Google Announces Free Product Listings on Google Shopping

Google Announces Free Product Listings on Google Shopping

How to create a Google Shopping feed

Google Shopping is quickly becoming the single largest repository of product information. In May of 2020 Google started accepting organic listings for the first time in 8 years creating a significant new organic landscape for eCommerce sites. 

This document outlines the steps to take in order to create a Google Shopping feed. At the end of the day the organic winners will be those sites that invest in original content that engages and converts the reader but the first step is to make sure that Google has full visibility to your product catalog.

Here’s the information you’ll want to add to your feed:

  • ID: A unique alphanumeric ID number for every product you sell.
  • Title: This may be the single most important element on the page in terms and should include the most important keywords.
  • Description: Google will accept up to 5,000 characters in the description and again it’s important to integrate the appropriate keywords that will match your customers’ search queries.
  • Link: A link to the product URL on the site.
  • Image link: Google wants the link so that it can easily display your photo within the search results. Use the primary image.
  • Price: It’s important that you deliver the feed frequently and that the price on your feed matches the price on your site. Assume that Google is checking.
  • Brand: This is the brand name of the product and it helps Google match search intent to the product.
  • GTIN: If available, include the Global Trade Item Number. This helps Google to group sellers of the same product. Google can help you find your item’s GTIN if you’re unsure.
  • Shipping Weight, Length, Width & Height: Google Shopping tries to estimate the total cost of shipping. Values like weight, and shipping dimensions are designed to work alongside your shipping settings inside of Google Merchant Center.
  • Sales Tax: This is required in the US only if needed to override the account tax settings for an individual item. Use Google Merchant Center to settings to maintain your overall sales tax information.

Is my site eligible for Google Shopping?

Yes! All retailers can opt-in to show their products across Google Shopping for free once the product feed is submitted to the Google Merchant Center. Keep in mind that Google does restrict listings for illegal, regulated, or sensitive products.

How do I get to page one of Google Shopping?

The feed is critical and serves as Google’s starting point, but frankly that’s just a ticket to the game. In order to get to page one, you’ll have to compete for the best and most relevant product listings against all the other providers with products relevant to the search. Here’s a list of things to consider.

  1. Perfect your product title: The product title may be the most important page element and is weighed heavily in Google’s ranking algorithm. Be sure to optimize the title around the keyword phrases your customers will use to search for the product. You may need to try a few different titles if you aren’t getting to page one.

  1. Test different images: An image can really help your product stick out on a busy SERP, so pay attention to your product photography and understand how to stand out relative to other photos on the SERP. 

  1. Provide as much product detail as possible: While the most important keywords should be in your title, the description is an opportunity to connect with a broader range of search queries. Include as many relevant feed attributes as you can , such as your product category, product type, color, condition, size, and color!

  1. Product Schema: Schema on your website allows search engines to better understand your page. You’ll need to include the required attributes like price, and you can also add reviews to this schema to show the average rating. This can help entice clicks.

Must Have Schema Elements

We have seen in the competitive analysis that most of the eCommerce stores are not using rich snippets effectively. Rich snippets are missing or major product elements are not included in most of the product pages.

The most important details related to a product (which can grab the customer attention) are as following:

product name

eCommerce stores should consider implementing all important Schema Markups to all the product pages.

5. Offers: If you offer free shipping, or the product is on sale, include this information in the shopping feed. This will help the products stand out from its competitors.

6. PayPal: Google now has a partnership with PayPal, to quickly help stores with set up and provide users with “high quality results”. If you need a faster, more secure payment method for products, this is definitely worth looking into.

How will organic Google Shopping results impact paid product listing ads (PLA)?

Retailers can and should continue to show their paid ads alongside these new free organic listings. This will allow retailers more flexibility in how they chose to promote their full inventory across Google. Once the products are approved in Google Merchant Center, a retailer can create a Google Shopping campaign within Google Ads and promote specific items across Google, paying only when a searcher arrives at their site.

Current Google Shopping advertisers will continue to show their ads on the Google Shopping tab, primarily towards the top and bottom of the pages. Nonpaid (organic) listings will take the remaining real estate within that tab.

The shopping results from the Google Search results page, partner search engines, Google images, the Google Display Network, Gmail, and YouTube will still only feature paid shopping ads at this time, so advertisers won’t expect to lose much of their shopping ads traffic.

What is the ‘Google Buy Button’ or ‘Purchases on Google’? 

Google has branded the tool as Purchases on Google but many people refer to it as the Google Buy Button. The buy button appears when people do product-related searches on Android phones or tablets. This is how the process unfolds:


  1. Google Shopping product listing ads appear on those searches and if one of the advertisers uses Purchases on Google, its ad will have a “Buy on Google” button.
  2. When the user clicks on the Buy on Google button they go to a landing page on Google, i.e. not the retailer’s website.
  3. The user can add the product to a shopping cart.
  4. They then complete the purchase using Google Wallet, all without ever leaving Google.
  5. The retailer then fulfills the order and is responsible for all communications and interactions with the customer in relation to that order.

    At the time of the launch, Google said it had analyzed conversion data and found the conversion rates for its Product Listing Ads were 50 percent lower on mobile than desktop.


    With the Google Buy Button, Google aimed to increase conversion rates on mobile. Google says its buy button simplifies the process of buying online as the purchaser already has an account with Google and a method of payment.

    This is easier than going to a retailer’s website and, potentially, creating a new account and/or entering delivery and payment information. 

How to Use Competitive Analysis in Ecommerce SEO Strategy

How to Use Competitive Analysis in Ecommerce SEO Strategy

Competitive drive is a key component to remain successful in the ecommerce industry. Knowing your biggest competitors and what they’re up to provides business owners with opportunities to take a competitive advantage. Including competitive analysis in your arsenal of ecommerce strategy tools has a positive impact on everything from on-site content to ROI. Evaluating your competitor’s strengths and weaknesses offers a window into areas of your own business that might need improvement.

When doing a competitive analysis, the eCommerce industry has more areas to consider than a basic brick-and-mortar operation. Social media marketing, SEO, product page content, mobile compatibility, and speed are just a few of the areas to include in the analysis. Comparing your own eCommerce business to competitors using unbiased analysis gives you a fresh perspective on site-wide SEO strategy. Use analysis tools like SEMRush™ or Google Search™ to identify your direct competitors, then select the top two or three to analyze.

Direct competitors are brands that provide products or services that are basically the same as yours, and operate in the same geographic region. Some eCommerce businesses add indirect competitors to the analysis. These are competitors who don’t sell similar products, but can fill the need or solve the problem your product does. Since the analysis process can be lengthy, it’s vital to focus most of your analysis on direct competitors. Take a look at the top five ways to use competitive analysis to improve your eCommerce SEO strategy. 

1. Overhaul Content

eCommerce competitive analysis narrows in on the content that attracts the most views and UGC for your direct competitors. Use this information to evaluate where your content is lacking, and then update it accordingly. Keep the following areas in mind when performing a competitor analysis for content:

  • Buying guides
  • Blogs
  • Videos
  • eBooks
  • Product descriptions

Compile an accurate comparison by spotlighting competitors’ focus, length, and keywords in content.


Focus on how you can incorporate some of their topical content into your ecommerce business, and include fresh information and perspectives. What is their primary content focus? Are they constantly updating their blogs or creating buying guides? If you have similar content that is unsuccessful, rework it or start over using insight from the competitor analysis. Creating insightful content that enriches your consumer’s experience increases conversions. You only need to find out what your competitor is doing and do it better.


Using competitor analysis can help you see where you may need to adjust content length based on competitor ranking in SERPs. Research done in 2017 showed that longer blogs ranked higher with search engines, but new information has determined that the length of content should be comparison based. In 2018, a study showed that ecommerce business blogs perform better if they use a similar word count, as compared to blogs on the same topic that ranked first in SERP.


Use competitive SEO analysis to find out how your competitor is adding fresh keywords into evergreen short and long-term keywords to improve SERP ranking. How your competitors use keywords impacts their SEO success: evaluate what types of keywords they are using and adjust yours. Are they using LSI keywords? Are their keywords evergreen, or fresh (trending)? Fresh keywords are an excellent short-term source of conversions, but evergreen keywords are more relevant for consistent conversions. Pay attention to all forms of content including image meta-tags and titles. To get a complete view of your SEO content versus your competitors, don’t forget titles, metadata, tags, and content relevance.

2. Increase Conversions

Ecommerce SEO competitor analysis can increase your conversions because innovation is a large part of maintaining customer satisfaction. Gaining an increase in customer satisfaction provides you with a competitive advantage. Analyze growth patterns and current ROI to find opportunities to convert consumers to your brand by using innovative strategies that increase ratings and positive UGC. Evaluate your competitors’ marketing goals and predictions, as well as their past and present strategies that have been successful. Your closest competitors can give you insight into different SEO strategies that can increase your ranking in SERP, and lead to more conversions. Chances are, if they are optimizing for voice or image search and getting good results, your ecommerce business would be safe optimizing for those areas, too.


3. Strengthen Marketing Strategy

Ecommerce SEO strategy needs to include social media marketing: 25.6 percent of referral traffic came from social media advertising (SMA) in 2017. While Google Search is still king, social media continues to keep pace, and with voice and image search not far behind, text search will have stiff competition. Use competitive analysis to determine the type, frequency, and cost of your competitors’ SMA campaigns. Figure out what types of SMA worked for your direct competitors, and which ones fell short. Brainstorm how your ecommerce business can expand on competitor methods and find opportunities to surpass their successes. Competitive analysis allows your ecommerce business to locate opportunities for the right social media strategy, based on real-time marketing data. It can also determine where current SMA strategy fell-short. Pay special attention to competitor social media data like:

  • Fans, followers, and subscriptions
  • Sharing patterns
  • Frequency and consistency of posts
  • Customer engagement
  • Photos and videos
  • Advertisements and videos

In your competitor analysis, include the advertising methods that did and didn’t work for the competition to give yourself a jump-off point for additions or changes.

4. Maximize Overall Site Speed

Another key benefit of using competitor analysis is the ability to compare your current load speed with your competitors. Run all URLs through analysis to help you zero in on areas that can be improved to increase load speed. Include the following in your ecommerce SEO competitor analysis:

  • Images: What format are they using? How did they optimize images for search engines and mobile devices? What size are their image files compared to yours? Are their image titles more effective?
  • Links: How do they use internal and external links? Do they allow social media linking? How are their links displayed (buttons, images, direct links)? Are their external links more trustworthy or relevant than yours?
  • URLs: Does your competitor include keywords in product URLs? Have they optimized URLs for SEO? What type of formatting do they use?

5. Rank Higher in SERP

Ecommerce SEO competitor analysis used effectively leads to higher SERP rankings. When you analyze your competitor’s content, speed, and marketing strategy you break down their SEO structure. Find out how your competitor uses keywords to drive search engine results, and decide what you can do to improve on their existing methods. Competitive analysis allows you to see how they are redirecting broken or missing links, improving loading times, and using cross browser compatibility. Consider researching the following in your ecommerce SERP competitive analysis:

  • Ease of use: Sites that are user-friendly rank higher because they provide a positive customer experience
  • Layout: Site layout is important to SERP rankings – the easier a sight is for the search engine algorithm to navigate matters
  • Platform: Technology used to create a site is just as important as SEO content to search engines
  • XML Sitemap: Sitemaps provide efficient and quick indexing for search engines
  • Snippets: They appear at the top of SERP, usually with an image, link, and description

Don’t forget to double check UGC at all stages of analysis. See what aspects of products or services their consumers are satisfied with, and what needs work. Satisfied consumers create more leads, visits, conversions, and SERP rankings.

Let us know how competitor analysis has helped your ecommerce business, and what steps had the biggest impact on conversions. If you need guidance fixing under-performing areas of your site, we can help. At eZdia we analyze content, customer experiences, traffic, and conversions, and identify problems that can negatively impact your ecommerce business. We can also help by developing rich content like strategy guides and blogs that engage and entertain your consumers.



Why Every Ecommerce Business Needs a Predictive Customer Behavior Analytics Model

Why Every Ecommerce Business Needs a Predictive Customer Behavior Analytics Model

The availability of consumer data has made it easier for retailers to direct ad campaigns and product recommendations to individual consumers and specific behavioral groups. Customer behavior data can help ecommerce businesses personalize promotional offers, drive conversions through behavioral pattern recognition, and find areas that are performing well and ones that need a tune-up. Every ecommerce business should have a personalized, predictive customer behavior analysis (CBA) model.


Benefits of a Solid CBA Model for Ecommerce

Insights from CBA can help ecommerce businesses personalize advertising, replicate long-term customers, reduce acquisition costs, and increase leads and conversions. Targeting email promotions, recommendations, and campaigns to individual consumers offers personalized solutions that drive further sales. Satisfied customers are more likely to recommend the brand to friends and relatives, or share products on social media or through email. Using big data analysis, ecommerce businesses can find consumers with behavior similar to their best customers. Etailers can replicate the marketing efforts made to obtain and keep lifetime customers to create leads and potentially gain conversions. Knowing what consumers are looking for and predicting future behavior that may influence purchases means less guesswork when creating marketing campaigns. Determining like behaviors and gleaning valuable insights when creating successful campaigns the first time around helps reduce the amount of money spent trying to acquire customers. Having a predictive CBA model in place assists an ecommerce business through:

  • Better personalization: being able to create marketing directed at customers as individuals
  • Driving relevant content: add content based on popular content areas, topics, or consumer feedback from a variety of platforms
  • Design ICPs: big data analysis helps create specific profiles through the accumulation of digital information using CBA strategies
  • Predicting future behavior: tracking and analyzing individual data can help ecommerce businesses predict the next step in a customer’s purchase evolution
  • Finding best marketing practices: gathering customer behavior data to see what strategies are working and what practices need an overhaul
  • Tracking the consumer journey: tracking from discovery to ordering and beyond, then using data to drive recommendation engines and direct advertising campaigns
  • Adjusting analytics models and data mining strategies: help determine where data mining strategies are successful and what type of customer information gathering works but that still maintains brand trust

Customer behavioral analysis is becoming extremely popular in ecommerce. An estimated 69 percent of ecommerce businesses uses predictive CBA for acquisition, growth, and retention rates. By focusing on individuals or specific behavior groups, retailers can increase consumer satisfaction and turn visitors into repeat customers.

Types of Customer Behavior Analytics Models to Choose From

Customer behavior models are meant to answer set questions based on customer data analysis. Up until recently, the RFM (recency, frequency, monetary) model was considered the most effective method for CBA models. The primary goal of RFM is to focus on sales, and ecommerce specialists have realized this model of analyzing customer behavior has become obsolete. With the increase in data available and consumers’ need for a personal connection to brands, models have evolved to include those that focus more on consumer satisfaction than sales. The idea behind the shift is that sales will follow satisfaction, making the consumer the most important factor. There are three main CBA models that work better for ecommerce than RFM:

1. Customer Journey Analytics

Analyze data from all channels the customer interacts with throughout the entirety of their experience with your brand. This model helps an ecommerce business determine what drives consumer purchase behavior. Track four major steps in the purchase path to get a full picture of consumers’ journeys:

  • Awareness: influences and triggers
  • Consideration: product and ecommerce brand research
  • Conversion: where and when purchase decision was made, what step in the journey
  • Evaluation: experience, feedback left, satisfaction rating, review, any user-generated feedback

Gathering data from each step of an individual customer’s journey can aid ecommerce businesses in developing or adjusting personalized marketing strategies.

2. Behavior Segmentation

The behavior segmentation model entails collecting data about the actions customer take, based on behavior patterns during the purchase-decision process. The data is used to help classify behavior groups and develop engagement strategies. Behavior classification is a better way to create groups of like-minded consumers without relying on age or geographic locations.

3. LTV

Modeling customer behavior analytics includes collecting customer’s data to determine their lifetime value (LTV) to an ecommerce business to obtain actionable insight on how to maintain customers with like behaviors long-term. Unlike RFM, which focuses primarily on immediate sales value gained by conversions, LTV is centered on the value of a consumer through their entire time with a brand.

Ways to Use Customer Behavior Analytics to Increase Conversions

The three most important attributes consumers consider when deciding where to shop online are best price, preferred website, and best delivery methods. An ecommerce site is more likely to be preferred if they are in tune with consumer behavior and display knowledge about consumers as individuals.

CBA_Purchase Decision Factors.png

One of the primary benefits of using predictive CBA is the opportunity to transform data insight into conversions for your ecommerce business. There are three key ways predictive analytics models can make the largest impact on ecommerce conversion rates:

  1. Increase customer satisfaction ratings: Predictive CB analysis obtains data from designated areas that can be used to increase customers’ personal experiences. The more satisfied consumers are, the more likely they are to recommend the brand to friends and relatives through email, social media, or word-of-mouth.
  2. Lead-to-marketing reconstruction: Predictive analytics can determine on which areas you need to focus the advertising budget, based on consumer purchase data and user-generated feedback. Consumer data can indicate how advertising campaigns have been successful and areas that need to be restructured or omitted to achieve better results. This provides an opportunity for ecommerce businesses to increase sales through personalized and behavioral group marketing campaigns.
  3. Attract potential partners: Positive statistics from data analysis methods can be leveraged to promote your brand on social media platforms, advertisements, and even turned into badges that can be placed on your website. A successful CBA model displays a knowledge of industry practices for optimizing success and makes your brand a trusted source of credible information on consumer behavior analytics methods.

Having partners increases brand visibility through recommendations for a product on their blogs, advertising, or recommendation engines. CBA is useful in the collection of data pertaining to what platforms draw the most user-generated feedback, which is vital for lead-to-marketing reconstruction. Knowing which one will work best for your company takes some research into current goals and possibly past data sets for customer satisfaction.

Customizing a Comprehensive CBA Model

Data points for each ecommerce business need to be customized to their end goals. The type of customer data they need depends on what type of goal they wish to accomplish by collecting customer behavioral data. For instance, if you’re trying to create a recommendation algorithm for an automatic feature based on consumer viewing history, you won’t need age or income information. One working example of this is Amazon’s recommendation algorithm, which uses the following data sets:

  • Purchase history
  • Shopping cart items
  • Rated and liked products
  • Views and purchases

Developing a list of areas vital to the goal they wish to accomplish helps ecommerce businesses develop a comprehensive CBA model that weeds out irrelevant data.

1. Include Unstructured Data

Using unstructured data allows etailers to gain insight from traditional forms of customer information, such as test data. These are available wherever user-generated feedback is found on social media, in comments, reviews, and other areas where consumers have direct interaction with a brand. Just because it isn’t necessarily big data, doesn’t mean it isn’t important data.

2. Know Your Goal

All the data in the world is useless if you don’t know what you’re looking for. Before developing an analytics strategy, ecommerce organizations should ask and answer the following questions:

  • What does the company plan to accomplish with customer behavioral data?
  • What metrics matter to the goal?
  • What are specific areas of concern?

Not having a clear view of what consumer data gathering is meant to accomplish but developing a strategy for behavioral data collection anyway is an exercise in futility. The influx of data alone just causes confusion. Determine motives. For example, is the company’s primary concern cart drop-off, conversion rates, or poor advertising campaign results? Obtaining clarity prior to the implementation of a CBA model saves time and supplies retailers with accurate data sets that apply to a specific goal.

3. Focus on Relevant Data Sources

Only use data that focuses directly on your end goal. Each data collection effort should center on resolving a very specific set of issues or accomplishing a business goal. Focusing on specific criteria provides ecommerce businesses with more reliable data results.

4. Sources of Data to Include

There are three primary ways to collect customer behavior data, and all three data areas must be considered while developing a comprehensive CBA model.

  1. Direct contact: phone, email, surveys, user-generated feedback, etc.
  2. Digital tracking: collecting data on behavior patterns such as purchase history, reordering, view habits, shares, etc.
  3. Competitor strategies: tracking competitor success with data collection methods, and adopting relevant methods.

With any form of digital data collection effort, and some direct efforts, software generally sorts results and breaks them into understandable pieces of data. This way, retailers can gain actionable insight faster and negate human error. Without data-sorting software applications, piecing through vast amounts of digital data could take months.

5. Build a Customized CBA Model

Customize your CBA model as much as possible to gather actionable insight into consumer behavior, what drives purchases, or why customers don’t follow through. Design a CBA model that uses analytics methods that are relevant to your brand. For a CBA model to be successful, it needs to fit your specific goal and provide accurate real-time data that applies to that goal. Using a cookie-cutter version of CBA isn’t going to produce tailored results, but there are some steps that should be included in every CBA build. In our 7 Steps to Building a Successful Customer Behavior Model, the primary steps to include during the build of a CBA model are broken down. The steps include tools like predictive and funnel analysis methods.

What Are Ecommerce Businesses Presently Using CBA For?

The level of actionable insight received from the end results of CBA helps retailers keep customers that generally drop off prior to conversion. In fact, 63 percent of ecommerce businesses use the data to increase customer satisfaction rates, while 46 percent use it to elevate loyalty in existing customers. One of the most popular ways to use predictive analytics rests in recommendation engines like the one created by Amazon. The majority of Netflix viewer activity is driven by their recommendation service, and it’s estimated it saves the company $1 billion per year by reducing turnover rates. A key benefit of CBA is customization to individual consumers, which makes it crucial to ecommerce success. One study shows that 52 percent of consumers often switch brands when advertisements are not personalized to their habits. The possibilities for data-driven success are endless using CBA, especially when combined with other customer behavior data analysis methods and AI technology.

What information on CBA did you find most helpful? We’d love to hear how CBA has changed your ecommerce company for the better and any tips you might have about creating a successful model. Want help reaching your goal? Please contact eZdia to learn how we can assist. We strive to offer services that help ecommerce businesses reach their full potential. This is done by developing engaging content, analyzing and developing data for algorithms, managing content, and providing other valuable ecommerce solutions.

Author: Kristin Ann Hassel