---
title: "Retail and e-commerce | Snowplow Signals"
description: "Snowplow Signals tells your storefront, search and support tools what each shopper is doing right now, so they respond before the shopper leaves."
source: https://signals.snowplow.io/solutions/retail
---

[Solutions](https://signals.snowplow.io/solutions) / Industry 

# Convert more shoppers, and keep more of the margin. 

Signals tells your storefront, search and support tools what each shopper is doing right now, so they respond while the shopper is still deciding.

[Start free](https://signals.snowplow.io/start) [Talk to us](https://signals.snowplow.io/start?intent=talk) 

How Signals helps

## What retailers can do with it.

[Conversion Higher cart conversion Recovery emails go out after the shopper has left. Signals tells your site or messaging tool when a full cart sits idle for ten minutes, so it can nudge the shopper while they are still browsing. How cart recovery works → ](https://signals.snowplow.io/solutions/retail/cart) [Conversion Higher search conversion Three failed searches in a row, and the fourth is ranked before the search hears about them. Signals tells your search engine what this shopper typed and skipped this visit, so it can broaden the results or offer help. How search inputs work → ](https://signals.snowplow.io/solutions/retail/search) [Conversion Higher checkout conversion A shopper stuck at checkout looks like one still deciding. Signals tells your storefront when a shopper stalls on one step or returns to the cart, so it can show the delivery date or offer help. How checkout prompts work → ](https://signals.snowplow.io/solutions/retail/checkout) [Margin Lower discount spend A site-wide code goes out before anyone knows who needs it. Signals tells your storefront when a shopper keeps returning to the price without buying, so your offer rules can save the code for them. How offer targeting works → ](https://signals.snowplow.io/solutions/retail/offers) [Margin Fewer returns One dress in three sizes means two returns the store learns about weeks later. Signals tells your storefront when a cart holds one item in several sizes, so it can offer the fit guide before checkout. How fit prompts work → ](https://signals.snowplow.io/solutions/retail/sizing) [Service Shorter handle times A shopper who opens chat starts by explaining what they were doing. Signals tells your support agent or chatbot the order, the cart and the pages viewed before they asked, so the answer starts from there. How support context works → ](https://signals.snowplow.io/solutions/retail/support) 

For your engineers

## One SDK, one read call. 

We host it, or it runs in your own AWS or GCP account. Signals collects events with its own SDKs, updates what it knows within a second of each event, and answers a read in 6ms at the median and 10ms at p95, in-region.

[Read the docs →](https://docs.snowplow.io/docs/signals/introduction/) 

```
const shopper = await signals.getServiceAttributes({
  name: "storefront",
  attribute_key: "domain_sessionid",
  identifier: sessionId,
});
// { failed_searches: 3, cart_value: 184.2, cart_idle_seconds: 412 }
```

Your storefront asks what this shopper is doing when it renders a page 

Search and recommendations get this visit's behavior with each request 

Braze, or any tool that takes a webhook gets a message the moment a rule fires 

Snowflake, BigQuery or Databricks runs the same definitions over past visits 

Questions

## What to settle before an engineer looks. 

### We already run a personalization or search vendor. Does Signals replace it?

No. Your vendor keeps ranking the results and choosing the offers. Signals tells it what this shopper has done in this visit, the failed searches and the cart among them, current to the last event, and you pass that in as a boost, a filter or a rule input.

### We already have a CDP. Why add this?

Keep it. A CDP builds audiences from past behavior and syncs them on its own schedule, so a page rendering right now learns about this visit too late. Signals sits underneath it, computes what the shopper is doing in this visit and serves it to your storefront on request, and it can send the moments it notices back to the CDP.

### Is this a data platform project?

No. Add the SDK to your storefront and app and collection starts, with nothing to build first. Your team defines what it wants to know about a shopper in the console, and an engineer reads it with one call and ships it through the CI/CD you have. Most of that wiring can be delegated to a coding agent, which is what the MCP server and agent skill are for.

### Where does the data live?

We host it, or it runs in your own AWS or GCP account, with the same architecture either way. Events are validated against your schemas on the way in and stay yours. Your warehouse runs the same definitions over past visits, so there is one set of numbers rather than two.

### What if next-day data is fine for us?

Then a warehouse tool is cheaper and you should buy that instead. If the recovery email can go out tomorrow and the offer can wait for tonight's segment refresh, Signals is more than you need. It is for the storefront, search and offers that have to respond while the shopper is still browsing.

## Try it on your own site. 

Add the SDK, define one thing you want to know about a visitor, and watch it update while you click around. The free tier runs on the real engine: no card, no sales call.

[Start free](https://signals.snowplow.io/start) [Talk to an engineer](https://signals.snowplow.io/start?intent=engineer)