The Background

Paid search is a key marketing tool within the travel sector and our client had a reasonably good idea of the traffic and conversions it was receiving directly via its paid search channel. However, they lacked visibility around the indirect and longer-term influence of paid search on its business.

The Challenge

The client ran an online search test, up-weighting activity in 30% of the country with higher search spend (via increased CPCs and average positions) and wanted answers to key questions such as:

• How long did it take for new customers obtained via the search
test to pay back when including their repeat purchases?

• Does a higher investment in paid search lead to increased
conversions via other channels, especially low cost channels
such as organic search and direct type in?

• Do synergies with other online channels and fulfilment through those channels help make the higher investment efficient?

• Are they better off investing in paid search at the normal level, paid search at the higher level or in TV?

Our Solution

To answer these questions, we:
• Segmented their transactional data into new and returning customers – as they behave very differently.
• Built econometric models of the data utilizing the test and control regions to maximize variation and also analyzed their customer level touch point data – millions of rows of online marketing interactions, website visits and sales.

The Results

Our solution generated data driven insights for the client to help identify the correct level of media buying optimizations. Investment in paid search drove up the volume of sales and had a huge impact on attracting new customers, providing a 30% uplift in acquisitions.
The extra investment in paid search however failed to pay back mainly because it was working in isolation from other marketing channels.

We found that the shape of customer repeat usage was such that if the investment did not pay back within a year, it never would.
We also found that lower CPC found at positions below ad rank 1-3 were efficient and that investment at this level was highly recommended and in fact more efficient than their TV buy.

The Take Aways

The client was able to shift investment from TV to paid search, buying at lower CPC at positions below ad rank 1-3 which was more efficient than their TV buy. Moving forward with their search investment at the correct level for their market created a multi-million $ improvement.

Download this case study to see how we used deep insights to optimise paid search investment saving $millions in marketing spend.