Wie Giesswein nach dem Post-COVID-Drop 12,2 Mio. € zusätzlich erzielte.
Ein Category-Entry-Point-Research-Programm, das Qualitätswahrnehmung als Growth-Hebel identifizierte und in eine dreijährige Testing-Roadmap übersetzte.
Live storefrontThe current shop carries the premium wool footwear story. The CRO system made quality, comfort, and purchase confidence more visible at decision points.
Protocol boardThe operating system connected CEP research to quality-led experiments.
Actual controlActual Research Hub screenshot from a PDP comparison test.
live
Growth MapSignale wandern von rohem Verhalten in eine getestete Roadmap.
live
Test 1
Test 2
Test 3
Winner rollout
TestgeschwindigkeitParallele Tests potenzieren Lernen, statt sequenziell zu warten.
€12.2MAdditional Revenue
3 YearsTimeframe
25.3xROI
€232.5K/moTop Test Impact
Since 2020Partnership
MarkeGiesswein
Primäres Ergebnis€12.2M Additional Revenue
Evidenzbasis€232.5K/mo Top Test Impact
Zeitraum3 Years Timeframe
The short version
Giesswein is a €100M+ annual revenue premium wool shoe brand that experienced a significant revenue drop after the post-COVID e-commerce correction. With no clear diagnosis for the decline, DRIP conducted a Category Entry Point analysis that revealed 'Initial Quality Perception' was the top purchase driver. Over three years of testing, we generated €12.2M in additional revenue — including a single test that added €232,500/month.
Giesswein Evidence Stack
Research blieb nicht abstrakt. Sie wurde sichtbare Arbeit.
Jede Case-Study-Schicht hält die Artefakte auf der Seite: aktuelle Shop-Screenshots, Research Boards, Priorisierungsoutputs, Test-Evidenz und Impact Charts.
ResearchTestsPrioritätRevenue
01Live-Shop
Giesswein had a premium product story. The site had to make that quality tangible fast.
Current storefrontThe premium wool promise is visible; CRO made it more decisive near purchase.
02Research-Diagnose
After the post-COVID correction, the site was underselling the strongest reason to buy.
Research outputThe research made quality perception actionable instead of generic.
03Roadmap-System
The roadmap turned quality perception into visible PDP, PLP, and cart proof.
Protocol boardThe operating system connected CEP research to quality-led experiments.
04Kommerzieller Proof
The biggest wins came from making premium value impossible to miss.
Der kommerzielle Proof hinter €12.2M Additional Revenue.
Die Seite hält Evidenz nah an der Story, damit der Growth Claim von denselben Screenshots, Tests und Research-Signalen getragen wird, die die Roadmap geformt haben.
Quality-led commercial proofThe quality-perception roadmap generated €12.2M in additional revenue, with one test adding €232.5K per month.
02Die Marke
Warum Giesswein ein schärferes Growth-System brauchte.
Giesswein is an Austrian premium footwear brand known for its innovative use of Merino wool in shoe construction. With over €100M in annual revenue, the brand has a strong DTC presence across European markets, selling directly through their own online shop.
The brand's unique selling proposition — sustainable, high-quality wool shoes that combine comfort with eco-consciousness — resonated strongly during the COVID e-commerce boom. But when the post-COVID correction hit, Giesswein experienced a meaningful revenue decline with no clear understanding of why.
Live shop context
Giesswein had a premium product story. The site had to make that quality tangible fast.
The live storefront already carries wool, comfort, and outdoor cues. The research showed the buying system needed stronger proof around material quality, durability, returns, and purchase confidence.
€100M+Annual Revenue Brand
95Comfort Importance
85Durability Importance
Current storefrontThe premium wool promise is visible; CRO made it more decisive near purchase.Driver mapResearch Hub pointed to comfort, delivery reliability, returns, and durability as concrete proof needs.Feature rankingThe ranking translated brand perception into exact testable proof surfaces.
03Die Herausforderung
Das Conversion-Problem hinter der Headline.
After the post-COVID e-commerce normalization, Giesswein's online revenue dropped significantly. The team didn't have a clear diagnosis: Was it a market shift? A product issue? A site experience problem? Without data-driven insights, they couldn't determine what had changed or how to fix it.
The brand was making decisions based on internal assumptions about what customers valued, but those assumptions hadn't been validated against actual customer behavior. The gap between what Giesswein thought customers cared about and what actually drove purchase decisions was unknown — and potentially large.
Research diagnosis
After the post-COVID correction, the site was underselling the strongest reason to buy.
Customers were not primarily asking for a broader sustainability story. They needed immediate confidence that the wool shoes were comfortable, premium, durable, and low-risk to try.
95Comfort
95Customer Service
90Delivery Reliability
Research outputThe research made quality perception actionable instead of generic.Quality mapComfort, returns, delivery, and durability became the roadmap's main proof requirements.
04Der Ansatz
Die Arbeit wurde zu einem research-gestützten Testing-System.
We began with a Category Entry Point (CEP) analysis — a deep research methodology that maps the specific situations, needs, and motivations that bring customers to the brand. This revealed a critical insight: 'Initial Quality Perception' was the number one purchase driver for Giesswein customers.
Customers weren't primarily buying for sustainability or eco-consciousness (which Giesswein had been emphasizing). They were buying because they perceived Giesswein shoes as premium quality. The Merino wool wasn't an eco-story — it was a quality-and-comfort story.
Armed with this insight, we designed a testing program that doubled down on showcasing quality throughout the customer journey. Every test hypothesis tied back to the core driver: making the quality of Giesswein products immediately visible and tangible in the digital experience.
Scientific operating system
The roadmap turned quality perception into visible PDP, PLP, and cart proof.
Instead of broad redesign work, the tests focused on precise confidence builders: Merino material cues, guarantee modules, comparison visuals, product videos, and checkout reassurance.
25.3xROI
3yProgram
€232.5K/moTop Test
Protocol boardThe operating system connected CEP research to quality-led experiments.Actual controlActual Research Hub screenshot from a PDP comparison test.Actual variantThe variant made the shoe comparison more concrete on mobile.
05DRIP Growth Protocol
How Giesswein turned quality perception into a three-year testing system
The program followed the DRIP thesis: predictive consumer research identified the real purchase driver, rapid A/B testing made that driver visible across PDP, PLP, and cart surfaces, and iterative prioritization kept the roadmap tied to revenue exposure.
live
VisitClickAddBuy
Predictive ResearchAufmerksamkeit, Einwände und Kaufmotive verdichten sich zu schärferen Hypothesen.
live
Test 1
Test 2
Test 3
Winner rollout
Rapid TestingMehrere aktive Tests erzeugen mehr valide Chancen auf Gewinner.
live
PDP92
Cart84
PLP73
Search61
Email44
Iterative PriorisierungDie wertvollste Evidenz wird in den nächsten Sprint priorisiert.
Quality of TestPredictive Consumer Research
Use CEP and feature research to find the proof customers needed before paying a premium.
Rate of TestingRapid A/B Testing
Translate quality proof into badges, comparison modules, video, material sections, and cart reassurance.
Success RateIterative Prioritization
Double down on the proofs that improved RPU and reduced perceived purchase risk.
OutputCompounding Learning
Jede validierte Änderung hebt die nächste Baseline und zeigt dem nächsten Sprint, was getestet werden sollte.
01Predictive Consumer Research
Find the purchase driver behind the revenue drop
The CEP and feature analyses reframed the problem from broad post-COVID softness to a sharper issue: visitors needed stronger proof that the shoes justified premium expectations.
Operating Insight
The main conversion lever was not more sustainability copy. It was immediate quality confidence.
95Comfort
85Durability
90Delivery
InputCEP analysis
Identify why buyers enter the category and what they must believe.
SignalQuality proof
Material, comfort, and durability carried more conversion weight.
HypothesisMake it tangible
Place quality proof close to purchase decisions.
Comfortimportance
95
Delivery reliabilityimportance
90
Durabilityimportance
85
Research Hub outputThe ranking made comfort, service, delivery, returns, and durability visible as roadmap inputs.
02Rapid A/B Testing
Turn premium proof into controlled experiments
The testing roadmap converted the quality hypothesis into PDP badges, guarantee modules, comparison images, cart reassurance, videos, and PLP action tests.
Operating Insight
The best tests made a premium product easier to trust without discounting it.
€232.5K/moTop Test
+7.7%PLP RPU
3yTesting
PDPMaterial proof
Surface wool and comfort cues earlier.
CartReduce risk
Add reassurance near commitment moments.
PLPShorten path
Let ready shoppers act without extra page loads.
Merino wool proof365-day guaranteeComparison imageEasy ordering reassuranceQuick add on PLP
Testing roadmapEach experiment made a specific part of the premium promise clearer.
03Iterative Prioritization
Compound the quality mechanism across the journey
Validated proof points fed the next roadmap. The program expanded from single PDP cues into a broader operating system for premium confidence.
Operating Insight
The more consistently the shop made quality tangible, the easier it became to justify premium price.
€12.2MRevenue
25.3xROI
3yProgram
ReadoutProof moved RPU
Measure whether the quality cue changed revenue per user.
PromoteExpand surface
Move the proof mechanism to adjacent pages.
BaselineHigher confidence
Use learnings to lift the next starting point.
Commercial proofThe impact chart shows how quality-led proof created recurring revenue.
CEP signalCEP
Initial quality perception became the decisive purchase frame after the post-COVID drop.
The site had to make premium wool quality visible before users compared price.
Feature signal95%
Comfort and customer service both scored 95% importance; delivery scored 90%.
Quality proof needed to include the product experience and the buying experience.
Risk signal85%
Returns, refunds, and durability each ranked in the top five feature concerns.
Premium positioning only converts when the downside feels controlled.
Revenue signal€232.5K/mo
One value perception badge produced €232,500 per month.
Small proof changes can outperform redesigns when they target the right psychological driver.
Feature importance ranking
Research Hub showed that premium footwear conversion depended on comfort, service reliability, delivery, returns, durability, and origin transparency.
1
Comfort / Tragekomfort
User Experience
The product had to feel like an immediate comfort upgrade.
65 Erwähnungen70% pos15% neu15% neg
95%Importance
2
Customer Service
Trust
Premium purchase confidence depended on support and issue resolution.
58 Erwähnungen20% pos20% neu60% neg
95%Importance
3
Delivery Speed & Reliability
Trust
Delivery reliability had to match the premium brand promise.
Durability proof helped justify the price and material promise.
35 Erwähnungen40% pos25% neu35% neg
85%Importance
06Wichtige Tests & Ergebnisse
So sahen die echten Tests aus.
Die Seite hält Kontroll-, Varianten- und Ergebnis-Screenshots sichtbar, damit die Fallstudie die Evidenz hinter jedem Claim zeigt.
Value Perception Badge on PDP
Added a prominent '100% Merino Wolle' badge directly on product detail pages. Rather than burying the material information in product descriptions, we made it immediately visible as a quality indicator — not an eco indicator. The badge leveraged the 'Initial Quality Perception' driver identified in the CEP analysis.
Without badge → With badge+€232,500/month
Actual controlActual Research Hub control screenshot from a PDP guarantee and trust test.Actual variantThe variant added a stronger guarantee badge below the primary purchase action.
Material & Maintenance Highlight
Created a dedicated section on product pages highlighting material properties and care instructions. This positioned Giesswein's wool as a premium, long-lasting material — reinforcing quality perception and addressing durability concerns that prevent purchase.
No material section → Prominent material highlight+€52,470/month
Actual controlActual mobile reference from the Research Hub experiment record.Actual variantThe variant made the comparison against regular shoes more concrete.
Quick Add-to-Cart on PLP
Added an 'In den Warenkorb' (Add to Cart) button directly beneath each product tile on the product listing page, enabling one-click add without visiting the product detail page. This leveraged cognitive ease, decision simplification, and action bias — reducing friction between browsing and buying by eliminating an entire page load from the purchase path.
Actual controlActual cart drawer control screenshot from Research Hub.Actual variantThe winning variant added reassurance that ordering is easy and secure.
Gesamte Wirkung
Der Output war keine schönere Website. Es war ein besseres Umsatzsystem.
Over three years, the optimization program generated €12.2M in additional revenue for Giesswein — a 25.3x ROI on the engagement.
The biggest single impact came from the Value Perception badge test, which alone added €232,500 per month in additional revenue. This single test demonstrates how finding the right psychological driver and amplifying it can have outsized impact.
Beyond the revenue numbers, the CEP analysis fundamentally shifted Giesswein's understanding of their own brand positioning. The insight that quality perception — not sustainability — was the primary purchase driver informed not just CRO but broader marketing and positioning decisions.
Das Fazit
Der Vorteil kam durch compounding Lernen.
The Giesswein case illustrates a common and costly mistake: brands often optimize for what they think customers care about, not what actually drives purchase decisions. Giesswein was emphasizing sustainability when customers were buying for quality. That misalignment meant the site was underselling its strongest value proposition.
The €232,500/month badge test is a perfect example of how small, specific changes grounded in real psychological research can have enormous impact. It wasn't a redesign. It wasn't a new feature. It was a badge — positioned correctly because the research told us exactly what mattered.
For premium brands experiencing post-COVID corrections: the fix often isn't to discount or diversify. It's to understand — with data — what your customers actually value, and make that value impossible to miss.
Commercial proof
The biggest wins came from making premium value impossible to miss.
A single badge generated €232,500 per month, and the full quality-led system produced €12.2M across three years.
€12.2MAdditional Revenue
€232.5K/moTop Test
25.3xROI
Impact chartThe commercial proof connects quality-led tests to recurring revenue impact.Cart controlActual control screenshot from a cart reassurance test.Cart variantThe winning variant added simple ordering reassurance in the cart drawer.
Working with DRIP ensures a continuous, data-driven approach to online shop development. Their optimization helps us focus on real value, combining internal and external ideas. As a German-speaking agency, they understand our audience better, and we appreciate their flexibility, collaboration, and quick response times.
Premium FootwearDRIP Growth Protocol / Giesswein
