Radiant Gowns

Radiant Gowns is a luxury formalwear brand—selling everything from bridal dresses to mother-of-the-bride designs and prom styles—primarily via independent boutiques. They also run consumer-facing sites that let people browse collections, then click “Find a Store.” Although this setup had worked for years, the team wanted deeper insights: were these websites actually driving store visits? How could they track interest in new dresses without depending on hunches or missed phone calls?

They decided to launch a retailer portal so boutiques could order inventory online, but they lacked in-house technical expertise. They had a CMS vendor for web dev, an agency for NetSuite integrations, and not much else. We stepped in with a “crawl, walk, run” approach to build out analytics incrementally. That let them see real-time user behavior across different brand sites and, eventually, roll out new marketing tactics that leveraged fresh data.

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Phase One (Crawl): Basic Tracking with Minimal IT Overhead

They were rolling out a new B2B portal but didn’t know how to measure its adoption without burdening their developers. We started by moving them from Universal Analytics (UA) to GA4, wiring everything through Google Tag Manager (GTM). This way, changes wouldn’t require rewriting site code every time.

We kept the event tracking simple at first—just capturing key steps like portal logins, style page views, and store-locator clicks. That gave Radiant Gowns a quick read on how many people landed on each brand site (bridal, prom, mother-of-the-bride) and whether they took any next steps. Although basic, this foundation answered immediate questions like, “Is our portal even getting any traction?” and “Are visitors actually looking at the styles we just launched?”

This initial phase was deliberately narrow so their small, non-technical team could digest the metrics quickly. With that essential baseline in place, Radiant Gowns began noticing new opportunities for more detailed style-level insights.

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Phase Two (Walk): Deep-Dive Into Style Patterns and Seasonal Trends

Once the brand had clarity on overall traffic and portal logins, they started asking more advanced questions about user engagement. Now that they knew people were visiting, they wanted to see what styles caught the most attention, which colors were trending, and how brand-new dresses fared compared to older ones still in production. They were essentially thinking:

• Which styles keep drawing interest season after season, even after being on the market a while?
• When does a fresh design start to pick up steam—can we catch that before wedding season fully ramps up?
• Are visitors who view a certain prom line also looking at mother-of-the-bride dresses, or are those segments totally separate?

We expanded GA4 event tagging to capture style-level details. Instead of a generic page view, each style had its own event with parameters like collection name, color, and release year. Through this, Radiant Gowns could see, for example:

Older Styles That Stayed Popular

• Some mother-of-the-bride gowns had been around a few years, yet still racked up steady views. This mattered because older dresses often get overshadowed by new launches, but data showed these weren’t just stragglers—they were driving consistent store locator clicks. It signaled that discontinuing them too soon could lose potential sales.

Breakdown of Colors and Collections Over Time

• The brand releases new styles each year, but the older lines sometimes remain in production if they keep selling. Now they could compare a gown introduced two seasons ago against one launched this year, to see whether the new arrival was catching on or if an older staple was still outperforming it.

Early Identification of Trending Dresses

• By setting up daily and weekly metrics, Radiant Gowns noticed when a style’s page views spiked—often tied to social media chatter or a small paid campaign. They could reach out to boutiques with, “This new gown just saw a major jump in interest; you might want to stock it before prom/wedding season hits full swing.”

Channel-Specific Visitor Behavior

• Different marketing tests—like Pinterest ads or a quick promotional boost on Instagram—brought in visitors with varying engagement levels. If a certain campaign brought high traffic but few store-locator clicks, Radiant Gowns realized it was generating window shoppers, not serious buyers. On the flip side, a moderate traffic source that had a high “store find” rate indicated more actionable leads.

To make all of this digestible for their non-technical team, we pulled the tagged events into a set of easy PowerBI dashboards. This bypassed the complexity of GA4’s native interface (which had frustrated them) and let them filter by brand, date range, or style ID.

Immediate Uses

• Planning New Style Launches: If a style introduced six months ago was already surging, they’d highlight it in marketing emails and encourage boutiques to stock up.
• Retaining Evergreen Designs: If a “last season” style kept racking up views, Radiant Gowns was more confident continuing its production.
• Channel Optimization: By mapping traffic source to style engagement, they moved more budget to channels that generated tangible store visits.

All of this happened because the brand trusted the simpler metrics we built in Phase One, then got curious about deeper questions. The data was flexible enough that, whenever they said, “We want to compare new vs. returning styles over the last 30 days,” we could spin up a new dashboard in days—not weeks—thanks to the GTM/GA4 structure and the visuals in PowerBI.

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Phase Three (Run): Automated Emails for “Hot Styles”

In the midst of analyzing which dresses were trending, Radiant Gowns realized they could do more than just keep these insights internal. Since boutiques are the ones actually selling dresses to consumers, why not pass along the online “popularity data” so retailers could proactively stock up?

They asked if there was a way to automate a monthly breakdown of top-viewed dresses. Specifically, they wanted:

• A short list of “hot styles” emailed to each boutique.
• Region-specific insights, so a store in the Midwest saw what was popular in its area.
• Instant links to the portal, so the boutique could place an order without calling a rep.

We built a system that combined the style-level GA4 data with a simple segmentation layer for each boutique’s region and product lines. Then we used the same data warehouse fueling PowerBI to auto-generate monthly emails. If in a given month, a certain prom style skyrocketed in the Southeast, the relevant boutiques in that region would get a rundown of its performance and a one-click path to reorder.

Once this email program took off, boutiques reported they were discovering dresses they didn’t realize were in high demand. Many store owners still ordered inventory based on past experience or anecdotal feedback. Now, with monthly “hot style” emails, they could see which collections brides were actually browsing online. Since each style included an immediate portal link, that also nudged portal usage upward (fewer phone calls, fewer manual spreadsheets).

For Radiant Gowns, this was a step beyond just logging website metrics. They had effectively looped their retailers into the brand’s data feed, turning analytics into a driver for real B2B sales. Plus, the monthly cadence gave them a reason to keep the data fresh and look for new trends. If a certain style kept reappearing month after month, they’d highlight it in marketing campaigns or develop spinoffs in similar fabrics.

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A Holistic Evolution

Radiant Gowns initially just wanted to confirm that their new portal was catching on, but the journey revealed how data could solve bigger industry-wide challenges:

• Attribution in a Non-Ecommerce Setting: Because final sales happen in a local boutique, Radiant Gowns struggled for years to connect website interest to actual orders. By focusing on style-level engagement and store-locator clicks, they at least got a reliable signal that visitors were interested enough to find a local shop.
• Spotting Seasonal and Localized Trends: Some dresses see a consistent trickle of interest all year; others spike right before a major prom or wedding season. Radiant Gowns used these patterns to time new collection releases more strategically, aligning with consumer momentum.
• Empowering Stores with Data: Automating monthly “hot style” emails gave boutiques better visibility into real-time market demand. That kind of data-driven collaboration was unusual in an old-school, phone-based sales environment.

Their in-house resources never grew into a dedicated dev or analytics team. Yet by sticking to a crawl–walk–run approach, they didn’t need one. We kept the technical footprint light (GTM for event setup, GA4 for data collection, PowerBI for user-friendly dashboards) and stepped in as needed. This incremental method helped them see quick wins, gain confidence, and then deepen the analytics footprint as new needs emerged.

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Why We Enjoyed This Project

The Radiant Gowns story is about a brand learning to adopt data tools without an internal tech army. They began by capturing a few essential metrics for the retailer portal and soon realized those same systems could reveal which styles were blowing up online—even the older ones still pulling big interest. Seeing that data in an accessible dashboard nudged them to experiment with marketing channels, track results, and—ultimately—share “hot style” intelligence directly with stores.

That’s the beauty of a crawl–walk–run approach for a traditionally offline business. Each step is small enough to be digestible but powerful enough to spark bigger ideas. By the final phase, Radiant Gowns wasn’t just measuring traffic; they were using web behavior to shape everything from which dresses they produce next season to how they communicate with hundreds of boutiques. And all this came from a brand that originally just wanted to see if anybody was using their new B2B portal.

It goes to show that the right analytics setup—especially one that doesn’t depend on heavy in-house code changes—can unlock far more than anyone imagines at the start. Radiant Gowns leveraged it to drive real growth across multiple lines, build stronger retailer relationships, and adapt swiftly to whatever season or style was heating up online.