What should you bid on a new Long-Tail AdGroup?

What is a common AdWords bid you'd set for long-tail keywords, knowing that you'll probably have to wait a long time before you get any impressions?

I came across this question recently, and I decided to dig into it.

Let's take the example of a single keyword, which we aren't sure how it'll perform.

We'll need to make a few more assumptions to get started. Hopefully, you can produce these based on similar activity in the account, or on your site.

You'll need an Average Order Value assumption first. If you're targeting a specific product, that product's price is a reasonable place to start. We'll assume $150.

You'll also need a Conversion Rate. You can either use data from a similar campaign, or your site's average. Don't worry--it can be a ballpark, as this will correct course quickly enough. We'll assume 3%.

You'll need to know how much you're willing to pay for advertising, as a percent of revenue. Too high, and your margin erodes. Too low, and you give up market share. This figure is called your Cost Of Sale (COS), and is just the reciprocal of your ROI target. Let's assume you're looking to spend no more than 15% of revenue for these ads.

Now, with the COS and the AOV, you can arrive at a Cost Per Acquisition (CPA) target. This is how many dollars you can spend per conversion. In our case, it's $150 * 15% = $22.50. That's how much we can spend to generate the clicks it takes to produce a conversion.

If we have the Conversion Rate, then we know how many clicks (on average) that takes. At 3% conversion rate, we average an order for every ~33 clicks, so the $22.50 is all we can pay for them and still hit our goal. That's $0.68 per click, and that makes for a GREAT starting point.

But how do you know when to give up? You can use Wolfram|Alpha to find out how many clicks it would take to get to 95% probability of a conversion:

Wolfram
Wolfram

The .03 on the left side of the equation is our conversion rate, and the .95 on the right is the certainty we'd like to hit. This solves to ~98 clicks, so we'll run our test to at least that many.

10/30/15 Update: Andrew Flicker corrected me, and I now direct you to the One Proportion Hypothesis Test for this type of calculation. Same idea, much easier to use.

If you get a conversion sooner than that (with some sort of upper bound), you can bid based on the real conversion rate. For example, if you got a conversion after 25 clicks, you can bid using that 4% number. If that's actually too high, you'll have collected the additional clicks to know that fairly quickly, so it turns itself back down before it spends very much. If you aren't getting a conversion, you can also bid as if you had only one. If you are at 90 clicks, for example, and still haven't seen a conversion, you could bid as if you had one, bid using the 1.11% rate, and your bid will be cautious, skeptical even, and your spend will be controlled.

Now, this was all for a single biddable entity, perhaps a keyword. What I'd recommend is to keep your long-tail keywords separate from your higher-action keywords, in different AdGroups. As you sift out higher-performers, move them to the main group. Slower-action keywords can stay in the long-tail. You may not even need to bid at the keyword level, if you use this strategy, as you can apply exactly the same logic to the AdGroup level. If the entire Group is a new test, then this will accumulate clicks more rapidly, and reduce costs.

Now, how ever broad you go will set how much risk you're taking on. If you have one keyword, like this example, you're probably only going to spend about $50. If you do that thousands of times, then that number goes up with it.

Have questions?  Drop me a line via the Contact form--I love to talk shop.

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Should I Offer Free Shipping on Our Site?

Should I Offer Free Shipping and If So What Should I Set the Threshold To?

At some point in everyone's ecommerce career they contemplate these questions when it comes to their ecommerce pricing strategies; should I offer free shipping, what should I set the shipping threshold to, how can I offer free shipping without losing money?

I've done a lot of testing on this topic using some pretty extensive A/B tests with completely different cart/checkout pathways (not just simple UI stuff). One e-commerce company I've worked with tested free shipping on all orders versus free shipping over $100. Their AOV was about $300 at the time and as we conducted the test if the profitability gap was significant, we'd be able to tell quickly, and turn off the test, so we went for it. The conversion rate gain was worth more than the lost margin by giving up either the collected shipping.

This did have an affect on the company's ability to be price competitive on low-ticket items. While Google Shopping does an okay job showing the "total price", not all competitors are feeding accurate shipping prices (especially if they also have a "handling fee"), and not all shoppers are looking at the "total price". This meant they would do well throughout the bulk of the catalog with great cross-sells to low-ticket items, but possibly draw less traffic to them.

Whether a higher or lower threshold makes sense for your site will depend on your margins, your conversion rates, and how those discounts (or up-charges, if you're going the other way) affect both. I recommend measuring success across the test and control group in terms of gross margin dollars. If, given the same number of visitors, one generates a statistically significant increase in margin dollars, then there's likely an improved strategy to be derived from that outcome.

If you generate a large amount of your profit from low-ticket, high-margin items, you probably won't go for universal free shipping, so you can keep your product prices competitive at that end. If they're just a contributor, but not a margin leader, then it might make sense to push the threshold lower, or off, and make it up on the big ticket items.

Once you set the threshold, however, be sure to look at your pricing logic right around that dollar amount. You'll want to be sure you're taking the threshold into account when you're setting prices just under it. But don't forget--a $70 item might have an AOV of $122 per conversion, if you average more than one item per cart, or your cross-sells are strong, so don't assume it's a $70 conversion average, just because that's the item's price.

To get around that, we turned the problem inside out, and generated something we called "Shaded Shipping"--that was a per-unit cost of shipping, averaged across all of the shipments that contained that item, including multiple units or multiple products. With the Shaded Shipping amount being updated weekly or so, we could use that to establish pricing that preserved profitability, but also allowed us to take advantage of multi-item purchase behavior through those averages.

This is a piece of a larger pricing strategy conversation, of course.  Hopefully this will help you working on answering the question, should I offer free shipping?  If it doesn't set you on the right path, drop me a line via the Contact form--I love to talk shop.

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How do Google Shopping Campaigns track performance?

Roy, we've got some questions on how Google tracks and exposes performance data for Google Shopping Campaigns. Could it be possible that performance follows SKUs in Google Shopping when product groups are split or aggregated – but not when they are moved via updating a value in the feed?

We’ve seen that it takes several hours to move between product groups in PLAs when done via updating a value in the data (in this case the custom label 0). We pushed a feed with updated values to merchant center at noon. At 3PM very few items had moved. By 4:30 about 1/2 of them had moved and by 10:30 all items had moved. This had to do with products moving from our store to our outlet, which has a different URL path. It appears to us a move like this doesn’t bring any of the performance data along with the SKU.

This is a great question and I expect it will evolve as Google continues its push with Google Shopping Campaigns in general. Performance data is kept at the product ID level since this is unique to your data i.e. your feed and account. Google stores the data at this level. However, in my experience it doesn't retroactively apply that data to new product groups based on new dimensions.

For example, say you just added a new custom_label for price tiers (a favorite optimization strategy of mine). The day after it's added, if you break out a Product Group by all of the values of this new field, all of the conversions will appear in "Everything Else". When you click to examine the products in that "Everything Else" bucket, it could be entirely empty, and the conversions are still there. However, if you broke it out by product_id at the top level (which none of us could literally do, as it's limited to 1000 leaves per branch, and most of the sites I work on have catalogs bigger than that), then you would absolutely see the product conversions at the level described.

However, if you refactor a taxonomy after a couple of weeks, conversions since that change are totally portable from one place to another. If you already have "store" and "outlet" branches, it may transport them cleanly. If "store" and "outlet" were totally new, then I think you'd have to do some kind of performance aggregation through AdWords Scripts or API reporting, since the ProductIDs would still have data, but the new Product Groups wouldn't. That may change in the future, as making the product_id performance totally portable would be really cool, and not technically difficult for Google to switch.

Have questions?  Drop me a line via the Contact form--I love to talk shop.

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What's a good benchmark CTR to measure performance?

Tips on Using CTR for Queries with Low Click Volume

I have I quick question regarding keyword research. I am currently working on setting up our process for identifying either successful or under-performing keywords in Bing. Most keywords won't see enough conversion/clicks to reach any kind of statistical so it certainly seems to be the case that click-through rate will mostly be what we monitor (at least for right now). So, in determining how to evaluate keywords by click-through rate, I was curious about how we can/should set a baseline. Specifically, when we are assessing broad match keywords, we should (in many cases) get enough impressions to easily test for differences, but I'm not quite sure what would be a sensible baseline CTR to test against. I was thinking maybe a 1% CTR would be a good level for our evaluations (testing each keyword for a significant difference above/below that), but our past data has been really inconsistent and recent campaigns we set up have seen many, if not most, CTRs in the tenths of a percent. So, I guess that is where I wanted to get your input/advice. From what you have seen, is there a standard you would recommend for separating high/low CTRs? Does it depend on the business, and if so, should I maybe just sort CTR performance into percentiles (identifying, say, the top and bottom 10% in CTR in each campaign/ad group)? Just curious to get your thoughts. Any guidance you can give would be awesome.

Unless there's huge volume, and otherwise great performance, CTRs under 1% are usually pretty weak. If I'm sifting my keywords, I will carefully consider anything under 0.5%, and see if I can guess why it's a miss. Maybe there's a tangential broad match that is hitting a different search intent? Worst case, I just exact-match negate it, but I'll usually try to figure out the behavior, and set up new filters for it.

Overall, you can get a sense of how keywords are doing by looking at the distribution within the account. You can search out under performers by filtering to keywords with at least a few hundred impressions, but few or no clicks (or, low CTR, obviously--same game). Take for example an account I've worked on recently, there are only 42 keywords in this AdWords account with over 1000 impressions, and a CTR of less than 1%. Those keywords generated $2327.46 in costs over $5165.77 in revenue. The COS of 45% would be tolerable within the account's goals. Filter that to CTRs of 0.75% or lower, and that trims off all but $571.02 of the cost, but also all but $764.95 of the revenue--and the COS becomes about 75%! As that's higher than the Gross Margin of the product line in this account, the account would have been more profitable during that period if this group of keywords hadn't existed.

There is one other major factor to consider, though. CTR is a function of two variables: keyword relevance, and ad copy. If the ad text is poor, it'll drive down the CTR of even the most brilliantly targeted keywords. If all of the keywords in an Ad Group are exhibiting the same weak performance, but the relevance and behavior seem right, then it might be worth running a test of a new Ad.

Thanks for the question and as always feel free to let me know how I can help.

Have questions?  Drop me a line via the Contact form--I love to talk shop.

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What's your feeling on affiliate marketing?

Thoughts on Affiliate Marketing Commission Structures

We've been using Link Connector for a while, but they really only have coupon sites in their network and 90% of the traffic they direct to us is dominated by RetailMeNot.  We are thinking about re-negotiating with Link Connector or dropping affiliate marketing all together or maybe trying out another network.  What's your feeling on affiliate marketing?

What are your thoughts on commission structures?  We are thinking of excluding commissions depending on how close to conversion the affiliate touch is i.e. if it comes within 24 hours then we exclude that touch from getting any commission.  Any insights you can share are appreciated Roy.

Most coupon sites are a scam.  There are probably only a handful of good ones to work with.  I've setup programs where we have a pretty low hurdle for our affiliates to get over.  We'd do a monthly check for how many customers are "new" visitors.  That is, if they were already on our site before the affiliate click, then they're not "new".  We require something like 5% new customers for our normal commission, and 1% for our lower tier commission.  Less than 1%, and we just drop you.  It's an almost laughably low bar, but it filters the scumbags out like crazy.

I wouldn't worry too much about the near-checkout touches, as a good content affiliate that lands a qualified visitor could get caught in that trap.  Rather, keep an eye on the coupons being used, and regularly rotate and police them.  You'll detect weak affiliates based on their average discount rates, new customer rates, and similar metrics.

I've used an attribution model to police the affiliates, too.  On average, the channel only holds about 60% of touched orders.  For comparison, retargeting display is 45-50%, and AdWords is 80-85%.  In addition, when you audit orders you should stay pretty solidly above 65%.  When we get lazy, it drops quickly.  In Google Analytics, you can tell where the channel is playing by comparing an even split with a last-touch.  If last touch rewards the channel a TON, then you have a couponer problem.  Content affiliates would normally be a shred higher in the funnel, and thus do well under either model, so the relative model performance can be used like a litmus test.

I've also run two networks at a time, for most of the history.  That's just because some quality participants can sometimes be found on different networks.  First it was GAN and CJ, then CJ and LinkShare after GAN shut down.  The problem is...most good bloggers can make more money with Amazon links than they can off of specialty retailers, as their visitors are probably going to buy something from Amazon in the next 24 hours.  With us, they'd have to buy something related to what they were reading.  Our conversion rates just aren't what Amazon can offer, even if our rates are competitive.  Still, with the small handful of good bloggers out there affiliate marketing can produce enough of a positive contribution that it's worth having the channel live.

Have questions?  Drop me a line via the Contact form--I love to talk shop.

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