Target
AI recommendations plugged into the list a customer is building, inside a seasonal window
For the 2026 back-to-school season, Target plugged AI recommendations into its teacher and back-to-college wish lists, on Target.com and in its app: wish list creations rose more than 50% year over year, items added to those lists more than doubled, and conversion on the key back-to-school pages rose nearly 20%, figures given by Target on its August 19, 2026 earnings call.
Key points
- Target plugged AI recommendations into its teacher and back-to-college wish lists for the 2026 back-to-school season.
- Rolled out on Target.com and in the app, with personalized content on the home screen.
- Wish list creations up more than 50% year over year, items added more than doubled.
- Conversion on the key back-to-school pages up nearly 20%, figures given on an earnings call.
Objective
Make list building the entry point of the back-to-school season: help a parent, a teacher or a student complete a list without forgetting an item, then turn that list into an order on the seasonal pages.
The deployment
For the 2026 back-to-school season, Target added AI recommendations to two lists its customers were already building by hand: a teacher's wish list and the gear list of a student leaving for college. On Target.com and in the app, the engine suggests items to add to the list in progress, with the goal of surfacing forgotten essentials. The feature was announced on June 24, 2026, alongside the opening of the back-to-school and back-to-college season, at the same time as personalized content on the app home screen. On August 19, 2026, on the second-quarter earnings call, Chief Merchandising Officer Cara Sylvester put numbers on the customer response: wish list creations up more than 50% year over year, items added to those lists more than doubled, conversion on the key back-to-school pages up nearly 20%. Target states these three figures right after citing the AI wish lists and the personalized home screen content together, without isolating the contribution of each piece. One point of context that should not be mistaken for a result of the feature: CEO Michael Fiddelke said on the same call that Target's digital traffic coming from outside AI platforms (OpenAI, Google Gemini and others, under agentic commerce partnerships) is growing more than 3.5 times faster than the industry year over year. That is an inbound market flow, measured at the site level, not the performance of the lists.
Results Proof A
The three figures come from the official transcript of Target's second-quarter 2026 earnings call (LSEG StreetEvents, published by Target on corporate.target.com, call held on August 19, 2026), where the Chief Merchandising Officer states them herself. They are repeated in the Q2 2026 Earnings Highlights document Target published the same day, and the feature is described in the official June 24, 2026 press release that opens the season. The business press (PYMNTS) confirmed the more than 50% rise in list creations on the day of the call. Earnings communication, primary source, several concurring sources.
How it works
Inferred typical approachThe internal detail is not public. Here is a proven approach that leads to the same result, to adapt to your stack.
The stack in detail
- plateforme Recommandations IA de wish list (in-house Target) Item recommendations plugged into the teacher and back-to-college wish lists, on Target.com and in the app. Target discloses neither the model nor the vendor.
- outil Wish lists et registres Target The persistent list object attached to the account, already in place, that the recommendation engine plugs into.
How it runs, concretely
For ops teams-
1Opening the list Customer
The customer creates a teacher wish list or a back-to-college list on Target.com or in the app.
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2Item suggestions AI
The engine proposes items to add, to surface forgotten essentials and seasonal ideas.
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3Personalizing the app entry point Platform
The app home screen serves personalized content during the back-to-school window.
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4From list to order Platform and merchandising
The key back-to-school and back-to-college pages carry the conversion of the lists that have been built.
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5Measurement Merchandising and data
Target tracks three counters: list creations, items added per list, conversion on the season's key pages.
The list in progress and the account it is attached to. If the list is empty and the account has no history, the recommendation has little left to work with and falls back on standard seasonal lists.
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
- customer account with purchase and browsing history
- persistent list object attached to the account (wish list, registry)
- product catalog finely attributed on season and grade level
- conversion measurement that can be isolated at the seasonal page level
Org prerequisites
- a merchandising team that owns the season, its assortment and its pages
- ability to ship the feature before the seasonal window opens, not during it
- prior agreement on which counters to track and on what may be attributed to them
Possible stack
- product recommendation engine, in-house or a personalization platform
- the site's list and registry engine
- personalized content tool on the app home screen
The plan, step by step
- Step 1Identify the existing list moments in the journey (back-to-school, holidays, baby, moving) and measure how many lists are created and abandoned empty.Deliverable: Map of list moments with a volume and an abandonment rate for each.
- Step 2Pick one season and enrich the product catalog with the attributes the recommendation needs to complete a list, not just to propose a similar product.Deliverable: Season assortment attributed and usable by a completion engine.
- Step 3Plug a recommendation engine into the list in progress, with the intent of completing what is missing, and test it on part of the traffic against a control group.Deliverable: Suggestions in production on a subset, read against a control.
- Step 4Instrument the three counters (list creations, items added, conversion on the season's pages) before the window opens, and separate the contribution of the list recommendation from that of the personalized content if both ship together.Deliverable: Read per component, decision on whether to run it again next season.
First step: Spot the list your customers already build by hand during a strong season, then plug the recommendation into that list object rather than into the product page.
The same pattern, proven in other industries
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Henkel
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Sources
- S1 LSEG StreetEvents Edited Transcript TGT.N - Q2 2026 Target Corp Earnings Call (transcription publiee par Target) Primary archive pending
- S2 Target (TGT) Q2 2026 Earnings Call Transcript Established press archive pending
- S3 Target Q2 2026 Earnings Highlights Primary archive pending
- S4 Target Kicks Off Back-to-School and Back-to-College with Style at the Center, Newness and More Partnerships, All at Incredible Value Primary archive pending
- S5 Target Touts Jump in Traffic From AI Platforms as Sales Climb Established press archive pending
An error, newer info, a source?
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