Bing Shopping API: How to Collect Product Data from Bing

Bing Shopping is often overlooked in ecommerce data workflows. Many teams focus on Google Shopping first because it has larger search volume and a more familiar advertising ecosystem. But Bing Shopping can still reveal useful product data: prices, sellers, product titles, ratings, shopping ads, delivery details, and how products appear for different markets.

For ecommerce teams, this data is useful when you want to monitor competitors, compare prices, track seller visibility, study product positioning, or build a recurring product intelligence workflow. A Bing Shopping API helps turn those search results into structured data that can be stored, compared, and analyzed over time.

In this article, “Bing Shopping API” refers to a SERP API that collects public Bing Shopping search results and returns them in a structured format. This is different from Microsoft’s Content API, which is used by advertisers to manage Microsoft Merchant Center catalogs, update product offers, pricing, availability, and catalog status.

What Product Data Can You Collect from Bing Shopping?

A Bing Shopping API can return structured product data from Bing Shopping results, including prices, seller information, ratings, reviews, product images, shopping ads, filters, related suggestions, and pagination. Some APIs also support sorting results by relevance or price and localizing results by country or market.

The most useful fields usually include:

For product intelligence, the key is not just collecting one snapshot. The value comes from collecting the same fields repeatedly under consistent search settings.

When Should You Use a Bing Shopping API?

A Bing Shopping API is most useful when your team needs search-visible product data rather than internal merchant catalog data.

For example, an ecommerce brand may want to know whether its products appear for important category keywords. A marketplace operator may want to monitor seller overlap across regions. A pricing team may want to compare product prices across competing retailers. An SEO team may want to understand which product titles, images, and merchants Bing tends to surface for commercial searches.

This kind of data is different from website crawling. Instead of visiting each retailer one by one, you collect the product listings that Bing Shopping shows for a specific search query. That makes it useful for search visibility analysis, seller monitoring, category research, and recurring market tracking.

A Practical Collection Workflow

A good Bing Shopping data workflow starts with search intent, not code.

Begin by defining the product queries you care about. These can be brand terms, category terms, model names, or commercial queries such as “wireless earbuds,” “running shoes,” “gaming laptop,” or “standing desk.” Avoid mixing very broad and very specific queries in the same report, because they often produce different types of product results.

Then choose your market settings. Bing supports market codes such as en-US, where the format combines language and country or region. Specifying the market helps return more relevant regional results.

A typical request may include:

engine=bing_shopping
q=wireless earbuds
market_code=en-US
language=en
sort_by=featured
page=1

For price research, you may also test sorted results, such as low-to-high or high-to-low pricing. For category research, filters can help narrow results by seller, brand, price range, or product type. Some Bing Shopping APIs return filter values that can be passed back into later requests.

Once the API returns results, store each product listing with a timestamp. This is important because shopping results can change as prices, promotions, seller availability, and ranking signals change.

How to Structure the Data

For analysis, do not store raw results only. Create a clean table that makes comparison easier.

A practical schema may include:

query
market_code
language
device
page
position
product_title
seller
price
currency
original_price
rating
review_count
delivery
product_url
image_url
collected_at

If your workflow compares Bing Shopping with Google Shopping, keep the field names consistent across both sources. For example, use the same field names for product_title, seller, price, position, rating, review_count, and collected_at. Talordata’s own comparison guide also recommends keeping country, language, location, device, and collection time consistent when comparing shopping data across Google and Bing.

This makes it easier to build dashboards, detect changes, and compare product visibility across search engines.

Common Use Cases

Price Monitoring

Bing Shopping data can help teams track whether competitors are changing prices for the same product or similar products. This is useful for categories where price changes frequently, such as electronics, home appliances, beauty products, sports gear, and consumer accessories.

The goal is not just to collect the lowest price. A better workflow tracks price, seller, position, and timestamp together. A lower price may matter more if the seller also appears near the top of the results.

Seller Visibility Tracking

For marketplace teams, Bing Shopping can show which sellers appear for important product or category searches. You can track how often a seller appears, which products they appear for, and whether their visibility changes across markets.

This is useful when monitoring authorized sellers, marketplace competition, or retail partner coverage.

Product Matching

Product names may vary between sellers. One listing may include a model number, while another may use a shorter title. To compare products correctly, teams often need matching logic based on title normalization, brand, model number, image, price range, and destination URL.

A Bing Shopping API gives you the search-facing product data, but your internal matching rules determine how reliable the comparison becomes.

Shopping Ads and Organic Product Visibility

Some Bing Shopping APIs can return both product listings and shopping ads. That makes it possible to compare paid visibility with broader shopping result visibility.

For ecommerce SEO and paid search teams, this can help answer questions such as:

• Which sellers appear in shopping ads?

• Which products appear without ads?

• Are ads pushing organic product listings lower?

• Do competitors use different titles or pricing in paid placements?

AI and Product Research Workflows

Structured Bing Shopping data can also support AI workflows. For example, an AI product research assistant may need current product titles, price ranges, seller options, ratings, and availability signals.

Instead of relying on stale datasets, teams can use a SERP API to collect fresh shopping result data and feed it into dashboards, internal tools, or AI-assisted research workflows.

What to Watch Out For

Bing Shopping data is dynamic. A single request should not be treated as a stable market report. Results can change based on market, device, time, sorting, filters, and product availability.

Keep these rules in mind:

• Use the same query format across collection runs.

• Keep market, language, device, and sorting settings consistent.

• Store timestamps for every result.

• Normalize product titles before comparing products.

• Separate sponsored results from standard shopping listings.

• Track position and price together, not separately.

• Review outliers manually before making pricing decisions.

Responsible use also matters. Product data collection should focus on publicly visible search results, reasonable request volumes, and legitimate business analysis.

Where Talordata Can Help

For teams that need repeatable shopping data collection, Talordata SERP API can help collect structured SERP data from search engines including Bing and Google. In a Bing Shopping workflow, the main value is consistency: teams can collect product titles, prices, sellers, ratings, links, and positions in a format that is easier to store, compare, and use in dashboards or AI workflows. Get free testing now

Conclusion

Bing Shopping may not be the first platform ecommerce teams analyze, but it can provide useful product visibility and pricing signals. A Bing Shopping API makes this data easier to collect by converting search result pages into structured fields such as product title, price, seller, rating, link, image, and position.

The best workflows are not built around one-time exports. They use consistent queries, market settings, sorting rules, timestamps, and normalized fields. With that foundation, teams can monitor prices, sellers, product visibility, shopping ads, and market changes over time.