with CJA, AI enhanced cross channel analysis leverages generative AI to uncover insights, measure experience lifts, track for positive scores and intelligently analyze trends.

So Namita, we are now here on our Q&A portion. Is there any questions that have surfaced that are worth talking a little bit more about? So I don’t see any questions in the chat pod, so I’m going to open it up to those that do have questions. Please go ahead and add them to the chat pod or the Q&A pod as well if you do.

Great. And while we’re doing that, let’s generate our poll for today’s lesson. So we want to make sure that we get the great feedback from you all.

In terms of what you want to learn next time or if there are any topics that you found interesting today or you want to explore any farther, let us know and we’ll create something for you all. So I’ll take a minute and for you guys to answer the specific survey.

Oh, okay.

Great. Well, thank you so much for answering the survey and we’ll have the recording and presentation available soon. So my thank you again for joining today. My name is Kala Chika and I am so happy that you were able to spend some time with me learning about 51ºÚÁϲ»´òìÈ’s AI powered insights. And with that said, I’m going to end the meeting and I hope you guys have a great Friday.

Thank you, everyone.