51黑料不打烊

Transcript

Hey, everyone. Thank you for joining. We鈥檙e going to wait just a couple of minutes for other attendees to filter in and then we鈥檒l jump into today鈥檚 content. Our session today is focused on maximizing user experience with 51黑料不打烊 targets. AI driven personalization. Like I said, we鈥檒l give it just a couple of minutes and let attendees filter in, and then we鈥檒l jump into today鈥檚 content.

There are a couple of webinars this week as well coming up. If you鈥檙e interested, I will put links to those in the chat if you have time to join. Definitely feel free to register and note that as you register for a session, if for any reason you can鈥檛 make it, you will receive the recording and presentation afterwards. So I鈥檒l put the links for this week鈥檚 webinars in the chat.

All right. Well, it looks like we are starting to get a good amount of people in the room, so we鈥檒l get started on the content focused on maximizing user experience with 51黑料不打烊 targets. I do have an personalization shortly. Our presenter today is my colleague John Magnusson. John currently works as a Solution Customer Success Manager with the Data and Integration team. John has been with 51黑料不打烊 for over 12 years and has been working with Analytics and Target, supporting 51黑料不打烊 customers across industry verticals. So with that, I鈥檒l go ahead and turn things over to John today to get us started. A couple of notes before he gets into his content though. Note that today鈥檚 session is listen only, but if you have any questions as we walk through the content, please feel free to post those in the chat and we鈥檒l either address them there, or we鈥檒l talk through them at the end, as we do have a little bit of time reserved for Q&A.

So thank you for your time and participation today, and I鈥檒l go ahead and turn things over to John.

Thank you Katy. Thank you for everyone that鈥檚 joining today. Good morning. Good afternoon. Good evening wherever you may be and looking forward to our discussion today. And hopefully you can take a little bit of a nugget or get motivated to try something new today with with target. So as Katie mentioned, I鈥檝e been with 51黑料不打烊 for a little over 12 years and currently focused on the analytics and target components. And today I want to walk through some of those features. So let鈥檚 get let鈥檚 get rolling. So the quick and quick look at the agenda today. Want to touch briefly on the engage, expand and embed concepts of building out a solid foundation for target. And then following that we will jump into the tools auto allocate, auto target, automated personalization and recommendations as we go through and then briefly touch on automated segments.

So as we all know, improving the customer experience is has never been more important among emerging market marketing capabilities. Personalization ranks at the top, to drive better customer experiences. And we know that poor personalization can hurt your bottom line, leading customers to switch from companies that fail to meet their expectations to those that do. And, personalization drives engagement. Conversion. However, like I say, before we dive into the more advanced features and target that capitalize on AI. I do want to do a quick review of the basic layout of the foundation for target. So there鈥檚 an e-book out there called The Winning Playbook for Experience Personalization. And you can see there鈥檚 a link at the bottom of this slide here, but also have, Katie drop us an icon, a link in the chat as well. But in this e-book she talks about three different components engage, expand, and embed.

At the at the engage phase, you want to build a team with defined roles, starting with an executive sponsor and a program owner, and then develop comprehensive goals and benchmarks beyond basic AB testing and establish foundational capabilities for data, content and intelligence. Respect customer data privacy. Of course, then as you grow and move into the expand phase this involves is where you want to involve stakeholders from various channels to enrich customer journeys, and then broaden your efforts using predictive analytics and personalization techniques, and then manage expanding data sets and adapt content to new interfaces, ensuring responsible data collection in a cookie. This world and when you expand, when you reach the embed phase, this reestablish a center of excellence to empower teams and maintain governance. Focus on broader strategic initiatives and use cases beyond traditional channels, and maintain high data quality and privacy standards. Leveraging AI for personalization at scale.

So to become a center of excellence for personalization, you need to establish a framework that empowers teams across the enterprise while maintaining governance over processes and technology. Foundation. A lot of people jump in and start doing some ad hoc testing and trying different things out, and that鈥檚 only going to get you so far. You really need to have this good foundation and this cycle with your testing to really advance, name, capitalize the most out of target.

A quick way to look at that here is this this right here. You want to sit down and identify what your opportunities are. Identify the value of valuable opportunities for targeted activities that are aligned with business objectives. And then define the strategy you want to find out. Define the objectives of an activity with specific visitor segmentation criteria and align experiences. Then work to design that activity.

Execute activity. Execute the activity within the target tool so the. Develop the code. Conduct the QA testing. Launch the activity, then analyze the results from that activity and find the insights and recommendations. And those are key as you take action on those results. And then that of course rolls you right around back in again to step one. And you want to just keep going through this loop over and over again as you continue to, broaden your testing and learn what to test next based on the previous tests.

So with that information, let鈥檚 dive into things of the seven capabilities within target. I would imagine most, if not everyone on this webinar today are familiar with at least some of the basics of a B testing, possibly experience targeting, or maybe even multivariate testing.

And those are fairly manual processes. So today we鈥檙e going to focus on the remaining four tools in target, which are 51黑料不打烊 Sensei or AI leveraged leveraging machine learning algorithms and automation to optimize and personalize at scale.

The enriched profile data automatically feeds into these personalization models. So today we鈥檒l cover auto allocate. This is where the winner receives more traffic as the test matures. Auto target each visitor sees what wins for them every visit. Automated personalization is the perfect offer for each visitor every time. And then, of course, personalized recommendations were personalized suggestions for each visitor.

So starting with auto allocate what is it? Auto allocate enables real time traffic shifts to the most successful experience as the test progresses. So unlike your classic or your traditional B testing, which requires predefined sample sizes and fixed test durations.

Auto allocate allows you to actively run continuously without manual adjustment. So this automated process identifies the winning experience more quickly and efficiently distributing it to a larger audience seamlessly. So how does it work? It uses a multi-armed bandit approach to maintain a testing pool, ensuring that the results are constantly validated. So you say you know what鈥檚 a multi-armed bandit? Imagine you鈥檙e at a casino with a row of slot machines, each with a different payout rate, and you want to find the machine that gives you the most money, but you also want to keep playing to win more. So this is similar to multi-armed bandit approach. Does it test you? In simple terms? It鈥檚 a smarter way to test different options, like website designs or marketing strategies to find the best one. Instead of testing all options equally, it鈥檒l quickly shift more traffic to the better performing ones. This way, you get to faster results and make the most out of your testing time. So again, like it鈥檚 playing like slot machines, but with a strategy that helps you win more by focusing on machines that are paying out the best. So this approach optimize is approximately 80% of the traffic, while keeping 20% at the test or control group to ensure the ongoing validation of the results. So auto Allocate begins its process after reaching a thousand visitors and 50 conversions, I believe, and it guarantees 95% confidence level. Once the winner is identified. So a significant improvement over your classic baby test. And then a quick look at how the how auto allocate ties back to our foundational engage expand and embed. Okay. So at the engage level auto allocate simplifies the initial setup by automatically distributing traffic to different variations of content or experiences. And of course this helps in quickly identifying which variations perform best without manual intervention. So it makes it just makes life easier to get started with personalization and reallocating traffic to the best performing variations in real time. Auto allocate provides immediate improvements in engagement and conversion rates. And this demonstrates effectiveness of personalization early on, which helps, of course, to build momentum and support for future initiatives. If you guys look great, if you鈥檙e just in, if you鈥檙e making your management look good, you鈥檙e going to continue to get additional support.

Auto allocate generates valuable data on customer preferences and behaviors from the outset. And these insights help in understanding what resonates with different audience segments. Building a strong foundation for more advanced and personalization strategies.

Then, if we look at the expand component as the organization grows in sophistication. Auto allocate is going to support more complex use cases by continue optimizing traffic distribution across multiple experiences. It鈥檚 also enhanced personalization efforts across different channels by ensuring that the best performing experiences are prioritized, leading to improved customer engagement conversion rates. And lastly, utilizes predictive analytics to refine and expand personalization strategies. This is. This helps to ensure that the most relevant experiences are delivered to users based on their behavior preferences.

At the embed level, which is the highest maturity level? Auto allocate scales. Personalization efforts. By automating the optimization process across a wide range of experiences and touchpoints, supports continuous improvement by leveraging AI to learn and adapt in real time, and integrates seamlessly with other enterprise systems and processes, enabling a holistic approach to personalization that spans the entire customer journey.

So let me demonstrate kind of how this is and why this is important. So we have a hypothetical ABC test here with 300,000 visitors that we need to allocate in two weeks. So it鈥檚 a manual A-B and test. Each experience would get 100,000 for the life of this test. And if you run this test for two weeks, it looks like you made 50,000 overall plus any future gains. So not bad. Like I say, any any test that鈥檚 a positive test is a good test.

However, with auto allocate as you run this test traffic shifts. Absolute values here haven鈥檛 changed. C is still the best and B is still the worst. What changes though is the number of people in those experiences. So we get over four times the revenue increase to auto allocate. This is again this is not real data. And these are hypothetical and rounded results. But this is representative of a real time test that has been run.

So yes we should use auto allocate our auto allocate.

When you want to reap the benefits of delivering the winning experience to more people faster. Also, it鈥檚 also great for time sensitive promotions or or product launches where you have a short time frame, like holidays or specific events where you鈥檝e got a limited time frame to get your content optimized and out the door.

So I鈥檓 going to jump over here now. I鈥檓 going to escape out of the slide show for one second and take us over to our sandbox. And fingers crossed everything goes well. Go live with activities. But I want to walk you through real quick the simplicity of this. We鈥檙e in target right now. We鈥檙e looking at the current activities we have, and I鈥檓 going to build a new activity. Let me change the resolution here. I think I鈥檓 probably a small screen blows up a little bit bigger.

So I鈥檓 going to say create an activity here. I鈥檓 just going to do a basic AB test. We鈥檝e got our web renewed visual experience composer and our test page.

So our content comes up here and I鈥檓 going to do like I say this is a simple. We鈥檙e going to keep this simple. But of course the sky鈥檚 the limit on what you can do with these things. But for today we are just going to do something simple. Says here be active with style.

Let鈥檚 say I want to I want to change this text here to let鈥檚 see here. Let鈥檚 just say over here, say find your style.

And we鈥檒l save. So now you can see I鈥檝e got experience A is B active with style. Experience B is find your style. And then we are going to say that鈥檚 all we want to change on this will hit next. Now instead of doing a manual allocation now is our chance to use auto allocate. So we鈥檒l select auto allocate and we鈥檒l hit next.

Here鈥檚 our chance to change our title text change. And we can give our objective if we like. We can set the priority. If you have other tests running on the same page, you might choose to set the priority. So it鈥檚 below other tests. You can schedule the start and end dates, or you can simply manually activate the activate as you like.

The reporting source is going to be target. And then my primary goal I鈥檓 going to put here conversion.

And then I鈥檓 going to put click as enablement is what I will be measuring. So I鈥檒l select my element. It takes me back here I鈥檓 going to I鈥檓 going to say shop now. So I want to know if people are going to click on this link based upon what my text says hit save. So now I have my metric. I鈥檓 trying to for my element, I鈥檓 looking for my conversion. And then that鈥檚 about it. I mean, I can put some more notes down here, but I鈥檓 going to save.

And it鈥檚 syncing with the system right now.

And I鈥檝e got my completed activity. You鈥檒l see it here. Now text change. Now I can go in here and activate deactivate edit delete make changes. But it鈥檚 there and it鈥檚 ready to go. So really the, the opportunity to to do an auto allocate type of activity is extremely simple. And so I encourage you to jump in to take a look at that. So let鈥檚 jump back to the presentation here.

And just to give you a quick example here. This is this is Swisscom. And they did an auto allocate test here where they built out five different experiences. Just the Galaxy nine, Galaxy nine with the simple pay option. Galaxy nine with the push sale bubble. Push device with our time urgency was D and then he was iTunes promo and the results were captured. After running the activity for just five days. And after those five days experience, D was the winner. And again, although the tests continue to run after the snapshot of results, Auto Allocate identified V as the winner for five days and was driving 40% of the traffic to that experience due to it having the highest conversion rate of 5.8.

So not bad. All right. So let鈥檚 look at auto target.

Auto target leverages machine learning to personalize user experiences. It integrates various data sources, including online, offline, behavioral, and contextual data to identify and deliver the most suitable experience for each user. So this feature can be applied to a wide range of user interactions from testing entire website layouts to customizing specific functionalities like navigation, all seamlessly integrated again into that a b testing workflow and auto targeted users for employees and and sample algorithm approach, which combines multiple machine learning techniques into a single model to enhance predictive accuracy. So basically, this method allows auto target to optimize experiences at a granular level, ensuring that each interaction is tailored to the individual users needs and preferences.

Looking at how auto target ties back to our our core components engage, expand, and embed auto Target simplifies the initial set up by using Advanced Machine learning to select the best performing experiences for multiple market defined options. So again, this helps in quickly identifying effective personalization strategies without an extensive manual effort.

It delivers quick wins. The most relevant experiences to visitors right from the start. Auto target can quickly demonstrate the value of personalization through improved engagement and conversion rates. Also, auto Target generates valuable data on customer preferences and behaviors from the onset. So these insights help in understanding what resonates with different audience segments, laying a strong foundation for more advanced personalization strategies.

When you reach the expand stage, Auto Target supports more sophisticated targeting strategies by continuing to continuously learning and optimizing which experiences work best for different audience segments.

Ensures that personalized, personalized experiences are consistent across the various channels, enhancing the overall customer journey and driving higher engagement, and then facilitates the expansion of personalization efforts by automating the delivery of tailored experiences at scale at the embed stage. The highest level maturity auto targeting integrates seamlessly with other enterprise systems. Enabling a unified approach to personalization uses AI to continuously refine and improve personalization strategies in real time. This ensures the experiences remain relevant and effective, and then of course, supports a holistic personalization strategy by combining data from various sources to deliver highly terrible experiences.

So again, looking at the power of target, you remember the auto allocate example we have before where we had 175,000. We ran through auto allocate. Well it just so happens there are some people who responded better to experiences A and B. So in this again hypothetical example, some segments prefer experiences that were on average poorer performing gold members for example, preferred A over C, I quite a bit.

So if we auto target these experiences and we automatically deliver an personalized experience, hey to gold members and experience B to weekend visitors and then the rest go to experience C, we see almost double the revenue of an auto allocate test and eight times the amount of the static AB test. Again, this is a hypothetical, but these are the results of a very these are very real results that you can see. So.

Again in auto target when you鈥檙e setting the auto target it鈥檚 going to be through the the classic AB interface. And you鈥檒l have the chance to select auto target for personalization. And then below that there is an auto allocation goal. And here you鈥檙e going to have a couple of different options to evaluate personalization algorithm and that maximize personalization traffic. And we鈥檒l jump in here in a second. But I want you to see this here. So with evaluate personalization algorithm here if your goal is to test the algorithm, use a 5,050% split of the visitors between the control and the target algorithm. So this split gives the more most accurate estimate of the lift, and is suggested for use with random experiences as your control.

This maximizes accuracy of lift between control and personalization. However, there is a downside in that you鈥檒l have relatively fewer visitors. They鈥檒l have the personalized experience because of the 5050 split.

On the flip side, you better maximize personalization traffic. If your goal is to create an always on activity, put 10% of the visitors into the control to ensure that there鈥檚 enough data for the algorithms to control negative. I鈥檓 sorry to continue learning over time.

The trade off here is that in exchange for personalizing a large portion of your traffic, you have less precision than what the exact lift is. No matter what your goal. This is the recommended traffic split. When using that specific experience as the control, you鈥檙e going to maximize the number of visitors who have the personalized experience. You鈥檙e going to maximize the lift, but you will have less accuracy as to what the lift is for the activity. And then last, of course, if you feel like you want to customize that a bit, maybe 80, 20, 70, 30, whatever you feel like might be more in line with your specific needs. You have that choice to do a custom allocation as well.

So if we jump back over to our workspace here and we say create activity, we鈥檒l do an AB test again. Again we鈥檙e going to stick with Visual and Experience Composer in the web. And we鈥檒l create the start of our test here.

Okay. So we鈥檝e got experience a an experience B I鈥檓 going to I鈥檓 going to add a third experience here. And what what I want to do here experience a I let me zoom out just one one click here okay. So we鈥檝e got we鈥檝e got down here.

We鈥檝e got our accident left across the top. We鈥檝e got men, women and then equipment down here we鈥檝e got women men and and then our best gear. So for experience I want this to line up I鈥檓 going to whoops zoom I鈥檓 going to I鈥檓 going to move this link here.

So that it鈥檚 over here okay. That should. So that lines up now with with what鈥檚 down below the top three. Pull down line up with our three options down below. So that鈥檚 experience a catering to women here. The experience B we鈥檙e going to we鈥檙e reverse that. We鈥檒l take our link here. And I鈥檓 going to change the layout. And I鈥檓 going to move what actually we鈥檝e got meant here now. So what I want I want men next. So I鈥檓 going to cancel that. So I think I鈥檝e had the experience a is met women and then man experience B is going to be men and we鈥檒l change this one down here as well. We鈥檒l grab this container and we鈥檙e going to change its layout.

So we鈥檒l drag it over here. And say yes okay. So that鈥檚 going to be experience B where we鈥檝e got men on the first pull down and men out here. And our experience C we鈥檒l go ahead and put equipment as the as the primary. So we鈥檙e going to change the layout, rearrange it. Little track equipment over here.

And we鈥檒l do the same thing down here. We鈥檙e going to have this container layout. Oops. Rearrange. It will drag this one over here.

All right. So just three different ideas that if I鈥檓 targeting maybe the majority of my, of my shoppers or women, but I might have some men that come in and have some interest. And so if I don鈥檛 know if changing that layout will have interested capturing them or, or possibly just simply having equipment lead the way, we鈥檒l try those regional layouts. So then we say next.

And here again we have the opportunity to go to Auto target. And then we can allocate how we want that.

I鈥檓 going to leave it. Let鈥檚 see I鈥檓 going to maximize on this. I鈥檓 going to put the 9010 and then I鈥檒l save it. Same thing as before. We can we can name it here.

Recently I mean you could obviously put a lot more detail there and date and time, things like that, to help to help us in governance and then set your priority, your duration. I鈥檓 going to again look at conversion. We鈥檒l select elements.

And then I鈥檓 looking to see if they click on Equipment and Women Shop now or any of these as well. We鈥檒l kind of track and see what happens with these.

And that鈥檚 really the gist of it. That鈥檚 pretty straightforward. Simple. Obviously you can do a lot more than that, but I want to show you how it how quick it is to set up and, and and this will show you here this is an A-B test using auto target. This is an AP test using auto allocate. These are AB tests that are manual. And then and the will move on to auto personalization. We鈥檒l walk through that one in just a minute. Here.

I do have a couple of examples here. But another another use case of a we鈥檒l have a short time. So I鈥檒l include those. We send it out but we can go through that later if we have time. So auto personalization this auto personalization or AP activities, they combine offerings for messages and use advanced machinery to match different offer variations to each visitor based on the individual customer profile to personalize content and drive Lyft. So similar to auto target, AP uses a random forest algorithm as a main as its main personalization algorithm algorithm to determine the best experience to show a visitor. So AP can be valuable in the discovery phase of testing. It also use is also useful to elevate, to allow machine learning to determine the most effective content and targeting diverse visitors. And then over time, the algorithm learns to predict the most effective content and display the content most likely to achieve your goals.

Tying it back to engage, expand, and embed.

I find that the content here is very similar to auto target because they do pretty similar things, and so they work in a similar fashion to help push you through the engage, expand and embed process. So all that you guys, you know, I鈥檓 going to skip this right now because of time, but it鈥檚 very similar to target in terms of how it gives you the advantage of getting in early with identifying unique use cases and personalizing for your customers.

So in this example here, there are four distinct locations with multiple different offers that could be delivered and using AP. The best performing content for each individual automatically gets displayed. Offers are ranked in order of performance impact per individual, and you can apply marker controls and set exclusions so content doesn鈥檛 get repeated across locations. AP is a great tool to use when you鈥檙e dynamically creating pages on the fly, and it can automatically order the content based on a visitor鈥檚 profile and their interests. This way, they see the most important content to them higher on the page. For example, that one note with AP, and then we鈥檒l see as we get into it. When you look for example at description, specifications, reviews and recommendations here on the right side, you wouldn鈥檛 necessarily be moving these these containers. Three organize. You would simply be moving the content in the description, the text and the HTML within those containers to the different locations. You could move recommendations to the top and specifications to report them. Depending on what the, AI algorithm determines is the best for that individual user.

And again, this well, this is a little different. Now that we鈥檙e not doing the AB test, we鈥檒l have and we鈥檒l go into automated personalization. So let鈥檚 jump out of here. And again back to the tool. So in this case we鈥檙e going to create an activity. We鈥檒l do an automated personalization activity. This time.

Launch that.

Site comes up.

Okay. So we鈥檒l keep this simple because of time. But let鈥檚 say a couple of different things. Let鈥檚 go with let鈥檚 say discover the finest yoga fashions from Luma. I鈥檓 just going to click on here and let鈥檚 say we鈥檙e going to change the text from Discover the Finest. We鈥檙e gonna say discover the greatest.

It will say that now you鈥檒l see there鈥檚 some hash marks behind it. Tell him telling me that that鈥檚 something that I鈥檝e changed. And let鈥檚 change the text here. The active lifestyle, say change text will say.

Active with who are style. Hit save. And let鈥檚 say we want to change the background color. Let鈥檚 choose change background color. And let鈥檚 say I want to do something I don鈥檛 know. Red鈥檚 probably not a good color to choose from, but we鈥檙e going to go with it today.

So what I鈥檝e highlighted three different things that I want to change. And each time you add a new element, you change. You can go over here and manage your content. And you can see with just those three elements, I鈥檝e changed. I鈥檝e already created, you know, 18 different experiences. And it鈥檚 going to it鈥檚 going to compound on itself. If I change another element, it might be 36 different experiences or even greater for you. It鈥檚 going to compound itself each time you add new elements. So I鈥檝e got three different elements here that I鈥檝e changed. We鈥檒l go ahead hit next.

And similar to Auto Target you can choose how you want to have your traffic at 5050 or 9010 or customize that allocation. We鈥檒l leave it as 5050 for now.

Again, name your activity AP test.

Primary goal. We鈥檙e looking for conversion. And I鈥檓 going to say click on an element. And I鈥檓 curious to see given the changes I鈥檝e made, how many people click on shop now or maybe down here with with one of these three. Well, I鈥檝e just got those two because we鈥檙e looking at fashion.

And say save now we can save our and save and close our test.

Once this is done sinking, there鈥檚 one additional thing I want to show you here. And an auto personalization that we don鈥檛 have in the others. So we added activity on. So you want to go back and look at this activity. Now you should have oops okay. Here鈥檚 this preview link here. This preview link will bring up all of your different experiences over here on the on the left hand side. And now you can click on these different experiences. And you鈥檙e going to see all of the changes that we鈥檝e made, the different.

Experiences that your customers are going to see. And then of course as this test runs.

The app is going to start learning different things from the from your customers or visitors and start to allocate accordingly or personalize their experience accordingly. So okay, so that鈥檚 the next we鈥檒l have.

And so there it鈥檚 there on the tool. It鈥檚 showing you know an auto personalization test there.

All right. So let鈥檚 jump back over to our slides here.

All right. So the last we鈥檙e going to touch on is recommendations and recommendations. Activities automatically display products services or content. That might be things that might interest your visitors based on previous user activity preferences or criteria.

So recommendations helps direct visitors to relevant items they might not, might not otherwise know about, and recommendations that you provide your your visitors with relevant content at the right time and in the right place.

So how does it work? Well, recommendations supports a wide range of recommendations, algorithms or criteria as they call it. They鈥檙e kind of one of the same and integrates with 51黑料不打烊 Analytics for comprehensive reporting and insights.

So this could be anything from a hotel recommending nearby restaurants after a customer makes a reservation. Maybe you鈥檙e a retail store and you鈥檙e recommending ranges and dishwashers after a customer has been viewing or purchased a fridge, possibly, or additional articles or books after an individual. An individual has shown interest in a certain item or an article. I think we鈥檝e all been there. We鈥檝e all seen recommendations at play everywhere we go in the web, so hopefully we鈥檙e all familiar with what it is.

The recommendations algorithms that you have to work with are popularity based. This was mostly view or most viewed or top seller. Your content based, which is content similarity item based. This is viewed, viewed or viewed. Bought or bought. Bought and bought of course, can be any conversion personalized. This is recently viewed site affinity or profile enhanced collaborative filtering. And then last of course putting your own where you can have the option to do custom, your own custom algorithm.

Again, tying this back to our three phases of maturity, the recommendations simplifies the initial set up by using the AI to automatically suggest relevant products, services, or content based on user behavior and preferences.

Honestly, it鈥檚 again similar to to title targeting and automated, personalization. So we won鈥檛 spend a ton of time here. Review that on your own. But these you know, all of these these tools make life just so much easier as you as you launch into further and deeper into target, build that foundation and just making everything, making some life better for you guys. So, I encourage you to kind of review that.

So and this is as kind of a it makes it look like it鈥檚 complex is but the reality is to get a setup with an activity is fairly simple. And target provides a guided workflow where you can select the audience and then the criteria and then design. And there there鈥檚 out-of-the-box templates that are available for each. And you can customize as much as you like or use your own design design criteria. So as you see here, we鈥檙e going to start where you build out your segments to build out your visitors. And then I like to think of the second second part here. The criteria. These are almost like your your little three by five recipes. You know, you build on a number of these things that you want to present to your customers based upon their browser history or whatever criteria you鈥檝e set up.

And then the tool gives you the easy option to select your layout how you want down the side, across the bottom, across the top, wherever however you want to lay it out. And you. Of course, you can do your own customization as well.

Now, because it does take a bit more time to configure a rack activity. I won鈥檛 have time to walk through that one today, but I, I do have a, there鈥檚 a webinar presented by Rob Hornick that does a fantastic job of walking you through an entire recommendations activity, setting everything up, how to put it into place. And so, I鈥檒l Katie, copy that link over in the chat for you guys. You have that. And actually if you鈥檙e if you鈥檙e thinking about doing recommendations, I would strongly encourage you take a few minutes and watch that video. It does a great job of of walking you through that process.

So at the foundation we have the algorithms we just reviewed. Then you can layer on refinements specific to your business. So for example you鈥檙e want only want to show items that are in X price range or never show out of stock items. So it gives you total control over the input and output with what you鈥檇 be recommending. And then you layer on your domain expertise and business rules. For example, use this slot to promote our, buy one, get one promotion. Let鈥檚 say. And in the end, it outputs a highly customized card like this. What鈥檚 great about target is you can test multiple algorithms against each other to see which drives higher engagement and revenue.

A quick, simple use case, but I鈥檒l leave that to be reviewed when the deck goes out. There is one final component I want to touch on, and this is automated segments. When you鈥檙e in target, you鈥檒l see a link for automated segments. And this will enable customers to understand how I activities are generating lift by exposing key attributes used in modeling and even automated segments that are created by AI.

So the automated segment report shows us how each experience performed to audiences that we may not have even thought about. So you can use these personalization insights to target retarget towards your conversion tool. It鈥檚 kind of like lifting the hood on an automated test and taking a look at why auto target or auto automated personalization decided to send traffic to different places, and it鈥檚 a chance to learn. Hey, that鈥檚 a that鈥檚 a segment I鈥檇 never thought about, and it鈥檚 really doing well for us and apply that segment in other tests down the road.

And then now that we鈥檝e covered all four of these, this with Granger is simply an example of some way that鈥檚 put all of these adoption all, you know, the full adoption of target to the test. They leverage to the target to reskin their website. And during the entire process, they used 51黑料不打烊 Target to validate each hypothesis, to ensure each change will create positive impact on the site.

They moved beyond basic testing, where they removed the site and that side navigation to a fully personalized experience throughout the customer journey. Replacing side navigation with personalized category tracks based on score, affinity or very scored category affinity, and then user profile based recommendations. Replacing more generic targeted recommendations at the bottom and ranked order. Offered personalized actions and custom home banner to welcome and re welcome customers back.

In the end, they made significant gains and increased the engagement. As you can see now on the right hand side with the results there, conversion rate and the customer satisfaction on their site. So I think personally target is one of the fastest ways to get a return on your investment. If you鈥檙e committed to putting it to use and put it to work. And, and you鈥檒l see quick gains, a quick wins on it. The ultimate goal, of course, here is to enable what we call 51黑料不打烊 Target everywhere, or the ability to have practitioners of all skill levels in all areas of the business easily pick up and use our testing, automation and personalization capabilities when and as needed for validating decision making and sharing insights and results.

Additionally, one thing that we didn鈥檛 talk about today, but it鈥檚 one of our standard features, is our collection of, I think it鈥檚 over 40 now. Easy to use API. So these APIs let you do some pretty cool and powerful things. For example, they enable off site testing, and you can also integrate your capabilities directly into 51黑料不打烊 Marketing Cloud or the third party systems. So these these APIs.

Enable you to work with the the Internet of Things, so to speak, or devices that are connected to the internet.

So this means you can personalize and optimize content for things like like ATM machines, digital consoles, set top boxes, digital kiosks and stores and airports of smart homes and other connected devices. So it鈥檚 kind of a cool thing to explore as well.

In summary, target delivers experience optimization everywhere consumers engage. It has a flexible architecture and a robust set of APIs, so you can personalize across any screen or device, including voice.

So my challenge today, kind of as we start to wrap up, is to go back to slide five. If you remember that. Same with the red circle that has the, the flow, if you will, of target testing and put that to work. So as a team focus on or identify one or more of these personalization tools auto target auto allocate that we鈥檝e covered today, pick one of those that aligns with one of your current use cases, or identify some use cases. And then put put that in line with that flow where you define your strategy, design your activity, execute on your activity, analyze the results, take action on the results, and start to repeat that cycle over and over again. Doing so, you鈥檒l not only have a good, solid foundation for going forward with target, but you鈥檒l start to see, really, you鈥檒l reduce your amount of work time needed and launching activities, and you鈥檒l see your creator results from those activities. So.

That鈥檚 what I鈥檝e got for you guys today. And I really appreciate everybody taking their time to take a time to join us. And hopefully, like I say, there was a nugget or two or some motivation to get out and try something new with Target and Katie, I don鈥檛 know if we had any questions that have come in in the chat or if you鈥檝e had a chance to address those or otherwise. We can address those here or take any other questions that may come in.

Yeah. Thanks, John. One second. Let me just kind of come through the questions that have come through and we鈥檒l see. What might be a good one to speak to all right.

So, one that just came in was can 51黑料不打烊 Target be implemented in an email sent through an agile email channel? And. And so from yeah, I was going to say if you have more to add that is fantastic. I would say at a high level, yes. So I would definitely recommend, working with your 51黑料不打烊 account team as you go to execute the strategy behind that use case, just to make sure all the boxes are checked and it goes off as expected.

Yeah, that鈥檚 a great answer. Yeah. It was so I. Thought, okay, the next one that came in is around target and referencing user profiles, which is does target create its own set of unified customer profiles, or does it require integration with CDP or 51黑料不打烊 Audiences, etc.? And my understanding is, yes, you can you can build and create your own segments and profiles directly off target. Obviously, the more you expand into to, to, Audience manager and ADP, where you have a broader, data set to work with, you can really begin to grow and expand those segments and get much more involved in what target can do. But yeah, at the core target, you can out some great segments straight from target. Yeah. And what I added on to that is well, is that target will use all of the profile data that it is available to it. So there鈥檚 some, you know, kind of default settings that it鈥檒l capture out of the box. But the more dynamic you are with customizing what you share with target, as far as, profile data from your visitors, whether that鈥檚 from CDP analytics, etc., or if you鈥檙e passing it directly to target, all of that will make your personalization efforts more robust, and it鈥檒l use whatever is available to it to make that decision. And when you start to use machine learning or having, you know, auto allocate an auto target and make those decisions for you.

All right. I think we鈥檝e got another question that鈥檚 come in. Let鈥檚 see.

Where the push how and can it be done with a TI. So that one I think starts to get a little bit out of at least my skill sets on that. I would check experiencing documentation or reach out to your 51黑料不打烊 team to kind of dig into the use case there. But there鈥檚 there鈥檚 a lot out there. So, the reason I say and work with your 51黑料不打烊 team is because they can kind of help guide you to the most efficient rule based on your use case. Let鈥檚 see, how do we learn more about these 50 APIs? Is there a list and descriptions of each somewhere? So yes, I would point you to experience League for that as well. I think what we鈥檒l do is we can, add a link into our follow up email after this event to kind of give you more details on those APIs in the descriptions. Yep. And to add on those, John. Okay. No, I was reviewing those. Yeah.

I鈥檓 going to continue to look for some more questions that have come in. But before, everyone kind of exits, I鈥檓 going to launch a quick poll just to get some feedback on this session and gauge what might be of interest, for future sessions, if you guys have a moment, just two questions to participate in. That would be fantastic. All right. Let鈥檚 see if there鈥檚 any other questions we missed.

Let鈥檚 see. Can target be implemented with Marketo to add personalization through email channel two. And so yes, there is a Target and Marketo integration available. I always actually like a record repeating myself here. But yeah, I would check experience for the documentation on that. Or if you reach out to your 51黑料不打烊 team, they can probably give you more concise direction on the capabilities of that integration and how to get it in place.

Anything else to add? John? I don鈥檛 think that there鈥檚 any questions we鈥檝e missed, but oh, hold on another moment to see if anything else comes through. No, I think that鈥檚 accurate. I know there鈥檚 an integration. I鈥檓 in the same boat you are in that, I get the request quite often where they want that, and we arrange, you know, engineering to work with them and do those things. I don鈥檛 know the, the specifics of that integration. So, yeah, check with your team and they can certainly help you. Yeah. I would recommend to, taking the time to kind of document and really detail out the use case, because sometimes those details around, like measurement are exactly what it is that you鈥檙e trying to optimize. Their personalize is going to change, the best, most efficient path for executing that use case. So start with that strategy. And experience League is there to help guide you along the way. If there鈥檚 questions around implementing action or execution. And then also your 51黑料不打烊 team can help based on what it is that you鈥檙e trying to accomplish.

Yeah, there鈥檚 a wealth of information and experience. I mean, every single thing you want to do is walk you through step by step video clips that can help training and it gives you everything you need. So utilize that heavily. All right. Well, it looks like we鈥檙e kind of at a pause. I don鈥檛 see any additional questions coming through in Q&A or in chat. I would recommend if anyone does have any questions, pop out our pop up. Feel free to reach out to your team. And we can talk you through it. But thanks everyone for your time today. The session recording, some resource links as well as the presentation will be sent out to everyone who has attended and those who have registered as well. So feel free to let us know if any other questions come up. But thank you again for your time. Thank you, John, for taking us through today鈥檚 content. That was absolutely great to see and help. Everyone has a great rest of your day. Take care. You bet. Thanks, Kelly. Thanks everyone.