Example 1: A commercial maintenance company targets a 20 mile radius around
downtown Denver. The marketing director might know that it costs 20% less
to sell to and service customers who are within 10 miles of downtown. He
can improve his results by increasing his bids by 20% for customers within
10 miles of downtown Denver, since these leads are more profitable.
Example 2: An online-only financial services company has modeled its
average customer lifetime value by zip code. The company’s search
specialist has been asked to achieve an average 8:1 return on ad spend
(ROAS), which they define as average lifetime value divided by average cost
per lead. The specialist downloads data from AdWords with cost per lead by
zip code and pairs it with lifetime value for each matching zip code
data). She looks for opportunities to improve her results by lowering
bids in zip codes where ROAS is below the target and increasing bids in zip
codes where ROAS is above the target. She makes her bid adjustment
decisions in the spreadsheet and implements them in her enhanced campaign, re-checking
the ROAS and volume impact for a few weeks and making changes as
necessary. With legacy campaigns, she would have to set up a new campaign for every zip code with different bids, increasing the level of campaign management complexity and effort required.
Example 3: A national multi-channel retail business has been running
AdWords campaigns to sell directly online and to drive people to its 400
local stores. The account has already set up location extensions, but it
wants to improve its ad visibility even more when customers are searching
within a short distance from its stores. With just a few clicks, its search
agency adds a single “2.0 mile around each location extension” target and
sets a +25% bid adjustment.