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How Does AppExchange Search Work?
Search is one of the most popular ways that Salesforce customers find
solutions on AppExchange. Learn how keyword relevance, engagement, listing experience, and
machine learning influence the search results that customers see. Then apply tips to help
customers discover your listing when they search for a solution to a business
problem.
What Influences AppExchange Search Results?
When someone searches AppExchange, four factors influence the results they see. Keyword relevance is the most important factor, followed by engagement, listing experience, and machine learning.

- Keyword Relevance
- Keyword relevance considers how closely customers’ search terms align with text on your listing. The more that the search terms align with your listing text, the higher its keyword relevance. Title, tagline, and brief description text is weighted more heavily than other listing text.
Example
A customer visits AppExchange to find an app for administering surveys. Their search
includes the words feedback and collection. AppExchange listings that include these words have a
higher keyword relevance than listings that don’t.
- Engagement
- Engagement is informed by your listing’s popularity and considers customer activities like screenshot views, test drives, and installs. We measure these activities daily and in aggregate over the past 30 days. The more customer activities that occur on your listing, the higher its engagement.
Example
A customer visits AppExchange to find a document generation app. After performing a
search, they visit two listings. The first listing has only a few low-resolution screenshots, so
the customer leaves without interacting. The second listing has high-resolution screenshots, a
video, and a free trial, and the customer interacts with each of them. In this scenario, the
customer’s behavior contributes to higher engagement for the second listing than the
first.
- Listing Experience
- Listing experience considers other aspects of your listing that aren’t included in keyword relevance and engagement factors. Some of these aspects relate to your Salesforce partnership, such as participation in the Pledge 1% program. Others relate to customers’ experiences with your solution, such as the number and quality of reviews on your listing or when your solution was last updated.
Example
A Salesforce partner lists a new telephony app on AppExchange. To promote awareness and
installs, the partner launches a marketing campaign. Then the partner sends follow-up emails to
customers who installed the app. The email thanks customers for trying the app and asks them to
share their feedback on AppExchange. The number of reviews grows and listing experience
increases.
- Machine Learning
- Machine learning uses AI to improve the search experience on AppExchange. Like other search providers, we don’t share details about our machine learning algorithm. But trust and customer success are central to the design of the algorithm. Trust means that the algorithm continuously tunes search results to ensure authenticity. Customer success means that the algorithm makes inferences about a customer’s search intent and prioritizes the results that are most likely to drive positive outcomes.
Example
A customer visits AppExchange and searches for a solution called Appy’s Maps. In the
search results, a competing solution appears alongside Appy’s Maps. This solution appears because
some who searched for Appy’s Maps eventually installed the competing solution. The machine
learning algorithm considers this outcome positive and associates the competing solution with
Appy’s Maps.
How Can I Make My Listing Easy to Find When Customers Search AppExchange?
Here are some tips to help your listing stand out in the AppExchange search results.
| Factor | Tips |
|---|---|
| Keyword Relevance |
|
| Engagement |
|
| Listing Experience |
|
Maintaining a strong search position is a marathon, not a sprint. All search factors work together, and can change over time. Periodically review your listing’s keywords, content, and analytics so that they contribute to machine learning. Make updates to those factors that you control.