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Media Awards Case Studies The Drum Awards For Media

AIA win in winning back post pandemic market share


By The Drum | Editorial

November 30, 2023 | 5 min read

Insurance brand AIA took a novel approach to win back market share in New Zealand following the pandemic and their innovative approach secured them the award for Data & Insight at The Drum Awards for Media. Here is the award-winning case study.

Example of AIA's work

Health and life insurance is a hugely competitive category in New Zealand, with a high number of heavy spending advertisers. Following the global pandemic, advertising investment was up significantly and multiple new players had entered the market. This was acutely felt in Paid Search, with a big increase in competitors bidding on life and health insurance keywords (up from 32 to 46 different advertisers).

As a result, AIA New Zealand's share of Search dropped and average cost per click had risen significantly. With continued focus on optimizing our budgets, we couldn’t spend our way out of this situation. AIA understood this but needed to hold lead volumes. Our objectives reflected this: deliver the same volume of leads through SEM with a flat budget, despite growing competition and CPC inflation (hold ROI flat).

A strong base of previous analytics work had identified how important TV was for Search. We knew a significant variance in AIA’s Search impressions can be explained by AIA’s TV activity. Not only that, but the quality of leads generated was highest immediately after AIA’s TV ads ran, within a relatively tight timeframe. So perhaps the solution was to run more TV? This wasn’t an option with a flat budget and significant TV price inflation. Growth would need to come from somewhere else.

As we reviewed the TV spend levels of our competitors a thought occurred to us: what if the relationship between TV and Search that we see for our brand is the same for the whole category? We analyzed historic Google data alongside competitor TV ad delivery and our insight was revealed: TV advertising appears to stimulate overall category searches in a similar way to our own advertising. It wasn’t just an AIA phenomenon; it appeared to be a category phenomenon.


So, if we couldn’t match our competitors’ spend all year, what if instead we could compete hardest in the highest intent moments for our category – the specific windows after our competitors run their TV ads? Strategy: own the intent moment after our big spending competitors advertise on TV. We weren’t going to get their TV spot lists, so instead we’d need:

Real-time TV competitive data, and

A means of using it to trigger paid search bids immediately.

To achieve this, we developed a unique solution engineered in steps: 1. Identify and analyze real-time TV data. First, we partnered with a supplier of real-time TV competitive data. We extracted 6 months of their data into our data warehouse, applied data harmonization, cleansing and transformation rules then analyzed it using AIA’s own performance data to see which days of the week, time zones, and competitor activity drove the greatest immediate impact on AIA search.

Establish competitive bid benchmarks. Armed with these insights, our analytics team then used competitive bid data to establish bid pricing rules by day, timezone, competitor and volume of ads. This meant we could build bid cost strategies based not just on our analytics but also on what it would take to compete in each micro moment.

Establish custom bid algorithms and real-time data feeds. The final step was to design and build the custom API between our data warehouse and ads platform. This sounds simple but was highly complex and involved multiple new technical developments by our team. For example, we had to build our own text recognition algorithm to read the TV ad data to distinguish between health and life insurance ads.

The result was a region-first paid search solution that capitalized on AIA’s competitors’ TV advertising.


Being most salient at the key intent moments helped AIA grow leads in the face of mounting competitive pressure. With the same overall spend level, attribution showed our “micro-intent” approach, compared to our baseline Search program, delivered…

More response: user volume increased

Better quality leads: conversion rate increased

Lower cost per lead

This meant that despite the same level of investment, we significantly exceeded our objective of holding Paid Search ROI, in fact, ROI from Paid Search increased. Entry credits

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