Daily Archives: September 22, 2021

Staying virtual; Wednesday’s daily brief

By | September 22, 2021


Search Engine Land’s daily brief features daily insights, news, tips, and essential bits of wisdom for today’s search marketer. If you would like to read this before the rest of the internet does, sign up here to get it delivered to your inbox daily.

Good morning, Marketers, being in-person isn’t worth anyone’s well-being.

That’s why we’re planning to continue virtual SMX and MarTech events in 2022. I want to be amongst my fellow search marketers as much as anyone, but there are very compelling reasons to continue with virtual conferences until we can be absolutely sure that we’re not compromising on safety. Chris Elwell, CEO of Third Door Media (Search Engine Land’s parent company), laid out these reasons in a two-part series of posts:

  • There’s no predicting the future of COVID with certainty, and that affects all the other reasons below.
  • The travel industry has been disrupted. Airlines are having a hard time rebounding, which means fewer, more expensive flights for the foreseeable future.
  • Over the last 18 months, virtual conferences have been successful for us. Search marketing conferences have translated well to the digital space.
  • Fewer in-person attendees means lower ROI, which any marketer should be able to appreciate.
  • The cost of participating in in-person events will rise. “Convention centers, decorators, caterers and all of the other participants in the ecosystem will be paying more to provide the appearance of safety,” Elwell explained. “Those costs will be passed on. Exhibitors will end up with the bill.”

When it’s safe to gather the way we all want to, I hope to be the first person to welcome you back to SMX, but until then, we’ll keep providing professional development opportunities via our virtual conferences. SMX Next will be kicking off on November 9, register and join us for actionable tactics to overcome today’s challenges and forward-thinking strategies that can help you prepare for 2022.

George Nguyen,
Editor


SEOs experiencing delays in data on Search Console performance reports

“We’re currently experiencing longer than usual delays in the Search Console performance report. This only affects reporting, not crawling, indexing, or ranking of websites,” said the Google Search Central Twitter account on the morning of Tuesday, September 21.

Many SEOs have noticed the change in their Search Console reports yesterday morning and have taken to social media to ask if they’re the only ones seeing the issue — clearly, they’re not. Based on chatter from the SEO community, the last day of data seems to be September 17 or 18.

Why we care. If your data isn’t updated, don’t worry just yet. The glitch will likely be fixed soon, but make sure to inform your clients and adjust your weekly reporting to ensure no misunderstandings or data mistakes. If you’re using the Search Console API, you maybe also see 404s until the glitch is remedied. Google assured SEOs that the glitch does not affect how sites are seen or indexed, just how the data is being relayed back to them. It’s also a good reminder to go into Search Console regularly to check your data and not just rely solely on tools that may pull the data into automated reports.

Read more here.


How to set up Google Analytics 4 using Google Tag Manager

Google Tag Manager (GTM) provides an easy, templated route to install GA4 on your site as well as create custom events. To help you get started, Tim Jensen, campaign manager at Clix Marketing, has shared how he gets GA4 tracking in place via GTM, as well as some basic customization options.

  • Step 1: To start, create a new tag with a Tag Type of “Google Analytics: GA4 Event.” Choose your GA4 ID under “Configuration Tag.”
  • Step 2: Next, enter the Event Name that you’d like to appear within the Google Analytics interface. In this case, we’re using “scroll” to align with the existing “scroll” event that GA4 tracks.
  • Step 3: Click on the Event Parameters section to expand it. Here, we can add a custom parameter to send further details about the event to Google Analytics. In this case, we’ll send through percentage values for when people scroll to specific points on a page.
  • Step 4: We’ll use “scroll_depth” for the Parameter Name. Next, the value will be {{Scroll Depth Threshold}}, a variable within GTM that will pull in the scroll percentages as people interact with the page and data is sent back in.
  • Step 5: We’ll need to create a trigger to determine the values we want to track. Click in the bottom Triggers section to start a new trigger, and select Scroll Depth Trigger. With the variety of screen sizes people may be browsing from, the percentage option is likely your best bet here. Add the numbers for the scroll points you want to track, separated by commas.
  • Step 6: Save the trigger, save your tag, and publish it live. You should now see more detailed scroll data populate when you look at the Events section in Analytics.

You can use the same basic model presented above to fire additional events into Google Analytics. Use the event name you’d like to populate into Google Analytics, and use parameters to populate further details. 

Read more here.


Product rich results without reviews, the Google Maps ghost and share of voice in modern marketing

Reviews aren’t necessary to use product schema for rich results. “You need either review, aggregateRating, or offers. If you have the product for sale (an ‘offer’) then that works,” Google’s John Mueller said. It may be difficult for lesser established brands to garner reviews, so at least now we know there are other ways to go about it.

“Sounded like a deep man’s voice with a slight Indian accent.” Some Google Maps users have reported that their voice navigation suddenly and briefly switched over to what sounds like a man with a slight Indian accent. This has happened to me as well, but I’m not sure I heard the same accent. Google says it’s aware of the issue and working on a fix, so there’s no need to fear…unless you believe in ghosts.

“Share of voice” in digital channels. Share of voice became a marketing staple decades ago, but the rise of digital muddied the waters. “This has led to renewed attention and debate around additional or alternative metrics.  Les Binet has been researching the value of share of search, which some like Mark Ritson advocate as a potential replacement and others like Shann Biglione at Zenith see as a different tool altogether,” said Marketoonist creator Tom Fishburne.


What We’re Reading: Maintaining your team’s productivity as the pandemic drags on

Are you more or less productive so far this year than you were in 2020? There seems to be no semblance of a consensus between my friends, colleagues, my partner or myself. “Well, I had a baby last year, so I was productive in different ways, I think,” Carolyn Lyden, our director of search content, told me. As for me, I’m not so sure — I worked hard last year, but I’m so much more efficient now that we’ve had over a year of pandemic life and virtual conferences under our belt. See? It’s not such an easy question to answer.

A HubSpot survey found that 39% of employees would say that their productivity level is the same as it was last year. A slightly smaller proportion (37%) said they are either a bit more or much more productive, and nearly a quarter (24%) consider themselves a slightly less or much less productive. There’s no explanation of survey methodology, so I have to assume it’s an internal survey — at any rate, Caroline Forsey, the manager of HubSpot’s marketing blog, sought to address these disparate experiences with a list of practices and strategies that managers can use to respond to changing productivity levels. Below are a few of the highlights.

  • Find daily or weekly activities your team can do together: This could be something as simple as a game of Two Truths and a Lie, a question of the day or collaborating on a themed music playlist. “Building a strong team culture is a critical component for increasing productivity, as it helps your employees feel more engaged at work and increases team morale,” Forsey wrote.
  • Paint a clear vision for your team’s future: The “unprecedented” part of the pandemic hasn’t totally faded, but at this point, we have a rough idea of what the near-term future looks like. “Employees had to adapt to a new working world, and now that they’ve adjusted, you need to paint an attainable future for them to work towards rather than ambiguity and uncertainty,” said Clint Fontanella, marketing manager at HubSpot.
  • Foster trust and boundaries: In remote environments, a lack of trust can turn into micromanagement. Without boundaries, remote work can quickly bleed into our leisure hours, which can be equally detrimental to productivity.
  • Acknowledge that productivity looks different for everyone: Here’s a personal example — Barry Schwartz can write and publish breaking industry news before I can finish reading it. While I also share that responsibility, I typically focus on longer, evergreen content. That means a lot of time spent communicating with professionals and companies and rounds of editing. Comparing us to one another simply doesn’t make sense. This is also true for employees that like to work 9 a.m. to 4 p.m. with no breaks and ones that need to leave for a few hours to drop their child off at daycare, for example.

New on Search Engine Land

About The Author

George Nguyen is an editor for Search Engine Land, covering organic search, podcasting and e-commerce. His background is in journalism and content marketing. Prior to entering the industry, he worked as a radio personality, writer, podcast host and public school teacher.



Source link : Searchengineland.com

How to Use Python to Forecast Demand, Traffic & More for SEO

By | September 22, 2021


Whether it’s search demand, revenue, or traffic from organic search, at some point in your SEO career, you’re bound to be asked to deliver a forecast.

In this column, you’ll learn how to do just that accurately and efficiently, thanks to Python.

We’re going to explore how to:

  • Pull and plot your data.
  • Use automated methods to estimate the best fit model parameters.
  • Apply the Augmented Dickey-Fuller method (ADF) to statistically test a time series.
  • Estimate the number of parameters for a SARIMA model.
  • Test your ****** and begin making forecasts.
  • Interpret and export your forecasts.

Before we get into it, let’s define the data. Regardless of the type of metric, we’re attempting to forecast, that data happens over time.

In most cases, this is likely to be over a series of dates. So effectively, the techniques we’re disclosing here are time series forecasting techniques.

So Why Forecast?

To answer a question with a question, why wouldn’t you forecast?

These techniques have been long used in finance for stock prices, for example, and in other fields. Why should SEO be any different?

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With multiple interests such as the budget holder and other colleagues – say, the SEO manager and marketing director – there will be expectations as to what the organic search channel can deliver and whether those expectations will be met, or not.

Forecasts provide a data-driven answer.

Helpful Forecasting Info for SEO Pros

Taking the data-driven approach using Python, there are a few things to bear in mind:

Forecasts work best when there is a lot of historical data.

The cadence of the data will determine the time frame needed for your forecast.

For example, if you have daily data like you would in your website analytics then you’ll have over 720 data points, which are fine.

With Google Trends, which has a weekly cadence, you’ll need at least 5 years to get 250 data points.

In any case, you should aim for a timeframe that gives you at least 200 data points (a number plucked from my personal experience).

****** like consistency.

If your data trend has a pattern — for example, it’s cyclical because there is seasonality — then your forecasts are more likely to be reliable.

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For that reason, forecasts don’t handle breakout trends very well because there’s no historical data to base the future on, as we’ll see later.

So how do forecasting ****** work? There are a few aspects the ****** will address about the time series data:

Autocorrelation

Autocorrelation is the extent to which the data point is similar to the data point that came before it.

This can give the model information as to how much impact an event in time has over the search traffic and whether the pattern is seasonal.

Seasonality

Seasonality informs the model as to whether there is a cyclical pattern, and the properties of the pattern, e.g.: how long, or the size of the variation between the highs and lows.

Stationarity

Stationarity is the measure of how the overall trend is changing over time. A non-stationary trend would show a general trend up or down, despite the highs and lows of the seasonal cycles.

With the above in mind, ****** will “do” things to the data to make it more of a straight line and therefore more predictable.

With the whistlestop theory out of the way, let’s start forecasting.

Exploring Your Data

# Import your libraries
import pandas as pd
from statsmodels.tsa.statespace.sarimax import SARIMAX
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf 
from statsmodels.tsa.seasonal import seasonal_decompose                        
from sklearn.metrics import mean_squared_error
from statsmodels.tools.eval_measures import rmse
import warnings
warnings.filterwarnings("ignore")
from pmdarima import auto_arima

We’re using Google Trends data, which is a CSV export.

These techniques can be used on any time series data, be it your own, your client’s or company’s clicks, revenues, etc.

# Import Google Trends Data
df = pd.read_csv("exports/keyword_gtrends_df.csv", index_col=0)
df.head()
Example data from Google Trends.Screenshot from Google Trends, September 2021

As we’d expect, the data from Google Trends is a very simple time series with ****, query, and hits spanning a 5-year period.

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It’s time to format the dataframe to go from long to wide.

This allows us to see the data with each search query as columns:

df_unstacked = ps_trends.set_index(["****", "query"]).unstack(level=-1)
df_unstacked.columns.set_names(['hits', 'query'], inplace=True)
ps_unstacked = df_unstacked.droplevel('hits', axis=1)
ps_unstacked.columns = [c.replace(' ', '_') for c in ps_unstacked.columns]
ps_unstacked = ps_unstacked.reset_index()
ps_unstacked.head()
Formatted dataframe.Screenshot from Google Trends, September 2021

We no longer have a hits column, as these are the values of the queries in their respective columns.

This format is not only useful for SARIMA (which we will be exploring here) but also for neural networks such as Long short-term memory (LSTM).

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Let’s plot the data:

ps_unstacked.plot(figsize=(10,5))
Plotting the data.Screenshot from Google Trends, September 2021

From the plot (above), you’ll note that the profiles of “PS4” and “PS5” are both different. For the non-gamers among you, “PS4” is the 4th generation of the Sony Playstation console, and “PS5” the fifth.

“PS4” searches are highly seasonal as they’re an established product and have a regular pattern apart from the end when the “PS5” emerges.

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The “PS5” didn’t exist 5 years ago, which would explain the absence of a trend in the first 4 years of the plot above.

I’ve chosen those two queries to help illustrate the difference in forecasting effectiveness for the two very different characteristics.

Decomposing the Trend

Let’s now decompose the seasonal (or non-seasonal) characteristics of each trend:

ps_unstacked.set_index("****", inplace=True)
ps_unstacked.index = pd.to_datetime(ps_unstacked.index)
query_col="ps5"

a = seasonal_decompose(ps_unstacked[query_col], model = "add")
a.plot();
Time series data.Screenshot from Google Trends, September 2021

The above shows the time series data and the overall smoothed trend arising from 2020.

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The seasonal trend box shows repeated peaks, which indicates that there is seasonality from 2016. However, it doesn’t seem particularly reliable given how flat the time series is from 2016 until 2020.

Also suspicious is the lack of noise, as the seasonal plot shows a virtually uniform pattern repeating periodically.

The Resid (which stands for “Residual”) shows any pattern of what’s left of the time series data after accounting for seasonality and trend, which in effect is nothing until 2020 as it’s at zero most of the time.

For “ps4”:

Time series data.Screenshot from Google Trends, September 2021

We can see fluctuation over the short term (Seasonality) and long term (Trend), with some noise (Resid).

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The next step is to use the Augmented Dickey-Fuller method (ADF) to statistically test whether a given Time series is stationary or not.

from pmdarima.arima import ADFTest

adf_test = ADFTest(alpha=0.05)
adf_test.should_diff(ps_unstacked[query_col])
PS4: (0.09760939899434763, True)
PS5: (0.01, False)

We can see the p-value of “PS5” shown above is more than 0.05, which means that the time series data is not stationary and therefore needs differencing.

“PS4,” on the other hand, is less than 0.05 at 0.01; it’s stationary and doesn’t require differencing.

The point of all of this is to understand the parameters that would be used if we were manually building a model to forecast Google searches.

Fitting Your SARIMA Model

Since we’ll be using automated methods to estimate the best fit model parameters (later), we’re now going to estimate the number of parameters for our SARIMA model.

I’ve chosen SARIMA because it’s easy to install. Although Facebook’s Prophet is elegant mathematically speaking (it uses Monte Carlo methods), it’s not maintained enough and many users may have problems trying to install it.

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In any case, SARIMA compares quite well to Prophet in terms of accuracy.

To estimate the parameters for our SARIMA model, note that we set m to 52 as there are 52 weeks in a year, which is how the periods are spaced in Google Trends.

We also set all of the parameters to start at 0 so that we can let the auto_arima do the heavy lifting and search for the values that best fit the data for forecasting.

ps5_s = auto_arima(ps_unstacked['ps4'],
           trace=True,
           m=52, # there are 52 periods per season (weekly data)
           start_p=0,
           start_d=0,
           start_q=0, 
           seasonal=False)

Response to above:

Performing stepwise search to minimize aic

 ARIMA(3,0,3)(0,0,0)[0]             : AIC=1842.301, Time=0.26 sec
 ARIMA(0,0,0)(0,0,0)[0]             : AIC=2651.089, Time=0.01 sec
...
 ARIMA(5,0,4)(0,0,0)[0] intercept   : AIC=1829.109, Time=0.51 sec

Best model:  ARIMA(4,0,3)(0,0,0)[0] intercept
Total fit time: 6.601 seconds

The printout above shows that the parameters that get the best results are:

PS4: ARIMA(4,0,3)(0,0,0)
PS5: ARIMA(3,1,3)(0,0,0)

The PS5 estimate is further detailed when printing out the model summary:

ps5_s.summary()
SARIMAX results.Screenshot from SARIMA, September 2021

What’s happening is this: The function is looking to minimize the probability of error measured by both the Akaike’s Information Criterion (AIC) and Bayesian Information Criterion.

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AIC = -2Log(L) + 2(p + q + k + 1)

Such that L is the likelihood of the data, k = 1 if c ≠ 0 and k = 0 if c = 0

BIC = AIC + [log(T) - 2] + (p + q + k + 1)

By minimizing AIC and BIC, we get the best-estimated parameters for p and q.

Test the Model

Now that we have the parameters, we can begin making forecasts. First, we’re going to see how the model performs over past data. This gives us some indication as to how well the model could perform for future periods.

ps4_order = ps4_s.get_params()['order']
ps4_seasorder = ps4_s.get_params()['seasonal_order']
ps5_order = ps5_s.get_params()['order']
ps5_seasorder = ps5_s.get_params()['seasonal_order']

params = {
    "ps4": {"order": ps4_order, "seasonal_order": ps4_seasorder},
    "ps5": {"order": ps5_order, "seasonal_order": ps5_seasorder}
}

results = []
fig, axs = plt.subplots(len(X.columns), 1, figsize=(24, 12))  

for i, col in enumerate(X.columns):
    #Fit best model for each column
    arima_model = SARIMAX(train_data[col],
                          order = params[col]["order"],
                          seasonal_order = params[col]["seasonal_order"])
    arima_result = arima_model.fit()

    #Predict
    arima_pred = arima_result.predict(start = len(train_data),
                                      end = len(X)-1, typ="levels")
                             .rename("ARIMA Predictions")

    #Plot predictions
    test_data[col].plot(figsize = (8,4), legend=True, ax=axs[i])
    arima_pred.plot(legend = True, ax=axs[i])
    arima_rmse_error = rmse(test_data[col], arima_pred)

    mean_value = X[col].mean()
    results.append((col, arima_pred, arima_rmse_error, mean_value))
    print(f'Column: {col} --> RMSE Error: {arima_rmse_error} - Mean: {mean_value}n')

Column: ps4 --> RMSE Error: 8.626764032898576 - Mean: 37.83461538461538
Column: ps5 --> RMSE Error: 27.552818032476257 - Mean: 3.973076923076923

The forecasts show the ****** are good when there is enough history until they suddenly change, as they have for PS4 from March onwards.

For PS5, the ****** are hopeless virtually from the get-go.

We know this because the Root Mean Squared Error (RMSE) is 8.62 for PS4, which is more than a third of the PS5 RMSE of 27.5. Given that Google Trends varies from 0 to 100, this is a 27% margin of error.

Forecast the Future

At this point, we’ll now make the foolhardy attempt to forecast the future based on the data we have to ****:

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oos_train_data = ps_unstacked
oos_train_data.tail()
Data to use to forecast.Screenshot from Google Trends, September 2021

As you can see from the table extract above, we’re now using all available data.

Now, we shall predict the next 6 months (defined as 26 weeks) in the code below:

oos_results = []
weeks_to_predict = 26
fig, axs = plt.subplots(len(ps_unstacked.columns), 1, figsize=(24, 12)) 

for i, col in enumerate(ps_unstacked.columns):
    #Fit best model for each column
    s = auto_arima(oos_train_data[col], trace=True)
    oos_arima_model = SARIMAX(oos_train_data[col],
                          order = s.get_params()['order'],
                          seasonal_order = s.get_params()['seasonal_order'])
    oos_arima_result = oos_arima_model.fit()
    #Predict
    oos_arima_pred = oos_arima_result.predict(start = len(oos_train_data),
                                      end = len(oos_train_data) + weeks_to_predict, typ="levels").rename("ARIMA Predictions")

    #Plot predictions
    oos_arima_pred.plot(legend = True, ax=axs[i])
    axs[i].legend([col]);
    mean_value = ps_unstacked[col].mean()

    oos_results.append((col, oos_arima_pred, mean_value))
    print(f'Column: {col} - Mean: {mean_value}n')

The output:

Performing stepwise search to minimize aic

 ARIMA(2,0,2)(0,0,0)[0] intercept   : AIC=1829.734, Time=0.21 sec
 ARIMA(0,0,0)(0,0,0)[0] intercept   : AIC=1999.661, Time=0.01 sec
...
 ARIMA(1,0,0)(0,0,0)[0]             : AIC=1865.936, Time=0.02 sec

Best model:  ARIMA(1,0,0)(0,0,0)[0] intercept
Total fit time: 0.722 seconds
Column: ps4 - Mean: 37.83461538461538
Performing stepwise search to minimize aic
 ARIMA(2,1,2)(0,0,0)[0] intercept   : AIC=1657.990, Time=0.19 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=1696.958, Time=0.01 sec
...
 ARIMA(4,1,4)(0,0,0)[0]             : AIC=1645.756, Time=0.56 sec

Best model:  ARIMA(3,1,3)(0,0,0)[0]          
Total fit time: 7.954 seconds
Column: ps5 - Mean: 3.973076923076923

This time, we automated the finding of the best fitting parameters and fed that directly into the model.

There’s been a lot of change in the last few weeks of the data. Although trends forecasted look likely, they don’t look super accurate, as shown below:

Graph of forecast from the data.Screenshot from Google Trends, September 2021

That’s in the case of those two keywords; if you were to try the code on your other data based on more established queries, they will probably provide more accurate forecasts on your own data.

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The forecast quality will be dependent on how stable the historic patterns are and will obviously not account for unforeseeable events like COVID-19.

Start Forecasting for SEO

If you weren’t excited by Python’s matplot data visualization tool, fear not! You can export the data and forecasts into Excel, Tableau, or another dashboard front end to make them look nicer.

To export your forecasts:

df_pred = pd.concat([pd.Series(res[1]) for res in oos_results], axis=1)
df_pred.columns = [x + str('_preds') for x in ps_unstacked.columns]
df_pred.to_csv('your_forecast_data.csv')

What we learned here is where forecasting using statistical ****** is useful or is likely to add value for forecasting, particularly in automated systems like dashboards – i.e., when there’s historical data and not when there is a sudden spike, like PS5.

More Resources:


Featured image: ImageFlow/Shutterstock





Source link : Searchenginejournal.com

AC installation and maintenance services in Dubai

By | September 22, 2021

Our Air Duct System Cleaning procedures are performed in accordance with NADCA (National Air Duct Cleaners Association) procedures of 2006 specifications and follow the rules of Dubai Municipality order no. 61/1991 as well.

Google rolls out ticket booking links, ‘Things to do’ ads and an eco-certified badge for hotels

By | September 22, 2021


Google is introducing new organic and paid features for travel and leisure businesses, including ticket booking links and pricing in search results, new “Things to do” ads and an eco-certified badge for hotel listings, the company announced Wednesday.

Ticket booking links. In addition to showing general information when users search for attractions, such as the Statue of Liberty or Tokyo Tower, for example, Google will now also show booking links for basic admission and other ticketing options (when available). 

Image: Google.

The company also has plans for a wider rollout of this feature: “In the months ahead, we’ll also begin showing information and booking links for experiences in a destination, like wine tasting in Paris or bike tours in California,” the search engine said.

Ticket booking links can be promoted at no cost. Attractions, tours and activities operators that want to participate can learn more over at Google’s Help Center.

Introducing Things to do ads. Google is also introducing a new paid product for travel and leisure businesses: Things to do ads (shown below).

Image: Google.

These ads will appear above the search results when users search for tours, activities and local attractions on Google Search. They show details such as images, reviews, pricing and include a booking link for the activity, and are shown to users based on their search terms, location and other related details.

Things to do ads are an automated format that use data from your inventory feed based on the ad group label. Advertisers can designate a budget and target users based on their country of residence and device type. Except for target impression share, all bidding strategies available for Search campaigns are also available for Things to do campaigns. Google’s Help Center has more details on how to get started with this new offering.

Eco-certified badges for hotel listings. Beginning this week, hotels that are certified for high standards of sustainability from certain independent organizations, such as EarthCheck or Green Key, will have an eco-certified badge next to their name, in the search results.

The eco-certified badge in search results and sustainability information in hotel profiles. Image: Google.

Additionally, users can view more information about a hotel’s sustainability efforts in the “About” tab of the hotel’s profile, as shown above. Hotel listing managers can add these attributes to their business profile by signing into Google My Business or by contacting Google My Business support.

Why we care. As the world gradually moves away from the pandemic, these offerings could help travel and leisure businesses bounce back from over a year and a half of disruption.

Ticketing booking links in search results may help attract reservations or sales for ticket sellers with competitive prices. In its announcement, Google drew similarities between this feature and free hotel booking links: “While it’s still early days, we’ve found that free hotel booking links result in increased engagement for both small and large partners,” the company said, “Hotels working with the booking engine WebHotelier saw more than $4.7M in additional revenue from free booking links this summer. With more than 6,000 active hotels, WebHotelier shared that they were ‘pleasantly surprised to receive reservations right from Google at no additional cost,’” the company said.

The new Things to do ad format is another tool that attractions operators can use to reach travelers that have shown an interest in a particular destination and can be a nice supplement to organic marketing efforts.

And, the eco-certified badge for hotel listings may distinguish business profiles in the search results, which can be a unique selling point for environmentally conscious travelers.


New on Search Engine Land

About The Author

George Nguyen is an editor for Search Engine Land, covering organic search, podcasting and e-commerce. His background is in journalism and content marketing. Prior to entering the industry, he worked as a radio personality, writer, podcast host and public school teacher.



Source link : Searchengineland.com

Author Authority: Is It a Google Ranking Factor?

By | September 22, 2021


Is there anything scarier reading an article providing medical advice from a journalism major fresh out of college with no medical background?

The truth is, not everything you read online is for your benefit. A lot of online content is just downright untrue. While authors may come from a harmless place, when certain copy is taken as the truth, it can become pretty harmful.

This is where the authority of the author (or author rank) begins to impact your content.

Here, we’re debunking the myths around author authority.

Read on to find out whether or not author authority is a ranking factor.

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The Claim: Author Authority as a Ranking Factor

When it comes to Google, it would make sense for them to value author authority as a ranking factor because of the E-A-T (Expertise, Authoritativeness, and Trustworthiness) guidelines.

But do search engines really care who created the content? And, does who the author is impact ranking algorithms?

Spoiler alert: There is not enough evidence to support this claim. But interest in this topic is growing.

Author Authority as a Ranking Factor: The Evidence

Let’s start with the first question, is author authority a ranking factor?

No, author authority is not a ranking factor. However, there are Google patents to help them identify authors for specific pages.

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In August 2005, Google filed a patent for Agent Rank. If you want to learn more about that, Bill Slawski breaks down Agent Rank here.

The short version? Google’s patent uses “digital signatures” to rank content based on reputation scores.

On June 20, 2011, Google confirmed it was supporting authorship markup. Remember rel=”author:?

In 2014, Mark Traphagen ran a study on authorship adoption to show authorship adoption by authors was slow. He found that 70% of authors did not connect their authorship with content.

Later in 2014, authorship markup was officially removed.

In 2016, Google’s Gary Illyes said at an SMX conference that Google is “not using authorship at all anymore” – but they know who the author is.

How does Google know this? Well, we learned in this 2021 video that Google looks at a number of factors (e.g., links to profile pages, structured data, other visible information on a page) as part of a process called reconciliation.

Other relevant evidence we found is from August 21, 2018, when Google’s John Mueller confirmed that Google does not use author reputation as a ranking factor.

Now, what about E-A-T. Reputation is different than “expertise” and “authoritativeness.”

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Reputation is how others view the author.

Expertise and authoritativeness are characteristics that Google uses to evaluate the author.

But recent patents show how authorship is evolving. For instance, in March 2020, Google filed a patent called Author Vectors to identify authors through internet-based writing styles.

In Slawki’s evaluation of the patent, he describes how the process works:

“Different authors can have different writing styles and different levels of expertise and interest in different topics.

Google is telling us with this new patent on author vectors that they may be able to identify the authors of unlabeled content.”

The fact is, we know that Google is getting better at determining who the authors of content might be with the updates to their Quality Rater Guidelines.

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But we don’t know why or how they are using this to support their ranking factors.

One thing we know for sure is that Google recommends adding an author’s URL to article schema.

Author Authority as a Ranking Signal: Our Verdict

Author Authority: Is It a Google Ranking Factor?

Author authority has had its ups and downs over the years. And with Google’s Quality Rater Guidelines related to E-A-T, it’s causing a bit of a gray area in SEO.

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While author authority may not directly impact your organic search rankings, it’s still smart to follow Google’s Quality Rater Guidelines to improve your content performance.


Featured image: Paulo Bobita/Search Engine Journal

Author Authority: Is It a Google Ranking Factor?





Source link : Searchenginejournal.com

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