PPC Day Parting: Some Considerations

Day parting is one of those things that I’m fairly sceptical about. Yes, it does make sense to skew ad budget towards well-converting periods of time, but then isn’t this too much of a simplistic view? I feel that our good old last click model, the usual suspect, can be misleading us into lowering bids during some important initial stages of the path to conversion. So I took some data and had a little play to see if this is true. Read on, if that’s your cup of tea.

Some Sample Data
The graph below can be a little confusing at first, but it simply plots the disparity between first click and last click conversion for different days of the week. If, for example, a campaign converts at 10% on first click, but only 5% on last, then it’d score very highly on the graph and hit the 200% mark. The higher the score, the more “first click heavy” a campaign is. Below the 100% mark, the campaign is last click heavy.

First click bias is happening to Paid Search on Thursdays in particular, and we might be tempted to cut bids. This may or may not be a good idea, but you need the visibility to make a decision. The opposite can also be true, as is the case with affiliates, represented by the blue line. They tend to convert disproportionately well at last click, but are more rarely the initial touchpoint.

Moral Of The Story
Day parting is often based on last click data and my ramblings above highlight its potential inaccuracy. Sometimes customers will research a product mid week, but actually buy it when they have spare time towards the weekend. Similarly with time parting, there is often a strong research phase during lunchtime but conversions occur in the evenings. For most of us, we’d need to have the ads running in both instances of the customer path to ensure a sale.

Ideally, the exercise above should be done on a regular basis and at campaign, not channel, level. Splitting Brand from Generics in PPC makes sense, as I imagine that Generics will experience much higher disparities. As always, the more you dig into the data, the more valuable and actionable it becomes.