Customer targeting solutions identify which customers or segments are most likely to respond to an offer, message, or product. They address the need to improve conversion by focusing outreach on relevant audiences.
Use cases
12
Examples
12
Industries
5
Timeline
7 mo
Data updated 1 day ago
Adoption over time
Documented cases per month
By case publish month · completed months only
7 cases documented across 37 months (Jul 23 – Jul 26), peaking at 3 in May 2026.
AI Use Cases Hub
2 earlier cases before Jul 23 not shown
Each column counts every documented case of this type by its publish month, across the full corpus. The in-progress current month is excluded from columns and surfaced separately, and cases published before the charted window are summarized as earlier cases instead of plotted.
3.8Innovativeness3.8/5Advanced3.8/5 - Advanced. This is more advanced than a standard chatbot or search case because it combines no-code CDP design, A2A integration, real-time campaign automation, and Gemini-driven marketing actions, but it is still an applied enterprise system rather than a breakthrough architecture.
beBit TECH built a no-code customer data platform on Google Cloud to unify fragmented customer and marketing data from ecommerce, loyalty, and chat systems.The platform uses BigQuery, Google Kubernetes Engine, and Vertex AI, with Gemini driving audience segmentation and product recommendations through an Agent-to-Agent integration model.It automatically launches and adapts LINE, email, and SMS campaigns in real time so marketers can act on customer behavior without manual IT support.
3Innovativeness3/5Differentiated3/5 - Differentiated. This is a practical retail recommendation-system modernization similar to other recent Google Cloud personalization cases; it is differentiated by careful rollout and business-rule handling, but not advanced enough for a 4 without evidence of a more novel architecture.
Thomann.io replaced a 13-year-old fragmented recommendation system with Vertex AI Recommendations to improve personalization across newsletters, mobile app, and website.The implementation used BigQuery, Bigtable, Google Analytics, Cloud Composer, and partner support from adesso.
3Innovativeness3/5Differentiated3/5 - Differentiated. A practical Google Cloud retail platform using GKE, BigQuery, and Vertex AI for scaling, reporting, personalization, and fraud-related scoring; similar to recent retail AI cases, so it is differentiated but not highly novel.
magicpin is an online location intelligence platform that allows customers to discover retailers in their vicinity and get discounts at them.Its 300,000 retail merchants use the platform to engage with customers or provide personalized offers, and the company uses Google Cloud to scale the platform, improve analytics, and power machine learning-driven personalization.
3Innovativeness3/5Differentiated3/5 - Differentiated. The implementation combines mapping, Street View, and live incline adjustment in connected fitness equipment to create a differentiated workout experience, but it is a focused product integration rather than a novel AI architecture.
ICON Health & Fitness, through its iFit division, built Google Maps into connected treadmills and gym bikes to make workouts more fun, social, and immersive so users would be more likely to stick with regular routines.The experience lets users select real-world routes, see progress on the map, share and revisit routes, and experience Street View imagery while exercising. The equipment also adjusts incline in real time based on route elevation.
3Innovativeness3/5Differentiated3/5 - Differentiated. Real-time connected retail analytics unifying online/offline touchpoints with automated personalized offers indicates useful workflow transformation, but the evidence does not specify advanced AI architectures beyond analytics and personalization.
Resulticks launched an AI-powered solution, SHOP, to transform customer engagement in the retail industry by connecting online and offline consumer touchpoints with real-time actionable data. Shoppers have viewed AI solutions in retail positively, and the modular platform consolidates shopper analytics, optimizing personalized interaction across live channels.
4.2Innovativeness4.2/5Advanced4.2/5 - Advanced. More advanced than a standard search or RAG pattern because it combines generative keyword expansion with embedding-based verification and sensitive-term guardrails in a production ad-targeting pipeline. Compared with recent enterprise Bedrock search cases, the novelty is moderate-to-high but not frontier-level.
Yahoo DSP enhanced search retargeting keyword expansion with generative AI on Amazon Bedrock.The new workflow replaces a Word2Vec plus LSH approach with LLM-based expansion, embedding similarity checks, and sensitive keyword filtering.It launched in production in Q1 2025 and improved keyword expansion rate and addressable audience reach.
2.9Innovativeness2.9/5Differentiated2.9/5 - Differentiated. Compared with recent Google Cloud data-platform cases, this is an established large-scale analytics and experimentation setup rather than a novel AI architecture; the main novelty is operational scale, not technique.
Spotify is a global audio streaming subscription service with hundreds of millions of users and more than 50 million songs and podcasts.The company uses Google Cloud data services to support recommendation, music discovery, personalization, and artist dashboards while managing large-scale data processing and experimentation.
3Innovativeness3/5Differentiated3/5 - Differentiated. Compared with recent Google Cloud retail personalization cases, this is a comparable applied ML personalization deployment with global scale and orchestration, but it uses established prediction and recommendation patterns rather than a novel architecture.
Richemont International SA used Google Cloud and AI/ML capabilities in an integrated Client Platform to improve customer experience across online, offline, and boutique journeys.Machine learning algorithms predicted which prospects or clients needed extra attention and what items to suggest, using engagement data such as email opens, clicks, SMS/MMS, and website visits.The solution was deployed across 11 brands in over 25 countries to support conversion, repurchase, and client loyalty.
3Innovativeness3/5Differentiated3/5 - Differentiated. The article shows a practical, value-driven combination of BigQuery analytics and Vertex AI personalization across multiple RVU brands, but it is an applied modernization rather than a novel AI architecture.
Uswitch, part of RVU, uses Google Cloud to support data-driven customer experiences in a highly competitive price-comparison market spanning energy, personal finance, insurance, and communications.The company migrated from on-premises Hadoop-based data infrastructure to BigQuery and later used Vertex AI as part of its machine-learning and personalization workflow to better understand customer behavior and segment users for product recommendations.By combining BigQuery-based analytics with Vertex AI, Uswitch aimed to make data science products repeatable across RVU brands and to simplify the presentation of deal options for customers.
2.8Innovativeness2.8/5Differentiated2.8/5 - Differentiated. This is a common but effective data-and-ML marketing analytics implementation: unify first-party data, score propensity, and activate targeted campaigns. It is similar to recent approved Google Cloud analytics cases rather than a novel architecture.
NTUC Income, a Singapore insurance cooperative, used Google Analytics 360 and BigQuery to unify customer interaction data and tailor marketing strategies to individual needs.Historical and streaming digital interactions were analyzed in BigQuery to segment customers by propensity to buy and preferred channel, enabling more personalized campaigns and sales activation.
How many customer targeting use cases are documented?
The AI Use Case Hub documents 12 real customer targeting deployments across 5 industries, with 12 detailed company examples you can browse.
Which industries adopt customer targeting the most?
Customer targeting is most common in Retail (58%), Tech & Comms (17%) and Finance (8%).
Which countries lead in customer targeting?
United States leads documented customer targeting deployments, followed by Singapore and Australia.
What technologies are used for customer targeting?
Teams most often build customer targeting with BigQuery, Vertex AI and Google Kubernetes Engine.
What AI capabilities power customer targeting?
Across the documented deployments, the most common capability patterns are Vision (8%) and Agent (8%).
What results do companies report from customer targeting?
Across the 12 deployments reporting outcomes, companies most often cite customer experience & trust (92%), new product / capability (58%) and speed & agility (42%). Where impact is quantified, the strongest evidence is in revenue & growth: a median +23% across 7 reported metrics.
