One of the toughest problems we face every day is making the right choice. For a customer, analyzing multiple products, identifying discounts, and reading reviews is a challenging task. In the process of evaluating multiple products, brands, and offers, buyers often end up confused. Recommending the right products to the buyers at the right time is the key to improving conversions, increasing the order size, and ensuring customer loyalty.
SAP Marketing Cloud provides a smart online solution for customers. Product and offer recommendations suggest customers based on their interests, buying history, and click through interactions. SAP Marketing consumes all these data points from commerce cloud and applies machine learning algorithms to understand the user metrics and recommend the right products.
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- Integration with multiple SAP products
- SAP Commerce Cloud
- Hybris C4C
- Provided rest API/OData to integrate with any non-SAP system
- An extensive set of ML algorithms which can be configured as per the business need
- Each scenario can be built applying multiple algorithms
- Activate and deactivate feasibility
- Analyze the model with the preview option. You can analyze the recommendation by using consumer, context, and item parameters.
- Customer satisfaction
- Increased cart value
- Increase cart conversion rate and sales
- Presenting customers content adjusted to their preferences
Understanding with an example
Seamless collaboration between SAP Marketing Cloud and Commerce Cloud has many benefits. These include:
- Understanding customer behavior
- Personalizing the storefront experience with relevant offers and products
- Increasing the conversion from abandoned cart to shopping cart
To better understand this topic let's understand the key pieces of data exchanged from Commerce Cloud and Marketing Cloud.
- Master data – Products & products hierarchies, customer & consent
- Transactional data – Saved carts, abandoned cart, and reviews
To drive more sales, SAP Marketing Cloud creates product and offer recommendations by applying machine learning algorithms on top of master and transactional data. These recommendations are published on commerce cloud storefront page, which helps track the visibility and success of the recommendations and sends the impression and click-through data to the marketing cloud. These key figures are used by marketing experts to evaluate their effectiveness.
OOTB SAP Marketing provides three main algorithms:
- Top-N Query: With this algorithm, you can recommend customers top viewed products, top sellers, and recently viewed products.
- Association Analysis: used to discover hidden analysis in large datasets, this grouping is based on browser session.
- Collaborative Filtering: It computes the similarities between consumers by considering their interaction.
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Building a recommendation model:
Building a model in 4 simple steps, below are key parameters that build a recommendation model:
Under 'Manage Recommendation' section select appropriate OOTB scenario and configure with the below parameters:
Below is a typical example of a top selling product recommendation model:
Results of top selling product recommendations:
Results of newly launched product recommendations:
Recommendations allow you to provide buying suggestions in real-time across multiple sales channels. This brings more sales by understanding user interactions.
About the Author
Sandeep Kumar Chekka is the Principal Engineer at TA Digital with 7+ years of core experience in designing, solutioning and developing complex eCommerce applications with personalized experience. He is certified professional in SAP Commerce Cloud, Marketing Cloud, Sales Cloud, SCPI and Salesforce B2C Commerce.
About TA Digital
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Over the past 20 years, TA Digital has positioned clients to achieve digital maturity by focusing on data, customer-centricity, and exponential return on investment; by melding exceptional user experience and data-driven methodologies with artificial intelligence and machine learning, we enable digital transformations that intelligently build upon the strategies we set into motion. We are known as a global leader that assists marketing and technology executives in understanding the digital ecosystem while identifying cultural and operational gaps within their business – ultimately ushering organizations toward a more mature model and profitable digital landscape.
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