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GlobalPay’s existing legacy marketing-automation setup – Gamooga – was sufficient for the business requirements at the time, particularly when the organization was primarily focused on its B2B-led model. The platform supported the team’s core communication and automation needs, and there was no immediate need to replace it simply for the sake of changing technology.

The business, however, began shifting its focus toward direct-to-consumer (D2C) growth. This changed the role marketing needed to play. Instead of primarily communicating with known customers and partners, GlobalPay needed to engage consumers across their journey – from a rate check or currency-order initiation to a remittance drop-off, card interaction, or other high-intent action.

That shift created a new set of requirements: real-time customer data, unified omnichannel engagement, behavioral triggers, a single customer view, and a direct connection between marketing intent and the sales team.

The question was therefore not simply about replacing an existing automation tool. It was about finding a reliable technology partner that could support GlobalPay’s next phase of growth – one that could integrate with its existing ecosystem, bring data and engagement together, scale across D2C channels, and evolve alongside the business.

This was the point at which GlobalPay partnered with Netcore, with the objective of building an engagement infrastructure designed not just for automation, but for real-time, customer-led D2C growth.

Just as important, the Netcore team took ownership of GlobalPay’s engagement outcomes rather than just shipping a build – treating GlobalPay’s KPIs and growth as its own, staying close through discovery, integration, and go-live, and steering decisions instead of waiting to be asked.

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None of this was a shortfall in demand or in effort. The team set out with four goals: reach interested shoppers while a new arrival is still new, create urgency around fast-moving stock without waiting for a scheduled send, route price changes to the shoppers who had actually looked at those items, and do all of it across three markets without adding campaign workload. Read together, the four describe a single requirement. Speed.

The catalog moved faster than the campaign calendar, and three moments paid for the gap:

• New arrivals, seen too late. By the time a weekly newsletter went out, a drop had been live for days, and the shoppers most likely to want it first had no early signal.
• Low-stock urgency, missed. Items selling out fast had no automatic nudge to shoppers who had already shown interest, so the moment passed unmarketed.
• Price changes, buried in blasts. When a price moved it was mentioned in a generic send rather than routed to the specific shoppers who had browsed or carted that exact item.

What made this structural rather than solvable with more effort: scheduled campaigns were never designed to serve as a real-time response layer on a catalog that changes daily. Every one of these moments depended on a marketer noticing it in time, across three storefronts, indefinitely.

A large share of redBus’s lifecycle email was landing in Gmail’s Promotions tab rather than the Primary inbox – buried, in practice, since that isn’t where travellers look first. The campaigns themselves were strong. What wasn’t keeping pace was their visibility: Gmail’s classifier wasn’t reading the relevance and engagement signals around those sent the way the content warranted, so fewer travellers saw the email in time to act on it.

The baseline carried the same story. Before any changes, the programme was opening at 22.00% with a 0.03% click rate on delivered email – below what its reach and relevance supported. Both teams agreed to go after the cause rather than patch around it.

AJobThing ran a high-frequency email programme across both audiences – employers and job seekers – but the team was making send decisions with limited visibility into what actually drove performance. Before Insight Agent, every performance review meant manually cross-referencing exports to compare one week against the next – a slow process that left little confidence in the picture it produced, and no early warning if the channel itself was starting to strain.

Campaign Intelligence Blind Spots

• Week-over-week comparisons: Comparing performance across Open Rate, CTR, Delivered, and Sent meant pulling and reconciling exports by hand, which made it hard to spot patterns quickly.
• Assumption over evidence: Send decisions ran on assumption rather than historical engagement data – the team couldn’t say with confidence which content, which segment, or which send time performed.
• Send-time guesswork: Optimal send times were hard to isolate, so scheduling ran on habit rather than evidence.
• Invisible content performance: Without a content-type view, the team couldn’t tell a Promotion send from a Hiring Kit send in performance terms.
• Undetected audience fatigue: Early signs of audience fatigue went unnoticed until unsubscribes had already climbed, putting sender reputation and domain deliverability at risk for every future send, not just the one that caused it.

To protect that reputation and get more out of every send, AJobThing needed a way to turn raw campaign metrics into fast, actionable decisions – without adding manual overhead to get there.