DFS
Welcome series personalized on zero-party data with AI product recommendation
DFS gained +4.2% conversion and +3.9% revenue on its welcome series by personalizing email content from zero-party data with Bloomreach Engagement and Loomi, a program run with the agency CACI.
Key points
- Welcome series personalized on zero-party data with AI product recommendation.
- Bloomreach Engagement and Loomi, plus an ML algorithm from the agency CACI.
- +4.2% conversion and +3.9% revenue versus control, +866% revenue on the CACI campaign.
- Evidence B, confirmed status.
Objective
Bridge the gap between rare, expensive furniture purchases by engaging new contacts early with content matched to what they are actually looking for.
The deployment
DFS, the UK's leading sofa retailer, engages its new contacts with an automated welcome series on Bloomreach Engagement. The brand first asks the customer what interests them (zero-party data via the what's your thing question), then the flow automatically sends the matching guide, for example a bedroom furniture buying guide if the customer is interested, and invites them into the showroom at the right point in the journey. Loomi provides the personalized product recommendation layer.
Results Proof B
Quantified Bloomreach customer story (welcome series) and a quantified case study from the agency CACI (ML email campaign). Two interested but concordant sources on DFS's AI email program; no third-party press, hence B.
How it works
Documented architectureThe stack in detail
- plateforme Bloomreach Engagement CRM engagement platform that collects the zero-party data and orchestrates the branched flows of the welcome series
- outil Bloomreach Loomi Bloomreach's AI layer that personalizes product recommendation in the emails
- outil Algorithme ML CACI model built by the agency on transactional history, behind the +866% revenue email campaign
- integrateur CACI DFS's data activation and engagement strategy partner
How it runs, concretely
For ops teams-
1Collecting the preference Customer
The customer answers the what's your thing question, which captures their furniture category of interest.
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2Branching the flow AI / Bloomreach platform
Bloomreach Engagement routes to the matching guide (for example bedroom) based on the answer.
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3Product recommendation AI (Loomi)
Loomi personalizes the recommended products in the email.
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4Bridge to the showroom CRM team / platform
The series invites the customer into the store at the right point in the journey for an in-person try.
The zero-party data (what the customer says they are looking for). Without this answer, the flow does not know which guide or category to push and falls back to a generic message.
How your customers perceive this type of use
Sourced studiesLe paradoxe est documente des deux cotes : 71% des consommateurs attendent des interactions personnalisees et 76% sont frustres quand elles manquent (McKinsey, 2021), mais 75% declarent ne pas acheter aupres d'organisations auxquelles ils ne confient pas leurs donnees (Cisco, 2024). La « creepy line » est localisee : messages recus quelques secondes apres une recherche et suivi de localisation sont les pratiques qui mettent le plus mal a l'aise (Periscope by McKinsey, 2019).
Acceptance conditions
- La confiance dans le traitement des donnees precede l'achat : 75% ne achetent pas sans elle (Cisco 2024)
- Un cadre legal protecteur rassure : 59% des consommateurs disent que des lois fortes sur la vie privee les rendent plus a l'aise pour partager des informations dans des applications IA (Cisco 2024)
- La personnalisation elle-meme est attendue quand elle est consentie : environ la moitie des consommateurs (US 55%, UK 52%) disent s'inscrire souvent ou parfois a des services personnalises (Periscope by McKinsey 2019)
Red lines
- Le message declenche quelques secondes apres une recherche ou un achat : deuxieme ou troisieme cause de malaise selon les pays (Periscope by McKinsey 2019)
- Le suivi de localisation percu comme de la surveillance : 40% de malaise en Allemagne et au Royaume-Uni (Periscope by McKinsey 2019)
- Le mesusage des donnees personnelles par l'IA, devenu la premiere inquietude des consommateurs, a 53% et en hausse (Qualtrics 2025)
Sources: McKinsey & Company 2021 · Periscope by McKinsey 2019 · Cisco 2024 · Qualtrics 2025
How to replicate
Inference, not sourcedData prerequisites
- a mechanism to collect zero-party data (preference questionnaire)
- a catalog structured by category
- opt-in email identifiers
Org prerequisites
- a CRM team able to build branched flows
- coordination between email and the physical network for considered purchases
Possible stack
- Bloomreach Engagement + Loomi
- any CRM platform with conditional flows and AI product recommendation
The plan, step by step
- Step 1Add the preference question at signup (which product category the contact is interested in).Deliverable: Zero-party data collection active on new signups
- Step 2Build the branched welcome flow that routes content (guides, categories) based on the answer.Deliverable: Conditional flow validated in test
- Step 3Wire the AI product recommendation into the series emails.Deliverable: Personalized emails in production
- Step 4Set a control group with no personalization to measure the real gap.Deliverable: Active A/B test with documented split
- Step 5Read conversion and revenue against the control, then generalize the series and add the showroom invitation at the right moment.Deliverable: Quantified readout and generalized welcome series
First step: Add a preference question at signup, then wire a welcome flow that routes content based on the answer.
The same pattern, proven in other industries
Function of Beauty
personalized product formulation by algorithm from a quiz
Henkel
genAI content supply chain for one-to-one personalization at scale
Hims & Hers
AI-driven personalized treatment routing on intake data (MedMatch)
Sources
- S1 DFS Uses Bloomreach Engagement to Increase Revenue Interested party archive pending
- S2 DFS Customer Success Story (CACI) Secondary archive pending
An error, newer info, a source?
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