Name: Understanding Commerce Marketing in SAP Marketing Cloud
Uploaded: 2019-10-24
Duration: 712 s

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<div><div><div>In this video you will learn how an integration between SAP Marketing Cloud and SAP Commerce Cloud helps you to deliver a personalized shopping experience for your customers. You will see how the integration of both solution allows you to enrich your customers profiles with their browsing behavior and other commerce-related interactions. <br><br>Based on this information Commerce Marketing then allows you to provide better, more contextual product and offer recommendations. Finally, you will also learn how the re-marketing capabilities of Commerce Marketing allow you to specifically engage with customers who have abandoned their shopping cart. </div></div></div>

Understanding Commerce Marketing in SAP Marketing Cloud

Hi. In this video, we'll explain how SAP marketing can drive a personalized online shopping experience leveraging a smooth integration between SAP Marketing Cloud Now SAP Commerce Cloud, a seamless collaboration between marketing and commerce, has many benefits. First, you can get a better understanding of your customers by enriching their profile with their browsing behavior on the web. Shop with information on past purchases and interactions on other channels. Second, by using this information, you can personalize their online experience with offer recommendations and product recommendations sent via SAP marketing cloud. This, in turn, can help increase the average basket size or card size, along with the possibility of increasing conversion by remarketing to customers who abandon their shopping carts. To better understand this topic. Let's start by identifying the key pieces of data that are exchanged between SAP Commerce Cloud and SAP Marketing Cloud. From commerce to marketing. Two main categories of data are transferred. First, master data, such as products. Product hierarchies. Customers, as well as their consent or permissions to receive personalized content, offers and emails. Second, transactional data such as customer orders, saved cards, abandoned cards, or even the product reviews that are written by customers in the opposite direction to facilitate the personalization of content. The following data entities flow from marketing to commerce. Campaign data and target group information are sent from marketing to commerce. You can define restrictions to drive the display of individual page components or entire pages based on real time campaign lookups. In SAP Marketing Cloud to drive more online sales, SAP Commerce Cloud Displays product and offer recommendations coming from Marketing Cloud and importantly, SAP Commerce Cloud tracks the visibility and success of the recommendations and sends the impressions and Clickthrough data to Marketing Cloud. Whenever the web Shop fetches product recommendations. Impressions are created. These key figures help marketing experts evaluate the effectiveness of recommendation scenarios created in SAP Marketing Cloud. So far, we went through a high level overview of what data is exchanged between SAP Marketing Cloud and SAP Commerce Cloud. Now let's explore the valuable scenarios that are possible as a result. The first is Profile Enrichment in SAP Marketing Cloud within contact profiles. User browsing activities on the Commerce storefront are captured as interactions, and can be tracked in a timeline to understand customer journeys. Additionally, within the commerce tab of a contact profile, you can also see browsing favorites reflecting the products in your web shop that a customer frequently viewed. You can also see their buying history along with recommendations for the products they might be interested in. The second scenario that's made possible through Commerce marketing is providing personalized product recommendations. You can provide smart product recommendations to your customers by considering contextual information, such as their purchase history and their browsing behavior, to find the most relevant products out of the box. SAP marketing leverages three main algorithms using top end query. You can, for example, recommend to your customers the top viewed coffee products by other customers in the last two weeks. The second algorithm is association analysis. It's used to discover hidden relationships in large data sets. With its insights, you can recommend to customers items that were frequently bought together. The grouping is based on browser sessions. The third algorithm is the collaborative filtering algorithm. It computes the similarity between consumers by considering all of their interactions with products. Consumers are then given recommendations that follow the pattern. People who bought this coffee machine also bought this coffee filter. Here, the aggregation or grouping is based on users. You can start building a model by defining different steps. Each step comprises of different algorithms mentioned earlier. There are four main types of steps. Recommend. Step types are used to find the right products based on the algorithms used. Filter results is used to remove any unwanted recommendations. A typical example is to remove recommendations of any product that's already in the shopping cart. The step type position products is used to assign a rank to a product. For example, if you want to push products of a certain brand into the recommendations, there are more step types available in SAP marketing Cloud to customize the rank or determine the categories of products in customers shopping carts. On the screen, you see an example of product recommendation model that's used to recommend coffee related products while promoting the Rafale Grande Aroma range of coffee beans and ground coffee. It uses all of the algorithms from the three different step types discussed earlier. The top viewed algorithm is used to filter the products that are viewed most on the shopping website. Algorithms for the position product step type are used to assign the first position to the rapidly grande aroma range of products. The filter product step type contains the algorithm to remove items that are already in the consumer's shopping cart. Once recommendations are built in marketing, they can be consumed in two main application areas. The first is within Commerce smart that and here there's a component called SAP marketing recommendation. This component can be used as a placeholder for the product carousel, and is populated with product recommendations pulled from a SAP marketing cloud. We see various recommendations in areas of marketing cloud available for configuration in commerce. Smart edit. The second application area is the Content Studio and marketing. Here you can send emails with personalized product recommendations. You'll find product recommendations as a building block that can be dragged and dropped into email content. Afterwards, you can select a relevant recommendation scenario to populate the email content with relevant personalized product recommendations. Now that we've covered product recommendations, let's look at the third commerce marketing scenario. Personalized offer recommendations offer recommendations for. Facilitate picking the best promotional offer for every customer, in every channel, in every moment. The keyword here is picking. This is because the objective of offer recommendations isn't to maintain promotional logic, but rather to select which of the offers is the best to display to the specific customer. In this specific channel at this moment. So what is an offer then? In SAP Marketing Cloud, an offer is composed of multiple ingredients. First, visual content to be displayed on different channels. For example, a banner, second a time validity, and third, an optional incentive which could be a coupon with a discount. An offer can optionally be linked to a product or product category. It can also be eligible to only a specific target group, or it could be valid only in a specific marketing location, which can refer to a physical store or an online web shop. Here you can see an example of an offer in SAP Marketing Cloud. This offer is designated to promote the Grand aroma line of coffee products from the company Gallo d'Oro. We can see a banner and placeholders for maintaining eligibility, validity and product related data. The marketing location can be attached to the offer. The offer shown on screen will be valid only for the physical store of Gallo d'Oro in Las Vegas. If a customer visits this Gallo d'Oro shop in Las Vegas, they would be able to benefit from the specific offer using the coupon tied to this offer. The coupon information is maintained in the offer as shown on screen. So conceptually, how do offer recommendations work? How do we pick the best offer? The trigger comes typically from commerce with a request the marketing to come up with an offer based on a specific customer context. As an example, commerce informs marketing that a coffee lover is browsing new flavors of coffee on the web shop. In turn, this information is used to start a search for the right offer by scanning the relevant offers in marketing. Considering the Pre-filter is defined in the recommendation algorithm, the recommendation algorithms are weighted individually. Accordingly, the offer with the best runtime filter match and the highest algorithm weight is identified as the best offer and sent over to Commerce Cloud. In our current example, the coffee lover customer is browsing new flavors of coffee. So the logic would be like this. If the specific offer grants 5% off and is tied to the target group of coffee lover customers, this offer will be sent to Commerce as the best offer. You can see how the pre filters related to special target groups are added in Marketing Cloud. Just like product recommendations offer, recommendations can also be consumed in commerce within Smart Edit through the standard SAP marketing offer recommendation component and in emails through the Offer Recommendation Basic block. The fourth scenario, leveraging the Commerce marketing integration, is the abandoned shopping cart scenario. The objective here is to drive sales by encouraging customers to go back to the shopping carts they abandoned earlier, and to complete the checkout process. For this scenario to happen, SAP Commerce Cloud periodically collects abandoned shopping cart data and sends it to SAP marketing cloud. It also sends the list of products that are in the cart, and the date on which the cart was last changed. This information is then used while building the email content in SAP Marketing cloud. Out of the box in the Content Studio, there's an abandoned shopping cart basic block that can be dragged and dropped to automatically create parts of emails. Be it in the header, body or footer. This allows you to personalize the email content with attributes of the cart and abandoned products. Then, the email is automatically populated with the right products and links to every customer during campaign execution. Once the email content is ready, you can use the abandoned Shopping Cart campaign template to send reminder emails about their incomplete checkouts. After a campaign execution, SAP marketing cloud creates interactions to track open emails as well as purchase products. This helps in tracking revenue from retargeting campaigns. You can monitor the number of created orders and the total number of orders to evaluate campaign performance. To recap, there are four key scenarios that drive a unique, personalized online experience for customers and give us deeper insights on their behavior. These scenarios are profile enrichment, personalized product recommendations, personalized offer recommendations, and email remarketing with information on abandoned shopping carts. This wraps it up for quick guide on commerce marketing.