We've packaged our public audit into a clear view of the main quantified gaps, the business rationale, and a concrete front-end feature prototype we can deliver for you.
Takealot’s public site signals are strong on assortment, promotions, delivery options, returns, self-service help, and merchandising. But your real-user performance data shows a large mobile-first gap that is big enough to justify a focused front-end project.
That means the web performance problem is not a niche desktop issue.
The LCP image/resource is starting far too late. That is a strong signal to fix above-the-fold resource discovery and JS/render ordering.
This is good news: hero/media prioritization and page shell changes can move the needle quickly.
Takealot is about 4.9× slower than Amazon on LCP.
This is severe enough that users are likely seeing visible content jumps.
Likely main-thread congestion, heavy event work, or too much client-side hydration/processing during interactions.
Good is ≤ 1.8s. The site feels blank for too long.
Near the 0.8s “good” guide. Not perfect, but not the biggest problem.
Observed PDP → Add to Basket progression lift after a 0.1s improvement study.
Takealot already does many e-commerce basics well. The most defensible wedge is performance because the current gap is large, publicly measurable, and directly aligned to the front-end roles you're building for.
Your help centre highlights questions like price changes at checkout, collection availability, and missing delivery options. That means there is room for earlier delivery and cost transparency on product and list pages.
You get a reduction in LCP and CLS, plus a front-end feature tied to lower support friction and clearer purchase confidence—not just a prettier UI.
This is the kind of feature we can deliver quickly. It addresses delivery eligibility confusion, collection availability, price clarity, and marketplace vs. Takealot fulfillment differences before the customer reaches checkout.
It's more actionable than a generic redesign because it maps to publicly visible help-centre pain points and to known checkout/drop-off behavior around delivery and price uncertainty.
Your final delivery options are calculated from area, seller type, item size, and membership benefits. Explaining that early reduces “why did my price/delivery change?” friction.
A clearer offer: “Know your real delivery promise before add-to-cart.”
Fewer questions about missing collection options, unexpected fees, and membership delivery eligibility.
A bounded front-end module with clear API contracts, measurable click-through, and A/B-testable outcomes.
Instrument field RUM, identify LCP resource chain, record top CLS shift sources, and baseline click-to-render timings for key interactions.
Ship homepage/PDP performance fixes: preload the LCP element, reduce dependent request chains, reserve layout space, and break up long interaction tasks.
Launch the delivery-promise widget on PDP/PLP, A/B test add-to-cart rate, support contact rate, and bounce-to-search/help behaviour.
Takealot CrUX summary via Calibre: https://calibreapp.com/tools/core-web-vitals-test/www.takealot.com
Amazon benchmark via Calibre: https://calibreapp.com/tools/core-web-vitals-test/www.amazon.com
Core Web Vitals thresholds and guidance: https://web.dev/articles/vitals
Optimize LCP: https://web.dev/articles/optimize-lcp
LCP subparts and resource load delay: https://web.dev/blog/common-misconceptions-lcp
Optimize CLS: https://web.dev/articles/optimize-cls
Optimize INP: https://web.dev/articles/optimize-inp
Milliseconds Make Millions case study: https://web.dev/case-studies/milliseconds-make-millions
Baymard product list benchmark: https://baymard.com/blog/current-state-product-list-and-filtering
Baymard checkout benchmark: https://baymard.com/blog/current-state-of-checkout-ux
Baymard shipping estimate on PDP: https://baymard.com/blog/show-shipping-costs-on-product-pages