Product Strategy

Scaling UX teams across Product & Engineering

Operations: Structure & resource requirements

• Review: Product roadmaps for full insight in product vision and delivery requirements

• Review: Engineering structures for design capability requirements

Initiative

End-to-end customer experience redesign, with CRM replatform and modernised journeys across:
Search, Engagement, Customer Accounts, E-commerce and Customer Assist

Search:

• Core conversion journey

• Focused on increasing product relevancy to requirements

Strategy

Strengthen the strategic pillar of being the leading digital-first supplier of high-quality, high-specificity proteins, building customer trust and reinforcing competitive positioning

Role

• Created UX strategy and ops model for digital transformation (search to cart)

• Recruited, scaled, and managed UX team (design, research, insights, IA, copy, design operations)

• Defined strategy hypotheses - Low filter use, positive friction, right product first time

• Delivered research roadmap and insight outcomes supported all product squads

• Advocated user-centred outcomes aligned to revenue - Prioritising search improvement

• Partnered with product, engineering, data science, delivery, and customer-facing teams

• Mentored team; advocating discovery and journey mapping as a core source of insights, cross-validated with data, user feedback and testing

• Managed stakeholder scope and expectations

• Explored AI/ML search integration with data science, mitigating existing revenue risk

Search challenge

• Time-poor researchers overwhelmed by many results in categories of 30,000+ products

• Up to 50+ filters with <4% engagement

• 99.5% single-keyword searches, reduced effectiveness of search capability and results

• Time-poor researchers tended to make quick product choices

• Average 1.2 result pages per session (~24 products), leaving relevant products undiscovered

Search objective

• Enable customers to find the right product, first time

Early outcomes

• Product Description Page (PDP) engagement: Improved by ~33% (1.2 → 1.6 per session, +7,600/day or ~2.8M/year if sustained)

• Customer sentiment: Increased from 3.2 → 4.3/5

• Quick View: Reduced irrelevant PDP visits, surfacing key attributes earlier

• Product relevancy: Improvements lowered replacements under guarantee, reducing avoidable costs and strengthening trust

• Bounce rate: Held steady at ~50%, within e-commerce benchmarks, despite added positive friction

Why PDP views mattered

• Mixed on/offline purchase behaviour meant PDP views was the strongest proxy for conversion

• Higher PDP engagement correlated with increased revenue and fewer guarantee replacements

Research & discovery

Methods

• Data (sessions, bounce, filter use, PDP views)

• Interviews, usability tests, surveys, sentiment tracking

• ~300 customer engagements, ~10k+ remote feedbacks across full initiative (direct & distributor markets)

• VOC feedback loops, session analysis, heatmaps

• Internal beta testing and leadership reporting

Key insights

• Bounce ~50%

• Search filter use correlated with higher PDP engagement, but customer adoption <4%

• Time poor scientists on avg. skipped refinements and went direct to product pages

• Researchers required key product attributes upfront

• Discovery confirmed the focus: fewer results, higher relevance

Design approach & solution exploration

Concepts & prototyping

• Pre-search autosuggest expanded single-word searches with attributes for higher relevance - the more information a customer gave the lower number of results but higher chance of refinement

• Product Quick View surfaced essential attributes to cut down pogoing between product pages

• Parent category fallback ensured continuity for broad searchers, supporting legacy behaviours (no revenue risk)

Iterations & Trade-offs:

• Positive friction: Customers invested slightly more upfront, saving time later

• Filter simplification: 50+ reduced to essentials at search results stage, shifting behaviour away from unused filters

• Brand alignment: A mid-project company rebrand increased workload, timescales and required additional UX resource to collaborate with brand and recreate a newly branded design system of elements and components

Implementation

• Hypotheses validated through customer survey and sentiment tracking, usability tests, beta trials, leadership feedback.

• UXD & UXR embedded across Product and Engineering squads

• 2 weeks sprints following product focus area roadmaps

• Regular stakeholder collaboration and review contributed to customer focused improvements

Phased rollout

• Single European market pilot

• Expanded across EMEA

• Similar pilot repeated before full APAC region

• Rolled out in NA after strong results

• Search squad worked with PMs, engineers, BAs, delivery managers from digital and customer support, sales and marketing stakeholders

Results

Quantitative

• PDP engagement/session: Improved by ~33% (equivalent to +7,600/day ~2.8M/year if sustained)

• Customer sentiment: 3.2 → 4.3/5

• Bounce rate: Held steady at ~50% despite added positive friction

• Filter use: Remained low <4%, but offset by stronger product signals via autosuggest and Quick View (PDP-equivalent engagement)

• Search abandonment rate: Reduced by ~17%

Qualitative

• Customers found products faster with greater confidence

• Quick View reduced irrelevant PDP visits

• Stakeholders observed fewer guarantee replacements

• Positive beta testing feedback supported global rollout

Business Impact

• Higher PDP engagement correlated with revenue growth

• Reduced replacements lowered avoidable costs and strengthened customer trust

• Reinforced positioning as a digital-first leader in high-specificity proteins

  
  

Learnings & Next Steps

What Worked

• Autosuggest & Quick View improved relevance, increased PDP views/year and improved customer satisfaction

• Surfacing reviews, PubMed citations, product validation data earlier in the customer journey

What Didn’t

• Mid-project rebrand overhead; scope vs resource mismatch

• Regular scope expansions extended initiative timescales

• Acquisition deprioritised digital focus

Next Steps

• Personalised autosuggest (segmentation, signed-in state, purchase history)

• Explore predictive models with AI/ML/Data Science to identify trending proteins and inform manufacturing and marketing decisions.