Giving Credit To Keywords Where Credit Is Due Follow-Up

In an earlier post on Search Engine Land called Giving Credit to Keywords Where Credit is due, I mentioned we’d be following up on this topic with some future analysis. The analysis we’ve done is in the form of a case study, below.

Most companies have difficulty justifying the purchase of general top-of-the-funnel keywords such as “slippers” because typically, these types of terms don’t seem to convert-at least when measured using the last click method.  Add this to the fact that general terms are usually more expensive than branded terms like “LL Bean” or “Victoria’s Secret” and you’ll see why some marketers avoid them altogether.

So when marketers look at these kinds of non-branded keywords in their standard analytics package, they can see that they get a lot of clicks and that they’re spending a lot of money.  But what they cannot measure is the value that these ads bring.  In most analytics programs, these keywords actually look like they’re having a negative impact on sales and driving the cost per conversion up.

General keywords show negative profit

Prior to working with us, our client simply couldn’t justify the expense of these types of terms.  The analytics package they were using employed the last click method of attribution, which only gives credit to the last ad clicked prior to conversion.  By using the last click method, their analytics showed that only branded keywords, specific product names, and model numbers were having a positive impact on the bottom line and that general keywords were showing a negative profit.

The first step was to have the client move from a last click attribution model to one that gives credit to all of the ads involved in the purchase path.  The purchase path is the chronological sequencing of all ad clicks, banner impressions, organic visits and direct visits that lead to conversion.  Once they moved to this model, the customer segmented their top 50 keywords into five different groups:

• Competitive terms
• Product names
• Brand terms
• Model numbers
• General terms

Suggesting custom attribution settings

After they grouped the ads based on the above categories, we showed them a report that attributes even credit to each ad in the purchase path and shows even attribution with exclusions.  Exclusions are when you exclude giving credit to certain ads even though they appear in the purchase path.  In our client’s case, they excluded giving credit to any of their brand terms that occurred at the end of the purchase path as these types of keywords are almost always used for navigation and have no impact on the sales process.

By doing this, they were able to see how profit levels for the general terms quickly moved from a negative to a positive return.   They were also able to identify the amount of additional profit they earned from general terms which they chose to reinvest to acquire more clicks and ultimately more profitable conversions.

In addition, we calculated the number of additional visitors and clicks they would be able to purchase with their newfound profit from general terms.  The report also outlined the average profit per order.

Armed with these reports, the client was convinced that, when valued correctly, the purchase of the general terms did yield a significant profit and they therefore invested more dollars into their general top-of-the-funnel terms.

Attribution model leads to increase in ad spend, visitors and profits

The results were impressive.  As a result of reallocation, the client was able to determine the real value that their general terms had. Where the client used to only invest in a few ad sources, they now advertise in many different sources, as their ability to measure an ad’s success wherever it occurs in the purchase path has been greatly increased.

They also enjoy competitive advantage by purchasing general terms that competitors simply find too expensive when evaluated under a last click model.

Most important, however, is the fact that by using attribution management, the company was able to increase their number of visitors by 222% and, most importantly, increase their net profit by 131% over the course of 24 months.