Monarch
Monarch improves sales and conversion through Media Mix analysis
The challenge
Monarch has always allocated media sales based on the traditional ‘last click’ method of reporting. However, for some time the company has wanted to understand the true value of their online media. Monarch wanted to understand the relationship between its online media channels and investigate how those relationships affected sales and conversion.
The Solution
Over the course of a year RedEye built a unique and leading edge data view of campaign information, which allowed Monarch to see the complete campaign history of users purchasing on their site. This unique data view allowed Monarch to understand which channels influenced the completion of a sale, tracking the full customer journey as opposed to just looking at the last click owner of a sale. This insight allowed Monarch to analyse the genuine attribution of media spend leading to each sale.
Through the development of Media Mix reporting, monarch.co.uk’s long term relationship with RedEye has really paid dividends. Using RedEye for all web metrics, campaign tracking and our e-CRM programme has given monarch.co.uk a true apples-for-apples insight into what is and isn’t working for us online. Adding Media Mix has allowed us to make further commercial decisions with confidence and has resulted in an improved understanding of customer behaviour plus delivered real cost savings with no negative impact on booking volumes.
Various data modelling techniques were employed to assess the correlation between different media channels. The two methods explained below show how RedEye was able to gain insight from the data which Monarch could use to maximise ROI.
Contribution Analysis Report
The first of these interpretations was a Contribution Analysis Report allowing Monarch to focus on a particular channel and see its genuine contribution to sales. Previously comparison sites had been seen as performing reasonably well, but there was no significant plan to increase the marketing spend attributed to these sites. RedEye’s analysis identified that 30% of the time, comparison sites had no assist from other channels and significantly over 40% of the time, when there was an assist, it was from a comparison site.
Assist Correlation Node Report
The second significant interpretation was an Assist Correlation Node Report, looking at how the different channels interacted. A search specific version showed the correlation between natural and paid search. The old search ‘theory’ indicates non brand terms have a major impact on brand conversion. The RedEye analysis proved this was not true for Monarch; with the Assist Correlation Node Report showing non brand terms had little influence in the conversion of brand terms or any other channel for that matter.
Using the combined analysis the following trends were revealed that would not have been possible without the Media Mix data view:
- Paid search destination terms were not converting and were also not influencing brand terms, which was contrary to popular search theories.
- Comparison sites were much less supported by other channels than other activities, thus delivering a greater proportion of stand alone sales that would not be gained elsewhere.
- Natural search (both destination and brand terms) were both influencing paid search brand terms as well as other channels.
As a result of this analysis, Monarch made the following media spend changes:
- Removed most of the budget from expensive paid search destination terms.
- This budget was then used to slightly increase paid search brand terms and significantly increase budgets for natural search work.
- Doubled the budget available for comparison sites.
The Results
Based on the strategies implemented above, Monarch saw the following results:
- Initially a 73% reduction in cost per passenger from search (PPC and SEO).
- More recently this led to a 14% increase in SEO investment returning a 55% increase in revenue and 45% increase in bookings.
- Investment into comparison website CPC media increased by over 100%. This led to a doubling in revenue and passengers from this activity proving its true scalability and as shown by the data, proving it is a more stand alone channel
