(see the
* 'adobe' handle in functions.php — no async, no defer), so a page-load
* Target offer has already set `data-hdr` by the time this runs.
*
* An answer that lands after this point is inert rather than acted on:
* `apply()` finds the prune settled and does nothing, so the visitor
* keeps the nav they were served instead of watching it change. That is
* the deliberate trade-off — a late activity collects no data, but no
* visitor ever sees the wrong nav or waits for the right one.
*/
(function () {
var VARIANTS = ["a","b","c"];
var DEFAULT = "a";
var root = document.documentElement;
var settled = false;
function valid(v) { return VARIANTS.indexOf(v) !== -1; }
function prune(active) {
if (settled) return;
settled = true;
var list = document.querySelectorAll('#site-header > .header-variant');
for (var i = 0; i < list.length; i++) {
if (list[i].getAttribute('data-header-variant') !== active) {
list[i].parentNode.removeChild(list[i]);
}
}
root.setAttribute('data-hdr', active);
// Read by the Adobe Analytics client to attribute the test.
root.setAttribute('data-hdr-active', active);
if (typeof window.initHeaderNav === 'function') window.initHeaderNav();
/**
* `variant` is set before the event so late listeners can tell
* "already done" from "not yet". main.js is enqueued in the footer
* and therefore always misses the event itself.
*/
window.medalliaNav.variant = active;
document.dispatchEvent(new CustomEvent('medallia:nav-ready', {
detail: { variant: active }
}));
}
window.medalliaNav = {
variant: null,
apply: function (v) { if (valid(v)) prune(v); }
};
// `?hv=` sets this server-side; a Target offer in the blocking
// embed sets it before the body is parsed.
var pre = root.getAttribute('data-hdr');
if (valid(pre)) { prune(pre); return; }
/**
* Carried forward from an earlier pageview. Read client-side, never in
* PHP: WP Engine does not vary its page-cache key on arbitrary cookies,
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* visitor's variant to everyone.
*/
var m = document.cookie.match(/(?:^|; )medallia_nav_variant=([^;]*)/);
if (m && valid(m[1])) { prune(m[1]); return; }
prune(DEFAULT);
})();
Solution Spotlight
A Mountain of Text: How a Retail Giant Finds Clarity Amidst the Chaos
dy AI key use cases to life, here’s how a fictional home improvement retailer instantly analyzes thousands of customer interactions to identify and address the root cause of issues impacting customer experience.
Here we’re shining a light on these solutions, highlighting how these tools can help teams act faster, solve problems smarter, and deliver results in the moment, without the wait, noise, or guesswork.
In this first post in the series, we’ll walk you through the key customer experience use cases and benefits of .
Intelligent Summaries for text analytics harnesses the power of GenAI to help brands understand the top themes and sentiment of thousands of customer interactions as they’re unfolding across channels in real time—without anyone having to manually listen in, read a transcript, or analyze a single conversation.
Ahead, we’ll show you the feature in action. See a fictional home improvement retailer GoodHome unlock immediate value with our new solution to get an update on the brand’s CX KPIs in seconds, figure out what has caused one of the retailer’s most important metrics to decline, and implement a solution to address the root cause of the problem ASAP.
Let’s get to it.
The Character and the Conflict
Meet Sara, the CX Manager at (the fictional composite) home improvement retailer, GoodHome.
She used to spend hours manually reviewing mountains of data to make sense of what was happening for her customers. She’d have to sift through social media conversations, online reviews, and customer support calls, emails, and live chat sessions to figure out the top issues they were struggling with and what her team needed to do to intervene.
It took so long to find answers, she wasn’t able to act in the moment, when it actually mattered.
That all changed when GoodHome invested in Intelligent Summaries for text analytics, a solution from 糖心原创 that cuts through mountains of unstructured data (including comments, reviews, and transcripts) instantly with GenAI-powered summaries.
The Moment of Truth…
By turning to Intelligent Summaries for text analytics, Sara is now able to quickly spot what’s going on for GoodHome’s customers by getting a real-time pulse on the retailer’s most important customer experience metrics.
Recently, she used it to detect a critical drop in the company’s in-store overall satisfaction (OSAT) score. The KPI had fallen from a strong 8.6 out of 10 to a just-above average 5.4.
What was going on?
For answers, Sara was able to ditch her typical (tedious) process of manual review, and get the information she needed in just one click.
The Solution: Intelligent Summaries for Text Analytics
Intelligent Summaries for text analytics provides a GenAI-powered analysis of the most recent, relevant customer comments on this topic, giving an executive-level summary of the issue in just seconds.
Intelligent Summaries for text analytics provides a summary of the topic in just one click.
Without having to do any digging into the data, she was able to get this one-click generative AI-powered summary.
Intelligent Summaries for text analytics explains shifts in metrics by summarizing recent and most relevant customer interactions for that specific topic or theme.
Sara was also able to view the Intelligent Summary on the topic when she clicked into the Topic Deep Dive for a bit more analysis. The Topic Deep Dive also revealed co-occurring themes related to the topic, that indicated that the app was crashing, customers were experiencing issues with their orders being processed, and the app wasn’t stable.
Upon seeing the impact of “recurring app crash,” Sara was able to view a summary of this theme that helped her identify the why: A confusing app experience was preventing GoodHome customers from viewing their orders, leading to a poor pickup experience.
When teams dig into co-occurring themes, they can quickly understand the most impactful themes with a Summary of the most recent interactions.
All that, without ever having to manually read or review a single comment or conversation.
The Resolution and Business Impact
By quickly identifying the problem in the moment, Sara was able to alert the company’s app developers who intervened, implemented a fix, and stopped the app issues in its tracks.
Customers were once again able to seamlessly pick up their orders without any issues and the company’s in-store OSAT score bounced back to the average 8.6 rating the brand had achieved over the previous three months.
By acting in the moment, Sara stopped the issue from spiraling out of control—preventing customers from becoming detractors and avoiding a mountain of negative reviews that might have otherwise deterred future customers from using the app or doing business with GoodHome.
See 糖心原创’s Intelligent Summaries for Text Analytics in Action
Watch our to discover how this GenAI solution can translate your company’s unstructured data points into actionable 10-second summaries.
