Evaluating Paid Search Performance In A Down Economy

Is it time to fire your paid search managers?

Many online advertisers are seeing their first-ever year-over-year declines in PPC performance. Because of this, and the general need to find something, anything that will generate more sales cost effectively, PPC programs are coming under serious scrutiny from corner offices. We welcome this scrutiny, and hope to provide some useful guidance on how to measure success in PPC in a tough economy.

Last week on RKGBlog, I argued that the wrong way to evaluate success is by the amount of management “activity.” Tail-chasing activities don’t help and in fact usually hurt performance. Productive work often looks like staring at a blinking cursor scratching one’s chin. So, if you can’t tell from the external appearances the difference between excellent work and day-dreaming, how can you tell whether you’re getting the most you can from your search program?

The answer is “in the details.” Here are 4 critical data elements to put under the magnifying glass:

Keyword-level performance. The ultimate measure of execution is in the keyword-level costs and sales. We’ve written extensively on how to look at this data fairly, and how to identify the signatures of poor performance. Follow the methodology outlined in those posts.

There are no valid reasons for the keyword-level performance to look bad. Within reason, the keyword level performance on the high traffic terms should be pretty close to your targets for those terms. Performance of the tail should make sense as well when clustered appropriately.

The classic non-explanation for wide discrepancies in keyword performance is: “Oh, those keywords that appear to be overspending are actually just early in the conversion funnel. Tail terms get credited for sales, but these terms play an important role in the consideration cycle.” Horse-feathers!

We’ve been trying to debunk this myth for years, but it has strong legs.

Try this approach: “Oh, I see, so please show me the keyword level performance data giving credit to the first click rather than the last. That should look nice and clean, right?!?” That should produce a great deal of coughing and sputtering. I suspect it won’t produce any data. When they say “we can’t pull that data,” ask: “This consideration cycle seems to be a big deal. Why wouldn’t you keep track of all the clicks given that it seems to be critical to managing the program effectively?” Expect more coughing, the words “brand-building,” “best practice…” and reference to “other factors…” If they actually do produce the data, expect to find that the keywords that were overbid based on last-click credit were also overbid based on first-click credit.

Keyword coverage. The length of the list isn’t the only issue. It’s trivially easy to generate 100K+ keyword lists using automated tools, but these lists are chock full of holes when put to human scrutiny. Take a look at the keyword list, or just one product category as a sample. Look for obvious two and three word combinations that aren’t in the list. Oftentimes the bulk of these auto-generated lists are four, five and six word key phrases that simply don’t matter, and obvious synonyms and industry specific jargon that the machines miss.

Landing pages. After the click, do you land on a page that shows the widest possible selection that responds to the search? Taking the users too deep is just as bad as landing them all on the homepage.

Ad copy. When you search for your products and categories, does the ad copy seem compelling and appropriately targeted? Remember that the goal of copy is to sell your store, not the product. Copy should answer the questions: “Why should I shop for widgets at your store?” You don’t need to sell them on widgets, they’re already looking for them. Copy is not a “game-changer” and many fall into the trap of focusing exclusively on it, but having targeted compelling copy does help.

If under this scrutiny the program looks good, does that mean you’re doing the best you can? Not necessarily. Ask your PPC managers:

  • Are we bidding based on margin-level information or just top line sales? The tighter your performance objectives are tied to the actual value of the traffic the better.
  • Do our efficiency targets make sense given the current climate? This is a big one! If the team isn’t hitting the current targets changing the targets won’t help, but assuming the program is hitting on all cylinders, what should we try to accomplish in search? Is it short-term profit maximization? Longer-term profit maximization (placing more value on new customers)? Top-line maximization at a neutral bottom line? These target decisions are the MOST important pieces of the puzzle if you have a PPC management team that can hit the targets.
  • Are we factoring in spillover to the phones intelligently? This usually impacts higher ticket purchases more than impulse purchases.
  • Are we dayparting, to take advantage of the fact that traffic value changes by time of day and day of week?
  • Have we tried out content advertising recently? Google Adsense can generate sales cost effectively these days for many advertisers. We haven’t seen it get to be more than 3% or 4% of Adwords, but who’s turning down 3% right now?
  • Have we tested separating exact match and broad match versions with higher bids on exact match? Same for Google.com only vs Network partners?

The economy is in the tank and consumer confidence is pretty close to zero. The time to look under the hood to find opportunities is now. If the current management team isn’t making the grade it’s a good time to move, but make sure you grade based on performance—not bluster.

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

Related Topics: Channel: SEM | Paid Search Column


About The Author: is Co-Founder and Chief Marketing Scientist of RKG, a technology and service leader in paid search, SEO, performance display, social media, and the science of online marketing. He also writes for the RKG Blog. Follow him on Twitter at @georgemichie1.

Connect with the author via: Email | Twitter | Google+


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