Lookalike audiences are thee Facebook feature when it comes to audience targeting. If you’re looking to scale your campaigns and more, it’s a must-consider option.
You can use lookalike targeting to find similar users to your core audience based on interests, click behavior, and conversion habits. The smaller the percentage of your core audience, the more similar your lookalike audience will be.
You have several options from which to generate lookalike audiences:
As you can see, you have a lot of choices to test different audience types and associated performance. The key challenge is to segment and structure the audiences to avoid overlaps and achieve the best delivery.
Something to note: Since frequency caps limit the daily number of times you can deliver an ad to a user, lookalike audiences won’t increase your overall reach. And, you’ll have less predictability when it comes to which ad wins each auction.
There’s a way to overcome these challenges, however. Make sure your strategy includes nested lookalikes and smart exclusions. Let’s go into more detail.
Let’s start with an example, where we exclude the next-highest percentage audience from our targeted lookalike audience. So, if you’re targeting lookalike 3% and lookalike 5%, then exclude the 3% audience from the campaign that’s targeting the 5% one.
With smart exclusions, we exclude the targeted audiences that we’re already using in other live campaigns. For example, if you’re running campaigns with 1% lookalike and 3% lookalike and want to launch a broader targeting campaign, then exclude the 3% lookalike.
When you’re planning your targeting strategy, make sure you’re segmenting your lookalike thresholds according to the value of the user, and excluding the targeted audiences from campaigns to avoid overlap. This’ll allow you to use lookalike audiences from different sources, increasing the overall reach and scalability of your campaigns.
For example, if you’re running a retargeting campaign based on a Website Custom Audience of all your site visitors, exclude this campaign from all of your acquisition initiatives, along with the associated lookalike audiences.
Here’s another scenario. Suppose you’re a travel website and the user funnel includes two conversions—registration and booking. You would segment the audiences based on your goals—perhaps based on the custom audience of the previous month’s bookers, conversion pixel data, and Website Custom Audience of people who registered but didn’t book. Your segmentation would look like this:
Custom audience segmentation:
You can use all of these audiences for your acquisition campaigns, along with interest-based and other targeting options.
Here’s the final campaign planning structure for this example. This takes into account that retargeting campaigns are running based on your Website Custom Audiences.
Fine-tuned campaign planning structure:
Creating effective lookalike audiences takes a bit of cunning and patience, but it’s not rocket science. With continued practice, refinement, and measurement, you can scale your campaigns to ensure you’re targeting audiences with the most relevant ads at the most relevant time, in a way that works the best for your business. If you haven’t yet implemented this feature, we strongly recommend you get started today!
This is a guest post from Emily Hodges, Marketing and Public Relations Manager at Kiip.
We’re four months out from the holiday season. Yet, in the ad industry, we all know that brands are already plotting their marketing strategies and how they can effectively capture their targeted mobile audiences for the biggest shopping season of the year.
Kiip recently launched a survey tool to gather relevant mobile consumer data. US-based Kiip redeemers are surveyed about their demographics, behaviors, lifestyle, reward preferences, and buying habits. So far, Kiip’s surveys have received nearly three million user responses!
Below are the results specifically on holiday shopping habits. Check them out and see which category you fall under when it comes to your gift purchases.
This is a guest post from Dionte Pounds, Account Manager at
A few months ago, Google unveiled a new tool that allows advertisers to interact directly with an audience across the search, Gmail, and YouTube networks. That tool was Customer Match (See my previous post about setup tips).
With this feature, advertisers could submit a list of email addresses from past customers or email subscribers directly into the AdWords interface. Then, advertisers could target individuals who’d already expressed interest in their products, across channels, as long as they were signed in to Google.
With this update, Google strengthened the ability of advertisers to leverage 1st-party data. The move echoed Facebook’s Custom Audiences, which has been in the market for years and proven very effective. While it provides Google-focused marketers a great way to use 1st-party data, Google’s added another feature that uses that data to find and target new customers.
That tool is Similar Audiences.
Similar Audiences are made up of groups of people who have characteristics with a remarketing audience you’ve previously created. For example, if you have a remarketing audience created for people who’ve visited your website via a paid ad click within the last 30 days, Google will automatically generate a new pool of prospects you can target if the starting audience is large enough.
Because paid ad traffic is cookied, Google tracks the browsing habits of that cookied traffic over the last 30 days and uses that to find shared interests and behaviors. For a new Similar Audience to be created, at least 500 cookies with enough similarities and characteristics must be active. In theory, a larger remarketing list should yield a better Similar Audience in terms of relevancy, because it’s pulling from a larger set of data being sent back for Google to use.
So, a Similar Audience taken from a Customer Match list should be an extremely relevant pool of new users that you can target to grow a business. However, there are some features that are disabled for a Similar to Customer Match audience that must be taken into consideration when planning new advertising strategy.
The first is that, like all Similar Audiences, you can’t target a Similar to Customer Match audience across the Search Network. Because Similar Audiences are based on the webpage browsing history of the cookied user, you’re limited to targeting on the Display Network and YouTube Network.
Speaking of the Display Network, you can only target Similar to Customer Match audiences on the Google Display Network and YouTube. This is where the use of 1st-party data is somewhat limiting in Google. Because the uploaded customer lists lack the cookies needed to track browsing behavior, Google can’t use that data to find an audience with related interests on the Display Network.
Still, you can utilize a similar audience across Gmail and YouTube ads, because these are networks entirely owned by Google where the user is signed in to the network (at least most of the time for YouTube). Because the data Google receives from these channels are different from Display Network, where 3rd-party groups simply opt in to the network, the way Google finds these users and tracks characteristics greatly varies.
Even with these limitations, I still highly recommend testing all similar audiences, but especially a similar audience built from Customer Match. It’s a great way to engage a new audience of individuals similar to that of your past customers.
Global mobile trends all point to the same conclusion – operating in channel-specific silos no longer works, and now’s the time for marketers to implement a strong cross-channel marketing strategy.
If you subscribe to this blog (and if you don’t, see that second little box on the right), you already know we’ve been evangelizing the message of “cross-device, cross-channel.” There’s a good reason for that.
As we approach the halfway point of 2016, it’s more important than ever that marketers not only use data to understand customer behavior, but also to act on that behavior to deliver engaging, personalized experiences.
On May 25, Nitin Rabadia – our Director of Audience Marketing EMEA, APAC – will explain how to use data to win the online battle for attention and revenue. Gleaning insights from our 2016 Global Mobile Report (available with webinar registration), Nitin will field your questions and discuss:
Register for the webinar today.
When we looked at performance marketing data from the first quarter of 2016, one thing became clear: cross-channel, cross-device targeting remains the most powerful differentiator for profitable marketing strategies.
To create our quarterly benchmark reports, we sample the Marin Global Online Advertising Index, composed of advertisers who invest more than $7 billion in annualized ad spend on the Marin platform. We analyze data from around the world to create our report. For Q1 2016, key findings include:
For detailed information on Q1 2016 search, social, and display mobile performance – including detailed data charts with YoY performance and up-to-date recommendations – download our Performance Marketer’s Benchmark Report Q2 2016 – Vital Search, Social, and Display Performance Data by Device.
Mother’s Day is almost here! With flowers, cards, and family visits close at hand, many brick and mortar retailers are gearing up for the shopping spike. The season of maternal appreciation extends to online retailers, who are also gussying up their search, social, and display campaigns to attract consumers around the world.
How did online retailers do in 2015, and what to expect this year?
In the week leading up to Mother’s Day 2015 (May 10th), clicks increased an average of 15% across retailers as click-through rates rose 6%. In addition, spend increased 9% during the same time period, peaking a few days before Mother’s Day.
Most notably, conversions saw a bump of 12%, peaking on the 5th at 18% above the monthly average. This noticeable bump for all retailers was more pronounced among those specialty retailers that Mother’s Day particularly impacts.
CPCs actually dropped slightly during this period, except for two days where they spiked, the 4th and 5th. The 5th proved to be a particularly important day for consumers and advertisers, showing abnormal surges along all metrics.
Perhaps consumers took account delivery times and the looming holiday date into account, giving themselves a few buffer days in case of delays in delivery and arrival.
These numbers dropped dramatically on Mother’s Day itself, and returned slowly to roughly average afterwards. Click-through rates remained elevated for Mother’s Day and a few days afterwards before returning to seasonal norms.
For retailers looking to maximize their Mother’s Day sales, here are a few key takeaways:
This is a guest post from Dionte Pounds, Account Manager at
Last month, I discussed how to use proper segmentation to optimize the performance of Dynamic Search Ads campaigns and why segmentation is vital for success. Segmentation also plays a large part in the success of shopping campaigns.
If you’re not already familiar, shopping campaigns promote your online inventory of products by matching search queries to ads that feature these products. These ads, known as product listing ads, can appear in Google search results or on the Google Shopping results page.
Shopping campaigns generally benefit from high click-through rates and low CPCs. With segmentation, the value of shopping campaigns increases. Reporting on specific product performance becomes even easier. Product bidding becomes more accurate. And, overall product management improves through better organization.
If you’re a digital advertiser new to shopping campaigns, the steps below can help you successfully leverage this campaign type.
Proper segmentation doesn’t actually begin in the AdWords interface. The foundation of a highly organized and structured shopping campaign truly starts with the data feed. The data feed contains all the product data that’s uploaded to the Google Merchant Center. The Merchant Center essentially houses all the product data and makes it available to Google and Google Shopping.
To make sure proper segmentation within AdWords is possible, include as much data as possible for each product. For segmentation purposes, it’s vital to include the brand, condition, Google Product Category, and product type attributes. You also have the ability to include up to five custom labels that you can segment by. We’ll touch more on that later.
I strongly recommend having values for not only the required data attributes, but as many of the optional attributes as well. Google is more likely to reward products with rich data with a higher impression share and better ad position. So, there are incentives for fleshing out your data feed as much as possible, beyond just functionality.
Once your foundation (accurate product data) is set, you first need to figure out what type of segmentation makes the most sense for your business. To go back to the online luxury jewelry store from my last article, if I’m selling different brands of jewelry, I know that select brands are more popular than others. Because of this, I want to be able to bid differently for each brand in my inventory.
So, for this example, it makes sense to first segment, or subdivide, my shopping campaign by the Brand attribute. Selecting the correct starting subdivision immediately improves my ability to bid better, as I now have organized product groups that provide insightful data that allow me to bid more accurately than if they were grouped together.
Let’s imagine my online jewelry store sells Cartier, among other brands. After first subdividing all my products by brand, I now have a product group specifically for Cartier products. While this is great, I know that I get different returns from different product types, such as rings, bracelets, or necklaces. So, I want to be able to set bids for each individual Cartier product group.
What I would then do is segment that Cartier group by the product type attribute. Now, I have the ability to bid for Cartier rings separate from Cartier bracelets. Once you have your first subdivision completed, you can continue to subdivide until you believe you have the correct product organization for your business.
Keep in mind that each time you subdivide by another attribute, the bid will be placed at the resulting product groups. While this gives you improved bidding and a clear understanding of what products drive revenue for your business, you don’t want to subdivide too much. This could make the product group too small to get any valuable data from and optimize around.
Earlier, I mentioned that in addition to the Google required data attributes, you have the ability to create up to five custom labels for each product. Utilizing these labels allows you to be a bit more creative with the segmentation of your shopping campaign than the standard parameters Google allows, and to better segment by attributes that make the most sense for your business goals.
For example, let’s say my jewelry store categorizes products by expected popularity. A product could be given a rating of High, Medium, or Low. By including this rating in the custom label column, I could then subdivide my initial brand segment by this custom label, and bid up for the most popular products and bid low for less popular items.
Let’s say my jewelry store sells Cartier watches. Imagine these product listing ads have a great click-through rate but a poor conversion rate due to the high price point. Over time, these clicks result in wasted spend and drag down the efficiency of the account. To avoid a poor ROI moving forward, I can exclude Cartier watches from my shopping campaign.
Product exclusion is an effective way of improving performance by removing items from your shopping campaign that carry low ROI. Product exclusion can also be used to organize your shopping campaigns. To exclude products, click the max CPC column for that particular product group and then check Excluded.
Thank you, Google! Your announcement of the Google Analytics 360 Suite is industry-wide confirmation that enterprise level marketing tools are necessary in order to get the most out of your advertising dollars. Of course, Marin Software has known this all along and believes marketers of all sizes can benefit from these tools.
All marketers want efficient ways to reach new and existing customers and to understand what works and what doesn’t. As Forrester Research reports: “Sophisticated marketers who use analytics platforms are 3X more likely to outperform their peers in achieving revenue goals.” Organizations need this kind of sophisticated software to enable marketing teams to align around goals that help them optimize, compete, and drive revenue.
At Marin, our focus is providing the technology and data needed for demand and revenue generation based steadfastly on our customer’s goals. We enable customers to make holistic creative, bid and budget optimization decisions across their campaigns, all from the same integrated platform.
Besides integrating well with Google, we have extensive experience working with Yahoo, Bing, Baidu, Facebook, Twitter, Instagram, and many other leading partners, including 10 of the largest global exchanges. Our commitment remains the same – helping marketers reach their goals across publishers, across channels (search, social and display) and devices (desktop, tablet, mobile).
Purpose-built to provide customers with complete transparency of campaign data and results, our mission aligns with Peter Drucker’s adage, “If you can’t measure it, you can’t manage it.” We provide digital marketers superlative cross-publisher data and measurement including:
Although Marin Software has had a legacy in search leadership, we’ve evolved our cross-publisher platform via industry-leading acquisitions to power digital marketing campaigns for the world’s biggest brands and agencies. We look forward to continuing to provide our customers with the tools and insights to profitably compete and reach their goals.
Google AdWords now lets you upload both Identifiers for Advertising (IDFAs) and advertising IDs in bulk so that you can target your mobile app users using the Google Display Network. Although you can use this feature to solicit new users under the right circumstances, its chief use is re-engaging your mobile app users.
After all, your current mobile app users are your easiest source of IDFAs and advertising IDs, meaning you’re going to struggle making the most of this feature if you don’t already have a user base.
Regardless, you shouldn’t see this as a limitation but rather a reminder of the importance of re-engaging your mobile app users.
This is mainly because re-engaging your mobile app users can boost the success rates of your mobile advertising – though it’s important to note that there are a number of reasons why Google AdWords is now particularly useful for this purpose. And, successfully re-engaging those users will contribute to creating a “consumable experience” that makes them want to keep coming back for more.
Generally speaking, you can convince your existing users with much greater ease than your potential users. In part, this is because you’ve accumulated goodwill with your existing users, meaning you’ll have a much easier time convincing them you’re trustworthy, likable, and reliable.
However, it’s also important to note that you have existing data on their purchasing patterns, meaning you can tailor your mobile advertising for the best results. Summed up, you should focus on existing rather than potential users because it costs you less time, effort, and other resources to convince them on average.
Re-engagement can be useful throughout an app’s lifecycle, meaning that the resources spent on such mobile advertising can prove useful longer than otherwise possible.
For example, you can use it to solicit new users for a similar app, build loyalty in existing users by making them more invested in an app they’re already using, and even bring back past users by reminding them of the app’s existence at an opportune time.
Simply put, re-engagement is so versatile that it can be used for all stages of an app’s promotion.
Finally, mobile advertising has become more important, with no signs of stopping in the foreseeable future. This is because the number of mobile app users is continuing to rise as mobile devices become more convenient and more powerful. As a result, you can expect a better rate of return by spending your dollars on mobile advertising rather than the other options out there.
With that said, just because you can count on this latest Google AdWords feature to be useful, it doesn’t mean you can slack off when it comes to creating your mobile advertising for re-engaging your mobile app users.
As always, if you want to convince your mobile app users to pay attention – and consider your brand a consumable experience – your advertising needs to show your app as useful and interesting. Furthermore, you need to use your existing data to figure out what will appeal the most to them before sending it out at the right times, which is where the rest of Google AdWords features will prove to be beneficial.
Every year, March Madness fever consumes millions of sports fans across America. Productivity plummets across workplaces, as employees catch a few minutes of the game on their computer or phone. In fact, it’s estimated that companies lose millions, if not billions, annually during the March Madness productivity dip.
For sports retailers, is there another story? How much does March Madness increase their sales, and can it offset losses in work output during this basketball-crazed month? To find out, we took a look at the retail vertical during 2015 and associated consumer behavior.
During March 2015, the retail industry saw a noticeable rise in clicks and advertising spend starting just before the 22nd – last year’s start of the regionals – through the end of the month and the finals.
During the regionals, there was a steady climb in clicks and spend, culminating and peaking near the end of March when the Final Four were decided. Click-through rates also almost doubled between the beginning of the month and the Final Four decision, showing that there was a strong correlation between US sports retailer and consumer activity, and when the games were decided.
In other words, the first small bump happened when the tournament began, and then rose and peaked close to the final four teams being decided, when consumers looked to buy products supporting their team of choice.
While these gains probably didn’t offset the productivity losses across employers nationwide, it’s clear that US sports retailers had a field day for interest in NCAA attire and merchandise.