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Thanks for watching this video demo of Dynamic Yield’s end to end experience optimization platform where I’ll be walking though a high level overview of our many capabilities, as well as highlighting the overall usability of the product. Outside of what I’ll be covering here, we have many additional features that we’d love to show you. So if you’re interested in learning more about them, please be sure to contact us for a one-on-one demo.
So in addition to tracking event-level interactions like page views and clicks in real time, Dynamic Yield also provides a helpful admin interface to upload data in a batch file format. Our clients leverage this capability to upload their product catalog and integrate offline purchase events from their store POS for both anonymous and identified users. They can import additional customer attributes from their CRM, CDP, or any other kind of user data store and integrate content from their digital asset manager or CMS through what we call a variation feed All of the data that we collect about your customers is made available in the Dynamic Yield audience explorer. Analysts can profile their user data and build customer segments in real time. They can define complex logical statements in a simple interface using out-of-the-box segmentation conditions that include things like the user’s onsite behavior, geographic location, device information, referring source, and their offline transactions. Dynamic Yield can also ingest additional user data from your CRM, CDP, DMP, or any other system of record to further enrich your data set. This allows our clients to consolidate their disparate data silos into a single view of the customer.
We power email personalization using three different modules. Our triggered email campaigns can support classic use cases like cart and browse abandonment, or they can be configured to trigger off of any customer event that occurs on your website. The Dynamic Content and recommendations modules let you serve personalized blocks of content and product recommendations in your email campaigns just like the campaigns that run on your site. Our acquisition modules let you test and personalize your prospecting and retargeting efforts. Dynamic links are used to optimize the click-through URL for an ad placement. You define which landing URLs you want to test and then we generate a link for you to use in the ad placement. When the user clicks the ad, the dynamic link will route them to one of the landing URLs that you’ve defined in your campaign. Later, I’ll elaborate on how you can use our dynamic allocation method to monitor performance in real time and automatically allocate more traffic to the landing URL that proves to be the most effective. Display ads lets you target your users with personalized ad creative while they’re browsing other sites. You define the targeting conditions and provide the creative that you want to test. Then we give you an Iframe to serve through your DSP. The Iframe populates with personalized ad creative by leveraging the DYID stored in the user’s browser. Landing pages let you test, personalize, and deploy entirely new landing pages to a sub-domain on your site. The pages themselves are hosted by Dynamic Yield, which allows teams to easily deploy new landing pages without any integration with their CMS. High-performing brands know that testing and optimization are critical to delivering a delightful customer experience. Once they begin to personalize that experience, however, they find that the number of possible user journeys becomes more diverse. That’s why Dynamic Yield is built to help brands scale their optimization efforts and ensure that every personalized touch point is also optimized to the maximum degree. Every experience in your campaigns can be A/B tested using our Bayesians statistical engine. Analysts can define the percentage of traffic that should be allocated to the control, as well as each test variation. They can also configure more advanced settings related to the significance level, test duration, and attribution. The variation stickiness defines whether the user should see the same test variation across multiple browsing sessions or just within the current session. Analysts can also provide one or more email addresses to receive an automatic notification once a winning variation is declared. Dynamic Yield chooses a winner by evaluating the performance of each variation as measured by the primary metric of success. Key metrics like click-through rate conversions and revenue are available to select by default. Custom metrics can also be configured using any kind of onsite activity or event. For my use case, I want to know which variation captures the most newsletter subscriptions. An A/B test is ideal for experimenting with changes that will continue to impact the user experience long after the test concludes, but sometimes you don’t have two weeks to monitor an A/B test and manually declare a winning variation. One example is on Black Friday every year when retailers are required to change their entire customer shopping experience and simultaneously optimize its performance. A/B testing in this scenario would require too much overhead and result in decision making that’s too slow to keep up with traffic. In this scenario, we encourage marketers to leverage our dynamic allocation method. With this approach, each variation is exposed to an equal amount of traffic when the experience is first published. We then monitor their performance in real time and use a multi-armed bandit algorithm to shift traffic away from variations that perform poorly and reallocate it to the variation that performs best. Dynamic allocation lets marketers harness the power of machine learning to scale their optimization efforts and automate their decision making using real time performance data. Dynamic Yield provide several interfaces to visualize your data and report on campaign performance. The dashboard summarizes your personalization efforts into one single view. I can see important metrics like the conversion rate, revenue per user, and AOV. Different calculations are shown depending on the length of my lockback window. Over here on the left, I can see the recent activity that took place in my Dynamic Yield campaigns. It shows every time a campaign was created, modified, or deleted. I can also view a detailed change log that includes the users and timestamps associated with every change in my Dynamic Yield account. Every time a user creates, modifies, or deletes a campaign, it will show up here. Even though I selected a 30-day lockback window in our dashboard, this log extends all the way back to when your account was created. It’s not limited to the duration of your lockback window. In the middle of our dashboard, we list all of your AB tests that are currently running, as well as those that have recently concluded and declared a winner. If I want to take a deeper dive into a specific campaign then I can click into a more detailed reporting view right from the dashboard. In this case, Dynamic Yield has declared a winner. I can scan the detailed test results in my report and then pause all traffic allocation to the losing variations with a click of a button. This will end the test and allocate a 100% of traffic to the winning variation. At the very top of this list, we identify any tests where Dynamic Yield has identified a personalization opportunity with its predictive targeting feature. We’ll talk about predictive targeting more later on in this video. At the bottom of the A/B test widget is where you can download a full log of your A/B test history. This includes a summary of every test that was created in Dynamic Yield so that you can easily format this information into your own internal reports, like an upcoming quarterly business review. Just like the activity log that we saw a minute ago, this file contains the full history of your A/B tests since your account was created. It’s not limited to the duration of your lookback window. On the right, we have a dedicated widget to highlight the impact of recommendations across your site. Direct revenue is for revenue from products that are purchased no more than 30 days after clicking on a recommended item. Assisted revenue is revenue from products that are purchased after clicking on a recommended item and then buying any product within the same session. We show the percentage of your total revenue that comes from direct recommendations revenue, as well as the total number of clicks on your recommendations and the page types in which at least one recommendation widget is live. Below the recommendations widget, we list all the experiences that are using dynamic allocation. These are different from A/B tests because we’re using a multi-armed bandit algorithm to allocate traffic in real time instead of allocating a fixed percentage of traffic to each variation.
In the bottom left of our dashboard is our audiences widget. The top 15 audiences from your audience manager are displayed based on their conversion rate and the average order value. Audiences at the top right are top performers. Their conversion rate is high and the revenue they spend on each purchase is high. The size of the bubble reflects the size of the audience. You can hover over the bubbles for more information. You can also click the audience manager and audience explorer buttons to drill down or see all of your audiences. To the right, I can see recent platform updates and click-through to more detailed release notes on the Dynamic Yield website. At the very bottom of our dashboard, we have our inspiration widget which features relevant use cases from our inspiration library. Users had access to more than 80 detailed use cases on our website to promote knowledge sharing and help enhance their personalization program. Our dashboard centralizes all of your personalization efforts into one single view, giving you informed look of how your program is performing and where it’s headed in the future. Right now I’m looking at a campaign for clothing retailer Land’s End. Campaign reports show high level performance metrics as well as the performance breakdown of each targeted experience in the campaign. You can modify the columns displayed in this table and export the data to a spreadsheet if you wish. In this campaign, we’re targeting users with a different visual navigation based on whether they have a stronger affinity for women’s, men’s, girls’, or boys’ clothing. I’ll click into the experience for boys’ clothing shoppers and take a look at the report for the variations that I’m testing. Right now I’m looking at the report for the current test version. This timeframe can be adjusted to any date range though. If you wanna see a full log of the conversions that were recorded by Dynamic Yield during this test, then you can use our revenue events log to download and analyze the full transaction history. I can analyze different performance metrics over time in this graph and see more detailed metrics in the data tables below. The primary metric for my test is shown by default, but I can also add any other secondary metrics that are relevant to my analysis. The probability to be best is the probability that a variation will outperform all other active variations based on the performance data that we’ve gathered. You can also break down these metrics by audience to see how each variation is performing at the segment level. If a variation performs particularly well for a specific segment, then it may be more optimal to target that segment with that test variation and personalize the experience. This is why Dynamic Yield gives marketers the ability to proactively identify personalization opportunities through what we call predictive targeting.
Every A/B test and Dynamic Yield has its variations performance automatically broken down by every segment defined in your audience manager. If predictive targeting sees that your campaign can benefit by exclusively targeting a segment with only one of your test variations, then our platform will notify the user via email and give them the option to add the new targeted experience with a single click. In this example, predictive targeting has notified the user that they can achieve an additional 7.8% uplift by targeting users in the search traffic audience with the control. All other users, however, should be targeted with the winning variation that’s being served through DY. When evaluating the results of your A/B tests, we also give you the option to filter out any outliers from the underlying data. This allows the analyst to confirm the validity of their test and ensure that their results are not being skewed by a handful of users. In this way, the campaign reporting view simplifies your analysis while also giving full transparency into the underlying data. Dynamic Yield uses recommendation strategies as the decisioning components of your recommendation campaigns. A single campaign can use multiple strategies. Like in the case where you want to test different strategies against each other. A single strategy can also be used in multiple recommendation campaigns. Therefore, it’s helpful to have a more global report of strategy performance to supplement your campaign level reports. The recommendation strategy report shows key performance metrics broken down by each individual strategy to help you understand which recommendations are the most effective across the site. Dynamic Yield continues to differentiate itself as a recommendations engine that not only provides advanced algorithms, but also flexible merchandising and filtering capabilities. I’ll define the page type where I plan to serve these recommendations since only some algorithms are valid on certain page types. For example, viewed together recommendations totally makes sense on a product page, but if I’m serving on the homepage, then viewed together recommendations wouldn’t be appropriate and therefore, shouldn’t be available to select as your algorithm. Dynamic Yield offers a wide variety of recommendation algorithms that are trained by customer data from both online and offline sources. This allows us to make better recommendations on the website based on what customers are purchasing in the store and vice versa. I can use something we call algorithmic fusion to power a single widget with multiple recommendation algorithms. For example, if my widget is a single row of four products, I can use the popularity algorithm to choose the first item, then collaborative filtering for the second, user affinity for the third, and recently viewed in the fourth. We also give you the option to shuffle the recommendation results. Instead of showing the product that receives the top score from our algorithm, we’ll randomly select one of the highest scoring products. This promotes product discovery and exposes your customers to more of your product catalog.
For my use case, I’ll use Dynamic Yields deep learning algorithm, which uses neural networks to identify patterns in customer behavior and then predict which product recommendations will lead to the most purchases. I’ll also choose to exclude recently viewed items, and then I’ll add some custom filtering logic for users in the men audience. I can implement rules that only includes some items or totally excludes some items from the recommendation results. I can also pin specific products to the top of my results and I can even choose to only apply the rule to certain slots in my widget. In my case, I want to only include items from the men product category and I want to apply this rule to all slots. I can search my product catalog and manually select the items, or use logical conditions to select them dynamically. I can set targeting conditions if I only want to apply this rule in a certain context or for users that fit a specific profile. In this case, I want to target users that fall into the men’s segment. I can schedule this rule to only apply at a certain time if I choose, but I’ll leave these conditions blank. I can add more rules to target my other key segments if I wish, and I also have the ability to pause a rule at any time. Once I saved the recommendation strategy, I can use it in a recommendations campaign and serve them as a widget through our client-side script.
Alternatively, I can choose to retrieve the recommendations as a JSON object through our API. This gives marketers the ability to merchandise and curate their sites’ recommendations, and developers, the flexibility to render those recommendations however they like. Now that I’ve configured my recommendation strategy, I’ll create a recommendation campaign to serve as the front end component of my homepage recommendation widget. I’ll target all users with this experience. And this time, I’ll use a template for my own library. I’ll use the variables tab to customize the template and select my underlying recommendation strategy. This makes it easy to test a new product recommendation strategy with the same widget, or keep the strategy the same, but make some aesthetic changes to the widget. After I click save and publish, the recommendations will be ready to serve. Emails can be triggered from Dynamic Yield to handle common use cases like cart and browser abandonment or as a response to any other kind of custom event. The cart abandonment email that I triggered during the front-end demonstration was powered by this campaign. The email is triggered once the user adds any product to their cart, but we delay sending the email for another three hours and cancel sending altogether if the user ends up purchasing the product or starting a new session. These rules are pretty standard for any cart abandonment email. But they can be easily configured to fit your use case. I can also set frequency capping rules to ensure that I don’t send too many emails to the customer too frequently. I can target a specific audience or group of audiences if I wish and then set up the variations that I want to test. I can easily change the variables just like any other DY template. I can also embed multiple recommendation widgets and choose the recommendation strategy that powers each one. You can set up granular merchandising rules in your email recommendation strategies, just like recommendations on your website. In the next segment, I’ll show how you can build these email recommendation strategies. For now, I’ll choose the user affinity strategy and populate the recommendations at the bottom of this email with products that cater to the user’s affinity profile. To make sure everything looks good, I’ll send a preview of each variation. Dynamic Yield can integrate with the client’s ESP to send triggered emails or send them through our default integration with SendGrid. Our clients also have the option to send blocks of personalized content and product recommendations through any ESP as well. After I click save and publish, the campaign will be live and ready to trigger based on the events we are collecting. In the previous example, we created a cart abandonment triggered email campaign. We displayed the items that the user abandoned at the top of that email and then selected a recommendation strategy for a second widget at the very bottom of the email. These strategies are defined in our email recommendations module. They can be embedded within a Dynamic Yield triggered email campaign, as we just saw a moment ago, or they can be copied and pasted into any other campaign, such as the daily batch and blast email.
You can use templates to customize the look and feel of the widget. Choose a recommendation algorithm and apply merchandising rules to the results. I’ll target this rule so that it only applies when the user belongs to my price sensitive audience. Once I hit save, this widget will be available to serve recommendations in both my Dynamic Yield triggered email campaigns, as well as any other email campaigns that I currently send through my ESP. Our dynamic content module is very similar. But instead of embedding a product recommendation widget, you can embed a block of content that’s personalized to the user. Notice that the campaign setup is just like a block of content on the website, whereas before we were personalizing a homepage hero banner and now we’re personalizing the banner at the top of an email campaign. Through flexible integration options and an intuitive workflow, our email modules demonstrate the cohesive, unified nature of the Dynamic Yield platform and enable our clients to scale their personalization efforts across all channels. Right now I’m in the app personalization module of our Blueberry iOS mobile app. I’ll add a new message to target users that abandoned their cart on web. I’ll trigger the message on page load and increase the size to cover the whole screen. I’ll target the web cart abandoners in men’s audiences. With DY’s native targeting, we can target across channels including web, mobile app, email, and display. I’ll create two variations to test my own template against the Dynamic Yield template. Once published, the messages will be served in the app without requiring any updates or approvals from the app store. In this next example, I’ll use Dynamic Yield’s visual edit tool to personalize the site navigation. I’ll provide the URL for the page where I want to preview the changes I’m about to make. I’ll target users in the women audience on all pages of the site. And then I’ll use the Wysiwyg editor to make changes. The tool makes it easy to navigate through the structure of different elements on the page. I can replace images, modify text, change colors, and even rearrange elements on the page. After I click save, I can preview the variations if I wish and then publish my test in the status page. Variations are marked with the default status of draft when they’re in development. They can be saved in Dynamic Yield and previewed on the website, but they won’t be deployed to the production site until their status is set to active. If I publish a variation to the site and then discover that there’s an issue, no problem, I can change the status from active to paused and seize traffic allocation to the problematic test variation. Once the variation is fixed, I can redeploy to the site by simply changing the status back to active again. In addition to personalizing individual touch points in the customer journey, Dynamic Yield gives you the ability to test and optimize multiple touch points across your entire conversion funnel with what we call multi-touch campaigns. In this example, I’m targeting users in the men audience and I’m testing to see which version of the user journey yields the most revenue. Think of each test variation as its own unique version of the customer experience. It has multiple touch points. It may include different types of campaigns. The variation displayed here has five touch points, three of which are dynamic content campaigns. One is custom code and the other is a notification. All the touch points, however, are created using the same simple workflow and leverage the same tools like our template editor and Wysiwyg editor. I’ll demonstrate how a new touch point is created by adding a welcome overlay to this user journey. I can target specific pages on the site. I can choose a template and modify its variables. Every type of campaign in Dynamic Yield can be tested, and multi-touch campaigns are no different. In this example, I’m allocating 10% of traffic to the control and 90% to variation one. Well, let’s say I want to test a second version of the user journey. I can easily duplicate a variation and modify its touch points. This makes it easy to scale and create as many variations as I need for my use case. Dynamic Yield clients leverage our platform to show users relevant items and guide them during their shopping journey. We track the performance of each product and then adapt to the context of user’s journey to make the most effective decisions.
Dynamic Yield is the Customer Experience Optimization Platform built for agility, providing all of the tools necessary to quickly build relevant digital experiences at every touchpoint under one roof. Through artificial intelligence, teams are then able to maximize the performance of each campaign, improving engagement, lifetime value, and revenue to help businesses gain a sustainable competitive advantage. In this high-level product demo, we walk through capabilities that help marketers quickly build and deliver better digital customer experiences at every touchpoint. If you are interested in seeing what else is under the hood, schedule a call to see an in-depth demo.