For two decades, retailers poured billions into personalization engines and homepages. The result? Bloated tech stacks, falling conversions, and an $8 billion leak after the click.
The Next Wave Of E-Commerce Innovation
The next wave of e-commerce won’t be won at the storefront — it will be decided in the post-click moment. This shift won’t be driven by keywords or static landing pages, but by AI agents: autonomous, context-aware systems that adapt in real time to a shopper’s intent. The race to own this new terrain is heating up. Spangle is betting on the application layer—agents embedded directly into shopping experiences—while former Twitter CEO Parag Agrawal’s startup Parallel is building the infrastructure they’ll rely on. Together, they’re shaping a fast-forming stack that could define how the next generation of AI tools discover, decide, and transact online.
The stakes are enormous. Spangle’s internal analysis pegs the value of fixing the post-click gap at $8 billion—a figure that reframes this not as incremental optimization, but as the single biggest opportunity in digital retail.
The Post-Click Problem
For all the hype about innovation, most e-commerce sites are barely holding it together. They’re Frankenstein platforms with 100+ SaaS tools, pop-ups and plugins. Each promises a sliver of personalization. Collectively, they slow pages, break the shopping journey and kill conversion.
Meanwhile, marketing ad tech has become a precision weapon. Meta’s ASC and Google’s PMax can target the exact shopper at the exact moment. Instagram’s algorithm can seed an obsession in seconds. But when you click, the magic collapses. You land on a static, tone-deaf page that ignores the ad you just saw.
That break—the moment after the click—is where billions are lost. Around 40% of e-commerce discovery traffic now comes from paid channels, and the cost per visit has jumped 9% year over year. Bounce rates are climbing, page views are dropping and conversion rates are down 11%.
Market Snapshot: The Post-Click Problem In Numbers
- Marketing spend — 15–20% of sales (industry benchmarks)
- Digital ad spend — 50–65% of e-commerce marketing budgets
- Paid traffic dependence — ~40% of e-commerce discovery from paid channels (CSQ Benchmark Report)
- Cost per visit — up 9% year-over-year (CSQ Benchmark Report)
- CAC — up 60% in five years (McKinsey)
- Bounce rates — rising; page views down, conversion rates 11% lower (CSQ Benchmark Report)
- Market opportunity — $8 billion, Spangle’s internal estimate for fixing the post-click gap
👉 The economics are clear: closing the post-click leak isn’t just about higher conversions — it’s about re-architecting retail AI infrastructure and unlocking the next era of post-click commerce.
From Personalization Failed To Retail AI Infrastructure
Retailers have long sold the promise of personalization—but what consumers got was personalization theater: static grids, irrelevant recommendations, and intrusive pop-ups.
The underlying problem was data. Most engines relied on sparse, backward-looking purchase histories. No single retailer had enough behavioral data to predict real intent, so the outputs looked generic, brittle, and outdated.
Personalization failed. The homepage doesn’t matter. TikTok is eating Meta’s lunch. And the future is post-click.
TikTok proved personalization is not about past purchase history but real-time signals. Its feed adapts in-session, learning from micro-behaviors minute by minute. Retailers relying on static, backward-looking engines can’t compete. Shoppers now expect that same level of real-time adaptation everywhere. TikTok’s approach is a direct challenge to Meta and Pinterest, whose ad models still depend on sending shoppers off-platform. Unless they solve the post-click gap, their ad products risk looking expensive and inefficient.
From Patchwork To Platform
Spangle’s bet is blunt: the next era of e-commerce will be won with infrastructure, not widgets. Its proprietary ProductGPT acts as a unified brain, interpreting ad creative, decoding styling cues, reading copy and responding to micro-signals in browsing behavior in real time.
Click on a dress with an asymmetrical hem? Your feed shifts toward similar styles. Click through an ad for “daytime wedding in NYC”? The site curates the experience around that occasion. And because Spangle runs natively on the brand’s domain—not as an external landing page—it preserves first-party data, maintains brand storytelling and closes the loop between marketing and merchandising.
📊 Spangle’s Results At A Glance
- 51% lift in conversion rates
- 46% increase in engagement
- 18% rise in average order value (AOV)
- 2x improvement in Return on Ad Spend (ROAS)
👉 Controlled A/B tests with brands like Revolve and SPARC Group prove Spangle’s model can turn expensive traffic into profitable growth. See full case studies here.
Most expensive, high-intent clicks die on static landing pages. Spangle doubles conversion by replacing them with adaptive, in-session experiences.
Starts with Ad Click (100%) → static landing pages → 60–90% bounce.
Ends with Spangle AI adaptive experience → 2× lift in in ROAS.
The chart makes clear what the numbers prove: Spangle closes the leak that costs retailers billions.
The Post-Click Battleground: Retail’s Agentic AI Arms Race
Tomorrow’s e-commerce winners won’t be judged by their storefronts. They’ll be decided in the split second after the click — where expensive, high-intent traffic is either captured or lost. That moment is becoming the new arms race, and agentic AI is the weapon retailers need to win it.
On the front end, Spangle transforms static shopping into living, in-session journeys that adapt in real time. On the back end, it keeps ad creative in sync with live inventory, spins up campaign-specific experiences without touching the core site roadmap, and feeds every interaction back into marketing systems to tighten the CAC-ROAS equation.
“The future of commerce will be intelligent, contextual, and agentic — and it demands a new infrastructure. We’re building it at Spangle,” says CEO Maju Kuruvilla.
👉 This closed loop — marketing intelligence feeding merchandising intelligence in real time — is what turns agentic AI from a buzzword into a battleground. Retailers who master it will squeeze more profit out of every click; those who don’t will keep leaking revenue to the bounce button.
Why Fashion Gets Hit First
Fashion is chaos—thousands of SKUs, constant turnover and a trend-sensitive consumer base. It’s the perfect stress test for post-click AI.
That’s why retailers like Revolve and Forever 21 tested Spangle first. For Revolve, the challenge was bridging fast-moving ad campaigns with equally dynamic onsite experiences. For Forever 21, it was aligning viral social ads with live inventory that changed daily. Both saw Spangle’s adaptive engine turn expensive traffic into measurable lifts in conversion, revenue per visit, and ROAS.
- Revolve: 60% lift in ROAS, 50% more revenue per visit, 30% boost in conversion by replacing static landing pages with Spangle-powered adaptive experiences.
- Forever 21: 66% jump in conversion, 18% rise in AOV, 21% more SKUs added to cart through real-time alignment of ads and inventory.
“As a data-driven, innovation-focused retailer, we’re always looking for ways to elevate the customer journey and maximize conversion. Spangle allowed us to extend the latest AI tech to our ad landing experiences without slowing down our internal roadmap. The results speak for themselves, all while staying true to the dynamic, trend-forward spirit that defines REVOLVE, ” Ryan Pabelona, Vice President of Performance Marketing at Revolve.
👉 If Spangle can tame fashion’s volatility, it can adapt to any inspiration-driven vertical—from beauty to home décor.
Spangle isn’t the only company shaping this new terrain. While it focuses on the post-click layer, others are racing to build the infrastructure these agents will depend on.
Parallel: Parag Agrawal’s Bet On AI Infrastructure
Former Twitter CEO Parag Agrawal’s startup, Parallel AI, is positioning itself as the research engine for AI agents in retail and beyond.
Parallel’s thesis: AI agents will soon outnumber human users online. Individuals may deploy dozens to act on their behalf, each capable of research, analysis and decision-making without human micromanagement.
Its first product, Deep Research, is an API that lets agents pull from multiple high-quality web sources and return structured, citation-backed analyses with confidence scores. The aim: eliminate hallucinations and stale data, giving agents a fresh, verifiable knowledge base for high-stakes workflows like commerce and finance.
Agrawal has raised $30 million from Khosla Ventures and others to scale a 25-person Palo Alto team. For platforms like Spangle, Parallel could become the invisible plumbing—supercharging commerce AI agents with trustworthy retrieval of product, trend and competitive data.
The AI Agent Stack
- Infrastructure Layer: Parallel (research and verification engine), LangChain and LlamaIndex (agent orchestration), AWS/Azure/GCP (compute and hosting).
- Application Layer: Spangle (post-click optimization), Cimulate (synthetic consumer simulations), Shopify and Amazon (in-house agentic layers).
- Competitive Frontier: As Forbes noted, the battle will be won by whoever controls the orchestration layer between discovery, decision and transaction.
The Broader Agentic AI Movement
Several startups are carving out niches in the agentic AI stack:
- Daydream:a conversational shopping company focused on top-of-funnel discovery.
- Cimulate: a predictive modeling company building simulations of consumer behavior.
- Fermat Commerce: a company that spins up campaign-specific stores outside retailer ecosystems.
- Unbounce: a legacy landing page company.
- Qeen.AI: a conversational assistant company playing at the feature layer, not the infrastructure layer.
Spangle’s lane is uncontested: the “check-in” moment when expensive, high-intent traffic hits a brand’s site. All of these players orbit one of two fronts — inspiration at the top of the funnel, or conversion after the click.
E-Commerce’s Two AI Battlegrounds
👉 Both matter. But the post-click side is wide open—and it’s where most revenue is leaking today.
The Team Behind Spangle AI
Spangle’s founding team isn’t new to retail’s most challenging problems. CEO Maju Kuruvilla scaled Amazon’s AI-powered logistics and later ran checkout startup Bolt. CTO Fei Wang helped build Saks Off Fifth’s data and ML stack after being on Alexa’s founding team at Amazon. CCO Karen Moon founded and exited a predictive analytics startup for retailers and was a Google Machine Learning Competition finalist. Head of Engineering Yufeng Gao built Saks’ native app and holds a PhD in physics.
In other words: this isn’t a team learning retail AI on the fly—they’ve been in the trenches of Amazon warehouses, fashion platforms, and checkout startups, and they’re now applying that to the $8B post-click leak.
“What excites us about Spangle is that it’s a first-principles solution for e-commerce’s biggest challenge, which it tackles with the most advanced and effective agentic AI solution we’ve seen,” said Scott Jacobson, Managing Director at Madrona Ventures.
The Provocation: E-Commerce Needs A Rebuild
Two decades of bolt-ons have left online retail bloated, brittle, and dangerously out of sync with shopper behavior. Agentic AI changes the rules by replacing the operating layer with something adaptive, contextual, and capable of reshaping the journey in real time.
Slick landing pages don’t win anymore. Influencers don’t win anymore. The battle is decided after the click—at the $8 billion leak bleeding retailers dry. Whoever closes it with agentic AI won’t just improve conversion. Platforms like Spangle AI will control the post-click moment—the most valuable choke point in digital commerce—and reset the economics of the internet.
