Compared to other data points, CTR is a relatively unreliable metric for judging the health of a paid search program over time because there are so many factors that need to be accounted for, some of which the advertiser has little to no control over.

If you’re worried about how your CTR looks month to month or year over year and are considering widespread copy changes, here are some factors to consider first:

Search Network Expansion:

For many, traffic from the engines’ search networks is growing faster than traffic on the engines themselves.  Click-through rates on this partner traffic also tend to be significantly lower,  by as much as 50-90%.  If you were to make no changes to how your ads were syndicated, you would see a lower CTR just from the expansion of the search network.  The upside is that the lower network CTR should not affect your quality score on the core domain.  As Google states: CTR on the Google Network only ever impacts Quality Score on the Google Network — not on Google.

So, if the network traffic is otherwise performing well, no changes are necessary, but its effect on overall CTR should be noted during trend analysis.  To get a more consistent view of your CTR over time, parse out the network traffic in the engines’ reporting tools.

Competitive Landscape Changes:

An ad’s position on the search results page has a major impact on its click-through rate for obvious reasons.  As Google’s Fred Vallaeys succinctly puts it: An ad in a higher position is predisposed to get a better CTR. If a high-bidding competitor swoops in and bumps you down a spot, you are virtually guaranteed to see your CTR fall.

If you are bidding based on efficiency and not position, as RKG recommends, you are left to accept the lower position or work to improve your quality score or conversion metric to try to overtake the competitor.  Copy or landing page changes could help, but if you’ve already optimized in those areas, you may be better off working to grow the program where there is a greater potential return on your sweat equity.  With any luck, the competitor will find they are overspending and you’ll see your position and CTR improve when they reduce their bids.

Note that Google’s Quality Score is normalized to account for the expected click-through rate differences in different positions, so all else being equal, falling a spot due to changes in the competitive landscape should not hurt QS.

Broad Match Expansion:

The engines, particularly Google, are regularly tweaking how their broad match algorithms function.  This requires them to balance ad relevance and revenue, but in recent years, the short-term revenue side has been winning out.  A broader and broader definition of broad match leads to keywords showing for more distantly related search queries and click-through rate suffers as a result.

The upside again is that Google is smart enough to take this into account when determining Quality Score, which is based only on the performance of the keyword when it matches the search query exactly.  (Consequently, changing a keyword’s match type from broad to exact will not improve its Quality Score.)  So, seeing a lower CTR due to increased broad matching is not a great reason to make copy or match type changes in and of itself.  At the same time though, this scenario may indicate that your account could use negative or tail keyword additions (with better targeted copy and landing pages) or increased parsing of exact and broad match traffic, assuming the broad match traffic converts at a lower rate.

Ideally, in our analysis we would account for the wider scope of broad match by looking only at CTR data when the search query matched the keyword exactly, regardless of the keyword’s match type.  Unfortunately, that’s not an easy task at adgroup or higher levels unless you are running mirror campaigns under different match types.  Even then, there may still be some bleed over.

Other Considerations:

• Portfolio effects: If you are frequently making keyword additions and bid changes, you may see your high level CTR stats improve or decline based on the type of keywords that receive more or less traffic after the changes.  In other words, click-through rate trends at an account, campaign or even adgroup level may not be telling you what you think they are.  For instance, growing incremental traffic while maintaining target efficiency is the goal for most advertisers, but it will lead to a lower account CTR as brand traffic with its higher click-through rates will make up a smaller proportion of the program.  Make sure to use as fine a level of detail as necessary to get an apples to apples comparison over time.
• Efficiency target changes: Aiming for a different ROI is likely to result in changes to your average ad positions and your CTR will change as a result.
• Content:  Impressions from contextual ads can fluctuate wildly, so content traffic should be parsed from search traffic by all means.

For an indication of what advertisers are up against, here’s a chart of RKG’s observed year over year change in Google Search impressions for a basket of well established clients versus the Y/Y change for Google Sites traffic under Comscore’s Core Search Report:

Even though we are taking regular steps to reduce poor quality broad match traffic, we have still seen our Adwords impressions balloon faster than clicks and much faster than traffic to Google and its partners as represented by the Comscore figures.  We’ve heard others recently ascribe lower CTRs to changes in consumer behavior during a weakened economic environment, but that would seem to be a minor effect relative to the changes coming from the engines.

Bottom line, if you are generating more traffic at the same efficiency despite lower click-through rates, chances are your program and copy are still in decent shape.  That’s not to say you should ignore the CTR metric, just be sure you know what the data is really saying before you change your optimization priorities.  Ideally,  PPC advertisers would have access to a more precise reading on quality score to steer by, but we’ll have to make do with what we have now.

Technorati Tags: broad-match, click-through rate, CTR, Google, paid-search, PPC, Quality-Score, search network

According to one industry observer, comScore is about to release numbers indicating Bing’s share has jumped 0.6% in the last month and a half, while Google and Yahoo each lost 0.3%. Now share of search volume and share of advertising revenue (factoring in syndication partners) are different animals, but from our data, comScore may be under-reporting the shift.

These data are aggregated so our higher spend clients dominate the results. We’ll take this apart to study medians later.

MSN/Bing’s growth from 5.3% in February of 2009, to 9.0% in February to date is highly impressive. Where much of that growth seemed to come at the expense of Yahoo early on after Bing’s launch, January and February show Google starting to feel the heat. Google’s share of paid search advertising dollars dropped from almost 81% in December to under 78% in the first week + of February.

Questions abound:

• Did Google buy a Superbowl ad because they’re seeing erosion of paid search share?
• Is Bing’s growth solely a function of Cashback, and is Cashback financially sustainable for them?
• While we and others have criticized Bing’s commercials, are they starting to have an impact?
• Has Google been distracted by all its other endeavors and taken its eye off its core product?
• Is there something screwy in our data that we haven’t accounted for?

Given that I pulled the data myself the odds of the last question being salient aren’t bad. One of our far more careful, thoughtful, judicious analysts will probably “tut, tut” me later.

Folks who budget search, and go so far as to budget by engine, might cause a shift in share simply by executive decision. But that’s not what we see in our data. Our clients don’t budget for the most part and don’t budget by engine at all. We’re spending more on Bing because the quantity and value of the traffic dictates spending more.

Paid search is only one piece of the advertising mix for Google, but it is a big piece. If this trend continues expect to see some refocusing from Google.

Love to hear if others are seeing similar trends!

Technorati Tags: Bing Market share, search engine market share

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Well, a lot has happened in the mobile space in the past year:  Apple sold another 20 million or so iPhones worldwide, Motorola and Verizon launched a $100 million marketing campaign for the Droid, AT&T claimed a nearly 7000% increase in data use over the last few years and Google finally debuted its self-branded Nexus One.  It’s a good time to revisit our numbers.

As we did previously, we are looking at click traffic from mobile devices with full internet browsers that trigger standard PPC ads.  In December of ‘08 we found just 0.4% of PPC clicks came from mobile users.  By October of ‘09 that figure had already jumped to nearly 1.3%.  (Although it’s dwarfed by Google, it was around this time that Yahoo began showing its full search ads on iPhone-like devices.)

In early January of this year, mobile’s share of PPC clicks is approaching 1.8%:

Traffic from devices running the Android OS has increased the most dramatically in recent months, with Android’s share of mobile PPC clicks rising from 6.3% in October to 10.5% this January.   Android’s quick rise corresponds with Motorola’s Droid launch as well as the launches of a slew of less-heralded Android devices late last year.

Share of Mobile PPC Traffic for Select Devices:

At the same time, iPhone’s mobile paid search traffic share (including iPod Touch) has been dominant, but somewhat flat, while Blackberry is down significantly.  It will be interesting to see if the Google Nexus One launch gives Android another big bump despite the reports of anemic first week sales.

So, we have this big increase in mobile traffic, it must be a boon to advertisers, right?  Unfortunately, it doesn’t look that way.  Conversion rates are very poor compared to traffic from desktop/laptop computers.   There are a couple of ways to think about the low conversion rates:

• Conversion Rates are Better than they Appear
  1. Cookie breakage as people browse on their mobile devices, but purchase on their home or work computers. 
  2. On some devices, there’s also more likely to be technical issues with tracking cookies or scripts that we don’t face on full PCs or Macs.
  3. People click on the ad, find the store nearest them and purchase in person.
• Conversion Rates Really are Awful
  1. Shopping on a tiny screen with two-thumb typing may be too much of a barrier for folks to overcome.
  2. People click on your ad and walk into someone else’s store to make the purchase.
  3. People are in someone else’s store want to compare prices online and click on your ad to show the clerk and get the benefit of the store’s price-matching policy.

If the advertiser doesn’t have brick and mortar stores, there’s a pretty good reason to be biased towards the negative assumptions. If you’re in that camp, it makes sense now for advertisers to parse out a duplicate campaign of top Google keywords to display on mobile devices while using the opt out option for existing campaigns.  The mobile campaign can be bid based on its own performance without diluting the results of the core program.

Mobile search growth is unlikely to slow anytime soon, while the lines between devices will continue to blur as more and more gadgets incorporate full web browsing functionality.  Google is clearly anticipating this as it pushes to get Android, with its close integration of Google services, on as many devices as possible.  Whether user behavior and our ability to track it begins to match what we see on traditional computers anytime soon is still an open question.

Technorati Tags: android, Google, iphone, market-share, mobile, paid-search, PPC, SEM, Yahoo

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A few years ago, I wrote a blog post referring to the syndication networks as “Sin-dication“. I’m trying to be nicer these days as a tribute to Alan.

FACT: The quality of traffic coming from the engine itself is of significantly higher quality than the traffic coming from their syndicate networks.

This has two important consequences:

1. Bidding the same for traffic on the Google syndication networks as on Google.com leaves opportunity on the table.
2. By not allowing advertisers to differentiate bids based on the quality of traffic driven by each network partner, Yahoo and Google lose money as do the advertisers.

THEORY

Let’s take some basic paid search principles and show how they apply to the syndicate partners. I’m going to make some simplifying assumptions to prevent us from getting mired in detail and missing the bigger picture.

The value of traffic in paid search varies terrifically by the user’s search and the ad it fires. Since we can’t really bid based on user search, let’s see what that looks like as a function of keyword:

Cool! Now let’s say the advertiser is willing to put 80% of the true value of the paid search income back into the marketing to drive the top line but still make some immediate profit.

Ideally, the bids would look like this:

By matching the bids to the value of the traffic on each KW we generate as much traffic as we can afford from each KW, thereby maximizing revenue within our efficiency requirements.

There is, however, another way to achieve the efficiency objective. Namely, by bidding to the average value of the collection of KWs.

In this simplest of models, assuming that the underbidding and overbidding don’t move the average value of the traffic — a big assumption and probably never the case — you still end up spending 80% of the paid search income on marketing, which was the target all along.

So if either way you hit the desired efficiency target, what’s the difference?

Volume. By wasting money on some terms, and missing opportunities for more sales on others, the poor bidding methodology hits its efficiency objectives but does so at the expense of sales volume.

Importantly: lower sales volume at the same efficiency by definition means lower advertising revenues for the engines.

APPLYING THEORY TO THE SYNDICATE NETWORKS

So, if what we’ve learned is that applying the same bids to traffic of varying quality leads to fewer sales/leads for the advertiser and less revenue for the engines how does that effect our view of the syndicate partners?

Let’s look at some data. I grabbed data from a handful of our clients and took median values by referring domain for click volume, and order volume as a percentage of the total traffic for each client. I also calculated median values for how each domain’s conversion rate compared to the average conversion rate for that ad network. The comparative conversion rate data is most interesting.

{Note: sites are ranked from left to right in descending order of traffic volume for the median client}

Notice that traffic from Google.com, AOL, Amazon, and Comcast tend to send much higher than average quality traffic: 16 – 20% better than average. At the same time, eBay, Ask and the Comparison Shopping Engines, tend to send significantly lower than average quality traffic.

For Adwords advertisers can and should bid differently on the Google.com domain and the rest of the network. Looking at these medians as if they were one advertiser’s data, we’d end up bidding the Google.com-only version of the account up by 16% and the Google.com + syndication network version of the account down by 33% (Google.com traffic is slightly more than 2/3 of the total, hence the disparity).

My sources tell me we’re among the very few folks in the space doing this, which is surprising as we’ve been doing it and advocating it to others for more than two and a half years.

This two-tier approach will help materially, but is in no way ideal bidding. In this, we end up underbidding on AOL and Amazon traffic, and still overbid on eBay, Ask and others. Better, but not great.

If we take a look at the same type of data from the Yahoo network we see even greater disparities.

Yahoo.com traffic is 35% better than the average Yahoo ad network traffic!!! The rest of the network averages ~ 42% below the aggregate average! But, even among those there are winners and losers.

Yahoo does not give us the opportunity to bid differently on the network, but does give us the opportunity to exclude traffic from particular domains. Given the lower traffic volume on Yahoo, it can be difficult to separate signal from noise to identify those domains that send particularly low quality traffic. Sometimes looking across multiple accounts helps us spot trends that we can’t really see looking at a single account’s data.

But excluding sites isn’t really what we’re after either. In most cases the traffic isn’t worth nothing, it’s just worth less. Paying the right amount for it would allow us to generate sales cost effectively from each domain in the syndicate network and most importantly, would allow us to push the gas harder on the high quality traffic provided by some.

Both Google and Yahoo claim to discount the CPCs from network partners, but our experience suggests the discounts aren’t adequate. Moreover, since any good bidding system bases bids on expected revenues, rather than sunk costs, the discounted CPCs wouldn’t solve the problem even if they were right. The bids might be right for the bad performers because of the discounts, but we’d still under bid on the higher quality traffic.

The obvious best solution for the advertisers and the engines is to allow us to bid differently by domains. Creating separate campaigns for each domain might be too much to manage, but account level percentage adjustments based on each advertisers data would be easy.

Perhaps the hang up is the publishers. If eBay’s revenue as a syndicate partner dropped by 40% they might use that space for display ads rather than search, and perhaps they’d rent that space through a non-Google exchange. Legacy revenue sharing agreements between the engines and the network partners may also be a barrier.

Whatever the case, we at RKG would like to have more control over what we pay for the traffic we get from the syndicate partners. We hope you folks will join us in this call!

This is one of those questions that has crossed my mind a number of times, but I never got around to looking at the data until now. I have no doubt that there are comScore studies out there, but with all due respect to those sharp folks, I don’t think their sample is representative of the general population.

METHODOLOGY

We took a random sample of our clients, and studied just over 1.8 million orders placed through our ads on those clients’ sites. We wanted to find out how loyal those buyers were to the engines they used. We eliminated all clicks from content networks, comparison shopping engines and other programs, so that we were left with only Google’s Adwords Network, Yahoo’s Network, MSN/Bing, and Ask.com’s Network. We also eliminated clicks on ads for different clients, and clicks more than 60 days prior to the order.

With this streamlined data set we answered the question:

For each engine, what fraction of the people using that engine first used that engine exclusively during their browsing?

We also looked to see whether the data looks different before Bing launched and subsequent to its launch.

FINDINGS

At first blush, the engines all appear to have fairly loyal followings.

This says that Google enjoys the highest level of loyalty with 97.6% of buyers who first touched a Google ad using only Google ads prior to their purchase.

However, this probably isn’t the right way to think about the phenomena. As we’ve noted previously, most PPC buyers touch only one ad before buying. The fact that they used only that one engine is perhaps less compelling evidence of ‘loyalty’ than we would see if we take a different tack.

In this next pass, we eliminated all the orders placed by people who only clicked one ad one time. What’s left answers the question:

For people who clicked more than one ad before buying, what fraction used the same engine/network for each click? And, has this changed since Bing launched?

These data paint a much more dramatic picture.

• Pretty clearly, Google users are much more loyal to their engine than the users of Yahoo and Bing.
• The degree of loyalty hasn’t shifted much since Bing’s launch, and one might surmise that Google folks are the ones testing the waters a bit as their loyalty number seems to have dipped a tiny bit.
• The Ask.com numbers are distorted by two factors: 1) on Ask.com Google ads intermix with their own; and 2) Most Ask.com traffic comes from their network, not Ask.com. As such, I’m eliminating them from the rest of the discussion (you’ll note some of the percentages below won’t quite add up to 100% as a result of excluding them after running the numbers.)

Once they leave, where do they go?

To do this analysis correctly, one would need to do a Markov chain analysis or something like it to look at transition probabilities. I didn’t have time to do that. Instead, I just looked at a gross measure of first engines and last engines within the set of multiple ad touch buyers. This obviates the distinction between Martha who went from Google => Yahoo => Google, and Ben who uses only Google. As such, having first engine and last engine the same is not identical to being an engine loyalist as studied above.

This is pretty cool data even given the crudeness of the study.

At first blush one might conclude that Yahoo and MSN are in big trouble: not only are Google users more loyal, but users of other engines are much more likely to move to Google than to any other. However, percentages lie. Google is the biggest so it’s normal that, for the subset that use more than one engine starting with MSN/Bing, most will go to Google.

The more interesting question is:

Do they migrate one way or another disproportionately to the size of the market share?

That’s what we show below. The expected percentage is calculated taking the relative market share — as defined by the entire data set from the first pass — of the other engines into account. We then calculated the percentage difference from the expected values.

This is kind of interesting! What strikes me is that MSN/Bing users are disproportionately more likely to move to Yahoo than to Google, and Yahoo users are disproportionately more likely to move to MSN/Bing. Perhaps this indicates that these folks really are committed to finding a Google alternative? I don’t know. I’m not entirely confident of my methodology/thought-process, and given that the first – last view is a pretty hacky approach we probably shouldn’t read too much into it.

CONCLUSIONS

Google appears to have by far the “stickiest” user base. This suggests that Bing has its work cut out for it if it is to gain share. That said, those who don’t use Google primarily are more likely to prefer other alternatives to Google than to follow the majority. The market demands a viable alternative.

WHO CARES?

This may be of most interest to the engines themselves — though they certainly already know this stuff — and perhaps to my friends at the big investment banks who want to gauge the future performance of those companies.

From a PPC management perspective, it’s kind of a non-issue. The lion’s share of the traffic comes from one touch buyers and engine loyalists. Moreover, by raw order count, the number of people who move from a first ad touch on engine X to a last ad touch on engine Y is almost exactly equal to the number moving from Y to X. This is true for all of the interaction Google <=> Yahoo; Yahoo <=> Bing; Bing <=> Google. As such the net effect on ad buying strategy is nil.

LIMITATIONS

Almost too many to list, but here are three more that might not be obvious.

• We’re only counting clicks on the advertisements. If someone clicks on a Google ad, then searches on Bing and clicks on a natural search link from that advertiser and makes a purchase, we’re calling them a Google-only user.
• Engine loyalty may have nothing to do with satisfaction levels. It is entirely possible that Yahoo and Bing are guaranteed to have lower loyalty numbers simply because of Google’s dominant market share. “Everyone” uses Google, so the curiosity-factor for Yahoo and Bing users has to be greater.
• We’re looking at Networks, here, so when Ebay switches from Yahoo’s network to Google’s people who used Ebay ads exclusively appear to have switched loyalties. Also, people who use off-beat search engines and bounce around might seem to be either loyalists if the sites all happen to be in one network, or switchers if they aren’t in the same network, even though their behavior is essentially the same.

I’d love to hear what others think about this. Am I looking at the numbers the right way? Am I thinking about the numbers the right way. Does this actually tell a story?

Technorati Tags: Bing Market share, MSN Yahoo agreement, search engine market share

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We’ve been asked this question a few times recently, and, since the immediate answer isn’t intuitive, we thought we’d clarify.

Occasionally, an increase in a term’s average CPC can occur simultaneously with a decrease in average position. This seems counterintuitive. Why would higher CPCs mean lower page positions? If the bid is higher, shouldn’t the ad be higher up on the page?

Yes and no. For exact terms, we don’t see this happen; the relationship between CPCs and average position is generally positive. We do see this with broad and phrase match keywords, however, since in both cases a keyword is involved not in one auction, but many. Given the nature of the PPC competitive landscape, each potential broad-match query will have its own auction, so a higher bid for ‘widget’ could move ‘widget’ up five positions, but only move ‘red widget’ (to which ‘widget’ is broad-matched with) up one space.

The overall decrease in average position occurs because a higher bid could lead to an increase in the number of auctions (different search queries) the keyword is now eligible for.  For example, let’s say that at 20 cents the broad-match ‘widget’ ad has too low a bid to appear for the search query ‘blue widget’. If we increase the ad’s bid by 10 cents, though, its new bid of 30 cents is high enough for ‘widget’ to cross the first-page threshold for ‘blue widget’. Since the increase in bid is small, the ad will most likely appear low on the page. For this example, let’s presume that Google displays the ad at position 10.

Since ‘average position’ is determined by adding the numerical position of each impression’s ad and then dividing by the total number of impressions, this outlying, position 10 spot will skew the numbers and lower the reported overall average position for ‘widget’. If, after increasing its bid by 10 cents, ‘widget’ on the query ‘widget’ moved from position 3 to 2 for 10 impressions, while on the query ‘blue widget’ it moved from not-appearing to position 10 for 2 impressions, then the average position for the term ‘widget’ would be 3.3 (40, the sum of the positions, divided by 12 impressions). In this situation, we increased the bid by 10 cents, but, to our dismay, the average position subsequently dropped from 3 to 3.3.

So, the ostensibly ‘worse’ average position is misleading. For no individual search query did the average position drop. However, the overall average position dropped because you now have your ad appearing in low positions for new search queries that were previously too pricey. This idea can be corroborated by analyzing your traffic. Did average position drop but impressions go up? If so, then you’re probably dealing with an increase in the number of auctions.

This is a good example of why the average position metric should be viewed with a grain of salt. While it is helpful in providing a general idea where your term is (such as position 1.3 versus 24.4), the number itself isn’t very useful in determining where exactly your term is for any individual auction. Nor does a lower average position necessarily indicate that the term is being pushed down the page. Indeed, such as in our example, a lower average position actually means you have more exposure and more traffic.

Technorati Tags: average position, bidding, broad-match, cps, Paid search bidding

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First a note about product features that I didn’t cover before, and Google kindly alerted me to. (Thanks!) Google Base Feeds (Now, referred to as Google Merchant Center Feeds) were developed to support Google Product Search. As such, most Alpha level features of the Plusbox product looked to utilize the existing data, and not much else. As the product evolved, Google allowed for additional, AdWords Plusbox specific columns to help the advertiser use one Google feed, to support two distinct Google products. The most notable ones are:

1. adwords_queryparam: Google will append “kw={keyword}” to the referring URL, for on-site analytics to handle. (Google example: If the destination URL in the offer is http://example.com/offer26?q=5 and the ad’s keyword is ‘digital cameras’, a user who clicks on the offer from a Product Plusbox will visit the URL
http://example.com/offer26?q=5&kw=digital+cameras).
2. adwords_prefer_for_query: Gives the advertiser the ability to specify key, user queries, and designate particular items as preferred for that query. Note that you can submit more than one keyword for each item. Also, you can use the same preferred query for multiple items in the feed. (Google example: If ‘red luggage’ is set as a preferred query, The item with this attribute will be preferred when the user’s query is ‘red luggage’ but not if the user’s query is ‘big red luggage’ or ‘luggage’).
3. adwords_redirect: Gives the advertiser the ability to specify a unique URL for AdWords Plusbox use only. The URL listed here will not be used in Google Product Search and will only be used in Plusbox ads. This allows advertisers to independently track each Google service utilizing this feed.

In my first and second posts on the topic, Plusbox ads had a fairly defined feel, with 3 product offering, text, and so forth. We’ve seen a number of new formats that they are testing.

Up to 6 products listed on top rail. Horizontal layout. Pricing. No item descriptions.

Up to 4 products listed on right rail. Horizontal & Vertical layout. Pricing. Titles present, but harder to read.

Finally, I found a few points in the data that are worth noting. First, it’s interesting to see the interaction between Google’s Plusbox serving frequency and overall user Plusbox engagement (expansions – that is to say, clicked on the Plus symbol of the ad, to see Plusbox product offering). Here I’ve outlined Plusbox ad impression volumes by week, compared to the same ad’s Plusbox expansion rate.

The product was fairly new in late 2008 / early 2009, but user expansion rates were consistently higher through much of 2009, until roughly July. Since then, we’ve seen much lower Plusbox expansion rates. Granted, expansion rates dropped from 0.7% to 0.2%, so the overall effect to the program is small. My hunch is that users were eager to test the new ad formats through the early quarter of 2009. Over time, they have grown weary of the product results displayed within, and have been less likely to reengage.

Second, it’s important to see if we are still seeing overall ad click-through-rates (CTRs) to be higher with the presence of the Plusbox ad, than without. (Regardless of any Plusbox icon expansion) Yes, this finding still holds true!

This still seems to be the most beneficial component of the current Plusbox product. There is some recent evidence that leads me to believe that the product is getting better. This could be a result of Google showing more appropriate products, testing the different layouts, or advertisers using the increased functionality to improve the results. User engagement with the Plusbox icon is still very limited, and I expect it to remain so for some time.

Technorati Tags: plus box, plusbox

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Over the past several months, our logs show that eBay had been testing Google ads for about 1% of its search ads, while the rest were being served by Yahoo.  Around 8/26, eBay flipped the switch and we began seeing the bulk of its clicks coming from Google ads.  Early this week we were still seeing eBay serve Yahoo paid search ads, but Google’s ads were providing roughly 90% of eBay’s search ad clicks.

While this move will likely benefit Google and eBay in the short-term, it is not necessarily a welcome change for advertisers.  We’ve noted before that syndication partners often have significantly lower conversion rates than the main engine providing their ads.  For many of our clients, eBay is no exception and we have often chosen to block our Yahoo ads from showing on eBay entirely.  Unfortunately, despite years of advertiser requests, Google does not provide the ability to block individual search partners in this manner.

Certain retailers are going to be hurt by this move more than others.  If your product offerings and keyword selection are particularly “eBay friendly” and you were previously blocking these ads on Yahoo, you could see a significant jump in costs without an equally significant increase in sales.  eBay has a ton of traffic and some retailers could easily see 5-10% of their paid clicks coming from the auction site.  We have been told that search network traffic is discounted based on quality, but that knowledge is not particularly helpful without more detail or the ability to make targeted search network exclusions.

As we’ve argued before, it is ultimately in the engines’ best interests to provide greater control and transparency to advertisers.  While RKG has tactics in place to handle the distinctions between Google and its search network, a perfect solution will not exist until greater control is available.  How many advertisers will decide not to bother with the search network at all if eBay starts eating up a big chunk of costs without delivering the sales?

Technorati Tags: ebay, Google, paid-search, PPC, search network, SEM, syndication partners, Yahoo

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In this vision, 5 or 10 years down the road, advertisers won’t have to specify keywords on which they’d like to advertise, nor would they have to pay for traffic that doesn’t convert. Advertising would be keyword free and paid for based on performance.

Sounds compelling! No keyword construction and maintenance, no worries about tying traffic to the right landing pages, no complicated bid management: Google will handle it all for you. Heck, they might dynamically write the ad copy that most resonates with each individual user! The advertiser just sits back, relaxes, and lets the good times roll.

Google would be wise to think this over carefully. Three considerations come to mind:

1. Could they really pull this off? For this to make sense, it would have to be more profitable for Google to pull all the strings than to allow advertisers to have the controls. For that to be the case Google would have to be able to do the advertising more efficiently than the advertisers do it for themselves, otherwise advertisers would not be willing to pay more for the same results. There are many reasons to question whether that’s plausible.
   • Would Google be quicker to recognize stock outages than the retailer is? For companies selling shoes, oftentimes the issue isn’t an outage as much as thinning of the most common sizes. For airlines, will Google know when certain routes are close to capacity for the likely selling window?
   • Would Google know a retailer’s shipping cut-off for Christmas delivery?
   • Would Google know when a retailer is having a promotion on certain products or across the board? Advertisers can anticipate these events and bid proactively…Google couldn’t.
   • Would Google know which products generated a great number of returns?
   • Not all orders are created equal: leads vary in quality, some keywords generate much bigger ticket orders than others, and the retailer has different margin structures built into its sale prices. Would Google allow different CPAs based on these factors? How would that work? Would Google have to know everyone’s profit margins on each product? Would they wait for leads to close to prove the value?
   • Would Google be able to pick the right landing page for each search? They don’t do this well in organic search at this point. Do a search for “nikon digital lenses“. What I see is a number of landing pages for Nikon products (not specifically lenses), and a handful of specific product pages (too deep). This hurts conversion rates. If Google generates lower conversion rates because they pick the wrong pages (could they even find search results pages?) how could they be more efficient than the advertiser?
2. Would the advertisers want this, even if Google could do it profitably?
   • For this to work, Google advertisers would all have to give Google real time conversion data. Privacy concerns aside, anyone familiar with selling through Amazon knows the legitimate business risks of sharing sales data with a mega company that could turn the tables on them.
   • Given the likelihood that Google will have to do a great deal of testing to determine what traffic to send where, and that experimentation by definition involves sending folks to the wrong place sometimes, are advertiser’s likely to be okay with the impact on their brand?
   • Anyone who works with affiliates will recognize the perils of revenue-sharing deals: does Google get paid for brand sales? How would orders that touch multiple channels be handled? What about frauds, cancels and returns?
   • The problems with revenue sharing deals go deeper than that. They create incentives to grab only the lowest hanging fruit from each orchard before moving on to the next. In this context the fruit left behind might represent the long tail: note that when Google Campaign budgets are applied, an already complex puzzle gets even more complex for Google. They simplify the problem by ignoring very specific keywords and just serve the general stuff. This hurts conversion rates. Or the fruit that gets ignored might be niche advertisers. “Too complicated to figure out which of 500 retailer’s ads work best for “Sony 60 inch TVs” — just serve ads for Best Buy, Wal-Mart, Amazon, etc and forget the little guys.”
   • Google would live in the world of revenue per impression for each ad/user search. If Google sees the same revenue per impression from multiple advertisers, they’re ambivalent as to which ads get served the highest, or make it to page one. The advertisers are decidedly not ambivalent. If Google decides Apex ads generate the same revenue as Acme ads they get to pick who wins.
3. Would Google want this to happen?
   • Given that Google might still control the lion’s share of the market 5 or 10 years from now, do you think the SEC and the DOJ might be interested in how Google decides which ads to serve? If the advertisers don’t control their own fate, it seems like Google gets to play king-maker, and that seems likely to incur the wrath of federal regulators.
   • Does Google really want to move away from being a technology company towards being a service provider? If they think they have client service issues now, wait ’til they’re controlling ad service! Yowza will they need to expand staffing!

Now, a wag will point out: “Of course RKG is against this kind of simplicity, it would cut the PPC agencies out of the loop!” Those wags are so annoying!

No question, complexity encourages outsourcing which benefits RKG. However, where there is marketing data there is a role for RKG, so we’d figure out something else to do.

What I find really troubling is Google belief that they could actually micro-manage an online economy more effectively than the players involved. There’s certainly no evidence to support the notion that Google can do PPC more efficiently than the best folks in the space. And free market economics certainly suggests that those with the greatest skin in the game certainly should be able to do it better than a disinterested machine.

Perhaps they see how badly some firms do search and think that without paying attention to the particulars, their algorithms could do better than those folks. They may be right in many cases, but I do believe that the weak agencies are being “outed” and that the bar is rising.

I’d love to hear what others think of Google’s vision!

Technorati Tags: Google monopoly

We reached that conclusion in 2003, and co-authored a stats paper on the subject in 2005.

It is funny to see the position bidding mavens jumping up and down crying foul, because this undermines the whole premise of their system. But the numbers don’t lie.

It is terrifically difficult to do this research and it requires a mountain of data to reach the right conclusion. It’s funny that Hal only mentions QS as a complicating factor. The much larger factor is that if the advertiser’s bidding system functions properly, it bids down poor performing KW and bids up great performing KW, making the top of the page look artificially better, if you’re not careful with your study.

Our research suggests it’s not just conversion rates that remain constant but AOV as well, indicating that the people who click on ads behave largely the same way regardless of where the ad was on the page.

Our research also suggests that the slightly worse performing positions Hal mentions are at the top of the page and the slightly better are at the bottom of the page. We reason that some fraction of folks click the first link or two by reflex without reading the copy and are therefore more likely to be disappointed once they get there. The folks clicking on the bottom links have either picked those ads for a reason — they recognize and like the company, or liked the copy — or they’ve already visited the sites from higher ranked ads and didn’t find what they want, so the competition has already fallen away.

In any case:

1. It’s pretty obvious why Google doesn’t want to go on record saying position 1 converts less well than other positions — a race to the bottom would hurt them; and
2. Trying to avoid position 1 really is silly. The traffic value differential is very small, much smaller than the error bars around the calculation of value for a given ad, hence immaterial to performance.

Let the value of traffic drive your bids, not the position. Position bidding both wastes money AND misses opportunities.

Technorati Tags: conversion rates by position, position bidding, PPC Bid management

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