Brands have never had more customer data, and have never had less to show for it. The signals are there. The unified profile is there. What's missing is the ability to act on both at once, in the moment, with the full context of who the customer is.
That distance, between what a brand knows about a customer and what it can actually do with that knowledge in the moment, is the Customer Decision Gap. The result is millions lost in missed revenue, wasted spend, and eroded trust.
We're excited to announce Amperity's spring launch: a set of capabilities that moves the platform from surfacing customer data to acting on it, in the moments that actually produce revenue.
Because the Customer Decision Gap shows up everywhere. The churn signal that sat in the data for two weeks. The cart abandonment that got a generic email the next morning. The customer who landed on your site and saw a default homepage. Every one of those is a revenue event that didn't happen. The spring releases are the next layer of a system built to close that gap.
Building on what's already in market
We already launched the Customer Data Assistant and real-time profile capabilities. Those releases addressed one dimension of the Customer Decision Gap: the time it takes to go from a question to an activated journey. Marketing and data teams got a natural language interface to customer data and the ability to build and launch journeys without waiting on a data queue.
But the gap has more than one dimension. Knowing how to build a journey doesn't tell you which journey to build. Acting on a signal doesn't help if the signal arrives hours after the moment passed. None of it holds if the AI tools executing downstream are working from five different versions of the customer.
The spring releases extend that foundation in three directions: surfacing the highest-value opportunities automatically, acting on customer signals while the window is still open, and giving every AI tool in the stack the same governed view of the customer. We've organized the launch around three outcomes: more insights, more moments captured, and more control.
More insights: from question to action in minutes
The first dimension of the Customer Decision Gap is the analysis backlog. Revenue risks stay buried because nobody has bandwidth to surface them. Predictive models take months to get to production. By the time the insight arrives, the opportunity has passed and the team is already chasing the next one.
Recommended Actions (Public Preview)
Recommended Actions continuously analyzes Amperity's unified customer data and surfaces the highest-value opportunities in plain English. Which customers are trending toward churn. Which segments are ready for a revenue play. Which journeys to launch now. Each recommendation comes with a suggested action attached, and connects directly to a journey that can go live immediately. Weeks of manual analysis compress into minutes.
This isn't a generic AI layer on top of raw data. Recommended Actions reasons over Amperity's AI-ready data foundation: unified profiles, resolved identity, governed context. The output is trustworthy because the foundation underneath it is. The recommendations a brand sees are grounded in its own customer data, not generic patterns scraped from a model trained on someone else's.
Self-Serve Predictive Models (GA)
Self-Serve Predictive Models gives data science teams back the months they used to spend on model deployment infrastructure. Build, train, and deploy directly in Amperity, in days rather than quarters, with validation and version history built in. Retail teams get predicted customer lifetime value (pCLV) and Product Affinity out of the box. Non-retail brands can access pCLV through the pilot now open.
Journey Goals (GA)
Journey Goals lets teams measure what's actually working, not just what converted. Track micro-moments that matter (loyalty signups, profile enrichment, engagement milestones), not just purchase. A/B split winners surface clearly, and audiences built from customers who met a goal activate directly for future campaigns. Spend follows what's working.
More moments captured: act before the window closes
The second dimension of the Customer Decision Gap is timing. Brands don't have a speed problem; they have a relevance problem. Reacting to a click isn't the same as responding to a customer. Closing this dimension means acting on signals while the window is still open, with the full context of who the customer is and what they've done before.
Cart Abandonment Journey (Preview)
Cart Abandonment Journey enables coordinated, multi-channel recovery triggered by live customer signals, not scheduled batch runs. The moment a cart is abandoned, the journey fires across channels while the customer is still reachable. No overnight batch. No generic follow-up. A coordinated response grounded in the full customer context Amperity holds.
Site Personalization (Preview)
When that same customer lands back on site, Site Personalization delivers an experience based on their complete customer profile from the moment they arrive. Not a default homepage. Not a segment of one inferred from last-click behavior. The full picture: purchase history, loyalty status, predicted value, product affinity. Together with Cart Abandonment Journey, these capabilities represent the execution layer of real-time marketing: the moment the gap between insight and experience finally closes.
More control: every AI tool working from the same governed foundation
The third dimension of the Customer Decision Gap is fragmentation. A brand can have perfect insights and fast execution, and still lose ground if the AI tools making decisions downstream are each working from a different, ungoverned slice of the customer. Each tool has its own pipeline. When the profile updates, half the stack is already behind. Security and legal become blockers. Every new AI initiative starts with a multi-week integration project.
Amperity MCP Server (Public Preview)
The Amperity Model Context Protocol (MCP) Server is the tool layer that connects any AI application to a governed view of the customer through one gateway. Engineers register once. Every AI tool, including Microsoft Copilot, Braze AI, Salesforce AgentForce, and custom large language models (LLMs), draws from the same accurate, governed customer context. No custom pipelines. No data duplication. Compliance is built into the architecture, not managed around it.
What was previously a month-long integration project per tool becomes a single connection. Because the data stays governed, security and legal stop being blockers and start being partners.
Most approaches to this problem add another layer: another integration, another governance process, another place where data can drift. The Amperity MCP Server takes the opposite approach. Every AI tool draws from the same governed foundation, with the right identity context applied to the right use case. Enforced at the architecture level, not managed around it.
Australian AWS Cloud Presence (GA)
For brands operating in Australia and regulated markets in the region, Amperity now runs on Amazon Web Services (AWS) infrastructure in Sydney (primary) and Melbourne (backup). Australian data stays in Australia. Local regulated industries can use the full Amperity platform and meet compliance requirements, with cross-continent data movement eliminated entirely.
Additional spring announcements
Several platform updates shipping alongside the launch are worth noting.
A single entry point into the platform
Amperity Home (GA) surfaces data health, platform usage, key metrics, and recent work the moment users log in. The Customer Data Agent is accessible directly from Home, so teams can move from question to action without switching context.
Self-serve answers on platform usage and cost
Amp Insights (Public Preview) gives leaders a natural language interface into how Amperity is being used and what it's producing. Ask which campaigns are consuming the most compute, or which workloads are generating the most customer value, and get immediate, auditable answers. No support ticket required.
A new revenue stream from first-party data
Audience Monetization (GA) lets brands build high-value audiences and activate them directly to demand-side platform (DSP) marketplaces like The Trade Desk, with automated refresh and compliance built in.
Shared taxonomy across audience programs
Custom Labels (GA) makes it possible to find every audience, query, campaign, and journey tied to a program in a single search.
The platform that closes the Customer Decision Gap
The Customer Decision Gap isn't a data problem. Brands have the data. It's an architecture problem: systems built to store and analyze, not to decide and act. And it compounds under AI. When the foundation is fragmented, AI doesn't fix the gap. It widens it, at machine speed.
The competitive advantage in marketing is no longer access to data. Every brand at scale has that. The advantage is the ability to close the gap in real time, with context that reflects everything the brand knows, across every AI tool in the stack. That's what this launch is built to deliver.
Ready to see the spring capabilities in action? Request a personalized demo.