From missed opportunities to 15× revenue: how Blimey scales upselling with Zentesimal

A business with signals no one could cover

Blimey operates in a very specific market: Chinese tourists who travel and book activities and experiences through their providers. A user who just booked a guided tour in Barcelona may be interested in a wine tasting that same afternoon. Another who has taken three trips with Blimey has a very defined buying profile. The signals were there.

The problem was capacity. Lorenzo and his team manually contacted some users when they could, but volume made it impossible to cover every case. Every interaction left unattended was an upselling or cross-selling opportunity that simply disappeared.

“It was not that we did not know what to do. We did not have enough hands to do it. We lost opportunities every day, and we knew it.”

The challenge: Chinese users, complex patterns, WeChat

Blimey has an extra layer of complexity. Their users are Chinese tourists, which means the main channel is not email or Western chat — it is WeChat. And the relevant behavior patterns are not obvious: you have to cross previous purchases, services booked, providers used, and churn risk signals to know when to act, how to do it, and which offer makes the most sense.

Automating messages was not enough. They needed a worker that could read each user’s context, detect the right moment, and personalize the interaction — on the right channel, with the right offer.

“Our users are Chinese, they operate on WeChat, and they have very specific buying patterns. We needed something that understood that complexity, not a generic tool sending mass messages.”

How the worker operates

Zentesimal built a worker specialized in the Blimey user lifecycle. The worker continuously monitors behavior patterns — purchase history, services booked, providers used, activity frequency — and detects two types of signals: upselling and cross-selling opportunities, and churn or abandonment risk.

When it identifies a signal, it acts. It contacts the user on WeChat with a message personalized to their context, follows up as needed, and escalates to the human team only when the situation requires it. Lorenzo’s team no longer has to hunt for opportunities — the worker brings them.

“What surprised me most was the personalization. It is not a bot sending the same message to everyone — it understands each user and acts accordingly. That makes the difference in a market like ours.”

The results

The impact was immediate. Moving from manual, partial coverage to full automated coverage, Blimey multiplied by 15 the revenue from upselling and cross-selling. No user with purchase signals is left unattended. And the quality of care improved: every interaction arrives at the right moment, with the right context.

“The 15× revenue speaks for itself. But equally important to me is that we now deliver care that was impossible before. Every user feels someone is looking out for them.”