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The four B:Match custom audience segments ensured that Fund Firm’s communications effectively reach a high-majority financial advisor audience – at times when this audience is focused on finding and absorbing information that is vital to their business success. Each segment was set live, in addition to a control group (users who belonged to neither segment A,B,C or D), via Doubleclick and the DMP.

Fund Firm had aggressive KPIs and ambitious goals based around three measures – which weren’t always aligned in terms of optimization. These key measures were:

rate of visitors landing on its website; total visitors landing on its website; and cost per visitor landing on its website. They were not looking purely at click through rate.

The data-powered campaign out-performed the advertiser’s previous campaigns with Bloomberg, and the data segments out-performed the control group. We saw 60x greater CTR lift and 12 times higher landing rate from the B:Match segments and 500% more landings than the control group. Most importantly the cost per landing for this campaign was at the peak among the media properties and the performance of the B:Match segments ensured that the client KPIs were met and exceeded.


Data informing media strategy

The B:Match results were presented to the client with resounding success. The results revealed key behavioral insights about the customer segment that informed the media strategy and execution. Insights included media consumption habits of financial advisors – illuminating how they work in real time during an actual business day. This is not how they respond on a panel or to a survey; this is going deep below the covers to understand the content that drives their professional activity day to day. The insights emerging from the data are just as significant as the data targeting itself.

The results showed that financial advisors were most interested in consuming content around Bonds, in particular government and muni bonds quotes pages (data listing pages on Financial advisors seldom read long form articles, instead preferring to consume vast quantities of financial and economic data.

Specific site areas where visitors were significantly more likely to be prospect’s Content Authorities included:

• Portfolio-focused videos

• Benchmark bond indexes

• Emerging markets news

• High-end real estate news

• Corporate bonds

• Financial advisors news

Data informing media execution

The insights derived from the data analysis were used to create audience targeting models that were executed via Bloomberg’s DMP. The analysis involved running 4 separate naïve Bayes models, depending on the criteria included in each model. Each model in turn was converted into a distinct audience targeting model in the DMP. For example, the reach model (segment A) looked exclusively at the types of pages that a Financial Advisor visits, controlling for all other variables. In contrast, the relevant model (segment B), analyzed types of pages combined with other features such as operating system, browser, country and time visited. Adding in these additional characteristics resulted in some interesting discoveries.

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