In e-commerce, how do you accompany customers on their purchase journey in the best way possible? How do you capture the relevant contextual information, perform effective and efficient data analysis, and provides real time individual experiences back to the customers? These are all covered by the Intelligent Selling Services for SAP Commerce Cloud solution.
In this blog post, I will give you a brief introduction to Intelligent Selling Services for SAP Commerce Cloud, what are the major functionality modules of the solution, and how it is used together with SAP Commerce Cloud to support personalized customer experiences.
Intelligent Selling Services for SAP Commerce Cloud, also known as ISS,
- is a cloud-native system with intuitive UI for easy data management and configuration
- captures and analyzes contextual and behavioural data across customer journey
- provides real-time customer experience merchandising and personalization
- can be directly integrated with SAP Commerce cloud based systems.
- is only available for professional and enterprise licenses of cloud version of commerce (i.e. not supporting on-prem)
Please also note that, ISS is rebranded from Context Driven Services, SAP Commerce Cloud (aka CDS)
ISS consists of 3 important functionality modules plus importing reporting features:
- Foundation: continuously tracks and stores customer behavior data so that they can be used in the other modules.
- Merchandising:promote and advertise products in a way that improves user experience and boosts e-commerce performance.
- Recommendation: one-to-one behaviourally driven product recommendations for known or unknown customers
- Reporting: provides insights core Key Performance Indicators as well as overview regarding test outcomes of A/B testing situations
Let’s take a look at each of the modules in further details.
enables to create, maintain, and continually extend customer data via difference resources, e.g.
- clickstream or commerce data coming from SAP Commerce Cloud
- from any other external system that can feed data into the foundation database of ISS
The collected data are then used for supporting the merchandising and recommendation modules coming next.
This module focuses on providing
- An Intuitive GUI for merchandisers to define and immediately visualizable content
- Metric-driven product selections with the merchandiser’s insight and experience
- Scheduling capability enables time targeting preferences with no manual interaction required
Possible use cases are:
- Enable product discovery by showing trending products.
- Promote products based on their real-time business KPI scores (e.g. conversion rate, product views, best selling products, mostly added into cart products, etc.).
Once a merchant has configured a possible mix with the intuitive UI:
A trending carousel can then automatically show the most up2date products according to the configuration:
this module applies machine learning analysis of shop visitors’ click-paths and behaviours to show the most relevant products during the shopping journey.
At the moment, there are 2 types of fully automatic recommendation features available:
- Related Products can recommend products related to the current product, based on collected clicks of all customers of the shop.
From the business users’ perspective, this feature can predict the next click of the current user, based on what other users have viewed next. Therefore, it can be used on the product detail page to keep the current user engaged in the exploration of the shop as it directly addresses product discovery. One typical example is to show a product carousel as follows:
- Personalized Products can recommend products according to the combination of user’s personal click history, product metadata, and the behaviour of the other users on shop.
Again the state-of-the-art ML sequence model and deep learning is applied to analyze the shopping journeys of all users to achieve personalized recommendations to each user.
Compared to Related Products feature, this feature provides 1-to-1 personalized product recommendation that is related to the current visitor in more shop pages, such as landing page, home page, category page or product page.
Please note, ISS portal page here states that there are 3 types of recommendations documented. The first one “Trending Products” is introduced in the previous module “Merchandising” already because it’s not based on machine learning analysis, but managed by merchants to directly influence the list of the recommended products.
This module provides graphical, intuitive and easy-to-use, yet powerful reporting functionalities regarding different strategies of product merchandising and recommendation.
As you can see above, 2 reporting tools are provided on the ISS GUI:
- Merchandising Reporting: enables to assess the click-through rate (CTR) for regarding predefined product mixes, strategies as well as the product carousels that are backed by the merchandising strategies and displayed on the online shop.
- A/B Test Reporting: can compare 2 product mixes within one product strategy against each other to illustrate the user impressions values, averaged CTR as well as the CTR range, and finally can even suggest which mix performs better.
If you want to know more?
Please take a look at the portal page of ISS:
Also pay attention to the up-coming live sessions on SAP Learning Hub about ISS.
That’s it and thanks for reading 😉