Please join us on Thursday February 18th at 1PM EST for what promises to be a lively discussion.

I plan to lay out our case for why as a rule a head-only program relying on broad match and negatives can’t compete with a well-managed head and tail.

We’ll also delineate the factors that create a more and less critical tail (snakes vs rabbits), and the mechanisms through which proper tools can be used to manage huge term lists profitably and cost effectively.

Come with hard questions!

George

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The desire to classify is hard to resist, so I’ll throw out 3 different Search Shopper Profiles for consideration.

1. Traditional Tracy: Tracy models her search shopping behavior on how she’s traditionally shopped. A trained brick and mortar shopper she visits stores one after another until she finds what she wants.

   Online, Tracy doesn’t search for a “silk blouse.” When she’s looking for a blouse she will visit: Anne Tyler, then Talbots, then Lands’ End, etc until she finds what she wants. Conversion rates on brand searches are huge because she’s already narrowed the list of stores based on past experience, offline marketing, word of mouth, etc.

   She is not interested in sifting through search results to find a retailer she likes. She’s a brand loyalist; not exclusive to any brand, but less interested in taking a chance on an unfamiliar store than others might be.

2. Yellow Page Paul: Paul knows that when he needs to shop the way to find a store is in the Yellow Pages. When Paul wants a new Barcalounger he looks for the type of store that would carry one: a furniture store. The directory wouldn’t have a listing for Barcalounger, so Paul’s been trained not to look for one.

   Paul didn’t go to the white pages to look for a specific store, he’s not a loyalist. He’s happy to buy from any retailer that has what he wants at a reasonable price.

3. Modern Mary: Modern Mary knows search engines and uses them well. She recognizes the efficiency of specificity and is far more likely to use three, four and five words to refine the SERP and get to exactly what she wants rapidly. More comfortable with computers, she likely reads the ad copy before clicking on links realizing that that can save time.

   Mary’s proficiency with search may also make her more likely to consult comparison shopping engines and coupon sites as a way of finding the best deal.

If there is any validity to these archetypes we should see some telling patterns in the data.

• We’d expect to see Tracy-like brand buyers have a disproportionate likelihood of having other brand search records on her browser, and we’d expect to find lower incidence of very specific search queries.
• We’d expect Paul to have other broad search phrases mixed with some navigational searches but a lower propensity for very specific search queries as well.
• Mary might reveal herself as a candidate for the buying cycle. She’s comfortable with search, and impatient with unproductive visits. She may be more likely to refine her search when the initial SERP doesn’t have what she wants. Indeed, it wouldn’t surprise me to find that the click to order interval for Mary is actually longer than for Paul; the reverse of what the standard buying cycle argument would suggest.

We’re blessed at the Rimm-Kaufman Group with a large slug of data from many great retailers, and a burning curiosity to find answers to these types of questions.

Sadly, the exigencies of running a business require me on occasion to do some productive work, so for the moment I throw these theories out to the wind with no empirical foundation to support them. If we can dig up sufficient evidence to back these crazy claims there will be a Part III of this post; if not, we’ll just forget this little incident took place :-)

George

• Shopper Profiles and Conversion Rates Part I If not a buying cycle, what is going on?...

Data driven marketing does not always demand an understanding of human motivation; drive by the numbers and the numbers will improve as a result. However, we often find that grasping at least some understanding of the underlying human behavior can help us better predict what strategies will and will not yield results.

Many explanations have been advanced to explain why measured conversion rates for general search terms are lower than those of more specific searches. No single explanation covers all the observed behavior, but one possible explanation had never occurred to me until recently.

Let’s run through the standard explanations and assess their strengths and weaknesses.

THE EXAMPLE CASE:

A furniture retailer finds that conversion rates for the keyword “furniture” and “round end table” are very different and that the value of the traffic on the latter is 4 times that of the former though the volume is far less.

POTENTIAL EXPLANATIONS:

1. The Buying Cycle: User’s searching for “furniture” haven’t decided what they want yet. These early stage shoppers will eventually return and purchase but those orders may be credited to other search keywords or other marketing programs entirely. The conversion differential is an illusion according to this argument.
   • Strength: It makes a great deal of logical sense, and serves as justification for larger ad budgets.
   • Weakness #1: In retail, the data just doesn’t support the explanation. I’m told it’s more prevalent in other verticals, but in retail this only explains a small portion of the effect.
   • Weakness #2: Do people really shop for “furniture”? “Honey, I’m going out to get some furniture, I’ll be back in a couple hours!” Seems to me, most people who are shopping are looking for something in particular. I could be wrong, but I’d bet far more people shop for an individual piece — the leather sofa — than look for a whole roomful.
2. Browsing not shopping: Users searching in general terms are mostly just kicking tires, whiling away the time virtually window shopping. People using more specific terms are serious buyers.
   • Strength: Compelling, and the conversion rates certainly support it.
   • Weakness: Wouldn’t we see more buying cycle behavior if this were the case? If people in “buying mode” shopped using more specific terms, where window shoppers search more generally, we’d see a strong buying cycle in the data, and we’d see the vast majority of sales take place after specific searches. The fact that we see little of the buying cycle pattern and the majority of sales on more general “head” Keywords suggests this is not the root either.
3. Failure to find what they seek: The more general the search, the more likely it is that the retailer’s selection isn’t comprehensive. With a SKU specific search retailers carrying that SKU have a 100% comprehensive selection for that particular model. No retailer has a 100% comprehensive selection of “furniture” so the chances of making a sale are reduced.
   • Strength: We see some evidence to support this. When a retailer carries a comprehensive selection of a particular manufacturer’s brand, conversion rates on “manufacturer brand” are often just as strong as searches on “manufacturer brand + category” or “manufacturer brand + product”.
   • Not really a “weakness” as much as a curiosity: Wouldn’t this lead to frustration and therefore convince people to search with more specificity?

This last question, and another observation led me to think searching skill as an explanation.

My wife is a brilliant person: scientist, artist, and CEO. While she is my superior in almost every respect, she doesn’t search as well as I do. The other day we tried to remember how Hamlet’s “To be or not to be” speech continued. We turned to our respective laptops to race to see who could find it first. I won, and I always win these races. I searched for “Hamlet ‘to be or not to be’ soliloquy”, she searched for “Shakespeare quotations”. I found what I wanted on the first click. She went through a number of different links before she found a site with the full-text. We both found what we wanted but it took her 4 times as many clicks as it took me.

Searching skill seems to explain both why we don’t see much of a buying cycle, and why we do see a huge volume of sales on general searches. We don’t see successively specific searches very often because people who haven’t learned to type in exactly what they want the first time likely haven’t learned that the next time they search either. Yet, because this skill-level represents the majority of the population the sales volume is there.

In Part II of this series I’ll lay out shopper profiles that might help explain some of what we see in the data.

• Shopper Profiles and Conversion Rates Part II Search shopper personas anyone?...

The big number that jumps out is a 15% Y/Y decline in search advertising revenue.  From Yahoo:

Marketing services revenues from Owned and Operated sites were $971 million for the fourth quarter of 2009, a 9 percent decrease compared to $1,063 million for the same period of 2008.  The decrease was primarily driven by a 15 percent decline in search advertising revenue and a 1 percent decline in display advertising revenue.

Results from a basket of RKG clients are in line with that figure with a spend decline of 20% across Yahoo and its partners in Q4.  By comparison, RKG’s spending on Google rose 14% Y/Y.

While a general improvement in the economic environment helped all three engines’ Q4 numbers, Yahoo continued to bleed share to Google and Bing at a fairly steady rate according to our data:

Interestingly, Yahoo’s reported numbers were bolstered by a relatively impressive 6% Y/Y lift in revenue from affiliate sites.  While they don’t break out specifics, it stands to reason that a big portion of that revenue is from Yahoo! Partners on search.  It’s unclear if the 15% decline in search advertising revenue includes the partner results; If so, it’s an even more impressive gain, but one with not so obvious costs.

To Yahoo’s credit, they have offered advertisers the ability to block poorly performing partner sites for some time now; however, if most advertisers are not parsing their data this finely, the expansion of Yahoo! Partners with lower and lower quality traffic will continue to drive down ROI and ultimately spend on Yahoo O&O sites where the bulk of their revenue potential is.  Advertisers and agencies taking advantage of Yahoo’s partner exclusions (like RKG) benefit from this scenario through reduced competition in bid auctions and lower CPCs.  Good for our clients, bad for Yahoo.

Fortunately for Yahoo, this effect is likely to diminish over time as more advertisers wise up to just how much money they are throwing away to poorly performing partner traffic.  Yahoo has also made a smart move recently by improving advertisers’ ability to manage bids on their network.  In addition to the existing all-or-nothing exclusions, we can now apply a percentage bid adjustment to partner traffic.  This is a very welcome move and the benefits should flow both ways in the long run.

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Many of our clients wrestle with the question: how should credit be attributed between marketing programs when more than one is involved in driving a sale.

It’s a tremendously important topic and as such RKG is close to unveiling a tool/service for helping advertisers study the actual behavior of buyers, and allocate credit as they see fit. More on that later.

Shop.org is conducting a survey to determine the current state of the online retail community on this issue. How many folks are doing attribution management and how sophisticated are their approaches. Importantly, the study group members will only see aggregated data, nothing that identifies the retailer or individual filling out the survey. Survey results will NOT be used to pepper folks with marketing material. That isn’t the intent of the study.

I’m serving as the self-appointed honesty cop, making sure that this study group doesn’t turn into a marketing message for attribution management solutions. Solution providers clearly have an interest in inflating the importance and complexity of the problem, and those who know me know that I don’t have any patience for that.

Shop.org is the retail industry’s interest group, so you retailers out there, please take 5 minutes to take the survey. It’s painless, I promise!

Many thanks,

George

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Today we describe a layered approach to evaluating a paid search program. The top level view might be the right view for the C-level execs who can’t get too deep in the weeds. The day-to-day management team should reflect on the high-level view, but also needs to roll up their sleeves and get dirty to make sure they’re getting the most out of the channel.

It’s ALL about the Competitive (”non-brand”) Search Keywords

The clear and actionable view of paid search at any level of detail comes from studying the performance of the competitive search program separated entirely from trademark/brand search. People searching for the advertiser by name aren’t driven by search, and paid search ads on those brand terms, already at the top of the page, can neither create more brand traffic, nor materially create incremental business from that cohort. “Brand” ads primarily cannibalize organic and affiliate links and managing those brand terms takes little effort. The real effort and the real incremental value of paid search is in the competitive (aka “non-brand”) side of the game, hence that is where the focus needs to be in the evaluation at every level.

The 10,000 Foot View

1. Is Competitive Paid Search hitting efficiency targets? If not, go no farther, the program is wasting money and heads should roll. Spending the budgeted amount for media regardless of the ROI is the wrong way to approach search.
2. Is the Competitive Paid Search program (herein after referred to as “the program”) growing in sales volume? If the efficiency targets are being hit and the program is getting bigger, that’s a positive sign. It isn’t a definitive sign one way or the other. The recession shrunk many well-managed programs; and programs can grow simply from channel shift without being particularly well handled.
3. Is the program growing as a percentage of total site sales? Much more telling than #2, growth as a percentage of the whole site shows that paid search is an increasingly important marketing channel which speaks well of its management. Again, shrinkage in this regard doesn’t necessarily mean the program is run poorly; as I discussed with Ken Cassar of Nielson: having a huge percentage of site sales coming through paid advertising isn’t necessarily healthy in the long run. Repeat customers are key to profitability, and they don’t typically come through competitive paid search ads.
4. Is the program growing relative to competitive natural search? One probably shouldn’t pit the SEO team against the paid search team as though they’re rivals, since great progress by one doesn’t necessarily imply the other isn’t doing well too, but it’s a metric to watch.

The Ten Foot View

Take a Keyword level performance report from Google for the last 2 or 3 months. (Whether this is first touch data, last touch data or some allocation doesn’t make much difference as we and others have shown time and time again.)

1. Do the high traffic terms each meet the efficiency objectives within reasonable tolerance for statistical noise?
2. In category/subcategory groupings do the lower traffic KW meet their efficiency targets in aggregate?
3. In other groupings meaningful to your business do the lower traffic KW meet the efficiency objectives?
4. Clustering by traffic volume, do the aggregates still make sense efficiency wise?

Bid management and analysis is half the battle, and the most complex half by far. This view will reveal the most damning evidence of problems if there are problems with the program. Do not accept “Buying cycle” arguments as an excuse for poor efficiency.

If the program looks good from each of the perspectives above then your paid search managers are handling at least the fundamentals of bid management well.

The Ten Inch View

1. How large is the list of active Keywords on Google? There exceptions to every rule, including this one, but 5 to 10 KW per product on the site is a pretty good benchmark. The Long Tail is valuable for almost every program, and broad match is no replacement for a comprehensive list.
2. What’s the quality of the KW coverage? Spot check some random sub-categories to see if all the obvious and not so obvious permutations are covered.
3. Are the Keywords machine generated gibberish? Machines do a lousy job of generating lists: leaving gaping holes, creating dangerous untargeted and inappropriate phrases and generating thousands of valueless phrases of more than 4 words. When evaluating the length of the list these should be ignored.
4. Are the landing pages appropriate? Placing visitors on a page equal in depth to their search improves conversion rates. Linking more general phrases to product pages leaves opportunity on the table. Only SKU specific Keywords should land on product pages. Ask for a KW - Ad Copy - Landing Page spreadsheet ranked by traffic volume descending. Check the landing pages for the highest traffic KW, and spot check others down the list.
5. Is the ad copy compelling and sufficiently specific? The goal is to sell the advertiser’s website, not the product. The user has already expressed interest in the product via their search, the goal is to convince them that your site is the best place to shop for it.

The Microscopic View

If the first three successively closer inspections reveals a healthy program, congratulations, you’re in the minority! Your program is healthy, but not necessarily in peak condition.

Peak fitness comes from applying the most sophisticated techniques in paid search, and executing them at the highest levels.

• Smart use of negative associations developed from user search strings. Proper databasing of referrer data and use of Google’s research tools should reveal opportunities to trim fat and raise bids on broad matched KW.
• Creating separate campaigns for exact matched and broad matched KW and bidding more on the exact match versions based on calculated performance differentials makes a material difference in performance.
• Placing the exact match version referenced above on Google.com only and pushing those bids more based on the syndication effects researched previously also produces positive returns on the effort. Using syndication partner exclusions on Yahoo while we wait with baited breath for their bidding differential option to go live will achieve the same effect.
• Anticipatory bidding based on seasonal shifts and shorter term promotions helps take the most advantage of those hot periods.
• Day parting done smartly (using the time of the click, not the time of the order) to exploit intra-day variance in performance values requires good analysis and superb tools.

These fine tuning mechanisms create the competitive advantages needed in a tough market place.

Still passed the test? Fantastic! Your paid search program is ripped!

But the fact that the program is managed beautifully doesn’t mean your business is getting all it can out of paid search.

• Are the managers aiming at the right targets?
• Are they factoring in returns and cancels?
• Are they measuring call center spillover?
• Are the targets set based on margin data? Handling co-op advertising dollars?
• Are lifetime value considerations factored in? By the type of first purchase?
• Is the tracking system catching all the sales?
• Is the cookie window sensible?
• Is attribution between channels handled correctly?

If you don’t know the answers it’s time to start asking why and when. Paid search remains the number 1 mechanism for generating incremental business online; the fact that it’s no longer new and sexy doesn’t mean it can be safely ignored.

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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.

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A quick poll:

How many marketers think sending the same email to the whole file will perform better than individually tailored emails based on each recipient’s past behavior?

How many think sending non-personalized direct mail pieces will outperform personalized outreaches?

Anybody? Anybody? … Bueller?

If no one thinks the monolithic approach would ever beat the segmentation approach, why is it that some PPC professionals advocate ditching the long tail KWs and letting broad match catch the tail?

The benefits of the long tail are exactly like the benefits of segmentation above.

1. The more targeted ad is served having better ad copy which raises CTR and therefore Quality Score. This leads to more traffic from the same number of user searches.
2. The more targeted user search necessitates a more targeted landing page. Not the “Fender Guitar” page, the “Fender 4 String Bass” page. That more targeted page will generate a higher conversion rate, hence more sales from the traffic generated.
3. Folks looking for “Fender 4 String Bass” will have a much higher conversion rate than those searching for “Fender guitars”. That fact coupled with #2 above means higher bids on the more targeted ads, putting the ads for the most targeted traffic higher on the page than the more general terms could be yielding more impressions and higher CTR.
4. With more of the highly targeted traffic going to the tail KWs the less targeted, more general KW will be bid down to more accurately reflect the value of the traffic. As I mentioned in my post on syndication partners, better resource allocation leads to a bigger program at the same efficiency.

The advantages of a thoroughly developed KW list are obvious in principal, but what about in practice?

The answer is an emphatic “YES!”

In a recent empirical study, we took apart campaigns from a number of our clients ranging in size, product types, price points and more, to see the degree to which the “Long Tail” matters. We found that the importance of those targeted searches varied tremendously from a low of 8% of the total business to a high of an amazing 83% of the total. The median of the study group was 31%.

Obviously if the program is tiny, and the tail is only 30% of the game, it might not be worth the attention, but for large programs even 8% is worth chasing. In the case above that 8% represented over $200K in sales per month.

Nevertheless, some argue that those targeted searches would be captured by broad match anyway, so there’s no real advantage gained by building out and maintaining a comprehensive KW list.

Not so.

As a test case, we took a client in the consumer electronics space who carries products from more than 1,000 different vendors. We then asked the question: what’s the traffic value differential between the highest traffic KW for each vendor brand, and the rest of the KW in that brand? In other words, if we just ran the highest traffic term for each brand on broad match to what degree would we be blending together traffic of vastly different quality?

The answer came back: “We’d be mixing apples and oranges”. In the test case and for vendor brands with enough traffic to have a tolerable signal to noise ratio the median variance in traffic quality (margin $ per click) between the brand-specific “head” and the brand-specific “tail” was 84%! And not always in the direction expected.

Throwing all the granular data into the head KW performance will very often result in significant overspending on one cohort of user searches and significant opportunity lost by underspending on the other cohort(s) of traffic.

So, there isn’t much question that for a substantial program the tail matters, and treating the head KW the same as its tail leads to significant mishandling of the bids.

The other rationale for ignoring the tail relates to the cost of building and maintaining it. “The time is better spent elsewhere.” Well, that could be true if either of the following is true: 1) it’s a small program with an insignificant tail, not worth the management cost to go after; or 2) you lack the power tools and algorithms necessary to build and manage the tail comprehensively with cost-effective human effort.

There is no solution for the first issue. If the whole program isn’t worth spending much time on, then certainly the tail isn’t worth it.

The second issue however is simply a resource and know-how constraint, which for a professional paid search manager should not be there. Telling folks “we don’t have the tools to manage your program effectively” is a hard message to deliver, but it’s the right message if it’s true. Telling clients: “The tail is unimportant,” or “The tail is effectively handled by broad match” simply isn’t honest for most sizable programs.

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Anyhow, we think these are as close to “must reads” as we had to offer last year. If we missed one of your favorites let us know :-)

Paid Search Practices

Why Budget Search?

Lifetime Value

Recognizing Signal and Noise in Paid Search

Importance of Landing Pages

Bid Management

Proper tail bidding requires flexible clusters

The Folly of Position Bidding

Managing thin data

The power and pitfalls of automated bidding

Good Rules and Bad Rules in bid management

The importance of atomic bidding

“Portfolio Bidding” has more than one meaning

Analytics and Research

Broad Match Bug

Buying Cycle in Paid Search

Multichannel Attribution and Paid Search

Impact of Geography on PPC: Online

Impact of Retail Store Presence in Paid Search

The Problems with Syndication Networks

Tracking

Call Center Spillover

The Gaping Hole in Analytics Tracking Packages

Thanks so much for reading our ramblings and for the invaluable commentary. We have some good ones coming down the pipe and more research studies on the way as well.

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Growth is good, particularly cost-effective growth, but the four month trend is hard to explain.

What happened in October? Might we conjecture that the first signs of economic growth spurred a certain amount of euphoria in consumer behavior? That theory might continue: consumers realized in November and December that economic growth and job growth aren’t synonymous and that as the economy continued to shed jobs people tightened their wallets again.

An alternative hypothesis might be that the extraordinary promotions in October and immediately after Thanksgiving cannibalized sales that would have come in December otherwise.

January may prove which of these two explanations is dominant. If the first explanation is correct we’d expect the growth rates to continue to decline in January. December job losses surprised economists, and have created some talk of a slide back into recession. If the second explanation is more correct we should see an uptick from the 8% figure. Promotions may have shifted the timing of the YoY growth, but overall for this basket Q4 was up ~18 - 20% over 2008Q4. A return to those levels would certainly be welcomed.

Stay tuned!

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