Making Sense Of Multi-Click Data
Click path reports can be very useful tools in understanding how customers behave. For instance, they can tell you how much time consumers spend research before they buy a product. They can also tell you if there are certain keywords that help other keywords convert, e.g. assisting keywords. Finally, when coupled with attribution, they can also help inform bidding decisions.
Since this is a vast topic, I am going to focus on three specific ways in which you can analyze this data and drive insights. Each of these techniques will help you understand a different facet of consumer behavior.
For this piece, I took a dataset from a large retail advertiser and classified the keywords into four buckets: generic (“shirts”), branded (“Ralph Lauren”), generic+branded (“Ralph Lauren shirts”), sale (“discount Ralph Lauren shirts”).
I then looked at 20,000 transactions over a period of 2 months. Each transaction was tracked for up to 10 Search touch points. How did the transactions breakout by the number of touch points?
Approximately 98% percent of transactions for this retailer take place within 5 Search touch points. The average path length was 1.72. This is a bit surprising and interesting. When similar studies (most notably a RKG one) were done in the past, the path length hovered in the 1.2-1.4 range. Similar analyses now reveal longer path lengths, in the 1.7-2 range. This indicates a savvier customer, more intent in researching a product before purchase.
Measuring The Branding Effect
Many advertisers are trying to measure the branding effect of generic keywords. The theory goes that many users start with a generic keyword and end up converting with a branded keyword. Does the data bear that theory out?
The graph above shows how transactions beginning with a certain keyword type ended. For instance, 89% of transactions that began with a generic+brand type keyword converted with the same keyword type, 5% of generic+brand type keyword converted with a brand keyword and so on.
Notice the strong diagonal (89%, 95%, 89% and 90%) indicating that for the most part users starting a transaction with a certain keyword type will end their transactions with the same keyword type.
What is the non-brand to brand spillover here? It is 7%. (NOTE: in the brand column, 22% of 117%, e.g. 7% of all brand conversions, began with a non branded term). In other words, the branding effect is small for this retailer.
Key Takeaways For The Advertiser
Clickstream analysis can provide valuable insights into consumer behavior. In this article, I have presented one way in which the data can be mined to provide useful insights.
If your average click path is long, it reveals a more bargain- and research-centric consumer. Ad copy must reflect this mindset. However, a shorter path length reveals a customer with immediate intent to buy. Ad copies for these types of terms must reflect the urgency in the consumers’ mindset.
While the branding effect for this dataset is small, it might be different for you. If you do see a strong branding effect, you must account for this in your bidding decisions for the generic terms. While looking at generic terms from a purely last-click perspective might make you more efficient, it might undercut the performance of your brand terms.
Analytics news and expert advice every Thursday.