May 2, 2019
How to Derive Business Insights Using SQL, BigQuery, and Google Data Studio
Every business wants to know more about their customers. Where do they live? What do they earn? What messaging resonates with them best? PPC advertising can answer all of these questions, and many more.
One of the biggest benefits of advertising on the Google Display Network is the additional insight you gain into demographic data. You can easily break down performance for Display or Remarketing campaigns by age brackets, gender, parental status, or any combination of those categories.
For this B2B advertiser, males drastically outperform females in terms of conversion volume and CPA. Drilling one level deeper, males from 45 to 64 years old perform even stronger than the average male. Based on these findings, we reduced our investment for female users and reinvested those dollars into 45 – 64 year old men. That demographic profile is something we can easily carry over to other advertising channels, like LinkedIn, Facebook, or even TV and print ads. Knowing our most valuable demographics ahead of time allows us to proactively increase our investment in those audience segments.
One of the quickest ways to gain insight into your audience is to check your average time lag to conversion. Some advertisers have very brief time lags, with over 75% of users converting within one day. Other advertisers have, on average, two or more weeks before a user converts.
This advertiser had one of the longest days to conversion I’ve ever seen. Because of that, I know that customers are shopping around, comparing options, revisiting and rethinking their purchase several times before they buy. Knowing that your target is spending a great deal of time comparison shopping and considering a purchase allows you to prioritize an incredibly granular remarketing strategy, and implement a comprehensive competitors campaign, to make sure you stay top of mind during the 12 day research process.
You can apply this knowledge to other marketing efforts as well. You might test an email list sign up as a micro-conversion to allow you to nurture previous visitors with promo emails. You could build a larger social media presence to ensure you’re reminding users of your brand during their other internet activity, or you could test landing pages that directly distinguish you from your competitors to clarify why a searcher should choose you.
Buried within AdWords geographic targeting options is a Location Groups setting, which allows you to gather data and make adjustments based on your audience’s income segment. Google uses IRS income data by zip code to slot searchers into income tiers, and accumulates data according to those tiers.
As you can see, our main non-brand customer for this client comes from the top-earning income segments of the population. However, with more time to consider a purchase, the middle-income bracket outperforms the other segments extraordinarily well, with over a 13 ROI for remarketing.
From this data, we hypothesized that middle-income customers take longer to purchase because the product represents a significant investment. Given more time to consider, they opt to choose this advertiser’s higher-quality product over cheaper alternatives. For this reason, we phased out all price-sensitive or discount language in our non-brand ad copy in favor of messaging focused on quality and luxury. Those discount-based attributes proved more effective at closing the deal with middle-income customers.
Paid search is data-heavy, but get beyond just the numbers to understanding the characteristics and population segments that make up that data. Paid search is a powerful tool to understanding your target market, and a clever account manager should leverage that insight to anticipate what that customer needs and adjust strategy accordingly.