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Five Years of Google Ranking Signals

LIghthouse

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Organic Search

1. Domain Age and Rate of Linking
2. Use of Keywords
3. Related Phrases
4. Keywords in Main Headings, Lists, and Titles
5. Page Speed
6. Watch Times for a Page
7. Context Terms on a Page
8. Language Models Using Ngrams
9. Gibberish Content
10. Authoritative Results
11. How Well Databases Answers Match Queries
12. Suspicious Activity to Increase Rankings
13. Popularity Scores for Events
14. The Amount of Weight from a Link is Based upon the Probability that someone might click upon it
15. Biometric Parameters while Viewing Results
16. Click-Throughs
17. Site Quality Scores
18. Disambiguating People
19. Effectiveness and Affinity
20. Quotes
21. Category Duration Visits
22. Repeat Clicks and Visit Durations
23. Environmental Information
24. Traffic Producing Links
25. Freshness
26. Media Consumption History
27. Geographic Coordinates
28. Low Quality
29. Television Viewing
30. Quality Rankings

Semantic Search

31. Searches using Structured Data
32. Related Entities
33. Nearby Locations
34. Attributes of Entities
35. Natural Language Search Results

Local Search

36. Travel Time for Local Results
37. Reverse Engineering of Spam Detection in Local Results
38. Surprisingness in Business Names in Local Search
39. Local Expert Reviews
40. Similar Local Entities
41. Distance from Mobile Location History
42. What People Search for at Locations Searched
43. Semantic Geotokens

Voice Search

44. Stressed Words

News Search

45. Originality

Google Ranking Signals

There are some other pages about Google Ranking Signals that don’t consider up-to-date information or sometimes use questionable critical thinking to argue that some of the signals that they include are actually something that Google considers. I’ve been blogging about patents from Google, Yahoo, Microsoft, and Apple since 2005, and have been exploring what those might say are ranking signals for over a decade.

Representatives from Google have stated that “Just because we have a patent on something, doesn’t mean we are using it.” The first time I heard them say that was after Go Daddy started advertising domain registrations of up to 10 years, because one Google patent (Information Retrieval Based on Historical Data) said that they might look at length of domain registration as a ranking signal, based on the thought that a “spammer would likely only register a domain for a period of one year.” (but actually, many people register domains for one year, and have their registrations on auto-renewal, so a one year registration is not evidence that a person registering a domain for just one year is a spammer.).

I’ve included some ranking signals that are a little older, but most of the things I’ve listed are from the past five years, often with blog posts I’ve written about them, and patents that go with them. This list is a compilation of blog posts that I have been working on for years, taking many hours of regular searching through patent filings, and reading blog posts from within the Search and SEO industries, and reading through many patents that I didn’t write about, and many that I have. If you have questions about any of the signals I’ve listed, please ask about them in the comments.

Some of the patents I have blogged about have not been implemented by Google yet, but could be. A company such as Google files a patent to protect the intellectual property behind their ideas, the work that their search engineers and testing teams put into those ideas. It is worth looking at, reading, and understanding many of these patents because they provide some insights into ideas that Google may have explored when developing ranking signals, and they may give you ideas of things that you may want to explore, and questions to keep in mind when you are working upon optimizing a site. Patents are made public to inspire people to innovate and invent and understand new ideas and inventions.

Organic Search

1. Domain Age and Rate of Linking

Google does have a patent called Document scoring based on document inception date, in which they tell us that they will often use the date that they first crawl a site, or the first time they see a document referenced in another site, as the age of that site. The patent also tells us that Google may look at the links pointed to a site, and calculate what the average rate of links pointed to a site may be and use that information to rank a site, based upon that linking.

2. Use of Keywords

Matt Cutts wrote a newsletter for librarians in which he explained how Google crawled the web, making an inverted index of the Web with terms found on Documents from the Web that it would match up with query terms when people performed searches. It shows us the importance of Keywords in queries and how Google finds words that contain those keywords as an important part of performing searches. A copy of that newsletter can be found here: https://www.analistaseo.es/wp-content/uploads/2014/09/How-Google-Index-Rank.pdf

3. Related Phrases

Google Recently updated its first phrase-based indexing patent, which tells us in its claims that pages with more related phrases on them rank higher than pages with less related phrases on them. That patent is: Phrase-based searching in an information retrieval system. Related phrases are phrases that are complete phrases that may predict the topic a page it appears upon is about. Google might look at the queries that a page is optimized for, and look at the highest ranking pages for those query terms, and see which meaningful complete phrases frequently occur (or co-occur) on those high ranking pages.

I wrote about the updating of this patent in the post Google Phrase-Based Indexing Updated. Google tells us about how they are indexing related phrases in an inverted index (like the term-based inverted index from #2) in the patent Index server architecture using tiered and sharded phrase posting lists

4. Keywords in Main Headings, Lists, and Titles

Semantic closeness illustrated

I wrote the post Google Defines Semantic Closeness as a Ranking Signal after reading the patent, Document ranking based on semantic distance between terms in a document. The Abstract of this patent tells us that:

Techniques are disclosed that locate implicitly defined semantic structures in a document, such as, for example, implicitly defined lists in an HTML document. The semantic structures can be used in the calculation of distance values between terms in the documents. The distance values may be used, for example, in the generation of ranking scores that indicate a relevance level of the document to a search query.

If a list in page has a heading on it, the items in that list are all considered to be equal distance away from the list. The words contained under a main heading on a page are all considered to be equal distance away from that main heading. All of the words on a page are considered to be equal distance away from the title to that page. So, a page that is titled “Ford” which has the word “motors” on that page is considered to be relevant for the phrase “Ford Motors.” Here is an example of how that semantic closeness works with a heading and a list:

5. Page Speed

Google has announced repeatedly that they consider Page Speed to be a ranking signal, including in the Google Blog post: Using site speed in web search ranking, and also in a patent that I wrote about in the post, Google’s Patent on Site Speed as a Ranking Signal.

The patent assigned to Google about Page Speed is Using resource load times in ranking search results. The patent tells us that this load time signal may be based upon measures of how long it takes a page to load on a range of devices:

The load time of an online resource can be based on a statistical measure of a sample of load times for a number of different types of devices that the page or resource might be viewed upon.

6. Watch Times for a page

While it may appear to be based upon videos, there is a Google Patent that tells us that it may rank pages higher if they are watched for longer periods of time than other pages. The post I wrote about this patent on is: Google Watch Times Algorithm For Rankings?, and the patent it is about is, Watch time based ranking.

A page may contain video or images or audio, and a watch time for those may make a difference too. Here’s a screenshot from the patent showing some examples:

Watch Time for a Page

7. Context Terms on a Page

I wrote the post Google Patents Context Vectors to Improve Search, about the patent User-context-based search engine.

The patent tells us that it may look at words that have more than one meaning in knowledge bases (such as bank, which could mean a building money is stored in, or the ground on one side of a river, or what a plane does when it turns in the air.) The search engine may take terms from that knowledge base that show what meaning was intended and collect them at “Context Terms” and it might look for those context terms when indexing pages those words are on, so that it indexes the correct meaning

8. Language Models Using Ngrams

Google may give pages quality scores based upon language models created from those pages when it looks at the ngrams on the pages of a site. This is similar to the Google Book Ngram Viewer.

I wrote about this in the post Using Ngram Phrase Models to Generate Site Quality Scores based upon the patent Predicting site quality

The closer the quality score for a page is to a high-quality page from a training set, the higher the page may rank.

9. Gibberish Content

This may sound a little like #8 above. Google may use ngrams to tell if the words on a page are gibberish, and reduce the ranking of a page. I wrote about this in a post titled, Google Scoring Gibberish Content to Demote Pages in Rankings?, about the patent Identifying gibberish content in resources.

Here is an ngram analysis using a well-known phrase, with 5 words in it:

The quick brown fox jumps
quick brown fox jumps over
brown fox jumps over the
fox jumps over the lazy
jumps over the lazy dog

Ngrams from a complete page might be collected like that, and from a collection of good pages and bad pages, to build language models (and Google has done that with a lot of books, as we see from the Google Ngram Viewer covering a very large collection of books.) It would be possible to tell which pages are gibberish from such a set of language models. This Gibberish content patent also mentions a keyword stuffing score that it would try to identify.

10. Authoritative Results
In the post Authoritative Search Results in Google Searches?, I wrote about the patent Obtaining authoritative search results, which tells us that Google might look at the results of a search, and if none of the Pages in the SERPs that appear are authoritative enough, it might search upon one of the query refinements that are listed with those results to see if they return any authoritative results.

If they do, the authoritative results may be merged into the original results. The way it describes authoritative results:

In general, an authoritative site is a site that the search system has determined to include particularly trusted, accurate, or reliable content. The search system can distinguish authoritative sites from low-quality sites that include resources with shallow content or that frequently include spam advertisements. Whether the search system considers a site to be authoritative will typically be query-dependent. For example, the search system can consider the site for the Centers for Disease Control, “cdc.gov,” to be an authoritative site for the query “cdc mosquito stop bites,” but may not consider the same site to be authoritative for the query “restaurant recommendations”. A search result that identifies a resource on a site that is authoritative for the query may be referred to as an authoritative search result.

11. How Well Databases Answers Match Queries

This patent doesn’t seem to have been implemented yet. But it might, and is worth thinking about. I wrote the post How Google May Rank Websites Based Upon Their Databases Answering Queries, based upon the patent Resource identification from organic and structured content. It tells us that Google might look at searches on a site, and how a site might answer them, to see if they are similar to the queries that Google receives from searchers. If they are, it might rank results from those sites higher. The patent also shows us that it might include the database results from such sites within Google Search results. If you start seeing that happening, you will know that Google decided to implement this patent. Here is the screenshot from the patent:

12. Suspicious Activity to Increase Rankings

Another time that Google publicly stated that “just because we have a patent doesn’t mean we use it, came shortly after I wrote about a patent in a post I called The Google Rank-Modifying Spammers Patent based upon the patent Ranking documents. It tells us about a transition rank that Google may assign to a site where they see activity that might be suspicious, such as keyword stuffing. Instead of improving the ranks of pages, they might decrease them, or rerank them randomly. The motivation behind it appears to be to have those people making changes to do more drastic things. The patent tells us:

Implementations consistent with the principles of the invention may rank documents based on a rank transition function. The ranking based on the rank transition function may be used to identify documents that are subjected to rank-modifying spamming. The rank transition may provide confusing indications of the impact on rank in response to rank-modifying spamming activities. Implementations consistent with the principles of the invention may also observe spammers’ reactions to rank changes to identify documents that are actively being manipulated.

13. Popularity Scores for Events
Might Google rank pages about events higher based upon how popular it might perceive that event to be? I wrote the post Ranking Events in Google Search Results about the patent Ranking events which told us about popularity of an event being something that would make a difference. The following Screenshot from the patent shows some of the signals that go into determining a popularity score for an event:

Some patents provide a list of the “Advantages” of following a process in the patent, as does this one:

The following advantages are described by the patent in following the approach it describes.

1) Events in a given location can be ranked so that popular or interesting events can be easily identified.
2) The ranking can be adjusted to ensure that highly-ranked events are diverse and different from one another.
3) Events matching a variety of event criteria can be ranked so that popular or interesting events can be easily identified.
4) The ranking can be provided to other systems or services that can use the ranking to enhance the user experience. For example, a search engine can use the ranking to identify the most popular events that are relevant to a received search query and present the most popular events to the user in response to the received query.
5) A recommendation engine can use the ranking to provide information identifying popular or interesting events to users that match the users’ interests.

14.The Amount of Weight from a Link is Based upon the Probability that someone might click upon it

I came across an update to the reasonable surfer patent, which focused more upon anchor text used in links than the earlier version of the patent, and told us that the amount of weight (PageRank) that might pass through a link was based upon the likelihood that someone might click upon that link.

The post is Google’s Reasonable Surfer Patent Updated based upon this patent Ranking documents based on user behavior and/or feature data. Since this is a continuation patent, it is worth looking at the claims in the patent to see what they say it is about. They do mention how ranking is affected, including the impact of anchor text and words before and after a link.

identifying: context relating to one or more words before or after the links, words in anchor text associated with the links, and a quantity of the words in the anchor text, the weight being determined based on whether the particular feature data corresponds to the stored feature data associated with the one or more links or whether the particular feature data corresponds to the stored feature data associated with the one or more other links, the rank being generated based on the weight; identifying, by the one or more devices, documents associated with a search query, the documents, associated with the search query, including the particular document; and providing, by the one or more devices, information associated with the particular document based on: the search query, and the generated rank.

15. Biometric Parameters while Viewing Results

This patent was one that I wondered about whether or not Google would implement, and suspect that many people would be upset if they did. I wrote about it in Satisfaction a Future Ranking Signal in Google Search Results?, based upon Ranking Query Results Using Biometric Parameters. Google may watch through a smart phone’s reverse camera to see the reaction of someone looking at results in response to a query, and if they appear to be unsatisfied with the results, those results may be demoted in future search results.

how satisfaction might be used with Search Results Pages

16. Click-Throughs

We’ve been told by Google Spokespeople that click-throughs are too noisy to use as a ranking signal, and yet a patent came out which describes how they might be used in such a way. With some thresholds, like clicks not counting until after the first 100, or a certain amount of time passes. The post I wrote about it in was Google Patents Click-Through Feedback on Search Results to Improve Rankings, based upon Modifying search result ranking based on a temporal element of user feedback

Rand Fishkin sent me a message saying that his experience has been that clicks were counting as ranking signals, but he was also seeing thresholds of around 500 clicks before clicks would make a difference. It’s difficult to tell with some signals, especially when Google makes statements about them not being signals in use.

Rand's tweet in response to my post, about his experiment.
Rand’s tweet in response to my post, about his experiment.

And Rand responded about what I said in the post about thresholds as well:

Threshold on click rates tweet.

17. Site Quality Scores

If you search for “seobythesea named entities” it is a signal that you have an expectation that you can find information about named entities on the site seobythesea.com.

If you do a site operator search such as “site:http://www.seobythesea.com named entities” you again are showing that you expect to be able to find information about a particular topic on this site. These are considered queries that refer to a particular site.

They are counted against queries that are considered to be associated with a particular site. So, if there are more referring queries than associated queries, the quality score for a site is higher.

If there are less referring queries than associated queries, then the quality score is lower. The post I wrote about this was How Google May Calculate Site Quality Scores (from Navneet Panda) based upon the patent Site quality score. A lower site quality score can mean a lower rank, as the patent tells us:

The site quality score for a site can be used as a signal to rank resources, or to rank search results that identify resources, that are found in one site relative to resources found in another site.

18. Disambiguating People

Like the patent about covering terms with more than one meaning by including context terms on their pages, when you write about people who may share a name with someone else, if they are also on sites such as Wikipedia, and disambiguated entries, make sure you include context terms on your page that makes it easier to tell which person you are write about.

The post I covered this in was Google Shows Us Context is King When Indexing People, based upon the patent Name disambiguation using context terms

19. Effectiveness and Affinity

If you search for something on a phone such as a song, and you have a music app on that phone that has that song upon it, Google may tell you what the song you are searching for is, and that you can access it on the app that you have loaded on your phone.

Social network affinities seem to be related to this. If you ask a question that might involve someone whom you might be connected to on a social network, they might be pointed out to you. See Effectiveness and Affinity as Search Ranking Signals (Better Search Experiences) about Ranking search results.

20. Quotes

Google seems to know who said what and has a patent on it.

See Google Searching Quotes of Entities on the patent Systems and methods for searching quotes of entities using a database.

21. Category Duration Visits

Could visits to specific Categories of a site have a positive effect on the rankings of those visited sites? We know that people from Google have said that use behavior signals like this tend to be noisy; but what are you to think when the patent I was writing about describes ways to reduce noise from such signals?

The post is A Panda Patent on Website and Category Visit Durations, and it is about a patent co-authored by Navneet Panda titled Website duration performance based on category durations.

22.Repeat Clicks and Visit Durations

I want to believe when Google Spokespeople say that Google doesn’t use click data to rank pages, but I keep on seeing patents from Navneet Panda that Google’s Panda Update was named after which describes user behavior that may have an impact.

The post is Click a Panda: High Quality Search Results based on Repeat Clicks and Visit Duration, and the patent it is about is one called Ranking search results

23 Environmental Information

Google can listen to a television playing, and respond to a question such as “Who is starring in this movie I am watching?

I wrote about it in Google to Use Environmental Information in Queries, and the post is based upon the patent
Answering questions using environmental context

24. Traffic Producing Links

Google might attempt to estimate how much traffic links to a site might bring to that site. If it believes that the links aren’t bringing much traffic, it may discount the value of those links.

I wrote about this in the post Did the Groundhog Update Just Take Place at Google?
It is about the patent Determining a quality measure for a resource

25. Freshness
New Google Freshness-Based Ranking Patent
Freshness based ranking

26. Media Consumption History

Google Media Consumption History Patent Filed
Query Response Using Media Consumption History

27. Geographic Coordinates

A patent called Determining geographic locations for place names in a fact repository was updated in a continuation patent, which I wrote about in Google Changes How they Understand Place Names in a Knowledge Graph. The claims from the patent were updated to include many mentions of “Geographic Coordinates” which indicated that including Latitude and Longitude information in Schema for a site might not be a bad idea. It’s impossible to say, based upon the patent that they use those signals in ordinary websites that aren’t knowledge base sites like a Wikipedia or an IMDB or Yahoo Finance. But it seemed very reasonable to believe that if they were hoping to see information in that form in those places that it wouldn’t hurt on Websites that were concerned about their locations as well (especially since knowledge bases seem to be the source of facts for many sites in places such as knowledge panels.)

28. Low Quality

How Google May Classify Sites as Low Quality Sites
Classifying sites as low quality sites

29. Television Watching

Google Granted Patent on Using What You Watch on TV as a Ranking Signal
System and method for enhancing user search results by determining a television program currently being displayed in proximity to an electronic device

30. Quality Rankings
How Google May Rank Web Sites Based on Quality Ratings
Website quality signal generation

Semantic Search

31. Searches using Structured Data

Google recently published a patent which showed how Structured data in the form of JSON-LD might be used on a page, and might cause Google to search for values of attributes of entities described in that structured data, such as what book was published by a certain author during a specific time period. The patent explained how Google could search through the structured data to find answers to a query like that. My post is Google Patent on Structured Data Focuses upon JSON-LD, and the patent it covers is <a href="http://patft.uspto.gov/netacgi/nph-Parser?Sect1=PTO1&Sect2=HITOFF&d=PALL&p=1&u=%2Fnetahtml%2FPTO%2Fsrchnum.htm&r=1&f=G&l=50&s1=9,754,048.PN.&OS=PN/9,754,048&RS=PN/9,754,048"Storing semi-structured data.

32. Related Entities

A search for an entity with a property or attribute that may not be the most noteworthy, but may be known may be findable in search results. In a post about this, I used and example query about “Where was George Washington a Surveyor?” since he is most well know for having been President. The post is Related Entity Scores in Knowledge Based Searches, based on the patent Providing search results based on sorted properties.

33.Nearby Locations

How Google May Interpret Queries Based on Locations and Entities (Tested)
Interpreting User Queries Based on Nearby Locations

34 Attributes of Entities
How Knowledge Base Entities can be Used in Searches
Identifying entities using search results

35. Natural Language Search Results
Direct Answers – Natural Language Search Results for Intent Queries
Natural Language Search Results for Intent Queries

Local Search

36. Travel Time for Local Results

How far someone may be will to travel to a place may be a reason why Google might increase the ranking of a business in local search results. I wrote about this in the post Ranking Local Businesses Based Upon Quality Measures including Travel Time based upon the patent Determining the quality of locations based on travel time investment.

Would you drive an hour away for a slice of pizza? If so, it must be pretty good pizza. The abstract from the patent tells us this:

…the quality measure of a given location may be determined based on the time investment a user is willing to make to visit the given location. For example, the time investment for a given location may be based on comparison of one or more actual distance values to reach the given location to one or more anticipated distance values to reach the given location.

37. Reverse Engineering of Spam Detection in Local Results

In the post How Google May Respond to Reverse Engineering of Spam Detection, I wrote about the patent Reverse engineering circumvention of spam detection algorithms. I remembered how Google responded when people brought up the Google Rank-Modifying Spammers Patent, that I wrote about in #13, telling people that just because they had a patent doesn’t mean they necessarily use it. This patent is slightly different from the Rank modifying spammer’s patent, in that it only applies to local search, and it may keep a spamming sight from appearing at all, or appearing if continued activity keeps on setting off flags. As the patent abstract tells us:

A spam score is assigned to a business listing when the listing is received at a search entity. A noise function is added to the spam score such that the spam score is varied. In the event that the spam score is greater than a first threshold, the listing is identified as fraudulent and the listing is not included in (or is removed from) the group of searchable business listings. In the event that the spam score is greater than a second threshold that is less than the first threshold, the listing may be flagged for inspection. The addition of the noise to the spam scores prevents potential spammers from reverse engineering the spam detecting algorithm such that more listings that are submitted to the search entity may be identified as fraudulent and not included in the group of searchable listings.

38. Surprisingness in Business Names in Local Search

Another patent that is about spam in local search is one I wrote about in the post Google Fights Keyword Stuffed Business Names Using a Surprisingnesss Value written about the patent Systems and methods of detecting keyword-stuffed business titles.

This patent targets keyword stuffed business names that include prominent business names to try to confuse the search engine. Examples include such names as “Locksmith restaurant,” and “Courtyard 422 Y st Marriott.”

39. Local Expert Reviews

I’ve been hearing people suggest that reviews can help a local search rank higher, and I have seen reviews considered equivalent to a mention in the Google patent on Location Prominence. But, I’ve now also seen a Google patent which tells us that a review from a local expert might also increase the rankings of a local entity in local results. My post was At Google Local Expert Reviews May Boost Local Search Results on the patent Identifying local experts for local search

40. Similar Local Entities

When you search for a local coffeehouse, Google may decide that it wants to show you similar local businesses, and may include some other coffee houses or other similar results in what you see also. I wrote a post on this called How Google May Determine Similar Local Entities, from the patent Detection of related local entities.

41. Distance from Mobile Location History

Google to Use Distance from Mobile Location History for Ranking in Local Search
Ranking Nearby Destinations Based on Visit Likelihood and Predicting Future Visits to Places From Location History

4239. What People Search for at Locations Searched

Search for a place that you might visit, and the query refinements that you might see may be based upon what people at that location you are considering visiting may have searched for. This doesn’t affect the rankings of the results you see, but instead the query refinements that you are shown. See Local Query Suggestions Based Upon Where People Search based on Local query suggestions.

42. Semantic Geotokens

Better Organic Search Results at Google Involving Geographic Location Queries
Semantic geotokens

Voice Search

44. Stressed Words in spoken queries

This may not be something you can optimize a page for, but it does show that Google is paying attention to voice search and where that might take us. In the post Google and Spoken Queries: Understanding Stressed Pronouns based upon the patent Resolving pronoun ambiguity in voice queries, we see that Google may be listening for our voices to emphasize certain words when we ask for something. Here is an example from the patent:

A voice query asks: “Who was Alexander Graham Bell’s father?”
The answer: “Alexander Melville Bell”
A followup voice query: “What is HIS birthday?”
The answer to the followup query: “Alexander Melville Bell’s birthday is 3/1/1819”

News Search

45. Originality in News Search

Originality Replaces Geography as Ranking Signal in Google News
Methods and apparatus for ranking documents


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10 Websites to Check Google Keyword Ranking

If you are reading this article, you already know the importance of checking Google keyword rankings and how keywords are performing on the most popular search engine. It is mandatory to find out which keyword is performing well on Google to create a strategy to improve your keyword rankings. Keyword tracking not only boosts your…
Source: https://dailyseoblog.com/websites-to-check-google-keyword-ranking/#utm_source=rss&utm_medium=rss

5 Trends In Digital Marketing That Are Sure To Impact The Industry In 2019 & Beyond… https://t.co/ytoTl4Hhhp

https://platform.twitter.com/widgets.js

from Twitter https://twitter.com/WebDesign499

Source: https://webdesign499com.wordpress.com/2018/06/19/5-trends-in-digital-marketing-that-are-sure-to-impact-the-industry-in-2019-beyond-https-t-co-ytotl4hhhp/