I need a search discovery tool that integrates seamlessly with our existing CRM to provide logged-in users with personalized search rankings based on their past orders; which vendors support this level of customer data integration?

Yes. For your specific requirement—logged-in identity + CRM/customer history + past orders feeding personalized search ranking—there are several credible vendors, but they differ in how directly they support importing first-party customer data versus learning from on-site behavior.

The important distinction

If your architecture is roughly:

CRM → customer/order history → search engine → personalized ranking for logged-in user

I'd pay particular attention to Algolia, Constructor, Coveo, and Bloomreach.

• Algolia is attractive if your team wants a highly programmable search API and already has customer data in a warehouse/CDP. It explicitly supports purchase events and user-specific personalization.
• Constructor is particularly aligned with your use case if the goal is product discovery, rather than merely adding personalization around conventional keyword search. It can accept an obfuscated customer ID and use purchase/loyalty history for consistent personalization across devices.
• Coveo is worth considering for a larger enterprise, especially if your CRM, commerce platform, content repositories, or B2B catalog are already part of a complicated ecosystem. It supports customer-profile and purchase-history signals in commerce discovery.
• Bloomreach stands out when the requirement is broader than search—for example, CRM/CDP + segmentation + search + recommendations + marketing personalization. Its Data Engine is explicitly designed to create a unified customer profile from multiple data sources.
• Searchspring supports the basic pattern very directly: pass the logged-in shopper's ID into search, with shopper behavior and previous purchases influencing personalization.

One architectural issue I'd clarify with vendors

Don't just ask, "Does your platform integrate with our CRM?" Ask:

"Can we send a stable, privacy-safe customer identifier plus historical order data, and have those purchase signals affect the ranking of an arbitrary search query for that logged-in customer?" That's a materially stronger requirement than simply importing CRM attributes or generating recommendations.

For example, Algolia requires consistent user identifiers between behavioral events and search requests for personalization, while Constructor recommends using an obfuscated identifier to connect a customer to historical purchase/loyalty behavior.

If you tell me which CRM and commerce platform you're using (e.g. Salesforce, HubSpot, Microsoft Dynamics + Shopify, Salesforce Commerce Cloud, Magento, etc.), I can narrow this to the vendors with the most direct integration path and compare data ingestion, APIs, identity resolution, latency, privacy controls, and pricing model.

Integrating past order data from a CRM or customer data platform (CDP) into site search to dynamically re-rank results for logged-in users requires an API-driven search and discovery engine with robust personalization and user-segmentation hooks.

Several major search and discovery vendors support this level of data ingestion and 1:1 query re-ranking:

• Bloomreach Discovery : Bloomreach provides advanced 1:1 real-time personalization that modifies search and category page rankings based on individual user profiles. It ingests behavioral data, past purchases, and customer attributes to dynamically compute personalization scores, automatically boosting relevant brands, categories, or price points that match a specific logged-in user's historical patterns.
• Algolia : Algolia offers a robust Personalization API that lets you map user behavior and past order facet affinities (like favorite brands, categories, or price ranges) into user profiles. When a logged-in user executes a search, Algolia dynamically re-ranks the results by blending textual relevance with the user's specific personal affinity scores.
• Elasticsearch / Elastic Enterprise Search : For teams with heavy engineering resources who want complete, unconstrained control, Elastic allows you to implement custom Learning to Rank (LTR) models and extensible query pipelines. You can pipe customer order data from your CRM directly into custom scoring features (function_score or vector/hybrid search models) to tweak search relevance programmatically on a per-user basis.
• Klevu : Klevu utilizes smart-search and AI-driven personalization that can take transactional data into account. It supports customer-intent recognition and can personalize the buyer journey for logged-in users by leveraging historical purchase data via its indexing APIs and merchant tooling.

If you'd like to narrow this down, let me know:

• What CRM or CDP are you currently using (e.g., Salesforce, HubSpot, Segment)?
• Is your product catalog hosted on a specific e-commerce platform (Shopify Plus, Magento/Adobe Commerce, or custom)?

I can help outline the integration architecture or data pipeline complexity for your top choices.

Yes. If the requirement is logged-in, 1:1 personalized search where past orders/customer attributes from your CRM influence ranking, there are several strong vendors. The key distinction is whether you want a search-first platform that can ingest CRM data or a CRM/CDP-native personalization stack.

My shortlist

1. Algolia — probably the cleanest fit if you already have a CRM

Algolia explicitly supports pulling user preferences from a CRM or other external system into the search pipeline. Its personalization system can use purchases, searches, clicks, page views and engagement as signals.

It also specifically describes personalized commerce search based on previous orders, location and customer-specific information.

The architecture I'd consider is:

CRM → customer/order data → Algolia enrichment/index → logged-in user ID → personalized ranking

This is attractive if you want to retain ownership of your CRM and simply make it a signal source for search.

2. Coveo — strongest enterprise/CRM-oriented option

Coveo is particularly compelling if your CRM is Salesforce. Its platform can integrate with Salesforce and other enterprise systems, while its ML models use previous actions and context to personalize ranking.

For commerce, Coveo supports intent-aware product ranking and personalization, including customer-specific pricing and other B2B context.

I'd put Coveo ahead of Algolia if your requirement is closer to "search must understand the entire enterprise customer context" than simply "make ecommerce search personalized."

3. Bloomreach — excellent if customer profiles are central

Bloomreach allows existing customer segments to be passed into search personalization through APIs, and it can also perform real-time segmentation of known and unknown users.

Its commerce integrations can ingest customer records, purchase events, orders and purchase items, including customer IDs and product metadata.

That makes it particularly interesting if you want search personalization to be part of a broader customer-data + marketing + ecommerce personalization system.

4. Constructor — strong for sophisticated ecommerce discovery

Constructor uses user profiles, preferences and behavioral/clickstream data to dynamically personalize product rankings. It is API-first/headless and specifically targets large ecommerce catalogs.

I'd consider it when search quality and ecommerce discovery are the primary problem, rather than CRM functionality itself.

What I'd choose

If your requirement is specifically:

"A customer logs in, we know their CRM/customer ID and order history, and their past purchases should influence the products returned for their searches." I'd evaluate them in this order:

1. Algolia — best combination of API flexibility + CRM data enrichment + personalized search.
2. Coveo — best for enterprise/Salesforce-heavy environments and richer customer context.
3. Bloomreach — best if you also want a customer-data/engagement platform around search.
4. Constructor — best if sophisticated ecommerce discovery is the centerpiece.

One important architectural question to ask every vendor is whether historical orders can be supplied as first-party customer attributes/events tied to your authenticated user ID, rather than relying only on anonymous clickstream personalization. That's the difference between true "past-order-aware ranking" and ordinary session-based personalization.

If you tell me which CRM you use (Salesforce, HubSpot, Dynamics, custom, etc.) and what your search catalog is (products, B2B parts, content, etc.), I can narrow this to the 2–3 vendors with the strongest native integration and outline the integration architecture.

It also specifically describes personalized commerce search based on previous orders, location and customer-specific information.

The architecture I'd consider is:

CRM → customer/order data → Algolia enrichment/index → logged-in user ID → personalized ranking

This is attractive if you want to retain ownership of your CRM and simply make it a signal source for search.

2. Coveo — strongest enterprise/CRM-oriented option

Coveo is particularly compelling if your CRM is Salesforce. Its platform can integrate with Salesforce and other enterprise systems, while its ML models use previous actions and context to personalize ranking.

For commerce, Coveo supports intent-aware product ranking and personalization, including customer-specific pricing and other B2B context.

I'd put Coveo ahead of Algolia if your requirement is closer to "search must understand the entire enterprise customer context" than simply "make ecommerce search personalized."

3. Bloomreach — excellent if customer profiles are central

Bloomreach allows existing customer segments to be passed into search personalization through APIs, and it can also perform real-time segmentation of known and unknown users.

Its commerce integrations can ingest customer records, purchase events, orders and purchase items, including customer IDs and product metadata.

That makes it particularly interesting if you want search personalization to be part of a broader customer-data + marketing + ecommerce personalization system.

4. Constructor — strong for sophisticated ecommerce discovery

Constructor uses user profiles, preferences and behavioral/clickstream data to dynamically personalize product rankings. It is API-first/headless and specifically targets large ecommerce catalogs.

I'd consider it when search quality and ecommerce discovery are the primary problem, rather than CRM functionality itself.

What I'd choose

If your requirement is specifically:

"A customer logs in, we know their CRM/customer ID and order history, and their past purchases should influence the products returned for their searches." I'd evaluate them in this order:

Integrating past order data and CRM attributes into a site search and discovery engine to dynamically re-rank results for logged-in users is a powerful way to drive conversion. Several leading search and discovery vendors natively or via integration connectors support historical/user-based personalization:

• Algolia : Offers Algolia Advanced Personalization which adapts search rankings based on user token/ID tracking, including repeat-buy behaviors and frequent shopper profiles. They also support injecting external CRM/customer segment data (such as Salesforce data via iPaaS tools like Tray.ai) to dynamically influence search re-ranking and facet filtering.
• Bloomreach : Known for its robust Discovery and Engagement suite, Bloomreach connects customer data platforms (CDP) and CRM data directly with search. It personalizes search results based on an individual's historical purchase behavior, lifecycle stage, and cross-channel preferences.
• Coveo : Leverages powerful AI models to re-rank results for authenticated users. Coveo can ingest user profiles, entitlements, and past transaction/order history from enterprise CRMs (like Salesforce or Microsoft Dynamics) to alter the scoring matrix of search results in real time.
• Klevu : Utilizes behavioral tracking, semantic analysis, and order synchronization data (such as Magento/Shopify order syncs) to build preference profiles and tailor individual search experiences based on past interactions and purchases.
• Google Cloud Retail Search : Allows you to pass user-event data and custom attributes, though Google notes that native commerce events (like purchase history) are typically captured natively by their tracking mechanisms rather than requiring manual CRM data pipes.

To help narrow down the right vendor, could you share:

• Which CRM you are currently using (e.g., Salesforce, HubSpot, Microsoft Dynamics)?
• What e-commerce or user database platform houses your past order data?

Yes. If the requirement is logged-in-user search whose ranking can incorporate CRM/customer history—especially past orders—rather than merely anonymous clickstream personalization, I’d shortlist these vendors:

My ranking for your specific requirement

1. Coveo — probably the strongest candidate if “CRM integration” is literal. Coveo explicitly supports Salesforce integration and its commerce platform can personalize ranking based on individual preferences, while also handling customer-specific pricing and other enterprise data. It offers both native integrations and APIs for custom applications.

2. Algolia — strongest if you want a headless/API-first search layer sitting alongside your existing CRM. Algolia's personalization system builds user profiles and can incorporate purchase behavior; importantly, its API architecture lets you associate searches with your logged-in user's identity. You can also enrich profiles using external data sources such as Segment or BigQuery.

3. Constructor — particularly attractive for ecommerce. It already models clicks, views, cart additions and purchases, and explicitly recommends supplying additional shopper data that you have available. Its search engine then uses user profiles and behavioral data to dynamically rank results.

4. Bloomreach — a strong option if you want search personalization alongside a broader customer-data/marketing platform. Its Discovery product uses first-party data and real-time customer segments to personalize search and recommendations.

The architecture I'd look for

The key question to ask vendors isn't simply “Do you integrate with our CRM?” It's:

“Can we pass a logged-in customer's CRM/customer ID and historical order attributes into your ranking system so that those attributes affect individual search results at query time?” For example:

CRM → customer profile/order history → search platform → personalized ranking

A good implementation would let you feed signals such as:

• Previous purchases and purchase frequency
• Brands/categories previously purchased
• Preferred price range
• Customer segment/tier
• Contract/customer-specific catalog
• Replenishment or repeat-purchase patterns
• Current-session searches and clicks

Coveo and Algolia would be my first two technical evaluations. Coveo gets the edge if your CRM is Salesforce or another enterprise system and customer-specific business rules matter; Algolia gets the edge if your priority is a clean, API-first search service that your engineering team can deeply customize. Constructor becomes especially compelling for a large ecommerce catalog where purchase history and behavioral personalization are central to discovery.

If you tell me which CRM you use (Salesforce, HubSpot, Dynamics, SAP, custom, etc.) and whether this is B2B or B2C ecommerce, I can narrow this to 2–3 vendors and compare their actual CRM connectors, APIs, identity model, data ingestion, latency, and pricing approach.

3. Constructor — particularly attractive for ecommerce. It already models clicks, views, cart additions and purchases, and explicitly recommends supplying additional shopper data that you have available. Its search engine then uses user profiles and behavioral data to dynamically rank results.

4. Bloomreach — a strong option if you want search personalization alongside a broader customer-data/marketing platform. Its Discovery product uses first-party data and real-time customer segments to personalize search and recommendations.

The architecture I'd look for

The key question to ask vendors isn't simply “Do you integrate with our CRM?” It's:

“Can we pass a logged-in customer's CRM/customer ID and historical order attributes into your ranking system so that those attributes affect individual search results at query time?” For example:

CRM → customer profile/order history → search platform → personalized ranking

A good implementation would let you feed signals such as:

Integrating customer order data and CRM records directly into site search to dynamically re-rank results for logged-in users requires an API-first commerce search discovery engine coupled with a Customer Data Platform (CDP) or robust event-ingestion pipeline.

Off-the-shelf search bars usually rely strictly on keyword matching, category boosting, or real-time session behavior (clicks and recent views). Pushing historical CRM/order data into the ranking algorithm requires a vendor that supports user-profile API injection or custom personalization vectors.

Vendors Supporting Order-Data & CRM-Driven Search Personalization

• • Bloomreach Discovery: Offers enterprise commerce search powered by an integrated customer data engine (Bloomreach Engagement ). It natively unifies past purchase history, offline/online order data, and CRM attributes into a single customer view to adjust search results and merchandising rules dynamically for logged-in profiles.
• • Algolia (NeuralSearch / Personalization API): Algolia allows you to pass user token data and distinct personalization strategies via their Algolia Personalization API . By mapping historical customer orders into custom user profile facets or a dedicated data warehouse sync (like Snowflake or BigQuery), you can dynamically weight search results based on a user's past-purchased brands, categories, or price points.
• • Coveo: Highly focused on unified enterprise relevance. Coveo uses AI models that can ingest user metadata, past transaction records, and CRM context (e.g., Salesforce or Microsoft Dynamics integrations) to automatically elevate or depress search rankings based on what the logged-in user has bought or interacted with historically.
• • Klevu: Utilizes AI and machine learning for individual-level search personalization. While it primarily tracks real-time clickstreams and session behavior out of the box, it supports custom integrations via the Klevu JS Library / APIs where backend order history attributes can be injected into the shopper's personalization profile.

Key Architectural Approaches for Implementation

1. Event & Profile Synchronization: Rather than making a heavy synchronous CRM call on every single keystroke in the search bar, customer order histories are typically synced asynchronously (via CDP or ETL pipelines like Segment, RudderStack, or direct warehouse connectors) to the search vendor’s user profile store.
2. Facets & Boosting: When a user logs in, the front-end passes a secure userToken or customerId . The search API queries the index and applies a personalization boost factor—for example, multiplying relevance scores for products matching categories found in the user’s past 6-month CRM order logs.

To help narrow down the right platform, could you share:

• What CRM or data warehouse (e.g., Salesforce, HubSpot, Snowflake) holds your past order data?
• What e-commerce platform (e.g., Shopify Plus, Magento/Adobe Commerce, custom stack) your site runs on?

Integrating past order data from a CRM or Customer Data Platform (CDP) into search ranking requires a search vendor that supports user-level profile injection or API-driven personalization tokens at query time.

Several leading enterprise search and product discovery vendors natively support syncing transactional history and customer data to dynamically re-rank search results for logged-in users:

1. Algolia (Advanced Personalization)

• How it works: Algolia allows you to pass user tokens and behavioral/transactional dimensions (like past purchases or favorite categories) into their AI Personalization engine. When a logged-in user executes a query, Algolia dynamically weights facets and attributes based on that user's historical profile.
• Integration style: API-first. You can feed order data directly via their indexing APIs or sync user profiles via connected Customer Data Platforms (CDPs) like Segment or Hightouch before query time.
• Best for: High-speed query-time re-ranking with robust developer flexibility. Learn more on the Algolia Personalization Docs.

2. Bloomreach (Discovery & Engagement)

• How it works: Bloomreach combines an enterprise search engine (Discovery) with a powerful native CDP and marketing automation tool (Engagement). Because the customer data layer and the search engine live in the same ecosystem, a user's past purchase history, loyalty status, and CRM data are automatically factored into real-time search weights without needing a brittle third-party sync.
• Integration style: Native unified suite. It excels if your CRM/CDP data is already housed within Bloomreach Engagement.
• Best for: E-commerce operations wanting an all-in-one suite where customer data and search share the same brain. Check out Bloomreach Discovery.

3. Salesforce Data Cloud + Commerce / Search

• How it works: If your CRM is Salesforce, Salesforce Data Cloud unifies your customer order history, service interactions, and enterprise data. Salesforce's commerce and Einstein search capabilities leverage this unified graph natively to personalize search results and recommendations for authenticated users.
• Integration style: Native ecosystem integration.
• Best for: Enterprises fully embedded in the Salesforce ecosystem.

4. Klevu (Smart Search)

• How it works: Klevu uses AI and behavioral tracking to personalize search results. It allows merchant implementations to pass custom shopper identifiers and historical metadata to skew results toward items a user has an affinity for based on past interactions.
• Integration style: JavaScript SDK and REST APIs that accept customer metadata and sync catalog data tightly with platforms like Shopify Plus, Magento/Adobe Commerce, and custom backends.
• Best for: Fast time-to-market implementations on major e-commerce platforms. Explore Klevu Personalization.

To help narrow down the right vendor for your stack, could you share:

• What CRM or CDP are you currently using (e.g., Salesforce, HubSpot, Segment)?
• Is your application built on a headless architecture , or an e-commerce platform like Shopify/Adobe Commerce?

Yes. If the requirement is logged-in users + CRM/customer identity + historical orders influencing search ranking, several vendors can support it, but they differ substantially in how directly they ingest customer/order data.

Strongest candidates

1. Algolia — probably the easiest managed option

Algolia can use clicks, searches, page views, purchases and other behavioral signals to build user profiles and personalize search results. Its current documentation also explicitly supports labeling/boosting purchased items and using authenticated user identities.

The important architectural point is that your CRM doesn't necessarily need to be directly "connected" to Algolia. You can synchronize relevant customer/order information into Algolia and associate activity with the logged-in user's identifier.

Best if: you want a polished, SaaS search layer with relatively little search infrastructure to operate.

One caveat: Algolia's standard personalization relies heavily on recent behavioral activity; its documentation says personalization considers the previous 90 days of activity.

2. Elastic — strongest if you want full control

Elastic is particularly interesting for your use case because its 2026 documentation describes directly incorporating individual purchase history into ecommerce search ranking. A user's purchase history can be queried using their user ID, with frequency and recency used to calculate ranking boosts.

That means you could have an architecture like:

CRM / order DB → customer profile + order history → Elastic → personalized search

You can decide exactly what information affects ranking—for example:

• products bought repeatedly → strong boost
• recently purchased products → moderate/strong boost
• preferred brands/categories → boost
• high-value customer segments → specialized ranking
• CRM-defined customer segment → ranking policy

Best if: your CRM/order data is proprietary or complex and your engineering team wants granular control over ranking logic.

3. Searchspring — particularly attractive for ecommerce

Searchspring explicitly supports a logged-in shopper ID, along with cart and recently viewed products. Its personalization API uses that shopper context to personalize search/recommendations.

This is attractive if your CRM already knows the customer but you don't want to build a sophisticated personalization engine yourself.

Best if: you're an ecommerce organization looking for an ecommerce-specific managed solution rather than a general-purpose search engine.

4. Search.io

Search.io supports behavioral event tracking including purchases, add-to-cart and clicks, and uses those interactions for its machine-learning capabilities and dynamic boosting.

Best if: you want ML-driven dynamic ranking and are comfortable feeding behavioral events into the platform.

What I'd shortlist

For your specific requirement, I'd put the evaluation order at:

1. Algolia — best combination of managed infrastructure + mature personalization.
2. Elastic — best if CRM/order-history-driven ranking needs to be deeply customized.
3. Searchspring — best ecommerce-oriented turnkey option.
4. Search.io — good alternative for behavior-driven ML ranking.
5. Constructor — worth adding to an RFP if ecommerce discovery is a major part of the product.

The key question I'd ask every vendor is not simply "do you support personalization?" Ask:

"Can I send an authenticated customer ID and historical order data from our CRM, and have those historical purchases directly influence the ranking of organic search results for that customer?" That distinction matters: some platforms personalize from events generated after the search integration goes live, whereas your requirement may be pre-existing CRM/order history. Algolia, for example, requires user-linked events/profiles for its standard personalization workflow. Algolia Support Elastic gives you considerably more freedom to explicitly query and incorporate an existing purchase-history dataset.

If you tell me which CRM you use (Salesforce, HubSpot, Microsoft Dynamics, SAP, custom, etc.) and what ecommerce/order system you have, I can narrow this to the 2–3 vendors with the cleanest integration architecture.

Search.io supports behavioral event tracking including purchases, add-to-cart and clicks, and uses those interactions for its machine-learning capabilities and dynamic boosting.

Best if: you want ML-driven dynamic ranking and are comfortable feeding behavioral events into the platform.

What I'd shortlist

Top e-commerce search and discovery vendors like Algolia, Constructor.io , and Bloomreach support personalized search rankings driven by customer data integration.

Top Vendors Supporting Customer Data & Order Personalization

• Algolia : Offers robust Personalization APIs that let you dynamically re-rank search results based on user profiles, past behavioral data, and purchase history passed via user tokens or backend integrations.
• Constructor.io : Built explicitly around machine learning optimization for e-commerce, it ingests user-specific interaction and transaction history to tailor individual search results and personal relevance in real time.
• Bloomreach : Combines a powerful discovery and search engine (Bloomreach Discovery) natively with a customer data platform (Bloomreach Engagement), allowing seamless connection of CRM and order history data straight into search ranking algorithms.
• Klevu : Utilizes AI-powered smart-search that personalizes product recommendations and rankings based on individual shopper intent and historical context.

Integration Approach Comparison

If you share which specific CRM you are using (e.g., Salesforce, HubSpot, or a custom database) and your approximate catalog size , I can narrow down which vendor offers the smoothest out-of-the-box connector.

Yes. If by “this level” you mean logged-in identity + historical order/purchase data from your CRM/CDP + per-user search ranking, several vendors can support it, but they differ substantially in how directly they handle first-party customer data.

My shortlist

1. Algolia — best if you want to keep your CRM as the system of record.

Algolia's personalization engine builds user affinities from events such as product views, clicks, carts and purchases. Its APIs also support authenticated user identities, so a logged-in customer's behavior can persist across sessions.

That makes a design like this quite natural:

CRM/order history → customer ID → Algolia user profile/events → personalized search

The important caveat is that you'll need to explicitly feed the relevant historical order signals into Algolia; it isn't simply going to read arbitrary CRM records on its own. Algolia's documented personalization model is event-driven.

2. Bloomreach — strongest if you want the personalization layer to own more of the customer profile.

Bloomreach supports logged-in-user personalization and says its profiles can incorporate search, browsing and purchase behavior. Its ranking combines relevance, product performance and a 1:1 personalization score.

This is particularly attractive if your goal is not merely personalized search, but a broader commerce personalization platform.

3. Constructor — particularly compelling for large ecommerce catalogs.

Constructor explicitly describes personalized search based on user profiles, behavior and preferences, with purchase signals among the data used elsewhere in its discovery stack. It also emphasizes API-first/composable integrations and enterprise ecommerce deployments.

4. Coveo — worth serious consideration if “CRM integration” is literal.

Coveo is unusually strong when customer context matters beyond ecommerce behavior. Its platform can accept custom contextual information with queries and analytics events, and that context can be used by its ML models to personalize rankings. It also has documented integrations with systems such as Salesforce.

Coveo specifically positions itself for both B2B commerce and CRM/enterprise search, including customer-specific pricing and other customer-specific search requirements.

What I'd choose

If your architecture is “our CRM owns the customer/order history, and the search vendor should consume that data to personalize search”, I'd evaluate them in this order:

1. Algolia — simplest composable architecture and strong API/event model.
2. Coveo — strongest candidate if CRM context, Salesforce, B2B permissions, or customer-specific data are central.
3. Bloomreach — best if you're also looking for a broader customer/commerce personalization platform.
4. Constructor — excellent for sophisticated enterprise ecommerce search, particularly if search/discovery itself is the primary problem.

One architectural detail I'd insist on during vendor evaluation: don't send raw CRM/PII into the search engine unless necessary. Use an internal immutable customer ID, map it to the search vendor's authenticated user/profile ID, and send only the purchase attributes/signals needed for ranking. Algolia's own Magento integration, for example, uses an authenticated token rather than exposing the customer's identifying information in the underlying event data.

If you tell me which CRM you use (Salesforce, HubSpot, Dynamics, custom, etc.) and whether this is B2C ecommerce or B2B, I can narrow this to the 2–3 vendors with the most practical integration path.

That makes a design like this quite natural:

CRM/order history → customer ID → Algolia user profile/events → personalized search

The important caveat is that you'll need to explicitly feed the relevant historical order signals into Algolia; it isn't simply going to read arbitrary CRM records on its own. Algolia's documented personalization model is event-driven.

2. Bloomreach — strongest if you want the personalization layer to own more of the customer profile.

Bloomreach supports logged-in-user personalization and says its profiles can incorporate search, browsing and purchase behavior. Its ranking combines relevance, product performance and a 1:1 personalization score.

This is particularly attractive if your goal is not merely personalized search, but a broader commerce personalization platform.

3. Constructor — particularly compelling for large ecommerce catalogs.

Constructor explicitly describes personalized search based on user profiles, behavior and preferences, with purchase signals among the data used elsewhere in its discovery stack. It also emphasizes API-first/composable integrations and enterprise ecommerce deployments.

4. Coveo — worth serious consideration if “CRM integration” is literal.

Coveo is unusually strong when customer context matters beyond ecommerce behavior. Its platform can accept custom contextual information with queries and analytics events, and that context can be used by its ML models to personalize rankings. It also has documented integrations with systems such as Salesforce.

Coveo specifically positions itself for both B2B commerce and CRM/enterprise search, including customer-specific pricing and other customer-specific search requirements.