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Smart Shopping for the Holidays – A Test & Learn Approach



With the December holidays approaching, it’s more important than ever to have confidence in your eCommerce strategies. It is no secret that online shopping is the new normal, and with fewer people traveling for the holidays, some families are putting more under their trees due to travel restrictions. As marketers, it’s no secret that a strong holiday strategy is vital for Q4 success.

Should you test Smart Shopping?

Is Smart Shopping for you? Well, it depends! Smart Shopping has a lot of incredible benefits and chances are your Return on Ad Spend (ROAS) will skyrocket. The higher your ROAS, the better because that depicts you’re spending advertising dollars efficiently.

One of the downsides of Smart Shopping is less control, but by combining Smart Shopping with regular shopping campaigns, you can expand your reach exponentially. Another downside is that because Smart Shopping is automated, it will show ads for top-performing products, putting you at risk for underselling less popular products. This can be bypassed by creating product-specific Smart Shopping campaigns.

So, what is Smart Shopping exactly? According to Google, Smart Shopping campaigns use events such as online purchases, signups, mobile purchases and store visits to automatically maximize conversion value for the daily budget you set. Essentially, Google uses pre-existing data to drive your revenue and maximize conversion value. What’s not to love? If you want to learn more, check out Connor Reagan’s post on running smart shopping campaigns along regular campaigns. 

My team tested smart shopping for a fitness equipment brand. We saw some pretty incredible results. Before I dig deeper, here are some best practices to be aware of before setting Smart Shopping campaigns.

4 Tips for Testing Smart Shopping

  1. Test things slowly – Don’t make any drastic changes close to peak holiday times; While new strategies are exciting, you don’t want to test too much at once.
  2. Push for Incremental Spend – Piggybacking off my previous point, Q4 is the time to push for incremental spend. You can use incremental spend to test Smart Shopping campaigns if you haven’t already and therefore, you won’t have much to lose.
  3. Create a standard Catch-all shopping campaign – Smart Shopping campaigns tend to spend more than regular campaigns. Creating a standard Catch-all shopping campaign to catch the excess once Smart Shopping budgets run out ensures you don’t miss out on anything. Target all products and set a low, manual bid ($0.15-$0.20).
  4. Implement Scripts – Here at Brainlabs, we are data nerds. I highly recommend running scripts to help automate or catch anomalies the human eye might otherwise miss.

Our Shopping Structure Before Smart Shopping

Before digging into the impact Smart Shopping had on our campaigns, let me paint a picture of our account before implementing smart shopping campaigns. When we started working with this brand, campaigns were divided into “All Products” search and shopping campaigns. They were broken down further into three categories – Mobile, Desktop and Tablet. While this proved effective, we wanted to test strategies that stretched efficiency even further.

While “All Products” campaigns (campaigns that catch excess traffic not targeted by more granular campaigns) perform well, the more product specific you can get, the better. Keep in mind you should still have a general catch-all campaign to catch the excess, even once you get Smart Shopping implemented. Getting more product specific allows you to more granularly target audiences, create product-specific ad copy, get more granular with keywords (if applicable) and more.

Before testing Smart Shopping, we also started breaking out top-performing products for both Search and Shopping into their own separate shopping campaigns. Not only did we see an increase in revenue through this change, but we also had more control where we sent users because we had more control over final URLs and budgets.

The Results

It’s no secret that Smart Shopping has been all the rage in the digital world. While we were getting decent results without using Smart Shopping, we couldn’t help but wonder – does this actually work? While we were a little nervous to try something new, we went ahead and launched a Smart Shopping campaign for one of our top-performing products. We implemented a mid- and upper-funnel campaign.

Overall, we saw a big increase in ROAS due to Smart Shopping campaigns. Some days, we even saw ROAS above 600%. It is recommended to still keep a regular catch-all shopping campaign to ensure no traffic is missed, and if you do test product-specific smart shopping campaigns to diversify your products. Our results were significant enough where I highly recommend any one to try it, however, I don’t recommend making changes hastily before peak times like the holidays.

In short, here are steps we took to implement smart shopping campaigns. These steps will ensure you test things slowly but surely.

  1.  Ensure both Search and Shopping campaigns running – Having both search and shopping campaigns is best practice to cover all your bases and reach the highest amount of users
  2. Create a catch-all campaign in both Search & Shopping  – As stated earlier, catch-all campaigns ensure you catch excess traffic that aren’t caught by product-specific campaigns
  3.  Create product specific campaigns for shopping and search for top performing products – You want to create product specific campaigns for your best sellers and exclude them from the catch all. This helps ensure all your products get shown, not just top performers
  4. Gauge performance – Midway through the process, ask yourself. What worked? What did not work? You might need to pivot your strategy a bit.
  5. Swap Smart Shopping product specific campaigns for currently running Shopping product-specific campaigns – Now, you’re ready to launch Smart Shopping campaigns. Making a gradual shift from regular shopping to Smart Shopping gives you extra cushion
  6. Review your test results – What worked? What didn’t work? Did you see ROAS increase significantly? Be sure to keep track of your data for Smart Shopping versus regular campaigns to note the impact. If ROAS has fluctuated, consider regrouping your campaigns. Be sure to give campaigns 2-4 weeks to run before making significant changes. 

All in all, testing out Smart Shopping will require some trial and error. Does it work best by having a singular catch all? Do you need product specific campaigns? Should you take a multifaceted approach with Smart Shopping, Regular shopping, and Search campaigns? There’s no secret formula, it will all depend on the brand and product. However, taking the above findings into consideration when planning  your eCommerce strategy is sure to bring success!


What can ChatGPT do?



ChatGPT Explained

ChatGPT is a large language model developed by OpenAI that is trained on a massive amount of text data. It is capable of generating human-like text and has been used in a variety of applications, such as chatbots, language translation, and text summarization.

One of the key features of ChatGPT is its ability to generate text that is similar to human writing. This is achieved through the use of a transformer architecture, which allows the model to understand the context and relationships between words in a sentence. The transformer architecture is a type of neural network that is designed to process sequential data, such as natural language.

Another important aspect of ChatGPT is its ability to generate text that is contextually relevant. This means that the model is able to understand the context of a conversation and generate responses that are appropriate to the conversation. This is accomplished by the use of a technique called “masked language modeling,” which allows the model to predict the next word in a sentence based on the context of the previous words.

One of the most popular applications of ChatGPT is in the creation of chatbots. Chatbots are computer programs that simulate human conversation and can be used in customer service, sales, and other applications. ChatGPT is particularly well-suited for this task because of its ability to generate human-like text and understand context.

Another application of ChatGPT is language translation. By training the model on a large amount of text data in multiple languages, it can be used to translate text from one language to another. The model is able to understand the meaning of the text and generate a translation that is grammatically correct and semantically equivalent.

In addition to chatbots and language translation, ChatGPT can also be used for text summarization. This is the process of taking a large amount of text and condensing it into a shorter, more concise version. ChatGPT is able to understand the main ideas of the text and generate a summary that captures the most important information.

Despite its many capabilities and applications, ChatGPT is not without its limitations. One of the main challenges with using language models like ChatGPT is the risk of generating text that is biased or offensive. This can occur when the model is trained on text data that contains biases or stereotypes. To address this, OpenAI has implemented a number of techniques to reduce bias in the training data and in the model itself.

In conclusion, ChatGPT is a powerful language model that is capable of generating human-like text and understanding context. It has a wide range of applications, including chatbots, language translation, and text summarization. While there are limitations to its use, ongoing research and development is aimed at improving the model’s performance and reducing the risk of bias.

** The above article has been written 100% by ChatGPT. This is an example of what can be done with AI. This was done to show the advanced text that can be written by an automated AI.

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Google December Product Reviews Update Affects More Than English Language Sites? via @sejournal, @martinibuster



Google’s Product Reviews update was announced to be rolling out to the English language. No mention was made as to if or when it would roll out to other languages. Mueller answered a question as to whether it is rolling out to other languages.

Google December 2021 Product Reviews Update

On December 1, 2021, Google announced on Twitter that a Product Review update would be rolling out that would focus on English language web pages.

The focus of the update was for improving the quality of reviews shown in Google search, specifically targeting review sites.

A Googler tweeted a description of the kinds of sites that would be targeted for demotion in the search rankings:

“Mainly relevant to sites that post articles reviewing products.

Think of sites like “best TVs under $200″.com.

Goal is to improve the quality and usefulness of reviews we show users.”


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Google also published a blog post with more guidance on the product review update that introduced two new best practices that Google’s algorithm would be looking for.

The first best practice was a requirement of evidence that a product was actually handled and reviewed.

The second best practice was to provide links to more than one place that a user could purchase the product.

The Twitter announcement stated that it was rolling out to English language websites. The blog post did not mention what languages it was rolling out to nor did the blog post specify that the product review update was limited to the English language.

Google’s Mueller Thinking About Product Reviews Update

Screenshot of Google's John Mueller trying to recall if December Product Review Update affects more than the English language

Screenshot of Google's John Mueller trying to recall if December Product Review Update affects more than the English language

Product Review Update Targets More Languages?

The person asking the question was rightly under the impression that the product review update only affected English language search results.


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But he asserted that he was seeing search volatility in the German language that appears to be related to Google’s December 2021 Product Review Update.

This is his question:

“I was seeing some movements in German search as well.

So I was wondering if there could also be an effect on websites in other languages by this product reviews update… because we had lots of movement and volatility in the last weeks.

…My question is, is it possible that the product reviews update affects other sites as well?”

John Mueller answered:

“I don’t know… like other languages?

My assumption was this was global and and across all languages.

But I don’t know what we announced in the blog post specifically.

But usually we try to push the engineering team to make a decision on that so that we can document it properly in the blog post.

I don’t know if that happened with the product reviews update. I don’t recall the complete blog post.

But it’s… from my point of view it seems like something that we could be doing in multiple languages and wouldn’t be tied to English.

And even if it were English initially, it feels like something that is relevant across the board, and we should try to find ways to roll that out to other languages over time as well.

So I’m not particularly surprised that you see changes in Germany.

But I also don’t know what we actually announced with regards to the locations and languages that are involved.”

Does Product Reviews Update Affect More Languages?

While the tweeted announcement specified that the product reviews update was limited to the English language the official blog post did not mention any such limitations.

Google’s John Mueller offered his opinion that the product reviews update is something that Google could do in multiple languages.

One must wonder if the tweet was meant to communicate that the update was rolling out first in English and subsequently to other languages.

It’s unclear if the product reviews update was rolled out globally to more languages. Hopefully Google will clarify this soon.


Google Blog Post About Product Reviews Update

Product reviews update and your site

Google’s New Product Reviews Guidelines

Write high quality product reviews

John Mueller Discusses If Product Reviews Update Is Global

Watch Mueller answer the question at the 14:00 Minute Mark

[embedded content]

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Survey says: Amazon, Google more trusted with your personal data than Apple is




MacRumors reveals that more people feel better with their personal data in the hands of Amazon and Google than Apple’s. Companies that the public really doesn’t trust when it comes to their personal data include Facebook, TikTok, and Instagram.

The survey asked over 1,000 internet users in the U.S. how much they trusted certain companies such as Facebook, TikTok, Instagram, WhatsApp, YouTube, Google, Microsoft, Apple, and Amazon to handle their user data and browsing activity responsibly.

Amazon and Google are considered by survey respondents to be more trustworthy than Apple

Those surveyed were asked whether they trusted these firms with their personal data “a great deal,” “a good amount,” “not much,” or “not at all.” Respondents could also answer that they had no opinion about a particular company. 18% of those polled said that they trust Apple “a great deal” which topped the 14% received by Google and Amazon.

However, 39% said that they trust Amazon  by “a good amount” with Google picking up 34% of the votes in that same category. Only 26% of those answering said that they trust Apple by “a good amount.” The first two responses, “a great deal” and “a good amount,” are considered positive replies for a company. “Not much” and “not at all” are considered negative responses.

By adding up the scores in the positive categories,

Apple tallied a score of 44% (18% said it trusted Apple with its personal data “a great deal” while 26% said it trusted Apple “a good amount”). But that placed the tech giant third after Amazon’s 53% and Google’s 48%. After Apple, Microsoft finished fourth with 43%, YouTube (which is owned by Google) was fifth with 35%, and Facebook was sixth at 20%.

Rounding out the remainder of the nine firms in the survey, Instagram placed seventh with a positive score of 19%, WhatsApp was eighth with a score of 15%, and TikTok was last at 12%.

Looking at the scoring for the two negative responses (“not much,” or “not at all”), Facebook had a combined negative score of 72% making it the least trusted company in the survey. TikTok was next at 63% with Instagram following at 60%. WhatsApp and YouTube were both in the middle of the pact at 53% followed next by Google and Microsoft at 47% and 42% respectively. Apple and Amazon each had the lowest combined negative scores at 40% each.

74% of those surveyed called targeted online ads invasive

The survey also found that a whopping 82% of respondents found targeted online ads annoying and 74% called them invasive. Just 27% found such ads helpful. This response doesn’t exactly track the 62% of iOS users who have used Apple’s App Tracking Transparency feature to opt-out of being tracked while browsing websites and using apps. The tracking allows third-party firms to send users targeted ads online which is something that they cannot do to users who have opted out.

The 38% of iOS users who decided not to opt out of being tracked might have done so because they find it convenient to receive targeted ads about a certain product that they looked up online. But is ATT actually doing anything?

Marketing strategy consultant Eric Seufert said last summer, “Anyone opting out of tracking right now is basically having the same level of data collected as they were before. Apple hasn’t actually deterred the behavior that they have called out as being so reprehensible, so they are kind of complicit in it happening.”

The Financial Times says that iPhone users are being lumped together by certain behaviors instead of unique ID numbers in order to send targeted ads. Facebook chief operating officer Sheryl Sandberg says that the company is working to rebuild its ad infrastructure “using more aggregate or anonymized data.”

Aggregated data is a collection of individual data that is used to create high-level data. Anonymized data is data that removes any information that can be used to identify the people in a group.

When consumers were asked how often do they think that their phones or other tech devices are listening in to them in ways that they didn’t agree to, 72% answered “very often” or “somewhat often.” 28% responded by saying “rarely” or “never.”

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