Mining the sales funnel for maximum marketing benefit

Very often online marketers can feel they're in a never ending cycle and seemingly hopless quest to achieve the best marketing mix across all ATL and BTL channels. The never ending struggle to optimise the monetisation of paid search in terms of sales and continuing customer engagement and questioning how best to measure real ROI against these two metrics.

What I've sometimes discovered both within my own teams of marketers and those I have met outside of work is that they often don't realise that the right tools and models can not only make ROI measurement easier they can actually improve ROI. The method is to adopt a true revenue attibution model across the entire marketing programme - both ATL & BTL.

Ok, I'm going to throw some numbers aroud, I've kept the ranges fairly wide so as not to exclude any particular industry. Web sales typically can account for anything between 10% - 30% of a company's revenue (I did say depending on the industry right? also depending on the online/offline split) but where this gets interesting is when we start to look at the level of influence the web has on a company's revenue, there are lots of reports out there so I'm going to go with an average of 40% of sales are influenced by the web.

Therefore, measuring the bidding process in relation only online transactions significantly under reports SEM’s overall contribution to company revenue. Also, remember that paid search conversions are influenced by more than just the last click; many times a conversion happens a few hours, days or weeks after a series of clicks and searches. So how do you measure and analyze paid search’s contribution to offline conversions that may account for equal—if not more—revenue than online sales?

The key is understanding the full sales cycle by incorporating data from advertising events that happen further up the funnel, but which still play an important role in leading to that final sale. Start with upstream, top-of-funnel influencers such as newsletter subscriptions and store locator searches. The cycle should then be followed all the way through, ending with downstream, bottom-of-funnel influencers such as call center conversions and returns.

To better illustrate this fundamental shift in revenue attribution and tracking, let’s take a closer look at 'See Through Windows', a market leader in domestic glazing ;-)

'See Through' knew many of its shoppers were looking online to buy glass, but would ultimately finish the transaction offline by calling the 0845-number or driving to their local 'See Through' store. 'See Through' quickly realised that under-tracking revenue resulting from online actions like “facility address look-ups” drove down both bidding and volume across their paid search program, and ultimately reduced sales and revenue at their retail locations. They knew they had to find a way to more accurately measure revenue and they found a method using activity attribution.
'See Through' identified website activities that influenced sales. These activities happened early in the sales cycle, and were either good predictors of future revenue, had intrinsic value, or both.  The activities were:
  • Product Detail/ Retail Unit Locator Page: Prospect visited the page of a certain facility to get the address.
  • Hold: Prospect entered name and address to reserve a unit at a Retail outlet.
  • Reservation: Prospect provided a deposit on a unit.
  • Revenue generating conversion: Actual purchase of glass unit.
'See More' surveyed their customers, analysed the glass market, and lined up sales data in context of the above activities.  With this analysis complete, they applied discrete values to the four activities, independent of an actual conversion.  Using the activity attribution method enabled them to:
  • Correlate keyword assists to discrete web activities for smarter bidding.
  • Smooth out sparse sale data by relying on more frequent activities that occur earlier in the sales process.
  • Gain earlier insight into the sales cycle and changing market conditions.
  • Understand which keywords contribute to a sale, even though they may not be the last click.
The following diagram, proposes a mapping, using consumers behavior lifecycle for the left column.
By leveraging keywords and web site activity that occurs earlier in the sales cycle, 'See More'  provides a more reliable stream of conversion data to its SEM team. This consistent conversion stream creates a more accurate picture of future revenue, and conversion rates allowing for smarter keyword bidding.

In addition, the earlier insight into changes in the auction environment allowed the bidding algorithm to react during the first few days of the sales cycle, eliminating inefficient spend while maintaining conversion volume.  Most importantly, 'See More' took activity on their website and created actionable data for reporting and bidding purposes