This is a guest post by Edwin Choi, VP of Marketing at Mobovida, a customer-driven, vertically integrated mobile accessory brand delivering fashion forward products direct to consumer. Check out our recent podcast interview with Edwin.
At CellularOutfitter.com, a leading online retail site owned by Mobovida, it can be said that we have created a new religion centering on conversion rates. This can best be exemplified by the giant (slightly altered) Wu-Tang decal we had in our old office:
C.R.R.E.A.M. = Conversion Rates Rule Everything Around Me!
Sadly, the decal didn’t survive the move to our new office. It’s hard to fault us for being dedicated to this metric: it’s a key reason why we have been growing like a weed during my five year tenure.
With higher conversion rates, we were able to exponentially lower our cost per acquisition for new customers and pour the cost savings into capturing more marketing share on our top-performing marketing channels. We’ve also built a considerable moat as competitors struggle to keep up with the rising costs of paid digital marketing (I’m looking at you, Google Adwords).
Read More: 13 Quick Tricks to Increase Conversion Rates that You Can Do Right Now
A huge proportion of our time during the last three years was dedicated to running hundreds of A/B tests through our constantly evolving conversion rate optimization process. A consistent, ever-present motivator for the blood, sweat and tears involved are the numerous learnings we were able to extract from our test data.
With a heavy combination of quantitative and qualitative data, we’re able to get into the minds of our customers and build marketing messages that resonate with our core personas. The wealth of information driven by Adobe Test & Target, Visual Website Optimizer and Optimizely tests helped us to intimately understand what makes our customers tick.
CellularOutfitter.com is built to handle large volumes of traffic and daily orders, so our testing methodology was perfect for scaling the business rapidly. However, what if each order was worth more to the business? One of our core metrics is the all-important “revenue per visit” metric, which combines both conversion rate and average order value into a telling key performance indicator.
We decided to extrapolate our learnings about the customer with a new goal in mind: raising the average order value.
We knew that our customers loved a great deal and that they often wanted to buy more things on the site, but they didn’t know that we had certain deals or products available. Because of this, we could hypothesize this through some of the data that we ran in previous tests as well as effective event-based, triggered marketing campaigns.
A light bulb went off in our heads: what is one of the most effective ways that retailers increase average order size in physical retail? It’s the “impulse buy” section at a checkout aisle:
This is the same reason why Fry’s Electronics, a large consumer electronics chain, sells candy in their checkout aisles as well. Customers waiting in line to complete their order are confronted with low-priced, high-margin items that they didn’t want or need. Close rates are high because the customer is already in line to make a purchase and the comparative cost of adding additional items to their current order is relatively small.
Also, it’s super easy! All you have to do is reach over to a bag of M&Ms, toss it onto the conveyor belt, and the retailer just gained $2 in average order value. It takes the customer just a few seconds and there’s very little decision criteria needed to add friction to the process.
We hypothesized that we could mimic this same experience online on our virtual shopping cart and increase the average order volume of our carts.
Our first test would give a massive discount if the customer added a certain amount of items to their cart. This was mainly powered by two widgets:
We launched the test and crossed our fingers.
It sharply decreased conversion rates and the net loss from losing those orders hurt one of the most important parts of our site. In order to confirm this, we double checked the amount of carts created before, during and after the test in order to make sure it wasn’t influenced by pre-cart site factors. It was flat:
We have losing results from our tests all the time, so we were excited to see what type of learnings we could dig up from this test. We started to look into the key losing metric: cart conversion rate. It plummeted 6.2%:
At the very least, we proved that we could sharply change consumer behavior! This means that the presentation was effective enough to alter the path of our customers.
Let’s dig in further!
We segmented the customers who reached this page into three groups and looked at their average order values:
We really spiked up average order values for users that somehow powered their way through promo redemption. The proportion was just too small to overcome the drastic drop in conversion rates. We cross verified this with coupon use event fires in Google Analytics, Adobe Analytics and our internal reporting system and everything checked out.
We had huge learnings from our first test, so we decided to refine the test and launch it again. This time, we had additional data and hypotheses. We knew we could significantly influence consumer behavior, but we were turning off too many people with the minimum order redemption requirement. We decided to build custom promotional tiers based on data:
As you can see from the above chart (KPIs blurred for privacy), we mined our cart data from our in-house business intelligence database and broke users out to certain cohorts based on the average number of items they had in their cart. We then asked ourselves this question:
Can we have a personalized widget that will get more people to “just buy one more item”?
This is akin to getting more people in the checkout line to buy a pack of gum. We also heavily reduced the amount of friction needed to add one more item to their cart. We had these hypotheses:
We decided to target certain Average Order “breakpoints” where the discounted AOV would still raise our sitewide based on our cohort data. The hypothesis was that we could upgrade cart AOV for our high volume cart cohorts if we tiered out our coupon code structure.
Learn More: LeadPages CEO Clay Collins Talks About How To Ramp Up Your Conversion Rates (Up To 75%!)
We relaunched the test with a new widget – this widget would count how many items were in a customer’s cart and give them a special discount upon adding one more item. The discount was mathematically derived to give the customer a great deal while at the same time raising our average cart values.
The results were astonishing for average order value – it increased by over 15% and it stayed there over time! It also resulted in flat conversion rates which meant that we had a massive double digit net gain.
As with any test, we had to cross verify the numbers with as many different data sources as possible to be sure that this was, indeed, the case. We built a custom dashboard that feeds data in real time from our business intelligence server to monitor “Big Carts.” A “Big Cart” is a cart defined as a shopping cart with an average order value at least two standard deviations above our typical rolling average:
After the test was launched, our number of “Big Carts” increased by over 20%! The widget coupon codes also became some of the most highly used coupon codes on our entire site. They also have some of the highest conversion rates and revenue per visitor metrics as verified by Adobe Analytics:
This single test has added millions of dollars in revenue to our site per year.
For every test that wins big, we always try to harvest the learnings from understanding our customers better into more wins down the road. The learnings from this test powered other similar winning tests on our mobile sites as well as countless hyper effective campaigns (for example, this was converted into a very effective e-mail campaign).
For us, this test also highlights the importance of testing for learnings instead of wins. When we lost heavily during the first test, our first reaction was not “Aw shucks, we lost. Let’s go for that win!”
It was “Let’s see what we can learn from this” – and the second home run test would not have happened without the first test.
Read more on this topic (and much more!) from Edwin Choi on his blog Marketing Muses.
What is your experience with AOV? Have you run any tests or learned anything new about it? Share in the comments below so we can all learn!
This is a guest post by Stevie Duffin-Lutgen, Marketing Analyst at Mobovida, a customer-driven, vertically integrated mobile accessory brand delivering fashion forward products direct to consumer. Check out our recent podcast interview with Edwin Choi, VP of Marketing at Mobovida.
If you’ve given up on display advertising as a source of profitable revenue, you’re not alone!
My predecessors in digital marketing at the heavily ROI-driven Mobovida didn’t have much success converting upper funnel banner clicks, instead wielding display for remarketing purposes only.
Mobovida owns CellularOutfitter, the largest online retailer of cell phone accessories. We sell a plethora of mobile accessories for just about every phone model, and wanted to know how we could revisit converting the cold display customer to reach a broader audience.
So when we heard that AdWords was going to start serving clicks on Gmail via Gmail Sponsored Promotions (GSP), we knew we had to jump on the opportunity. And I encourage anyone reading this to do the same!
Not only did the channel prove to be a valuable source of sales, but it allows the company to quickly test its Mobovida product line against fresh audiences. The data provided via quick-to-launch channels such as Gmail Ads has been invaluable for powering Mobovida’s agile product development model.
Although the channel started off losing $14 per $1 spent, we were able to methodically reach our tipping point ROAS (Return on Ad-Spend) of $2 per $1 after 13 rounds of testing.
Glad you asked!
If low-quality display clicks have ever given you nightmares, rest assured – surgically targeted Gmail ads with effective creative are not quite the same animal.
When Internet surfers are perusing their inboxes and see a banner for a Galaxy S7 Edge phone case (one of our top-selling categories), there’s always the possibility that they’re more annoyed with the banner interrupting their experience than they are interested in the offer (even if they actually need a case).
But GSP mobile ads are served up in the often retail-laden “Promotions” folder of Gmail user inboxes:
My drip e-mails from Nordstrom, ModCloth (can you guess my demographic?) and my other favorite shops are already in this folder, so when I access it I’m in a curious, if not full-on shopping, mood.
My job is to drive sustainable revenue at a 3:1 ROI target by harnessing Internet traffic. So when my team suggested working with Gmail ads – a branch of the Google Display pipe dream – I knew it was going to be a challenge.
But with some permission to adjust my ROI targets and experiment with a sizeable test budget, in one month I took our Gmail ad campaigns from 0-30% ROAS up to 3200%:
Here’s how it got done, in this order.
Display advertising is only as good as its targets, and one place where we struck gold in 2015 was Facebook, largely thanks to sweat equity and – you guessed it – rapidly testing endless permutations of targeting and creative.
Since Facebook ads are not triggered by search queries, we were alarmed at our ability to grow and scale the account after hammering out the right ad-demo-target formula, and truth be told it exhibited some game-changing potential:
I needed somewhere to start in Gmail and using my team’s experience in conquering Facebook was integral. I knew which products were converting for which demos and on which devices, which was a huge advantage! If you don’t have the luxury of previous team experience, consult your Google Analytics Audience -> Demographics tab for an expanded date range and look at your top contributing genders and age groups.
So I consulted some historical analytics and confirmed that older women using specific phone models converted best on our site. Thanks to Gmail campaign-level phone model targeting and ad group demo levers, I was able to carve out a solid beginning baseline upon which to test the various GSP ad types.
Learn More: The Complete Guide to Gmail Ads (How We Got $.10 CPCs & Leads As Low As $7)
So I got my initial targets down; the stage was set. Then I was confronted with the myriad Gmail ad options laid out in the AdWords Gmail ad gallery:
The question was: Which to choose?
The answer? Choose them all as there is no right or wrong option.
Every ad type is going to start in the same way: a “collapsed” format at the top of your audience’s inbox. It’s going to have an e-mail subject line and description that must spawn an initial, curious click. Once clicked, your ad will be revealed in the “expanded” format, but wait! – you’ve already been charged.
A challenge for us was finding the ad type/copy/creative combo that drove enough interaction on the first, charged click, and also resulted in click-throughs to the website. You can monitor this metric in Columns -> Gmail Metrics -> Gmail Clicks to Website in AdWords for a more accurate picture of CTR and conversion rate.
Thanks to our vast, SKU-level analytics data, I was able to break each phone model down by its top products, so I knew which items I wanted to serve on each phone model’s campaign.
A good example is screen protectors for the Galaxy S5 campaign: we know that our S5 users love screen protectors and especially tempered glass, so we started with simple single promotion ads:
We experimented with similar visuals and language across the available ad types, but the return was not there. We spent over $600 to make $35 on the ad pictured above, so something wasn’t working.
I didn’t know what was wrong, but my hypothesis was that our current aggressive ad types might’ve been too abrasive for Gmail users, who are opening what looks like an e-mail.
While there’s no true A/B tool available within AdWords for display at the moment, I had to test a different ad vibe. I created a toned-down version of my sales pitch and tried for straightforward instead of sensational. However, I still didn’t see the return I wanted with screen protectors no matter the ad type or my copy.
Here’s when a search marketer bangs their head against the wall and cries out, “I am done with display!” But I’m lucky – I have a lot of products to test, and I was determined to test them until something worked. So I switched to phone cases.
My original poster boy for phone model-targeted campaign success was the Samsung Galaxy Note 4.
I started with a toned-down version of our single promotion ads to test the waters, paired with a top selling Note 4 case:
And all of a sudden I was breaking even – I spent $375 and made $377, but that pesky “Ad Copy CTR” was driving my costs up significantly.
Is it possible as a PPC marketer to see a 25, 35, 45% CTR and see red?
In Gmail it is.
Earlier, I warned against the dangers of the collapsed versus expanded GSP ad formats, because advertisers are charged on the first click. That first click opens the e-mail ad in all its full, expanded glory – it doesn’t give you an URL pointing to your website. Consequently, Gmail ads are technically charged per impression.
And our flashy, monster-percent-off e-mail ad subject lines were attracting way too much attention. I needed to reduce junk or purely curious clicks significantly. I brainstormed with another member of my team and it was clear that we needed to heavily qualify every incoming click.
As a possible solution, we tested product price in the e-mail subject line:
Our Ad Copy CTR was still hovering above 30%, and volume was building, but we were still breaking even.
Now, I don’t know about you, but I’m only willing to pay so much for a customer. I don’t want to pay for tons of clicks from accidental swipes or rubberneckers.
So when my tragically effective subject lines were burning holes in the company’s pocket, my co-manager suggested I turn on target CPA bidding at a realistic level, just to give it a whirl. If I could not sacrifice good old-fashioned compelling copy for lower costs, AdWords needed to give me predictably higher-quality clicks based on its learnings from my unprofitable, proliferating conversions.
Our costs went down overall, but so did our impressions and conversions, perhaps from a lack of sufficient conversions learnings since our volume-per-campaign was still low on average.
From inception, we’d seen a pretty steady 5% average conversion rate, and target CPA results did show conversion rate lift (scroll down to see charts), but since our impressions were down to boot, our Gmail Clicks to Website metric was looking especially anemic.
The next thought was: could I turn manual CPC bidding back on for low conversion campaigns, create highly compelling expanded ad copy, and increase ROI by getting visitors through my ad and letting the PDP landing page do the rest of the work?
How do you qualify a customer? You tell them what to expect.
We’d been doing that with our subject line copy by discussing price, but we’d been repeating it inside the expanded ad as well. Could it be that seeing price again was discouraging? Does our audience not want to see dollar signs? Do they want more information than we’re giving them and we’re wasting the expanded headline space with fear-inducing redundancy?
I decided that it could certainly be one or many of those things, so we tested a new ad format – what came to be called the “WasNow2b” format, in which we still list the “Was $X, Now $Y” in the subject line, but add fear- or doubt-reducing value propositions in the expanded ad:
We started to see days of high return interspersed with those of low return, with the Gmail Clicks to Website metric increasing.
This wasn’t too surprising (gaining some traction only to hit shaky ground) because admittedly, our mobile experience isn’t the most flawless, and we’re driving pure mobile traffic from these Gmail ads.
On our site, landing on a PDP can be a damning experience for the older demographic, since navigating to our many other products or categories can be cumbersome. And our site visitors like to see options, especially in a wealth of colors.
I bolstered our concerns using Hotjar visitor recordings, and witnessed many Gmail visitors landing on the PDP we provided, only to scroll around the site in what looked like a baffled mess.
We hypothesized that if we offered more case color options in our Note 4 ads, we might see lift.
The Gmail Catalog Ad is the Single Promotion Ad’s more versatile sibling. Their formats are comparable: both have a top image, headline, and description available in the expanded format, but the Catalog ad allows for more items to be featured beneath the main product image. If our Single Promotion ads had been gaining traction, my learnings would’ve been derived from not only their targeting successes and failures, but those of their formatting.
So – plain and simple – I chose to experiment with the Catalog ads to mitigate the strain of variability:
After running the Note 4 Catalog ads, we were closer to our 3:1 ROI target than ever before:
Read More: Are You Exploiting YouTube’s Cheap Advertising Platform Yet?
The good scientist keeps a thorough lab notebook, and so should you.
Maintaining strict documentation of our ad copy, targets, and results was paramount to success in Gmail, even though it took time to develop the format that worked for us (and it’s still evolving!). We tracked progress in a Google doc with tabs for new ad buildouts:
…and our results. Our “Results” tab listed the testing round, campaign, ad group, ad name, a link or screenshot to ad creative, KPI data per ad, and, most importantly, a column for hypotheses derived from the round and a column for next moves.
For each round, we paid special attention to the average conversion rate and average “true” CTR. Note that “true” CTR is not collapsed ad clicks divided by impressions, but expanded ad clicks – Gmail Clicks to Website – over collapsed ad, or subject line, clicks.
To see how this worked, take a look at the evolution of our conversion rates and true CTRs over the course of four rounds involving pivotal changes:
Across the first and second rounds in this series, we were funneling in target CPA bids across campaigns with relatively higher conversion counts, and still mostly using a mix of single promotion ads. You can see lift in conversion rate on average, but true CTR decreased.
From the second to third rounds, we’d begun to test more and more catalog ads, which boosted our true CTR as predicted, but did not result in a lift in our average conversion rates.
Moving into the fourth round, we’d scaled the use of our fear-reducing expanded ad copy across all campaigns and introduced target CPA bidding to campaigns with 20 or more conversions. This combination was powerful, illustrated by the jump in both true CTR and conversion rate.
None of our progress would have been possible without this weekly ritual of recording KPIs for each ad, followed by synthesizing new hypotheses.
As the conversions rolled in, I gradually migrated campaigns from Manual CPC to Target CPA, at about $2 above that campaign’s average CPA. This approach, combined with our winning ad and copy formats, was enough to garner sales, but there was more that could be done at the ad group level since I can control who sees my ads at the level of gender, age and parental status.
Over time we’d see trends evolve – like 35-year-old women who love to click a particular ad in a particular campaign, but rarely convert. To save on costs, I removed those demo groups. You can do this by clicking on your campaign in AdWords, navigating to the Display Network tab, selecting Age, Gender or Parental Status, and hitting the “enabled” button on your demo group:
Narrowing in on winning demo groups while diversifying our offerings in creative refreshes has become more important moving forward, since we’ve noticed that hitting one demo with one phone model with the same ad for more than two months slows impressions and exhausts sales. But if you’re willing to venture into the unknown with display ads, Gmail is a potentially lucrative (and far less painful!) place to start when compared with the broader display network.
Play to your strengths, rigorously control your testing and documentation of both the collapsed and expanded ads and targeting, mine your efforts for learnings from your analytics platforms, and fine-tune your targets using the Display Network tab in AdWords. You may find yourself with a new Google revenue channel, and no keyword bids are necessary!
Have you tried Gmail Ads? What’s been your experience, good or bad? Let us know in the comments below!
Check out another great Gmail Ads post right here on the Growth Everywhere blog.
This post originally appeared on Single Grain, a growth marketing agency focused on scaling customer acquisition.
If you are an online retail company, you should be intimately familiar with Google Shopping. Google Shopping was first released as Froogle in 2002 and was a key growth driver of Google advertising revenue before it transitioned over to its current “pay to play” model in mid 2012.
Google Shopping is one of the biggest revenue drivers for both retailers and Google. Its engaging format drives a high click-through rate and qualifies visitors before they visit your site. The advantageous above-the-fold placement grants the valuable possibility of high traffic levels.
Too bad it’s not a secret—the ROAS (Return On Advertising Spending) on these placements are lucrative, so the competition is fierce. The average cost per click has been rapidly increasing ever since its release and it’s only getting worse.
Google has also been testing a “16-pack” of Google Shopping results that will only serve to exacerbate the pain of participating in this cost per click battleground. What’s a ROAS fanatical growth marketer to do?
When CellularOutfitter.com first participated in Google Shopping ads, it was driving a fraction of revenue. As we optimized the campaign structure over hundreds of iterations, we slowly developed a Google Shopping campaign structure that was perfectly optimized for squeezing every last penny of return from your Googlebase feed.
We will outline the structure below in our plan for the Ultimate Google Shopping Restructure.
Elite performance in the Google Shopping auction starts off with the quality and cleanliness of your Googlebase feed. Google will harvest and index your feed and serve up results based on what it deems to be the most appropriate match to the user’s search query. If you have a greater volume of optimized attributes versus your competitors, this will be a key advantage in impression share, cost per click and click-through rates.
Item title: Most e-commerce companies will simply import their product titles into this column. However, savvy paid search marketers know that Google Shopping results will bold any sort of keyword matches to help users find what they are looking for. With this in mind, you can harvest your top search queries in terms of revenue or traffic contribution from your top text ad keywords and start to optimize your product titles for the best visibility and impression share.
You will reap the dual benefits of increased CTR % as users will see more bolded keywords with your title versus the competition’s as well as higher impression share % as Google gets a more exact match to the user’s search query. Optimizing your feed in descending order of revenue contribution also ensures that your business will get the best bang for your buck.
Example: We had a high revenue contribution query of “Galaxy S6 TPU cases” doing well for our text ads. We then modified one of our highest converting products to exhibit this exact keyword in the title and descriptions in order to give this SKU more visibility in the Google Shopping results.
Learn More: The Complete Guide to Gmail Ads (How We Got $.10 CPCs & Leads As Low As $7)
Item description: With the above in mind, do the same thing with your descriptions and ensure that they are keyword rich for a few of the keyword variants that are driving the highest proportion of sales. If you are doing frequent ad rotations (which you should to figure out what type of messaging resonates well with your customer base), you can also incorporate some of those learnings into your product descriptions as well.
Example: When we ran our A/B tests for thousands of ad groups we found out that the “Up to 88% off Retail Prices” gave the highest click-through rate and conversion rates. Subsequently, most of our initial ad copy now has this value proposition and we are incorporating it into our SKU descriptions.
(CTR % over time)
The Googlebase contains a multitude of optional fields, including color, product variants and product sale information. Filling these in will allow users to better narrow their search results within the Google Shopping interface as well as grant the Googlebot better information about your products.
Having these fields appropriately filled in will also give you an edge over your competitors as most retailers are too lazy to fill them in.
Make copious use of the “Google Label” fields. The Google label fields allow you to segment your Googlebase feed into different sections that you can exploit for different optimization techniques. A few use cases I would suggest are:
The default method of setting up the Googlebase feed places an inordinate amount of control in Google’s hands: they decide which SKUs show up for which search query, they decide what is served in what proportion, and you only have a few levers to pull in order to influence performance.
However, the Alpha/Beta structure allows paid search marketers to continually harvest insights about their campaigns and maximize performance while incrementally wrestling control away from Google. As a good rule of thumb, the more draconian you are about controlling your paid search traffic, the better your performance will be.
Let’s explore the Alpha/Beta structure! If we were to map it out, it would look something like this:
First, we need to create a “catch all” campaign and assign it a low bid. This campaign will simply bid on all products in the feed at the lowest acceptable bid possible in order to get a decent amount of impressions.
For the sake of this discussion, let’s pretend that you have a catalog of 500,000 clothing SKUs and you set the bid to .50 CPC. The campaign priority for this campaign will be “Low”—this campaign level setting allows Google to better make sense of how it should deliver Google Shopping traffic if there are multiple Shopping campaigns utilizing the same Googlebase feed.
The goal of this campaign is to:
For your next campaign, we’re going to utilize either your Google custom labels or the Brand/Category columns that are present in your Googlebase in order to better sculpt your traffic. We will continue to go along with our clothing store retailer analogy and start to build out campaigns that represent the next tier of keywords that might present themselves in the sales cycle. For example, a prospective customer might search the following in order to buy a pair of dress socks:
Online clothing store > online clothing store socks > dress socks > men’s dress socks > black men’s dress socks > black Spiderman men’s dress socks
The first layer should be designed to capture search queries in the upper funnel, so a campaign structure might look like this:
And so on and so forth. The categories should have the following attributes:
By adding this layer into your Google Shopping campaign structure, you are effectively sculpting your Google Shopping traffic and will earn the following benefits:
Read More: LeadPages CEO Clay Collins Talks About How To Ramp Up Your Conversion Rates (Up To 75%!) [Podcast]
As your Catch All and First Layer campaigns collect data, they will start to provide enough user-driven insights for you to flesh out the remaining, more specific layers of your Shopping campaign. The eventual goal is to create SKU-specific ad groups that are targeting one ID; this will allow you to get the most granular performance and the best shaping of search queries to SKU IDs.
Example: you decide to click on Dimensions > Search Queries in your “Category – Socks” campaign. You see masses of search queries corresponding to rough subcategories. For simplicity’s sake, let’s vastly simplify some of your findings and assume you gathered enough data for the findings to be significant and that all five groupings have large amounts of traffic:
We now have enough data to add all five subcategories to a new campaign designed for your 2nd Layer in the Alpha/Beta hierarchy. These campaigns will have their serving priority set to “Medium” and have a higher bid than the Catch All or Category campaigns. You might structure the bids this way:
By structuring the bids this way, the profitable search queries catered toward “pink socks,” “men’s socks,” etc. will be funneled toward even more specific ad groups with their own corresponding bids, bid adjustments, negative keywords and promotion text. Performance will continue to rise as the more specific subcategory ad groups continue to gather SKU specific data.
You might be asking yourself: why would we want to bid so high on women’s socks even though the ROI isn’t there? We would want to do this because:
(Wow, terrible performance! Turned out this SKU was not priced appropriately.)
Lastly, once your subcategories start to gather significant amounts of data, you will gain the ability to build the most important piece of the Alpha/Beta structure—your SKU specific campaigns. These campaigns and ad groups will be focused toward individual SKUs that drive the performance of your entire account.
You will enjoy total control over the performance of these SKUs and will be easily able to monitor their performance with a microscope. This campaign will drive highly-defined search queries that sit at the very end of the user’s purchasing funnel. It will also be easy for you to dominate impression share % for high contribution SKUs.
The campaign should have its Priority set to “High” and have the highest bids across your entire slew of Shopping campaigns. This will ensure that AdWords will always show the SKUs you want when you want.
You can build out these ad groups by proceeding to the Dimensions > Product ID breakdown tab in any of your higher hierarchy Shopping ad groups. You will see a complete breakdown of the SKUs along with their performance:
Once you export a list of your top contributors, sort them into the following categories:
As your SKU specific campaigns gather more data, you will start to exhibit dictator-like control over your Shopping campaign traffic. An ideal situation is your generalized “Alpha” campaigns consistently and efficiently directing traffic to the places in the account structure where they can best performance while serving as “miners” looking for golden nuggets—the gold nuggets are SKUs that deserve to be in their own ad group so they can be optimized for even more traffic.
As your negative keyword structure and bids start to further sharpen your traffic stream, a greater proportion of your traffic will be funneled toward the highest ROI ad groups located in your SKU specific campaigns.
In the course of running Google Shopping campaigns for years and spending millions in ad spend, we developed a few novel techniques for further improving the ROI of our Shopping campaigns.
When mining for product IDs to add to our SKU specific campaigns, there are often SKUs that lose money no matter what. Since we don’t have infinite time or the wherewithal to figure out how to make them profitable, we might want to completely exclude them from being served.
However, when excluding product IDs across hundreds of ad groups and campaigns it can be a chore to keep track of the SKUs that you don’t wish to exclude. We wound up playing a frustrating game of “whack of mole” where a SKU excluded from one campaign would simply pop up in another.
We had our developers build a special tool for us—the SKU Exclusion Tool. PPC marketers wishing to exclude a SKU would upload a list in .CSV format. Upon our nightly feed generation, those SKUs would be completely removed from our Googlebase feed. This allowed us to quickly eliminate losing SKUs with 100% certainty and saved countless ad dollars.
Mining search queries amongst all your Shopping campaigns can be a massive endeavor. With the enormous scope of search queries that can crop up in the Search Query dimension, it can be difficult to come up with negative keyword additions that would move the needle.
We found that certain unprofitable terms such as “free” would show up countless times, but they were dispersed amongst thousands of unique search terms. This made it impossible to truly determine the ROI of the word “free.”
We had our developers build a tool that would separate each word in a list of search queries into a separate entity and then pivot all of the KPIs we needed and attach it to each instance. We could then export this list into an Excel file and quickly build a negative keyword list that would save tens of thousands of dollars. The word free might appear in the export like this:
“Free” – Appeared in 15,201 search queries. Contributed 150,304 impressions, 10,521 clicks, $5,260 in ad spend, $217 in revenue.
Once we added “free” and a host of other broad/phrase match negatives to the Shopping campaign, we would immediately save tens of thousands of unprofitable ad spend per month. We would immediately reinvest these dollars in capturing more impression share for the other profitable areas of the Adwords account.
This is a new technique we just started to implement. Export your list of search queries from your Google search campaigns into one Excel file, and export another list of your Shopping campaign search queries into another.
Perform a VLOOKUP and try to find mismatches amongst the files. These represent proven opportunities that you can quickly exploit.
You might find search queries that are performing well for your Shopping campaigns that aren’t added as keywords in your Search campaigns. Adding these search queries as keywords to your Search campaigns will boost your revenue as these proven winners will now start to appear in Google’s search results with greater frequency. You will also appear for both Shopping ad results as well as Search results and capture more impression real estate.
On the other hand, you might have search queries that are performing well for Google Search but are not contributing to your Google Shopping campaigns. For example, some of the below might be huge contributors to our fictional Google Adwords Search campaigns:
If you do carry these products, this would be a great opportunity to go into your Googlebase feed and modify the title, descriptions and attributes of these products to more prominently feature the search queries in question. Once Google starts serving the right products to the right search queries, build them out into their own ad groups and spike up the bid to dominate the impressions for that particular search query tree.
With the Search and Shopping results looking more competitive by the day, aim for granular, hyper optimized to annihilate your competition. For our core search queries, we often have 4-6 products appear in the results which can take up 75-90% of the available Google Shopping impressions.
This case study is a guest post by Edwin Choi, VP of Marketing at Mobovida, a customer-driven, vertically integrated mobile accessory brand delivering fashion forward products direct to consumer.
Let us know your experience with Google Shopping Campaign in the comments section below!
Hi everyone, on today’s show we have my friend Edwin Choi, VP of Marketing at Mobovida, a progressive vertically integrated online retailer of mobile accessories.
In today’s episode we’ll be talking about how Edwin and his team make use of the data they’ve been gathering to develop products that really speak to their core audience, like their top selling product, the “Mobovida Wallet Case.” We also chat about Mobovida’s 62% YoY growth rate, their two favorite marketing channels, and how they manage to continuously grow in such a competitive space.
Download podcast transcript [PDF] here: Edwin Choi Reveals Mobovida’s $10M/Year Customer Acquisition Recipe TRANSCRIPT
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