Predict visitor intent in real time, so you can personalise your site, ads and emails at scale with the tools you already use.
Yet most retailers are stuck treating every visitor the same due to data, cost or complexity.鈥
Catch-all conversion tactics are used on every visitor all the time. The experience is inappropriate.
Campaigns are triggered based on onsite actions without context. The content is irrelevant.
Traditional analytics show what visitors do, not why they do it. The view is incomplete.
Now any ecommerce team can personalise based on the context and expected actions of each visitor.
Use the button below to schedule a demo at a time that suits you.
Book a demoIf you have an ecommerce site and an analytics platform, you don鈥檛 need anything else to get value from our product.
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All you need to do to get setup is add our script to your store or tag management solution, such as Google Tag Manager.
黑料大事记 works through a single <10kb script and has minimal if any impact on Core Web Vitals.
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While an impact is possible (as it is with any script ever) the impact of our script can be minimised easily with async or deferred script loading.
All Intent Metrics and Segments can be pulled into your existing tech stack. This includes marketing, analytics and onsite tools. Or any other system that allows custom dimensions and variables.
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We鈥檙e working on expanding our Integration Library within the platform. In the meantime, you are able to set up your own integration to push intent specific events by dataLayer or front-end data.
No. The Intent Metrics we produce come from the sessions and data we model. You are able to look back at previous periods of intent data in platform, but historical intent data may only be useful to your insights rather than your customers.
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Be aware, If you decide not to renew your contract with us, we may get rid of your data within 30 days. We share back ups and are always open to talking about this though.
Si. Oui. Ja. While our model tracks text from each visitor鈥檚 browser, it has been designed to read the code elements from the website directly. The language inputs only make up a few of the +250 the signal we track.