E-commerce & Retail
Most teams estimate peak-day capacity from last year’s numbers, and whether the estimate held is only discovered on the day. We generate the traffic you expect in advance, complete with geographic distribution. You see where the system slows weeks before the campaign.
On the day traffic rises, the attack surface grows with it, and both risks materialise at once. With controlled DDoS simulation we measure the level at which your defences weaken, before the campaign opens.
In an e-commerce team releasing frequently, the easiest thing to break is the most valuable: search, cart, payment. We bring those flows into automated regression scope, so a break shows up before release rather than in production.
The digital channel is tested rigorously while in-store tills and POS devices usually pass on a manual check. We run device testing through robotic automation, validating that the same payment flow works across thousands of points of sale.
Loadmance is a cloud-based performance testing product that generates campaign load in a pattern close to real user distribution. It applies load from multiple regions rather than a single point, and you set the ramp profile: the sudden spike at doors-open, or a gradual climb across several hours. Because it converts measurements into an Apdex score, the performance conversation runs on satisfied customers rather than milliseconds.
Ddosphere is a security resilience product that tests your defences through controlled attack scenarios. Intensity levels are raised in stages, and the point at which bandwidth or the protection layer stops being sufficient is measured. Because the attack surface grows on the day traffic rises, this is one of the most concrete preparations you can make before a campaign.
AutoRunner is a test automation product that runs critical flows automatically on every release. In an e-commerce team releasing frequently, the easiest thing to break is the most valuable: search, product detail, cart, payment, returns. AutoRunner keeps those flows in regression scope, so when one breaks the team learns it from the pipeline report rather than from a customer complaint.
Roboclick is a testing robot that physically validates in-store till terminals and POS devices. It performs card read, PIN entry and receipt validation steps with a robotic arm and checks the screen through optical character recognition. When a new device model joins the fleet, the scenarios do not need rewriting; calibration is enough.
WCAG 2.2 criteria are tested for both compliance and conversion. A payment step that cannot be accessed is not only a compliance risk but a lost sale.
The common approach is to take a first measurement at least a few weeks before the campaign and repeat the test once the bottlenecks found have been addressed. A single test does not show whether the improvement worked. The comparative second run is what makes the capacity decision reliable.
Yes. Test scope, intensity level and stop thresholds are defined in advance, and testing usually runs in a separate production-like environment. If work in production is required, the test window is moved to a low-traffic period and automatic stop is configured on threshold breach.
Yes. Roboclick physically tests POS and SoftPOS devices, including key presses, card reads and screen validation. The in-store payment flow is validated to the same quality standard as the online one.
Loadmance converts response times into an Apdex score and relates the results to your SLA commitments. The report has two layers: detailed telemetry for the engineer and a capacity and risk summary for management. One test yields both a technical action and an investment decision.
Load testing measures system performance under the user volume you expect. Stress testing pushes the system beyond its capacity to establish the breaking point and the recovery behaviour. In campaign preparation the two are run together: one validates the normal case, the other the worst.
Complete the form; let’s discuss your campaign calendar, target concurrent-user count, and cart-to-payment flows. We’ll show which component is stressed at each traffic threshold through a measurable scenario.
