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Google’s John Mueller discusses product price as a ranking factor and explains whether it can impact the positions of ecommerce stores in search results.

This topic came up during the Google SEO office-hours hangout recorded on October 8.

It’s a poignant topic considering the rising cost of goods these days.

Many companies are finding themselves in the position of having to raise prices due to increased operational costs, scarcity of parts and materials, and other reasons that are out of their control.

Let’s say two businesses are selling the same product online, but one of them has to drastically increase the price because of extenuating circumstances.

Suddenly the product they were selling $100 is selling for $500. However, the other business is still selling it for $100.

Assuming all else is equal in terms of SEO, could the price gap have an impact on rankings?

It’s easy to think Google may want to direct searchers toward the lower price.

According to Mueller, that assumption would be wrong.

Here’s what he has to say.

Related: Google Ranking Factors: Fact or Fiction

Google’s John Mueller on Price As a Ranking Factor

It’s no secret that Google can recognize the prices of products on sales pages.

There’s structured data created for that purpose, and you’ll often see prices listed directly in search results.

Although Google can understand how much a product costs, it does not use that information to rank the product page.

Mueller says:

“Purely from a web search point of view, no, it’s not the case that we would try to recognize the price on a page and use that as a ranking factor.

So it’s not the case that we would say we’ll take the cheaper one and rank that higher. I don’t think that would really make sense.”

He adds that product pages also show up in shopping results, which are ranked different from Google’s regular set of search results.

As it relates to shopping search results, Mueller says he doesn’t know how they’re ordered.

It’s possible that price is a factor for shopping searches, but he has no idea.

Users can definitely sort shopping search results by price, though. So that’s always something to consider when it comes to the cost of items.

“However, a lot of these products also end up in the product search results, which could be because you submit a feed, or maybe because we recognize the product information on these pages, and the product search results I don’t know how they’re ordered.

It might be that they take the price into account, or things like availability, all of the other factors that kind of come in as attributes in product search.”

The key takeaway is price is not a factor for web search.

Mueller doesn’t rule out the possibility of it being a factor for shopping search, but he can’t confirm anything.

“So, from a web search point of view, we don’t take price into account. From a product search point of view it’s possible.

The tricky part, I think, as an SEO, is these different aspects of search are often combined in one search results page. Where you’ll see normal web results, and maybe you’ll see some product review results on the side, or maybe you’ll see some mix of that.”

Hear his full response in the video below:

Featured Image: Screenshot from chúng tôi October 2023. 

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Google Maps Integrates Local Product Data

Google Maps Integrates Local Product Data

Google’s Froogle last year became one of only a select few shopping sites to start to offer local, “offline” information (ShopLocal and CNet are the others) in addition to traditional e-commerce. Shopping engines across the spectrum recognize — and shopping maestro Brian Smith of Comparison Engines agrees — that offline inventory information is really the (I hate to say it but here goes . . . ) “Holy Grail” of online shopping.

People research online and then want to buy locally. Why? Because they don’t want to pay shipping, want to be able to return it locally, want it that day and, in many cases, trust the local retailer (even if it’s a big box) more than some anonymous online merchant, regardless of how many happy faces that retailer may have.

As has been widely reported, comScore is launching a tracking study (qSearch Retail) that will formally connect the relationship between search/online shopping and offline buying. This ongoing study will only confirm the increasingly powerful relationship between the Internet and local shopping. It’s estimated that almost $350 billion in offline transactions were influenced by the Internet in 2005.

Google’s/Froogle’s offline inventory data is being provided by chúng tôi and by ShopLocal. And now Google has integrated StepUp data into Maps/Local with a cool twist – product images. It’s buried and there are some kinks, but it’s very promising. Here’s an example.

First let’s talk about the obvious problems (recognizing this is a beta/first step):

3. I’m not able to search or category browse the micro-site/landing page for that product

Assume these problems get solved. Now let’s talk about how this starts to point to a really interesting opportunity for the consumer, the local retailer and Google – and how it much more clearly connects online and offline.

Assuming that the information offered by StepUp and Google is accurate and as the coverage becomes greater, and as the integration becomes more intuitive and “elegant,” this provides demonstrable value for both the user and the merchant.

I’ve argued for some time that all shopping engines will need to provide the “where can I buy it locally?” data to fully satisfy the dominant consumer use case. Right now there are a host of practical “infrastructure” problems that companies like ShopLocal, Channel Intelligence and StepUp are trying to solve. But it’s only a matter of time before “platform agnostic” becomes the norm and local/offline inventory information becomes a must-have for shopping sites.

Google Updates Search Snippets For Product Review Pages

Google updates search results for product review pages by listing an item’s pros and cons in the search snippet.

In addition, there’s structured data to go along with this update, but it’s not 100% mandatory to qualify for the new snippets.

While the new pros and cons structured data is recommended, Google says it will try to pull the information into the snippets automatically.

Here’s what’s changing and how to manually add the structured data to your product review pages.

New Search Snippets For Product Review Pages

Google is displaying more detailed snippets for product review pages with new lines of text listing pros and cons.

In a blog post, Google states:

“Product reviews often contain a list of pros and cons, which our research has shown to be popular with shoppers when making their purchasing decisions. Because of their importance to users, Google Search may highlight pros and cons in the product review snippet in Search results.”

An example of the new search snippet is shown below:

Google can create these new snippets automatically, as long as the information appears somewhere on the page.

You can make the information clear to Google by marking up your product review pages with pros and cons structured data.

New Pros & Cons Structured Data

In conjunction with the update to product review search snippets, Google is introducing a new type of structured data.

As a best practice, it’s always recommended to use Google-supported structured data when possible, even if it’s not a requirement.

To manually tell Google about the pros and cons of an editorial product review, add the positiveNotes and/or negativeNotes properties to your nested product review.

Examples of both types of markup code are shown below:

See Google’s official documentation for more information about applying this markup.

If you add pros and cons structured data, you must follow these guidelines:

Currently, only editorial product review pages are eligible for the pros and cons appearance in Search, not merchant product pages or customer product reviews.

There must be at least two statements about the product. It can be any combination of positive and/or negative statements (for example, ItemList markup with two positive statements is valid).

The pros and cons must be visible to users on the page.

Google Says Selling Links Can Harm Site Ranking In Search Results

Earlier in the month Google began its unofficial crackdown on pages which openly sell links in an effort to capitalize on their Google PageRank. Google’s crackdown; lowering the PageRank of those pages.

Danny Sullivan eluded to this in October and we published the unofficial confirmation of this via Matt Cutts a couple of days after the widespread shrinking Google PageRank across such sites. But yesterday Google made it official (thanks Philipp), adding to its Google Webmaster Help Center that selling links that pass PageRank can penalize a site not only in its Google Toolbar PageRank status, but also in Google search results.

Some SEOs and webmasters engage in the practice of buying and selling links that pass PageRank, disregarding the quality of the links, the sources, and the long-term impact it will have on their sites. Buying or selling links that pass PageRank is in violation of Google’s webmaster guidelines and can negatively impact a site’s ranking in search results.

Adding a rel=”nofollow” attribute to the tag

Redirecting the links to an intermediate page that is blocked from search engines with a chúng tôi file

Google works hard to ensure that it fully discounts links intended to manipulate search engine results, such excessive link exchanges and purchased links that pass PageRank.

So, there you have it. Officially and according to Google, selling links that pass PageRank can not only harm your site’s toolbar PageRank, but also damage your site’s rankings in the Google index.

If you feel that Google is targeting your site and you do not deserve such a penalty, the request reconsideration from Google.

Of course, Google could always be bluffing, and I do not recommend that people stop buying links. I do think however that in addition to buying links, you should also look into a well rounded approach to link building. Here are some tips:

Google Cuts Perks As Big Tech Braces For A Potential Recession

Throughout the twenty-tens, Google was essentially used as a synonym for company perks . Frequently being dubbed as a ‘playground for grownups’ and winning Fortune’s ‘Best Company to Work For’ title a total of eight times, the company blazed the trail for workplace benefits.

But Google’s not alone. Companies like Meta , Apple, and Amazon have all been forced to make spending cuts in recent months to remain competitive — and experts predict the worst may still be yet to come.

While Google is still turning a profit, a slowdown in ad spending has led the company’s CEO, Sundar Pichai, to make difficult decisions. Aside from scaling back employee benefits, Google has also recently reported a hiring freeze and headcount cuts may be soon to follow.

Google, a company renowned for its lavish company perks like free cooking classes and on-site massage treatments, has been forced to limit employee travel and make cuts to social events, ahead of a possible economic downturn.

However, while Google’s benefits program is still not something to sniff at, the company has been forced to join the ranks of other companies and pull the reigns on employee spending.

According to a recent report from ‘Information’, an email recently sent out to Google’s senior managers instructed them to limit employee travel to “business critical trips”. Social functions, team offsite meetings, and travel to in-person events that offer a virtual option will also be vetoed, according to the leaked source.

A Google spokesperson told Business Insider that they “recently shared guidance about taking a responsible approach on expense management, including travel and events”, and that “different product areas and functions are implementing this in a way that works best for their teams.” 

Does Pichai’s Plan to ‘Simplify the Company’ Involve Layoffs?

Unfortunately, as a national recession looms, the company’s CEO, Sundar Pichai, is considering cutting more than travel expenses and socials.

According to leaked audio messages from Google’s last all-hands meeting, Pichai unveiled a plan to “simplify the company” to improve employee productivity. At this year’s Code Conference, Piachi doubled down, hinting that cuts to products and personnel may be made to “improve Google’s efficiency by 20%”.

“Sometimes there are areas to make progress [where] you have three people making decisions, understanding that and bringing it down to two or one improves efficiency by 20%.” – Google’s CEO, Sundar Pichai

Speaking at the conference, Pichai explained that the company has become “slower” after its headcount ballooned.

Therefore, to speed up results as resources remain limited, Google’s execs are now considering trimming down staff members and combining major apps like YouTube Music and Google Play Music into one product. 

How is the Rest of Big Tech Responding?

While Google’s stocks are still performing quite well, a sudden fall in ad spending has caused its earnings and revenue to be weaker than expected for the last two consecutive quarters.

But Google isn’t the only major tech company that’s been impacted by surging inflation, rising interest rates, and a drop in consumer spending. From mass layoffs and hiring freezes to seismic budget cuts, a number of big tech firms have been forced to tighten their belts as they brace for an uncertain future.

Delivery juggernaut, Amazon, has recently cut a staggering 10,000 employees and been forced to abandon dozens of its existing and planned US facilities, resulting in almost 25 million square feet of unusable space.

In addition, Facebook’s parent company Meta recently announced that it will reduce hiring for most mid-to-senior level positions, following a budget cut of $3 billion earlier in the year.

Finally, major firms like Apple, Microsoft and Peloton have all recently made cuts to personnel, with more layoffs across the sector expected to take place in the coming months.

To stay up to date with how big tech is responding to the economic downturn, read our guide to tech company layoffs in 2023.

Dense Ranking In Power Query

There are several ways to rank things, dense ranking is when items that compare equally receive the same ranking number, with subsequent items receiving the next ranking number, with no gaps between numbers, like this:

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The queries in this Excel file can be copied/pasted into the Power BI Desktop Advanced Editor and will work there too.

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I have some data in an Excel table for students who are studying Spanish or English. They’ve just taken exams and I’m going to use Power Query to create a dense ranking for the scores they received in those exams.

I’m going to use two queries to load the table of data. The first query called Scores just gives us the same 20 row table, sorted first by Course (ascending) and then by Score (descending).

The 2nd query is called Ranks and this is where most of the work is done. After loading the same source table, the first thing to do is remove all columns except Course and Score.

Then remove duplicates in the Score column

Next, Group By the Course column

To give us a table with two rows, one for each course. Each row in the Count column contains a table that contains all the scores for that course.

Now the best part, by adding a Custom Column and using this to add an Index Column to each item in the Count column, because each item is a table of scores for that course,

you end up with another table in each row of the new custom column (called Rank) that has assigned a ranking (index) to every score for each course.

Now by expanding the tables in the Rank column you end up with this, a table with each score in each course ranked.

There are only 13 rows in this table but we have 20 rows in our source data so we need to merge (join) the Scores and Rank tables together into a new query

The result is a table in every row of our new query that includes the rank for that combination of Course and Score.

Expanding the column of tables gives this

Can you see the problem? All the ranks are wrong.

So what is going on? I’m not 100% sure. I’ve read several blog posts and articles where similar issues are described, and I’ve seen this same kind of problem occur with sorting and removing duplicates.

My understanding is that Power Query presents one view of how data is stored, as in the end result of the Ranks query above, but it actually stores it in another way/order.

This does seem odd but the explanation I’ve seen given is that PQ uses lazy evaluation – it only really evaluates something when it is actually needed. So as you are going through building a query with various steps, the data you see in the preview isn’t necessarily the data you’re going to get when you run the query for real.

I’m not convinced that this is desirable behaviour, but the solution appears to be to use Table.Buffer. Table.Buffer takes a table and stores it in memory after evaluating it. This seems to be the key point.

As you add steps to your query, and as that query is run, each step is evaluated and the data in the step may be evaluated many times. What does it mean to evaluate? It means PQ checks the data to see what it is. But there appears to be no guarantee that the data is stored in an expected, ordered state.

You could sort a list but in a subsequent step that sorting is lost. Or as we have here, we’ve created a ranking that isn’t applied correctly, even though when you examine the table in the Ranks column, it shows you the correct rank.

What is really puzzling is that as the query is doing a merge, it is matching up two columns, the Course and the Score, so shouldn’t it follow that the Dense Rank value in that row in the Ranks table should be correct?

The fact that it isn’t would imply that the join isn’t working properly. If the join can attach the correct Course and Score from the Ranks table to the Scores table, why is the Dense rank value wrong?

Anyway, the fix is to wrap the Ranks table in Table.Buffer inside the join step.

Buffering the table like this means the table is held in computer memory in a known state. The query evaluates Ranks once and then does not evaluate it again. The order of the elements in the table won’t change.

With Table.Buffer in place, the result of the join is now correct.

We might have to ask Microsoft what is actually going on here.

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