Last year about this time we identified what we thought was a bug in Google’s ad serving algorithm.

We noticed that as we lowered bids on high traffic general terms that didn’t convert well, much more specific keyword ads started being served in their place. This had three annoying consequences:

1. The more specific KW had a higher bid, hence we end up paying more for the traffic than it’s worth to us;
2. The landing page is less targeted, so we’re taking bad traffic and landing it on the wrong page, making it even less valuable traffic; and
3. Because of the poor quality traffic pouring in on what had been a high quality term, we bid that term down meaning we also get less of the high quality traffic that the term normally draws.

Our reps at Google at the time told us that this couldn’t happen, that the exact matched KW would always get precedence over the broad mis-match so what we were seeing…er…wasn’t happening…

We knew we were right about the phenomena and given their protestations that exact matches always won we suspected it was a mistake on Google’s part. We asked them if there was logic that makes exceptions to the exact match precedence if the ad is paused. They said “yes”. We then suggested that back in the day when there was a minimum bid, that an ad bid below that minimum might also be considered paused. They concurred. Then we suggested that when the minimum bid was replaced by the first page minimum bid perhaps the code wasn’t updated and any ad that didn’t meet that minimum would be treated as “paused”. They said at the time “That shouldn’t be the case; that isn’t what we intended; if it’s a mistake we’ll fix it.”

Googlers in good authority now tell me that that bug doesn’t exist — there’s no reference to the first page minimum in the code that drives the rankings. Instead the explanation is simply that the more specific KW must have a higher QS than the exact matched more general KW…or that its combination of bid and QS are higher than the exact matched term. We’re told that this shouldn’t really happen if the QS of the generic ad is good, but our data suggests otherwise. Just a cursory look at our data showed plenty of instances where an exact match ad with a QS of 10 was passed over for a broad matched ad with a higher bid.

Managing this self-competition with the current tool set is not just cumbersome, it’s impossible to do well. Adding every keyword as an exact matched negative for every other keyword in the account is unworkable. Bombing in all the general keywords as exact match negatives for all the more specific keywords is doable but time intensive and therefore costly.

Many advertisers simply give-up on broad match to prevent the shenanigans. We’d say that’s throwing out the baby with the bathwater, but understand the frustration. We do what we can, but anyone claiming to have this problem solved is either delusional or deceptive.

As I noted a couple of years back, one way Google could effectively self-destruct would be to go too far down the path of allowing higher bid/lower CTR KWs to take precedence over the right KWs. As described above, not only does spraying the traffic around unpredictably make all bidding systems less efficient and hence spend less money to reach the same efficiency target, the bigger danger is in alienating the shoppers who use the ads.

Advertisers bid ads down for good reason, like when inventory is thin, and if other ads take their place and continue drawing in traffic that won’t convert it is a disservice to the user as well as the advertiser.

Sometimes I rant around the office saying things like: “Advertisers should SUE! Here we’ve given Google instructions as to how much we’re willing to pay for people who type in “Foo Bar”, but when someone types in “Foo Bar” Google decides to serve my ad for “Left-handed steel foo bar” which has a much higher bid!!! That should be illegal!!!”

IMPORTANT NOTE: I should be ignored when I rant on like this — other times, too, but particularly when I rant. Anyone looking at the Adwords terms of use for ten seconds would realize that Google can serve whatever ad it wants so there’s no point in calling a lawyer.

Google’s engineers genuinely believe they can algorithmically pick better ads to serve than the advertisers can. This may be true for badly managed accounts, but is not true for well-managed programs. If this notion that sometimes humans are smarter than the machines is offensive to engineers, perhaps it could be framed in the language of “crowd sourcing.”

If the engineering team is willing to acknowledge that some folks might actually choose ads, landing pages and bids rationally, there may be a profit maximization angle as well. Google is not “evil”, it is a publicly traded company looking to grow its top and bottom line just like us. My argument here isn’t that they can’t do this legally, nor is it that they shouldn’t do it ethically. The argument is that this isn’t a good business decision on their part.

Bing’s path to victory lies not in stealing Google’s organic traffic, but in taking Google’s shopping traffic. That’s what Cashback is about, and if Google places short term revenue maximization over long-term ad relevance they’re opening the door for Bing to step through.

If average users decide that “Google is great for research, but go to Bing for shopping” Microsoft’s big investment might just pay off.

Technorati Tags: george-michie, Get Elastic, interview, Linda Bustos, paid-search

• Interview with Linda Bustos of Elastic Path Linda is one of the smartest people in online marketing....

Lance shares our “No Bull” approach to marketing his services and I’m glad to share this Q & A with our readers.

George: How do you know when your site isn’t converting as well as it could?

Lance Loveday

Lance: One potential sign that your conversion rate is putting you at a competitive disadvantage is if competitors are always showing up in the first 2-3 spots in the paid search results, but your ROI goals require you to show up lower on the page. While they could be spending blindly, it could also be a sign that competitors are seeing higher conversion rates. Because higher conversion = lower CPA = higher margin = higher CPA tolerance = higher bids = you get outranked.

But regardless of where you’re starting from, our approach is to never be satisfied, and always seek incremental improvement. Why settle for an average conversion rate when you could have an above average one – or an above average conversion rate when you could have an industry-leading one? The beauty of conversion optimization is that it’s a one-time cost with an ongoing benefit, so you don’t have to move the needle much on a conversion engagement to have a positive ROI when you have the benefit of time on your side.

We try to realistically model the projected outcomes of projects for clients to set expectations on the range of results they could expect to see. Although we try to be conservative with our estimates (better to under promise and over deliver) it can be a bit scary for us to put those numbers out there because that’s usually what becomes the benchmark for success for the project. But we like a challenge, and there’s nothing like the concreteness of a defined target to help bring focus to a project.

George: In assessing a site how should we think about the roles of KPIs vs competitive benchmarks vs user studies?

Lance: We like to use a combination of metrics to calculate success, as relying on too few metrics can lead to unintended consequences. Enron’s earnings, for example, looked great right up until they went under. So an effective dashboard should contain a balance of business metrics (revenue, transactions, margins), site metrics (conversion rate, bounce rate – by source), and user metrics (usability testing results, long-term survey response trends). We don’t put much stock in competitive conversion rate benchmarks, as variances in business models, traffic mix, and site strategy between sites almost always results in an apples-to-oranges comparison. A low-converting site can be very profitable, which is ultimately the most important competitive metric.
We also like to take a funnel view of the site as whole and measure throughput at various levels. Not all sites lend themselves to that kind of analysis, but it’s an incredibly powerful way of looking at your site if you have the capability. Taking that view allows us to model out the impact that small changes to a given page template can have on overall results.

George: If my website isn’t converting well am I better served by doing a complete re-design, or by taking an incremental test-driven approach to improve each template?

Lance: In a majority of cases it makes sense to take the more incremental approach. There’s almost always some upside to be found in every site. But we have worked with a few sites where the problems have been so structural that we’ve recommended a full-scale redesign.

George: How much can one raise the bar by moving the pixels around on the page? I know when we were in the design consulting business we often found it a challenge to measurably improve conversion rates unless the old design was really bad.

Lance: You’re touching on the core stumbling block we face as conversion consultants: Most people don’t believe that small interface tweaks (pushing pixels) can have a material impact on business results (more money). But a whole host of studies including this one and our own experience has shown otherwise.

For example, we just doubled conversion for a long-time lead generation client of ours, for whom we’d previously tripled conversion. So ultimately they increased lead volume by 6X with no loss of lead quality. Granted that didn’t happen overnight. But it’s a good example of the kind of game-changing impact you can have with a well-executed conversion optimization initiative. The average e-commerce site may not have that kind of upside potential, but it’s not uncommon for us to increase conversion by 30-50% on e-commerce sites.

George: Wow! 30% is a big lift!

Lance: One problem we’re seeing is that people are focusing so much on the sexiness of the new testing technologies that they are rushing to conduct some tests without ensuring they’re doing it properly – and then when they get mediocre results concluding that either a) there’s no room for improvement, or b) testing doesn’t work. But like anything, tests can be done poorly or done well. Doing it right means clearly identifying objectives, developing good testing plans, deploying the right kind of test method, testing user experiences (not just design tweaks) that have a high probability of success, ensuring statistically significant results and so on. It takes a lot of different skill sets all working together to do testing well – strategic, technical, creative, analytic and more. Sorry, I’ll get off the soapbox now.

Also, I prefer to be called a Site Whisperer. Pixel Pusher is just so demeaning.

George: Got it, “Site Whisperer” it is! :-)

George: Is there a particular piece of a website that is most often the stumbling block for conversion? Search results? Category pages? Product pages? Navigation? Shopping tools? The checkout process?

Lance: It can vary by site, but the checkout process is a common place to find very high dropoff rates. Depending on which stats you want to believe, the average shopping cart abandonment rate (across all industries) is 50-60%. My eyes get big any time I see a cart abandonment rate of 50% or higher, as that’s usually an indicator that we’re going to be able to make a huge difference for that client.

But we’ve seen plenty of examples where the category pages and/or product pages were the primary bottleneck as well.

George: What design flaw do you run across most often?

Lance: This is more of a strategy flaw than a design flaw, but I’ll go with requiring a separate login/registration step. It’s a real conversion killer from a user experience standpoint, and usually has such limited value to the site owner since users generally provide their email address anyhow later in the checkout/registration process.

George: How often do you find that platform constraints are ultimately the root of design problems?

Lance: More often than I’d like. But I’m really encouraged by some of the new testing platforms that enable us to bypass platform constraints. There are now some really elegant way to run tests on your site and increase conversion regardless of what platform you’re on. We’re running a test on a client’s shopping cart now that has required zero IT support or back-end changes.

George: How important is speed these days, and in your view is this mostly a “solved problem?”

Lance: NO WAY is this a solved problem. Load time is still a major conversion inhibitor for a number of sites – and its impact on conversion is growing as user expectations continue to evolve. This release from Akamai is about the link between site performance and customer satisfaction for financial services firms, but the lessons apply to all types of sites.

George: Any other general advice you can give?

Lance: Here’s my attempt at a Top 10 list:

1. Start small. But start.
2. Don’t test for the sake of testing. Test for impact and learning.
3. Test hypotheses, not opinions.
4. Don’t underestimate the power of persuasive design.
5. Spend 90% of your time in planning/strategy/test design/crafting great designs, and then 10% of your time running the test.
6. Test among good options.
7. Don’t settle for the 5% gain if a 50% gain is possible.
8. Be willing to be wrong.
9. Get out of your comfort zone.
10. Make testing an everyday thing.

George: Thanks so much for taking the time, Lance! Lance’s very well received book: Web Design for ROI is available at Amazon and other fine book stores!

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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

Yahoo’s new system for managing bids on syndication partner sites is a big win for advertisers and an even bigger win for Yahoo.

We’re already seeing results. By segmenting traffic into Yahoo.com only campaigns on standard match and Yahoo.com + syndicates on Advanced Match, with appropriate bid differentials applied to the traffic from the partner network, we’re seeing materially improved results. By improvement we mean more sales at the same efficiency, meaning higher revenue for Yahoo, in fact much higher.

Let’s take a look at how Yahoo poisoned its own well:

{Okay, in truth, this is historically inaccurate. Yahoo bought Overture. Overture already served a wide network of sites including Yahoo.com, so there was never a time when they served ads only on Yahoo.com. I’m simplifying history to make a point: trying to grow revenue by expanding syndicate partnerships has actually reduced Yahoo’s ad revenue — at least from folks who actually pay attention to their bidding}

In the beginning there were ads on Yahoo.com. The quantity of traffic was good and the quality of traffic was high. But as the SERPs became saturated with ads the growth of advertising revenue slowed.

CASCADE FAILURE STEP 1: Contaminating the pool

Yahoo began to expand its network of syndication partners who would serve Yahoo’s ads for a price. The idea was that this greatly expanded exposure of Yahoo ads and would therefore increase advertising revenue.

For the sake of simplicity we’ll say the syndicate partners keep 80% of the ad spend on their site and Yahoo gets 20%.

This all makes sense, but there’s a problem: the quality of traffic on these syndicate partners was not as good. For smart advertisers who bid to the value of the traffic they must either advertise at lower returns on investment on higher advertising spends, or they must adjust bids to discount the poor quality traffic.

CASCADE FAILURE STEP 2: Inflexibility

Had they given advertisers the opportunity to bid less only on the low quality traffic, the network expansions would have been money makers, but until a few weeks ago that wasn’t an option. So, advertisers had to drop bids on all the traffic, and as we’ll see below, Yahoo ended up losing money as a result.

Let’s use the example of Acme. Initially, Acme was able to spend $5,000 a week on Yahoo and that money all went to Yahoo. Let’s say they got 10,000 clicks and could afford to spend an average of $0.50/click for that high quality traffic.

Now let’s see what happens when Yahoo adds syndication partners who drive an additional 10,000 clicks but whose traffic value is half that of the Yahoo.com traffic.

CASCADE FAILURE STEP 3: Smart Advertisers react

At first blush, in this simplest model, we see that because the advertiser is forced to bid an average of 37.5 cents across both Yahoo and the syndicates to maintain efficiency, Yahoo loses money. They lose money because while the advertiser ends up spending more money and generating more sales, Yahoo now has to split revenue with the syndicates proportionally to the volume of traffic, rather than the quality of traffic.

But wait! In reality, the effect is quite a bit worse than this because at an average bid of 37.5 cents, the advertiser won’t generate the 10K clicks on Yahoo.com it generated with a 50 cent bid average. If we say that because of the bidding, differentials (over paying on the syndicates generates 12K clicks, under spending on Yahoo gets 8K clicks) the numbers get even worse.

But wait! Getting more of the low quality traffic and less of the high quality traffic pushes down the average value, forcing bids down and shrinking revenue…

and, the lower bids shrink traffic across the board…

CASCADE FAILURE STEP 4: Add more syndicate partners

Seeing its revenue decline, Yahoo adds more partners to make up the difference => go to Step 1

WHY SMART PRICING FAILS TO SOLVE THE PROBLEM:

Through smart pricing, Yahoo applies some discount to the click fees incurred based on aggregated performance differences between the partners. This, it was hoped, would keep advertisers from dropping bids across the board. It didn’t work for two main reasons:

1. The discounts weren’t even close to what they needed to be to make up for the quality differentials; and
2. Good bidding systems don’t set bids based on cost data, they base bids on the value per click, hence after the fact discounts don’t make the systems think the traffic is of any higher quality.
   The discounts may encourage advertisers to aim a bit higher since they’re mysteriously ending up more efficient than they intended due to the rebates, but that overshooting doesn’t close the gap all the way.
3. Advertisers couldn’t see what the discounts were, so absent controls there was no reason to trust that everything would be okay at the end of the day.

ESCAPING THE LOOP: Providing controls

By giving the advertisers the ability to control the bid differential applied to the syndication partners, the advertisers can spend more money profitably and Yahoo gets to keep the windfall. No fun for the partner network, but tough turkey.

This isn’t just theory, we’re seeing it in practice. Early early returns suggest Yahoo may see a 15 – 30% lift in revenue from the advertisers and agencies attentive enough to take advantage of the tools. This growth will be a product of the huge percentage of traffic coming from the network partners and the huge differential in the average value of those cohorts.

Now, we’d love it if we could do it by syndication partner, rather than one discount across the board, but we’ll take this as a big step in the right direction.

Exciting stuff from an unexpected source!

Technorati Tags: Yahoo-Syndication-Network

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How can you support this claim?  Can you discredit the possibility that the apparent improvement is just noise? How can you apply that authoritative label of “significant”?

Here I’d like to walk you through a basic test of significance that you can use to de-mystify changes in your paid search data.

If you’d like to skip the math, click here.

Let’s start with a situational example… say you’ve added Google Site Links to your brand ads and you want to show that brand click-through rate (CTR) has improved as a result.

1. First, you need to know what value brand CTR is potentially improving from.  Let’s call this value mu (pronounced myoo), and you can choose it in a variety of ways: the average or median CTR over the past month, the average or median CTR from this time of year last year, etc. It should really be whatever value you believe CTR to truly center around.
2. Next, you need data points.That is, you need several days of CTR data since the Site Links have been running. How many days is up to you. Generally, more is better, but I’ll touch on that later. The number of days you have is n. Take the average of the CTRs from those days; this is called xbar. Lastly, take the standard deviation (excel function stdev) of these CTRs and call it s.
3. Now we can compute a t-score, and with it, the probability that the change in CTR you’re seeing is or isn’t attributable to chance. Set t = |xbar – mu| / (s/squareroot(n)). Then use the function tdist in excel, and for the arguments, plug in t, n-1, and 1. The number that this function returns is the probability that the change in CTR is simply due to chance, aka noise. If this probability is very small, then we say CTR has changed significantly.

Enough Math! Is The Change In My Data Significant?

I’ve prepared an excel spreadsheet that handles the arithmetic. In this model, change the gray shaded cells to reflect your data. Enter the data that you think has fundamentally changed in column C. Only include data points since the change began. Then, in cell G2, enter the value from which you believe the data to have changed. That is, the average value of the data before the change.

The value p, produced in cell G7, is the probability that the change you’re seeing is only due to chance, and thus meaningless. Typically, a p-level must be below 5% to be considered significant. (If you want to be super, super sure, you can use 1% or 0.1% instead.) In other words, if your p-value is 5% or less, you can confidently say that the change in your data is real, definite, and due to something other than statistical noise. It’s a pretty safe bet that whatever initiative you took – whether it was switching landing pages, altering ad copy, or refining your bidding – was the catalyst for the improvement instead.

Allow me to fill in the spreadsheet with an example. For an imaginary online retailer, brand CTR hovers around 4.4%, so I fill in cell G2 with the value 4.4. The retailer enables Google Site Links, and CTRs for the 3 days afterward are 4.3, 5.2, and 5. So I enter those three data points into column C. And voila… the p-level comes back as 12.66%. This says that there is a 12.66% chance that the rise in CTR was due only to noise.

Not significant. Sorry, click-through-rates haven’t really increased, or at least, we can’t be very confident that the observed change is anything more than random noise.

But… three days is not much data. As smart analysts, we are cautious when examining trends over only a few days, and this significance test incorporates such wisdom. As the number of data points (n) you use increases, p-levels fall. For example, if all the numbers in the above example were the same except that you used 7 days instead of 3 (so n=7), the corresponding probability drops to 2.6%. In this instance, it’s very unlikely (2.6% unlikely) that the increase in CTR was due to noise, so here you can rather confidently say, “Yes, CTR has increased, and it wasn’t due to chance. It was probably due to the site links.”

Technorati Tags: Paid Search analytics, Paid-Search-statistics

Linda Bustos

The move to content management systems is another important one, empowering the non-techie to update and maintain a website without waiting for (and paying for) a web developer to make changes. It also helped the web development industry as demand grew for CMS builds and integrations.

George: How has social media changed the way Elastic Path thinks about eCommerce?

Linda: We are often asked by our ecommerce software customers which Web 2.0 (social media) activities have the most impact for retail vs. what is just hype. That prompted me to write a post titled Top Ten Web 2.0 Activities for Ecommerce. I believe customer reviews should be a staple for most online stores, “entry level” social media, while tactics like Facebook and social shopping are lower on my list for impact. Though some retailers have had success with social networks et cetera, it’s very difficult unless you’re an established brand like Victoria’s Secret or Zappos.

George: That’s been my take as well. Twitter and Facebook may be more valuable for public relations and responding to customer feedback than for driving sales directly. Pushing offers to followers seems like giving discounts to the wrong folks.

George: As with paid search, it seems like the number of eCommerce platform providers has exploded over the years. In search it’s a bit like the wild west, with some agencies charging outrageous fees for lousy service, and it seems like the same is true in your space. We had a plaform provider charge a client $20K to implement our tracking tags — usually a half-hour project for a decent programmer. Are quality standards rising from your perspective? Price rationality?

Linda: I can’t speak for any other ecommerce platform vendors, but I can speak to Elastic Path and how we approach the situation. Our software leverages open source components and we actually offer source code to our customers. So if you’d rather use your own developers to customize the features that we offer in our core product than use our Professional Services division, you have that option. That’s one advantage we have over some of our competitors who do charge a lot to make any changes to the code because it’s proprietary and requires consultants trained in their product.

George: You’re so much nicer than I am! :-)

George: Which of the newer features that have come out over the last year or so have had the biggest impact?

Linda: I haven’t seen anything “new” that is groundbreaking on the front-end in the last few years. What I think will have the most impact for retailers is A/B split testing, thanks to tools like the free Google Website Optimizer. This surely helps retailers make more money, but also helps customers on the front end as A/B testing will bring out which are the most clear value propositions, attractive offers, most usable designs and persuasive product copy.

George: What’s your take on product demo videos on product pages? Seems to me there could be a trade-off between page load speed and the quantity of stuff that can go on a page. The conversion rate hit because of slowness might outweigh the conversion rate benefit of the product demo.

Linda: Totally agree, your piece on tracking implications of page load times is relevant as well.

George: Are people adding these features just to “keep up with the Joneses”?

Linda: I think that does happen all too often. Making design and merchandising decisions on gut feel, rather than using the data freely available through web analytics and testing tools is a real mistake.

Unfortunately, I estimate that many if not most online retailers still don’t have a dedicated web analyst, nor do they know how to extract insights from their data rather than just viewing pages of reports. Even fewer have web optimization teams that have a good process for testing and continual improvement. The retailers that do, however, have an advantage and the bright side is if you invest in these activities now, you can still have an “edge,” which is harder to get in paid search and SEO anymore.

George: Better conversion rates feed nicely into paid search efforts, too, as they allow for more aggressive bidding without sacrificing efficiency.

George: Many folks seem to be ginning up conversion rates by slashing prices, and with the economy in the tank, many of our clients find themselves in brutal price/offer wars with their competition. Are there any secrets to staying competitive without having to give away the store?

Linda: One of my favorite retail quotes is from Kevin Hillstrom.

“Discounts and promotions are taxes placed upon brands for being unremarkable.”

It’s tough to have a good value proposition in an online world – where there’s so much transparency of alternative sites to buy from. But a clear and attractive reason why a customer should buy from you (at full price) rather than any one of your competitors is very important to keeping your margin. One of the most popular ways is to offer free shipping. But that’s becoming ubiquitous and expected. So retailers are offering free overnight and free return shipping. And that doesn’t help the bottom line either. Loyalty programs can be effective, as can incredible customer service (which prompts glowing word-of-mouth through social media).

But perhaps the best way to stay competitive without discounting is to invest in conversion optimization. If you can convert more of the traffic you are already getting just by changing the site design, features and content, maybe you don’t have to discount at all.

George: Great points. It does seem like it’s particularly difficult for SKU merchants. When you’re selling the exact same product as your competitor, charging more is a tough, tough sell. Having superb customer service and fulfillment certainly helps, but those can’t be marketing slogans — they have to be real. Word of mouth is increasingly fast and powerful in this era which can be a blessing or a curse.

George: Do you see anything new that could change the world of eCommerce?

Linda: Mobile. Mobile commerce is not a new channel, rather a new screen that consumers are going to become more and more comfortable with. No longer do you need to be at your desk or at home to transact online. More people own a mobile phone than a personal computer, and people have their mobile devices wherever they are at all hours of the day. And new devices like Kindle and iPad make the user experiences on mobile devices much better than tiny smartphone screens. So ebusinesses will need to understand mobile design and usability for their websites and also consider what applications will be most helpful to their customers. We’re not at critical mass yet, but we will be very soon.

George: I recently had a terrific, candid discussion with Jason Spero of AdMob — a mobile display advertising network that Google is trying to acquire. Jason said he’d been predicting the rise of mobile eCommerce for the last 10 years; for the first 6 years he was wrong, but now, he says, it looks like he may be right.

George: How important is having a mobile strategy in 2010?

Linda:

The last 3 years have been dubbed the “Year of the Mobile” it seems – and mobile commerce has certainly grabbed the attention of the largest retailers. Every month we hear of Internet Retailer 500 companies launching new mobile sites, redesigns or mobile applications.

While I still don’t think customers are ready to purchase online through mobile sites (biggest reasons are fear of data security and usability issues), I think it’s important for retailers to get their feet wet now to get over the learning curve. Having a mobile presence is also helpful for customer research on-the-go, and can be very helpful for customer service (think of order-tracking, back-in-stock and in-store pickup notifications, etc). Even if you don’t drive sales through the smaller screen, it can add value to your business, especially if you’re a multichannel retailer with brick-and-mortar stores.

George: Linda, thank you so much for taking the time!

Folks, this is a great opportunity to ask Linda questions, so fire away!

Technorati Tags: Elastic Path, Get Elastic, Linda Bustos

• Get Elastic Interview with George Michie A few weeks ago, Rimm-Kaufman Group CEO George Michie had the pleasure of interviewing Linda Bustos of Elastic Path and...

How could anyone oppose ROI maximization? Don’t all advertisers want better ROI?

Well, no.

Volume and Efficiency are always at tension and how that tension is balanced is highly revealing.

There are two fundamental approaches to this problem:

1. Spend the budget and “maximize the ROI”; and
2. Hit the efficiency target and maximize the budget within that constraint.

RKG has always believed that the ROI target should take precedence.

Let’s say Acme has a budget of $100,000 and an ROI target of 4 to 1.

The first approach: Spend the $100K and maximize the ROI.

Scenario 1: Spending $100K leads to significantly lower than 4 to 1 ROI:

Does it make sense to keep spending when the ROI turns south? The law of diminishing marginal returns tells us that next dollar of additional advertising wisely spent will always return less than the previous dollar. When we hit the point that the next dollar returns less than the target efficiency, or less than break even, why would we keep spending? If the returns are poor it’s like feeding money into a shredder simply because the goal was to “get rid of” some fixed amount of money.

Scenario 2: Spending $100K leads to significantly higher than 4 to 1 ROI:

Does it make sense to stop spending money if the ROI is great?

When the delay between investment and return is long or uncertain, budgets are essential. For advertisers taking direct sales the paid search returns happen before the media expense is billed, so the cash flow argument doesn’t carry much weight. And, when the returns are cash rather than leads, there isn’t even uncertainty as to the value of those returns.

It’s like having a commissioned sales force but telling them to go home when they’ve hit some salary cap. If the commission rates make sense, why would anyone apply the brakes?

“Maximizing ROI” implies exactly this kind of budget first mentality.

When client prospects approach us with budgets that are inconsistent with their ROI objectives we tell them that up front. Imagine a company selling all varieties of bungee cords and hoping to spend $250K a month at a 5 to 1 ROI. We’d tell them that given the market opportunity we can only do one or the other, and would recommend pursuing the 5 to 1 ROI as far as the market will allow.

Most other agencies would say: “Those are aggressive goals, but we’re up for the challenge. We’ll spend the budget and see what we can do about the ROI.” Next month, the discussion will center on how they can “Maximize the ROI”…while still spending like a drunken sailor. That’s good for the agency’s revenue, and good for the engines, but I’m not sure it’s in the advertiser’s best interest.

Maximizing ROI isn’t necessarily even the most profitable approach. By definition ROI is a ratio. A 5 to 1 ratio seems better than a 4 to 1, but looks can be deceiving. Let’s say the advertiser has 40 points of margin on a sale. On $10K in advertising the advertiser generates $50K in sales. $50K in sales yields $20K in margin, less the advertising means $10K in marketing income. Cool.

But if the additional aggressiveness of a 4 to 1 target gets them significantly higher on the page and hence more top line, the extra volume may generate more marketing income. Depending on the bidding landscape, perhaps $20K in advertising well-spent generates $80K. That 4 to 1 ratio, generates $32K in margin and $12K in marking income. A smaller ratio, but more money, and as every banker knows, you don’t put percentages in the bank.

According to the square root rule, profit is maximized when the ratio of margin generated to advertising spend is 2 to 1. In practice we find running that lean cuts the top line too much for $ maximization. At RKG, we’ve found that a ratio of margin to advertising of 1.4 – 1.7 tends to maximize marketing income.

The “Spend the budget first” mentality does make sense in some contexts. When the return on investment is uncertain (brand building) or delayed (lead generation), budgeting is wise. Too often, though, budgets are used simply because they are customary.

For this channel, any company with measured, immediate ROI goals should place those goals first and let the ad spend rise and fall with the market opportunities.

Language matters in paid search. The language agencies use in describing themselves can reveal a great deal. “We’ll maximize your ROI” screams: “We think about spending your budget first and hitting your efficiency targets last.”

Next week, I will take on baseball, motherhood and apple pie :-)

Technorati Tags: paid search budgets, PPC ROI

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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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