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Paid Search Buying Cycle: More thoughts

RKG has developed a reputation for straight-talk. We figure if we provide a valuable service at a fair price, business will be good. And, it has been, and we can sleep at night.

As part of this approach we’ve launched “truth campaigns” to debunk various myths that are propagated in the industry by those who follow a different business strategy.

The PPC buying cycle is one of those notions we’ve tried to put in proper perspective. The buying cycle argument suggests that people first search for general product categories, then get more specific later as they get closer to placing an order. Its advocates then argue that the more specific keywords steal credit from the more general keywords that got the ball rolling, and that it is therefore a good idea to spend well beyond your efficiency targets on general keywords, because while it doesn’t appear that they’re driving enough sales to justify their costs, the sales those keywords drive are simply being credited elsewhere.

There are two fundamental problems with this line of reasoning:

  1. This pattern is far less common than proponents suggest. Some less-than-scrupulous agencies have convinced their clients that changing the efficiency thresholds for these “early stage” keywords by factor of 200% or more makes good sense. We’ve seen instances where retailers with 30% cost to sales targets were convinced that a 300% cost to sales ratio (a 1000% increase!) on these keywords was acceptable.The reality is that even giving full credit to the first ad touched rather than last would rarely move the needle more than 10%. The highest we’ve ever seen is ~40%, which would mean moving the efficiency target from 30% to 42% on that KW.
  2. Here’s keyword data we pulled for one of our clients (we’re actually bidding to margin for this client hence the varied cost to sales values):

  3. Second, it’s not clear how much credit early stage touches actually deserve. There are several grounds for questioning the value of that first touch.
    • In some cases, the initial search is in a completely different product category from the subsequent search (first search: “Cuisinart”, second search “ipod nano 8 GB”). We investigated a sampling of these mismatches and in each case the purchase was related to the second search, not the first.
    • If, after the initial visit, the customer still hadn’t decided where to shop, and was willing to see a wide range of retail offerings for the specific type of product he or she wants to buy, exactly what was the value of the first visit? Why do we care whether the initial process of narrowing choices happens on your site (and on your dime) or your competitor’s?

Perhaps there is branding value to the initial, unproductive visit. Perhaps, people who shopped around on your site are more likely to click on your link when the do the follow up, more targeted search, than are folks who bounced around someone else’s site.

If we then are willing to give some credit (10%, 20%?) to the first PPC click how does that affect the math?

Now we see that if giving all the credit to first rather than last only moves the needle 5 or 10% most of the time, giving 20% credit to the first click instead of 100% only raises your efficiency threshold 1% or 2%. In our example before the retailer could afford 30.3 – 30.6% on these early cycle terms rather than 30%. In our most extreme case giving 20% credit on the keyword that saw a 40% lift when all the credit went to the first touch, our efficiency target go from 30% to 32.4%.

These micro tweaks are unlikely to have much impact on bids and positioning on the page, hence may have zero impact on traffic.

Worth investigating, sure, but if someone tells you that a huge difference in efficiency targets is warranted and profitable based on the buying cycle they’re either ignorant or lying.

I’ll take up the topic of branding value in a subsequent post. I’m wrestling to understand how branding is justified when tracking proves it to be unprofitable…

Love to hear other thoughts, opinions and data on the subject!

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Comments
11 Responses to “Paid Search Buying Cycle: More thoughts”
  1. james says:

    Hi George,

    Long time lurker here – we’ve met at a few conferences too.

    The data presented only measures the very first ppc visit and the very last ppc visit by the same user in a specific time period, right? when do the cookies you are using to track this data expire?

    Also, when we start to attribute the revenue to other sources too – like organic clicks, press release clicks, editorial stuff, cpm buys, offline ads if using unique urls, etc – do you think the efficiency needle has the ability to move more than ppc appears to move if all this other media is included?

    Also…like any funnel, do you think that everything in between the first and last click in ppc would experience the same minimal efficiency if revenue is attributed across all ‘interstitial’ visits within the ‘buying cycle’?

    Thanks for this post – tons to think about with it. Especially with PPC. Awesome study!

  2. Hi James, thanks for the great questions/thoughts.

    Initially I didn’t restrict the cookie window at all. Even doing that the bar doesn’t move that much, but I saw more of the phenomena where the initial keyword had nothing whatsoever to do with what they ended up buying.

    I decided the right way to go was with the retailer’s own cookie window. If they’re not willing to give credit to clicks more than X days old then so be it.

    It does make the notion of “first” a bit slippery though, as it’s really the first touch within the cookie window prior to the order.

    The vast majority of multi-click behavior involves clicks on the same ad, and the number of folks touching more than two competitive search ads is negligible.

    We have started to study cross-channel interactions and find that there is much more important movement between channels than we see within PPC search, and because the channels behave very differently (PPC, natural, email, affiliates, display, catalog) there are tendencies for some channels to be the initiators and others to be closers/cannibalizers. I’m speaking on the topic at IRCE and plan to share our findings there.

    The credit allocation scheme between channels tends to be quite important shifting channel credit by as much as 25%.

  3. Great article – interesting analysis.

    Just began diving into this same type of data in ClickEquations and seeing slightly more variance in A/S Ratios than your sample.

    For example, in one mid-sized retailer about 1/3 of the keywords show differences of greater than 10% (in positive or negative directions) between the first vs last model. A small number of these keywords have triple digit % differences – so it may be a few but on those the impact may be quite significant. These are using 30-day cookie windows on 90-days worth of data.

    The more surprising stuff is in the # of times and places in the chain the same keywords appear – far more frequently than people think are they paying for the same keyword multiple times in one conversion. Which of course tends to flatten the differences between first and last and linear if they’re both/all the same keyword!

    We haven’t seen enough yet to draw conclusions, but the kind of deep look you’ve taken and insights you suggest are certainly ones everyone is going to have to wrestle with soon – first within PPC and then as your earlier comment mentions across channels. All of this adds another serious layer of complexity to the process – unless your premise turns out to be right and it doesn’t matter much and we can all prove that :-)

    Thanks again for your post.

  4. james says:

    George, Thanks for the detailed response. I hope you blog about the cross-channel findings! ;) Have a fantastic week!

  5. Thanks Craig,

    It’s certainly the case that the degree of variance changes from client to client, so it’s always important to “see for yourself”.

    It’s also important to bear in mind that for keywords with low levels of traffic the % swings are going to be huge because either you got credit for that one order or you didn’t. A smart bid system isn’t bidding those keywords individually anyway, but aggregating that data with like keywords. As such, much of the washing around will occur within the same aggregation bucket and make no difference to the bidding.

    We think the right approach is to watch this phenomena atomically for the highest traffic terms and collectively for others. Testing some adjustments based on the data may reveal whether fractional allocation within PPC makes sense.

    George

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