Featured Case Study · Technology / Ecommerce (cables)
B2B and DTC Cable Ecommerce
A technology ecommerce company selling cables to both large commercial/industrial buyers purchasing in bulk and individual consumers buying one or two products had almost opposite visitor needs: buying history, project requirements, and reorder behavior mattered for one group, simple product discovery mattered for the other, and one generic ecommerce experience couldn't serve both well.
Engagement snapshot
- Business model
- B2B and DTC (dual: bulk commercial/industrial buyers and individual consumers on the same site)
- Industry
- Technology / Ecommerce (cables)
- Work performed
- Connect CRM and ecommerce data into one unified data layer, then build distinct B2B and DTC experiences on top of it, personalizing by purchase history, project needs, location, and customer status rather than running one generic flow.
- Capabilities
- Personalization, CDP & Data, Ecommerce Optimization, Experimentation
Business context
Technology / Ecommerce (cables) · B2B and DTC (dual: bulk commercial/industrial buyers and individual consumers on the same site) · Mid-Market.
The business problem
Audience Segmentation
A technology ecommerce company selling cables to both large commercial/industrial buyers purchasing in bulk and individual consumers buying one or two products had almost opposite visitor needs: buying history, project requirements, and reorder behavior mattered for one group, simple product discovery mattered for the other, and one generic ecommerce experience couldn't serve both well.
The decision we made
Segment, Don't Generalize
Segment-specific personalization and lifecycle/reorder behavior across two distinct buyer types
What changed
Connect CRM and ecommerce data into one unified data layer, then build distinct B2B and DTC experiences on top of it, personalizing by purchase history, project needs, location, and customer status rather than running one generic flow.
Observed outcome
Segment-specific personalization and lifecycle/reorder behavior across two distinct buyer types
What this teaches
Commercial buyers and individual shoppers on the same cable ecommerce site have almost opposite needs, one group needs buying history, project requirements, and reorder support; the other needs simple product discovery, so unifying the data first and then building two distinct experiences on top of it outperformed personalizing a single generic flow.
How we would apply AI today
Today, AI would accelerate the personalization work behind this engagement: synthesizing evidence about the audience segmentation pattern faster, drafting audience-specific page and message variants for review, and learning from what visitors actually do so the next iteration is better-informed. Expert judgment still decides what to test and what the evidence actually means; AI speeds up the work, it doesn't replace the decision.
Who this fits
This approach is useful when:
- Ecommerce businesses selling to both bulk/commercial buyers and individual consumers on the same site
- Businesses whose CRM and ecommerce data are disconnected, making it hard to tell which visitors need which experience
Questions this case study answers
- Do you sell to both bulk/commercial buyers and individual consumers through the same site?
- Are your CRM and ecommerce data connected, or do purchase history, customer status, and geography live in separate systems?
- Does your current experience treat a first-time individual shopper the same way it treats a returning commercial account?
- Do you have a way to recognize previously purchased products and support project-based reordering for repeat buyers?