Recommendation Share

Recommendation Share is a percentage-based metric that shows how often an AI system actively recommends a product or brand in response to analyzed shopping queries. It measures the share of AI responses in which the product is clearly presented as a suitable or preferred option.

The metric can be calculated as the share of AI responses containing a recommendation of the product in the total number of reviewed responses.

Recommendation Share = number of AI responses recommending the product / total number of analyzed AI responses × 100%

For example, if a product is recommended in 25 out of 100 analyzed responses, its Recommendation Share is 25%.

What Counts as an AI Recommendation?

A product can be considered recommended when an AI system:

• identifies it as suitable for the user,
• includes it in a list of recommended options,
• presents it as one of the best choices,
• suggests it for a specific use case,
• explains why it matches the user’s needs.

Simply appearing in a response should not be counted as a recommendation. A product may be mentioned only as an example, a comparison point, or an option the system does not advise choosing.

Why Is Recommendation Share Important?

Recommendation Share shows whether product visibility in AI translates into a positive assessment of its fit for the customer.

AI systems may consider factors such as:

• product specifications and intended use,
• price,
• availability,
• customer reviews,
• brand reputation,
• shipping and return conditions,
• fit with the user’s requirements.

A high Recommendation Share may indicate that a product is not only recognized by AI, but also perceived as a valuable and credible option.

Recommendation Share vs. Mention Share

• Mention Share measures how often a product appears.
• Recommendation Share measures how often it is actively recommended.
• Selection Share measures how often it is chosen as the final option.

A product may appear in many responses but be recommended in only some of them. The difference between Mention Share and Recommendation Share helps determine whether visibility leads to a positive recommendation.

How to Measure Recommendation Share

The measurement process may include:

1. Selecting products and competitors.
2. Preparing a set of shopping scenarios.
3. Collecting answers from selected AI systems.
4. Marking responses in which the product appears.
5. Separately marking responses containing a clear recommendation.
6. Calculating the result for a product, brand, category, or market.

It is also useful to analyze the product’s position on recommendation lists and the reasons given by the AI system.

How to Improve Recommendation Share

A brand can increase its chances of being recommended by:

1. Providing complete and comparable product data.
2. Creating content addressing specific purchase needs.
3. Building product and brand credibility.
4. Collecting reviews and increasing visibility in trusted external sources.
5. Keeping price and availability information current.
6. Clearly communicating product differentiators.
7. Improving delivery, return, and customer service conditions.

Summary

Recommendation Share is a metric that shows how often AI actively recommends a product or brand in analyzed responses. It distinguishes simple visibility from situations where the system considers the product a suitable choice.