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.

Opinions expressed in the article are those of the guest author and not necessarily Search Engine Land.

Related Topics: Channel: Analytics | Search & Analytics

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About The Author: is Director, Business Analytics at Adobe. He leads a global team that manages the performance of over $2 BN dollars of ad spend on search, social and display media at Adobe.

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  • TSG

    How long is the cookie expiration window for this client?

  • http://blog.efrontier.com sidshah

    I looked at transactions covering 2 months. Typically we see about 95% of all transactions in a 30 day window. So 60 days is a good length.

  • http://www.rimmkaufman.com George Michie

    Good stuff, Sid, thanks for the hat tip! There is some confusion in the literature about what is meant by the term “brand”. You’re using it here to denote that the user searched for a specific brand of apparel it seems. Often when people talk about the generic to brand search path they’re talking about the advertiser’s brand name, not the manufacturer’s brand name. In that context “Ralph Lauren” is only a brand search for Ralph Lauren, for every other retailer that sells Ralph Lauren clothing it is a competitive non-brand search phrase. Just wanted to make sure everyone was on the same page here.

    Also, your findings line up with ours in another sense as well. We find that customers who buy from more than one of our clients are disproportionately likely to use the same type of search phrase whenever they shop. Would be interested in your comments on this notion of user personas: http://www.rimmkaufman.com/rkgblog/2010/02/02/shopper-profiles-personas/ and http://www.rimmkaufman.com/rkgblog/2010/05/10/search-behavior-from-the-rkg-labs/

  • Henry Huang

    Hi Sid,

    This may be a silly question, but noticing your second graph (Starting and Ending Converting Keywords), you mentioned the high percentage along the diagonal. Seeing that 70% of orders converted after one keyword (from first graph), was the data for the second chart parsed of one touch-point conversions?

    Thank you,

    -Henry

  • http://blog.efrontier.com sidshah

    @Henry: In this example one click conversions are considered to have first and last click from the same keyword. I dont expect things to change much if we execlude one click conversions because the level of self assists (first and last keyword for a multi click conversion) in this example was about 80%. This is most likely due to Google Autocomplete.

    @ George: I like the idea of different personas but in search the lowest level of aggregation is the keyword/match type and the broad matched high volume keywords attract all kinds of users. So how does one action on it ? Any ideas ?

  • Henry Huang

    @SidShah – wow, that’s great to know. It is interesting to see that even in multi click conversions that 80% still have the same first and last keyword conversions. I would’ve thought that it would be less than that (maybe around 60-80% range). Thanks for sharing, I definitely enjoyed reading your post.

 

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