> ## Documentation Index
> Fetch the complete documentation index at: https://docs.scentxp.com/llms.txt
> Use this file to discover all available pages before exploring further.

# CRM & Personalization

## Scent Intelligence for your CRM

Traditional CRMs react to what people buy. WikiParfum helps you understand **why they buy** — turning olfactive preference data into hyper-personalized communications, deeper emotional connections, and better customer retention.

WikiParfum links all preferences and interactions to a stable user identifier. This means the olfactive intelligence built during a website session doesn't end when the user leaves — it can be reused across channels, sessions, and over time.

### The business impact

* **Higher open rates and click-through** — messages that resonate with personal scent preferences outperform generic promotions
* **Better retention** — identify patterns to reconnect with customers before they churn
* **Smarter upsell and cross-sell** — suggest complementary products based on each customer's olfactive profile
* **Stronger brand perception** — customers feel understood and valued

### What you can build

* **CRM enrichment** — append olfactive preferences (favorite families, ingredient affinities) to user profiles
* **Personalized email campaigns** — recommend fragrances in newsletters based on each user's taste profile
* **Lifecycle journeys** — trigger re-engagement with new launches that match a user's olfactive preferences
* **Taste-based segmentation** — group users by olfactive profile for targeted marketing
* **Cross-channel consistency** — same recommendations on web, mobile, in-store, and email

## How it works

The foundation is the session identity established via `initSession` (see [Sessions & User Identity](/wikiparfum-api/getting-started/sessions)).

```
1. User visits your site → initSession with userId
2. User explores, views perfumes, interacts with ingredients
3. WikiParfum captures these as olfactive preference signals
4. On next visit (or in email/CRM) → same userId retrieves evolved preferences
```

Because preferences are linked to a stable identifier, taste-based personalization remains relevant even as product assortments evolve.

## CRM integration

WikiParfum enhances your existing CRM — no replacement, no heavy setup, no data migration. The connection is anonymous: WikiParfum never sees or stores your customer data. All personal information stays within your system.

Compatible with: Salesforce, Adobe Campaign, Bloomreach, Emarsys, Insider, Klaviyo, HubSpot, and more.

## User-scoped recommendations

Pass `userId` to any recommendation query to get results influenced by the user's accumulated interaction history.

<CodeGroup>
  ```graphql Query theme={null}
  query PersonalizedRecommendations {
    recommendedPerfumes(
      userId: "4ad05833-01e9-412f-b6fd-a02c2ce1f7cd"
      search: { lang: "EN", perfumes: "424" }
    ) {
      id
      name
      brand {
        name
      }
      family {
        name
      }
    }
  }
  ```

  ```json Response theme={null}
  {
    "data": {
      "recommendedPerfumes": [
        {
          "id": "734",
          "name": "HYPNOTIC POISON",
          "brand": {
            "name": "Dior"
          },
          "family": {
            "name": "AMBERY (ORIENTAL)"
          }
        },
        {
          "id": "36141",
          "name": "GUCCI GUILTY ABSOLU DE PARFUM POUR FEMME",
          "brand": {
            "name": "Gucci"
          },
          "family": {
            "name": "AMBERY (ORIENTAL)"
          }
        }
      ]
    }
  }
  ```
</CodeGroup>

Over time, recommendations become increasingly aligned with each user's evolving taste profile — without requiring explicit questionnaires or profile setup.

## CRM integration patterns

### Newsletter personalization

Use recommendation data to populate email templates with fragrances tailored to each recipient:

1. Retrieve the user's `userId` from your CRM
2. Call `recommendedPerfumes` with recent interactions or catalog additions
3. Include the recommended perfumes (with images and family data) in the email template

### New launch targeting

When a new fragrance launches, identify which users are most likely to enjoy it:

1. Get the new perfume's olfactive profile (family, ingredients)
2. Match against users whose preference history aligns with that profile
3. Target those users with launch communications

### Segmentation by olfactive profile

Group users by their dominant family affinities or ingredient preferences for targeted campaigns — for example, sending woody fragrance promotions to users who consistently engage with woody perfumes.

## Cross-session persistence

The same `userId` works across:

* **Multiple website visits** — preferences accumulate over time
* **Different devices** — as long as the user is identified
* **Different touchpoints** — web, mobile app, in-store kiosk, email

This enables a consistent understanding of taste that improves with every interaction, regardless of where it happens.