Product Lead, Agentic Personalization

The Customer Problem When you open Spotify, the homepage is different for every user. When you browse Nike's site, products are ranked by what Nike thinks you'll buy. When you log into Chase, offers are tailored to your spending. This is web and mobile personalization: deciding in real time what each customer sees inside your product. It directly moves conversion, retention, and revenue at the point where customers are already engaged.

Almost nobody does it well outside of companies that build it from scratch. A single experiment (testing whether high-spend users convert better seeing a loyalty upsell on the account page) requires an analyst to build the segment, a PM to write targeting rules, a designer to create variants, an engineer to instrument the test, and someone to monitor results. Most teams run 5–10 per quarter and end up with a handful of static rules covering a fraction of their users.

The tools that exist (Optimizely, Dynamic Yield, Adobe Target) handle infrastructure but don't solve the coordination problem. Every experiment, audience, and piece of content still requires manual configuration. They also force companies to duplicate customer data into a proprietary store, adding months of implementation and permanent vendor lock-in.

Why Agents are the Solution The shift from "AI assists a step" to "AI owns a workflow" is already happening in software engineering. Personalization is ready for the same shift.

Today: A growth team testing whether high-intent visitors convert better with a comparison page spends 2–4 weeks coordinating across tools and people. They run maybe 8 experiments that quarter.

With agents: The growth lead describes the goal. An agent identifies the audience from existing customer data, generates variants informed by past performance, configures the experiment with holdout groups, launches it, monitors significance, and reallocates traffic. The human reviews before launch. Calendar time: hours. The same team runs 50+ experiments per quarter because the coordination cost drops to nearly zero.

Agents handle the full chain as one workflow: audience, content, experiment, optimization. Instead of a bunch of manual steps, agents rethink this as a continuously improving closed loop.

Why Hightouch, Why Now More than 1,000 enterprises—like Domino’s, Chime, Spotify, Ramp, Whoop, Grammarly, use Hightouch to run their marketing. Earlier this year we launched the Agentic Marketing Platform with two pillars:

Agentic Advertising Studio. Agents analyze performance, competitor creative, and brand guidelines, then assemble ad concepts across Meta, Google, TikTok, and LinkedIn. Agentic Lifecycle Studio. Agents identify lifecycle opportunities, draft audiences, assemble on-brand email and SMS campaigns, and orchestrate them through Braze, Iterable, Salesforce, and Adobe. Advertising covers paid media. Lifecycle covers email, SMS, and push. Agentic Personalization is the third pillar: what customers experience inside the website and mobile app. Different buyer (product and growth teams), different workflow (real-time experimentation), different technical surface (browser SDKs and edge delivery). Same agent infrastructure and data foundation.