Introducing Pave + Rippling: Make data-driven compensation decisions in real time

Announcements
June 21, 2023
1
min read

Note: This post originally appeared on the Rippling blog and has been republished here with permission.

Compensation management can be painful and time-consuming. Too often, businesses must rely on tedious benchmarking surveys and outdated data sets. As a result, it can be difficult to make informed compensation decisions.

That’s why we’re excited to announce Rippling’s integration with Pave, the leading data-driven compensation platform that allows you to benchmark, plan, and communicate your compensation in real time. Now, you can manage compensation from end to end, leveraging real-time employee compensation data from Rippling.

With this new integration, Rippling’s employee data automatically syncs into Pave’s compensation management system, empowering employees to make equitable pay decisions.

Automatically sync employee data from Rippling to Pave

Employee data transfers seamlessly from Rippling to Pave—including organizational details like team and level—so you don’t have to rely on exports, navigate spreadsheets, or manually adjust and reconcile compensation data. With up-to-date data, your managers can effectively work through merit cycles, and your employees can easily visualize their total compensation.

To get started, download the Pave app from the Rippling app store.

Learn more about Pave’s end-to-end compensation platform

Become a compensation expert with the latest insights powered by Pave.

(function (h, o, t, j, a, r) { h.hj = h.hj || function () { (h.hj.q = h.hj.q || []).push(arguments) }; h._hjSettings = { hjid: 2412860, hjsv: 6 }; a = o.getElementsByTagName('head')[0]; r = o.createElement('script'); r.async = 1; r.src = t + h._hjSettings.hjid + j + h._hjSettings.hjsv; a.appendChild(r); })(window, document, 'https://static.hotjar.com/c/hotjar-', '.js?sv='); !function () { var analytics = window.analytics = window.analytics || []; if (!analytics.initialize) if (analytics.invoked) window.console && console.error && console.error("Segment snippet included twice."); else { analytics.invoked = !0; analytics.methods = ["trackSubmit", "trackClick", "trackLink", "trackForm", "pageview", "identify", "reset", "group", "track", "ready", "alias", "debug", "page", "once", "off", "on", "addSourceMiddleware", "addIntegrationMiddleware", "setAnonymousId", "addDestinationMiddleware"]; analytics.factory = function (e) { return function () { var t = Array.prototype.slice.call(arguments); t.unshift(e); analytics.push(t); return analytics } }; for (var e = 0; e < analytics.methods.length; e++) { var key = analytics.methods[e]; analytics[key] = analytics.factory(key) } analytics.load = function (key, e) { var t = document.createElement("script"); t.type = "text/javascript"; t.async = !0; t.src = "https://cdn.segment.com/analytics.js/v1/" + key + "/analytics.min.js"; var n = document.getElementsByTagName("script")[0]; n.parentNode.insertBefore(t, n); analytics._loadOptions = e }; analytics.SNIPPET_VERSION = "4.13.1"; analytics.load("0KGQyN5tZ344emH53H3kxq9XcOO1bKKw"); analytics.page(); } }(); $(document).ready(function () { $('[data-analytics]').on('click', function (e) { var properties var event = $(this).attr('data-analytics') $.each(this.attributes, function (_, attribute) { if (attribute.name.startsWith('data-property-')) { if (!properties) properties = {} var property = attribute.name.split('data-property-')[1] properties[property] = attribute.value } }) analytics.track(event, properties) }) }); var isMobile = /iPhone|iPad|iPod|Android/i.test(navigator.userAgent); if (isMobile) { var dropdown = document.querySelectorAll('.navbar__dropdown'); for (var i = 0; i < dropdown.length; i++) { dropdown[i].addEventListener('click', function(e) { e.stopPropagation(); this.classList.toggle('w--open'); }); } }