Tapestry
generation and optimization of personalized marketing language
Tapestry, parent company of Coach and Kate Spade, uses Persado to generate and optimize its e-commerce copy, with a 3 to 5% increase in online revenue on personalized web content.
Objective
Grow e-commerce revenue by replacing generic site copy with AI-generated and optimized language, emotional and conversational, close to what an in-store advisor would say.
The deployment
Tapestry, parent company of Coach and Kate Spade, uses Persado to generate and optimize the marketing language of its e-commerce. The platform draws on a dataset from transaction analytics and the interactions of 150 million American consumers to produce emotional and conversational copy. The generated content is deployed on checkout pages, landing pages, site banners and buttons, email campaigns, and post-purchase thank-you pages. Each variant is tested to keep the best-performing wording. Tapestry reports an increase in e-commerce revenue and a decrease in cart abandonment thanks to the personalized content.
Results Proof C
Two trade press titles (Consumer Goods Technology, Chain Store Age) naming Tapestry and the 3 to 5% e-commerce revenue figure, with a named program manager. Conversion and abandonment figures reported qualitatively.
How it works
Documented architectureThe stack in detail
- plateforme Persado genAI platform for generating and optimizing marketing language: emotional and conversational variants tested location by location.
- llm Modele de langage Persado Proprietary model trained on transaction analytics and the interactions of 150 million US consumers.
- infra Site e-commerce et ESP de Tapestry Connected locations: checkout, landing pages, banners and buttons, emails, post-purchase thank-you pages.
How it runs, concretely
For ops teams-
1Choice of location marketing / e-commerce
The team targets a high-stakes location (checkout, landing, banner, email).
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2Variant generation AI
Persado generates several emotional and conversational wordings from its model.
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3Test and selection AI / marketing
The variants are tested; the best-performing wording is kept.
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4Deployment and measurement e-commerce
The winning content goes live, revenue and abandonment impact is tracked.
The measured performance of each variant (click, conversion, revenue). Without enough traffic volume to decide the tests, the selection of the best wording loses its reliability.
How your customers perceive this type of use
Sourced studiesUn ecart net separe les annonceurs des consommateurs : 77% des annonceurs voient l'IA positivement contre 38% des consommateurs (Yahoo/Publicis, 2024). Les mesures implicites confirment le rejet declare : en EEG, les pubs generees par IA produisent une activation memorielle plus faible que les pubs traditionnelles et sont decrites comme agacantes, ennuyeuses et confuses (NIQ, 2024). La disclosure a un effet ambivalent : elle augmente fortement la confiance quand elle est remarquee (Yahoo/Publicis), mais 27% des jeunes consommateurs disent faire moins confiance a une entreprise dont la pub est creee par IA (IAB, 2024).
Acceptance conditions
- Une disclosure visible : quand la mention IA est remarquee, la confiance globale envers l'entreprise augmente de 96% (Yahoo/Publicis 2024)
- Une qualite visuelle suffisante : les visuels IA de basse qualite augmentent l'effort cognitif et distraient du message (NIQ 2024)
Red lines
- Le contenu IA non declare puis identifie : 72% des consommateurs disent que l'IA rend l'authenticite difficile a etablir (Yahoo/Publicis 2024) et les marques utilisant des pubs IA sont plus souvent jugees inauthentiques ou non ethiques par les consommateurs que par les dirigeants (IAB 2024)
- Les mannequins et personnes generes par IA : 46% des consommateurs n'en veulent pas dans la publicite, l'inquietude premiere etant les standards de beaute irrealistes (Attest 2025)
Sources: Yahoo / Publicis Media (terrain Ebco) 2024 · IAB (avec Attest) 2024 · NIQ (NielsenIQ) 2024 · Attest 2025
How to replicate
Inference, not sourcedData prerequisites
- enough e-commerce traffic to decide the tests
- access to the copy locations of the site and emails
- performance history per location
Org prerequisites
- marketing/e-commerce team driving the tests
- validation process for the generated content
- integration of the platform with the CMS and the ESP
Possible stack
- Persado or equivalent NLG platform
- A/B testing tool
- e-commerce CMS and ESP
The plan, step by step
- Step 1Choose one or two high-stakes, high-traffic locations (checkout, email) and confirm that the volume allows the tests to be decided.Deliverable: List of prioritized locations and test plan.
- Step 2Connect the platform to the CMS and the ESP, frame the process for the brand to validate the tone.Deliverable: Working integration and validation guidelines for the generated content.
- Step 3Generate the variants and test them against the existing copy, location by location.Deliverable: First tests concluded, winning wordings identified.
- Step 4Deploy the winners, read the revenue and cart abandonment impact, decide on extension to other locations.Deliverable: Quantified review per location and extension roadmap.
First step: Connect a language generator to a high-traffic location (checkout or email) and compare to the existing copy.
The same pattern, proven in other industries
Leroy Merlin (Adeo)
genAI enrichment of product attributes and pages
eBay
GenAI generation of a product listing from a photo
Intuit
done-for-you genAI assistant integrated into the product to reduce friction and convert
Sources
- S1 Tapestry and Marks and Spencer Power E-Commerce Personalization With Generative AI Established press archive pending
- S2 Tapestry personalizes checkout content with generative AI Established press archive pending
An error, newer info, a source?
This page lives on its accuracy. If a figure has moved, if the deployment has changed, or if you have a higher-quality source, tell us. Every sourced correction is verified before publication.