This article was published in 2026 and references a historical event from 2018, included here for context and accuracy.
- Tension: Retailers cling to proven bestsellers while overlooking hidden products their own data says customers want.
- Noise: The assumption that promotional visibility equals demand keeps marketers recycling the same product lineup every season.
- Direct Message: Your next breakthrough campaign lives in the inventory you’ve been ignoring.
To learn more about our editorial approach, explore The Direct Message methodology.
In 2018, Teleflora’s marketing team made a discovery that contradicted everything they thought they knew about their best Valentine’s Day products. A heart-shaped rose arrangement called “Heart & Soul” was generating significant customer interest despite receiving almost no visibility on their website.
The bouquet had been sitting quietly in inventory for years, overlooked in favor of the usual promotional staples. When Teleflora finally gave it the spotlight, it became their highest-performing Valentine’s Day promotion that season.
This wasn’t luck. It was what happens when data intelligence finally gets the last word over marketing instinct.
The Teleflora case study, while nearly a decade old, carries a message that has only grown more urgent.
As retailers now swim in unprecedented volumes of customer data, the gap between what they assume sells and what actually resonates continues to widen.
The tools exist to close that gap. The question is whether marketers are willing to let the numbers challenge their assumptions.
The comfort of familiar choices
Every industry has its defaults. In flower delivery, those defaults are built around holidays and the products that have historically performed well during them. Roses for Valentine’s Day. Lilies for Mother’s Day. The promotional calendar runs on autopilot because the stakes feel too high to experiment.
Tommy Lamb, who joined Teleflora as director of CRM and loyalty in 2016, walked into exactly this situation.
The company’s email marketing relied on welcome messages, holiday reminders, and broad batch-and-blast campaigns. There was no personalization infrastructure, no behavioral triggers, and no mechanism to surface products that customers might actually want.
The marketing strategy was built on assumption rather than evidence.
This pattern is common across retail. Marketers gravitate toward products with proven track records because promoting them feels safe.
If roses sold well last year, roses will anchor this year’s campaign.
The logic is circular, self-reinforcing, and blind to opportunity. It also ignores a fundamental reality: customer preferences are not static, and the products generating the most organic interest often sit outside the promotional spotlight.
Teleflora’s transformation began when Lamb partnered with Bluecore, a retail marketing platform he had used successfully in previous roles. The implementation combined product data with individual customer profiles, creating a foundation for understanding not just what people had purchased, but what they were likely to purchase next.
Machine learning models began identifying which audiences would respond to specific products at specific times.
What emerged was a shift from guesswork to prediction. The two-touch email program expanded into a system of fifty unique trigger-based interactions.
Customers who had opted out of email could be reached through social channels. The entire approach moved from broadcasting to listening.
When assumptions drown out the data
The noise in retail marketing is not a lack of information. It is an excess of assumptions that prevent information from being used.
Marketers assume they know which products will perform because those products have always performed.
They assume that promotional visibility creates demand rather than simply capturing demand that already exists. They assume that expanding into new product recommendations is risky because it deviates from the proven playbook.
These assumptions create a feedback loop. Products that receive promotion generate sales. Products that receive no promotion generate fewer sales.
The data then appears to confirm that the promoted products are the bestsellers, even though the comparison is never fair. Unlisted inventory never gets the chance to prove itself.
Teleflora’s “Heart & Soul” bouquet broke this cycle because the company had finally built the infrastructure to see past its own promotional habits.
The product surfaced not through intuition or seasonal tradition, but through data analysis that revealed genuine customer interest in an arrangement that marketing had essentially abandoned. When given proper visibility in a personalized Valentine’s Day campaign, it outperformed every other promotion.
The lesson extends far beyond flower delivery. Research from cloudHQ’s 2026 Email Statistics Report shows that personalized campaigns outperform generic blasts by 5.5x in conversion rates.
The gap is not marginal. Brands that continue relying on one-size-fits-all promotions are leaving substantial revenue on the table while their data quietly points toward better options.
Letting inventory speak for itself
The most valuable products in your catalog may be the ones you stopped promoting years ago.
This insight reframes the entire conversation around product marketing. The conventional approach treats promotional decisions as a form of curation, where marketers select which products deserve attention based on historical performance and seasonal logic.
The data-driven approach inverts this relationship. Instead of telling customers what to want, it reveals what they already want and builds campaigns around that reality.
Jared Blank, then SVP of marketing and data insights at Bluecore, put it directly: the ability to surface products that don’t get much attention and send them to a broader audience is appealing precisely because it unlocks dormant value. Retailers accumulate inventory with untapped potential.
The challenge is building systems that can identify that potential before the products become clearance items.
Building intelligence into the promotional calendar
The Teleflora case illustrates a broader principle that applies across retail categories. Gift retailers face unique challenges because purchase cycles are often tied to annual occasions rather than ongoing needs.
A customer who buys flowers for Valentine’s Day may not return until Mother’s Day or an anniversary. This creates pressure to maximize every interaction, which paradoxically leads many brands to double down on the same products rather than experimenting with recommendations.
Predictive analytics changes this equation. By analyzing past behavior, browsing patterns, and product affinity, machine learning can anticipate which items a specific customer is most likely to purchase, even if those items have never been actively promoted to them.
The result is a promotional strategy that feels personalized because it actually is.
Email marketing remains the highest-ROI owned channel available to most retailers, delivering roughly $36 for every dollar spent. That return increases significantly when campaigns shift from volume-based thinking to relevance-based execution.
Sending rose promotions to customers who have historically purchased tulips wastes both marketing spend and customer attention.
The technology to avoid this waste has existed for years. The barrier is organizational willingness to trust the data over the defaults.
Teleflora’s journey from underdeveloped email strategy to sophisticated trigger-based personalization took commitment and investment. But the payoff was immediate and measurable. A forgotten product became a campaign centerpiece.
Customers received recommendations that matched their preferences rather than generic holiday messaging. The marketing function transformed from a broadcast operation into an intelligence operation.
The flowers-from-weeds metaphor in the original 2018 coverage was apt. Every retailer has inventory that deserves more attention than it receives. The data knows which products those are. The only remaining question is whether marketers are ready to listen.
