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    TikTok abandons plans to bring shopping to the U.S.

    Written on July 5, 2022 at 8:31 pm, by admin

    While popular in Asia, TikTok has abandoned its plans to bring shopping into the U.S. after its failed launch in the UK. The company had also planned to expand into mainland Europe, but those plans have also been scrapped.  

    What happened in the UK. Several team members in the UK working on the launch were either demoted after taking leave, were sick with stress or were removed from client projects altogether. The Financial Times reports:

    TikTok has abandoned plans to expand its live e-commerce initiative in Europe and the US, after the social media platform’s foray into QVC-style shopping in the UK was hit by internal problems and struggled to gain traction with consumers […] 

    But the expansion plans have been dropped after the UK project failed to meet targets and influencers dropped out of the scheme, three people said.

    Many TikTok Shop livestreams have achieved poor sales despite the company offering subsidies and cash incentives to encourage brands and influencers to sell through the app.

    Cultural differences. TikTok and the UK have had their share of culture clashes. Last month it was reported that there were complaints about an “aggressive corporate ethos that runs counter to typical working practices in the UK.”

    Joshua Ma, a senior ByteDance executive who ran TikTok Shop in Europe, was replaced, as the company investigated comments made to London-based staff that he “didn’t believe” in maternity leave.

    At least 20 members of the London ecommerce team — around half of all its original staff — have left since TikTok Shop’s launch, while others say they are on the brink of quitting. Two employees have been paid settlements over working conditions […]

    Ecommerce team members in London said they were expected to frequently work more than 12 hours a day, starting early to accommodate calls with China and ending late as livestreams were more successful in the evening, with “feedback reports” to be filed immediately after.

    Convenient timing. This news comes just a week after it was reported that the FCC in the US asked Apple and Google to remove TikTok from their app stores after it was leaked that the app was accessing U.S. user data. Since then, TikTok has allegedly committed to making changes. Could the decision to abandon bringing shopping into the U.S. is at least partially related to privacy and data concerns?

    Why we care. Advertisers and ecommerce business owners who were looking forward to promoting their stores using the new shopping feature will need to restrategize. But given the trouble TikTok has been in lately, it wouldn’t be a surprise if more advertisers started moving away from the platform. We’re not sure what big moves TikTok’s parent company ByteDance will be making in response to these developments, but we’ll be watching.

    The post TikTok abandons plans to bring shopping to the U.S. appeared first on Search Engine Land.

    Courtesy of Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing




    Why you should invest in branded keywords even if your company ranks #1 on Google

    Written on July 5, 2022 at 8:31 pm, by admin

    Search marketing is generally seen as a core tactic within digital marketing programs.

    Capturing the intent of users actively searching your products or solutions is a no-brainer, right?

    What seems like a simple question, can often be complicated.

    Whether it’s due to budget-constrained environments or the brand having a stellar SEO team, I’ve fielded the same question for nearly every brand/client I’ve worked with:

    “Why should I pay for brand search clicks, if our organic search listing is right at the top of the page?”

    To be fair to those non-PPC stakeholders who may be reading this article, it is a valid question to ask. ]

    Logically, I understand why it is appealing to capture free clicks and focus PPC budgets on tactics that will pull in new customers; however, there is a 1+1=3 effect to SEO and PPC results showing in conjunction. 

    Let’s look at some of the top reasons why brands should remain invested in their brand keywords. 

    1. SERP authority

    Search engines are a part of daily life. Billions rely on search engines to answer information gaps in real-time, every day. They are a brilliant resource that only increases in terms of adoption and value.

    Given how frequently we use search to solve problems, our brains naturally establish a correlation between the position of a search result and its relevance to our original query. 

    While a bit dated, Microsoft and Google have published studies confirming this notion.

    If you still aren’t convinced, just look Google’s and Microsoft’s recent earnings – clearly, advertisers perceive this placement as valuable.

    Search ads sit above the organic listings in premium real estate – directly below the search bar. Search ads are typically the first thing a user sees after initiating a search. 

    Not only does your ad box out competition from this prime real estate – which lowers the effectiveness of their competing offers – but in addition you have freedom to control the message users are seeing.

    While site content can be optimized for search results, PPC provides full autonomy over the presentation of your brand’s message.

    The impact of brand SERP management increases as the size of our screens shrinks. 

    Mobile searches continue to rise across nearly all categories of searches. Controlling the top result of a mobile brand search is critical, as the searcher’s attention is concentrated on the limited listings that make it above the fold.

    Combine your search ad with the top organic listing (due to your team’s great SEO work) and you are now controlling the top of the SERP and with it, the prospect’s (nearly) full attention. 

    2. Auction dynamics

    This is the most straightforward rationale for brand search keywords. Simply put, you’re getting much more bang for your buck with brand investment. 

    Take a step back and think about the purpose of search engines. They try to provide users with the optimal experience, meaning the most relevant results for a query. If you’ve run competitor keywords before, you’ve likely seen this in action. 

    Engines charge a surplus for clicks on competitor terms, based on the lower relevance of your ad and landing page. In my experience, competitor clicks have ranged from 5-25x more expensive than brand clicks. 

    Consider the SERP authority previously discussed and the economies of search engine auctions. Even if your brand budget is reallocated towards generic or competitor terms, it is possible (if not likely) that you will see an aggregate decrease in site visits. 

    Such approaches lean exclusively on SEO for brand visit volume, while reducing the effectiveness of your PPC budget.

    Even if SEO captured 100% of the visits previously driven by PPC ads, the incrementality of reinvesting in nonbrand/competitor terms is unlikely to move the needle, due to the relative inefficiency of nonbrand/competitor investments. 

    To illustrate this, I’ve created a realistic simulation below of how redistribution of brand investment into nonbrand campaigns could actually yield lower total traffic to the site, due to the relative inefficiency of nonbrand volume.

    In this scenario, the $1,000 that had previously been allocated to brand campaigns is now shifted to nonbrand. Despite SEO results capturing 95% of all brand traffic, we still see about even total traffic month over month, as we netted 800 fewer PPC clicks by shifting from brand to nonbrand. 

    Setting up clean measurement for your own business is critical, should you find your brand investment threatened.

    Work with your analytics teams so that you show how any shifts in investment are impacting total traffic to the site. 

    3. Meet the customer where they search

    Though digital marketing has evolved in terms of the complexity of tactics, it’s important to remember why search marketing is so effective.

    Folks searching for your brands are seeking out additional information about your brand. They are telling you what they are interested in learning more about.

    We don’t need to identify those who are close to purchase, as they are revealing that via their chosen queries. 

    Search is often referred to as a pull channel because we do not need to push ads in front of users. This is an opportunity to meet those expressing interest in your brand with a catered message. 

    Using an offline analogy: it’s similar to someone approaching the front door of your store, but you don’t have the lights on and have the blinds down.

    Never take your most engaged audiences for granted. Open the store up and show them what you believe is the best offer to get them in the door. 

    4. Messaging autonomy

    It’s been mentioned in a couple of the aforementioned arguments, but the value of catering your message for users who are interested in your brand is far greater than the cost of the click.

    In order to establish SERP authority or convert those prospects interested in your brand, you need a catered message that shows prominently at the top of the page. 

    I’ve been lucky to work with some incredible SEO professionals and it is fascinating what they do to influence a site’s performance; however, there is an element of certainty with PPC that cannot be replicated.

    We all tune into this blog on a recurring basis, as we are aware there are variables on search engines that are out of our control, which can have a meaningful impact on site performance.

    PPC provides an opportunity to customize the message, based on known attributes of the user. Whether its an early interaction in which you want to establish your brand’s credibility or someone who has been to your site many times, PPC provides you the flexibility and autonomy to control how your brand is perceived on some of the most frequently visited sites on the internet. 

    Brand search is a critical tactic that advertisers need to maintain support for at all costs.

    There are few other places on the internet where prospects are actively seeking out more information regarding your business.

    If in fact you are being asked a similar question, here are some tips for how to manage that conversation:

    The post Why you should invest in branded keywords even if your company ranks #1 on Google appeared first on Search Engine Land.

    Courtesy of Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing




    Top 5 secrets your search bar is hiding

    Written on July 5, 2022 at 8:31 pm, by admin

    Your search bar is your secret weapon. How can you help improve the site search experience when someone has landed on your site? What can you do to ensure that you’re meeting their needs while on your site?

    Once you segment the types of visitors on your site – informational, search intentional, navigational and transactional – you’ll know what they want to get out of your site and improve their search experience. You’ll also get better insights into what you can accomplish with those individual visitors.

    Join Jonathan Meyer, senior solutions engineer at Hawksearch by Bridgeline, as he reveals how to optimize your site and transform searches into sales in his informative SMX Advanced session. By breaking down the four types of search personas, you’ll better understand how to leverage your website’s architecture to serve your ideal client best while increasing your chances for brand loyalty. 

    After this presentation, you’ll be able to break down your site into the key elements to drive conversion, direct traffic to precisely the right product for purchase and identify new opportunities to raise the value of every shopping cart.

    Watch the session and learn how to improve customer trust by having resources available to deliver what your visitor is looking for. 

    The post Top 5 secrets your search bar is hiding appeared first on Search Engine Land.

    Courtesy of Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing




    How to use Google Ads Competitive Metrics

    Written on July 1, 2022 at 8:10 am, by admin

    If you’ve been running Google Ads for any amount of time, you’re probably aware that there’s generally a competitive aspect to any campaign that makes you want to monitor your vanity metrics around where you rank.

    In one of my former jobs, a senior leader would walk by every day and ask who ranked number one that day, us or the competition.

    The question wasn’t are we lowering our cost per acquisition or improving the overall metrics of the account, it was pure vanity and the desire to “win.”

    While “winning” in this way isn’t actually meaningful, it is important to pick your head up out of the sand from time to time and understand how your campaigns are performing vs. the competition. This helps you know who your actual competition is and how aggressive they are.

    These data points can be used to understand fluctuations in your own data. If your click-through rate is down, but you really haven’t changed anything, that doesn’t mean your competition isn’t on the move. Google recognized the importance of these data points and has been rolling out more competitive metrics into the Google Ads platform. 

    In the reporting section of Google Ads, you will find these metrics under “Competitive Metrics” and “Auction Insights” from the metrics and dimensions menu list. Underneath these two areas are some really great tools for understanding how you stack up to your competition. Here are a few of my favorites. 

    Search Outranking Share

    This data will let you know what specific domains are outranking your ads and how often. You can use this data at all levels (account, campaign, ad group, and keyword).

    We find this particularly helpful when there is a direct brand selling with various affiliates. Since seeing all the various competitors is difficult given a host of factors, it can also be helpful to understand and identify competitors that were not on your radar. 

    Search Overlap Rate and Position Above Rate

    A couple of other similar, but important metrics available are Search Overlap Rate and Position Above Rate. 

    Search Overlap Rate helps you understand how often a brand or domain also appears with you. This will give you a better understanding of the options your consumers have on the search results page.

    The Position Above Rate metric provides insights into how often that brands ad appears above your own ad when they are both shown together. The last part is really important.

    The data is not just telling you how often that brands ad has a higher rank than your average rank, but specifically when you are bidding against one another for the same customer search result.

    Why is this important?

    Because it helps give some insight into direct competition.

    In the below data Listingbaby.com outranks the clients ads almost 90% of the time.

    The action I would take based on this would be to understand their ad copy and landing page experience to evaluate the search experience. Looking for potential variances that would impact quality score. 

    Under the competitive metrics there are also a lot of valuable data points. The data points we use the most are around Impression Share.

    Understanding impression share helps give your brand insight into the total market opportunity.

    Impression share is the percentage of total impressions that your ad was displayed vs. what was possible.

    For example, if there are 100 searches for “running shoes” and your ad showed for 50 of those your impression share would be 50%. 

    The next piece that Google provides insight into is why your ad did not show for the other potential search results. They break it down into two buckets:

    These are really the two key levers to your ad being shown more. We use this all the time to help customers understand, “How high is up?” “What is the total market opportunity?” “How much could we spend if we exceed our ROI targets?” 

    For your keyword strategy the exact match impression share metric can help you understand how well aligned your keywords are to what consumers are searching for.

    Having exact match keywords will give you a better ability to control bids and the entire experience. It’s certainly not possible to have this be 100%, but monitoring this metric gives you a sense of how closely aligned you are and when your customers might be altering what they search for or how Google is changing the algorithm.  

    Click share is another important competitive metric that is provided. “Click share” is the clicks you’ve received on the Search Network divided by the estimated maximum number of clicks that you could have received.

    These impression share metrics are available for both display and search campaigns.

    They are also available for absolute top impressions (true #1 ranking). Reminder, it is a vanity trap to just chase the absolute number one position, unless you are crushing your KPIs, then it’s game on.

    These metrics can be used in the following various areas of your business.

    These data points put campaigns into context

    These data points help advertisers understand what volume is available and who you competing against.

    Using this data can help inform a variety of business units beyond just search marketing.

    Use the data wisely, keep your ego in check and go out and win! 

    The post How to use Google Ads Competitive Metrics appeared first on Search Engine Land.

    Courtesy of Search Engine Land: News & Info About SEO, PPC, SEM, Search Engines & Search Marketing




    Twitter’s algorithm ranking factors: A definitive guide

    Written on July 1, 2022 at 8:10 am, by admin

    Twitter patents and other publications reveal likely aspects of how tweets become promoted in the timeline feeds of users.

    Some of Twitter’s timeline ranking factors are very surprising, and adjusting your approach to Tweeting may help you to gain greater visibility of your Tweets.

    Based upon a number of key patents and other sources, I have outlined a number of probable ranking factors for Twitter’s algorithm herein.

    The Twitter timeline

    Twitter first began using an algorithm-based timeline back in 2016 when it switched from what was purely a chronological feed of Tweets from all the accounts one followed. The change ranked users’ timelines to allow them to see “the best Tweets first.” Twitter has since experimented with variations of this up to the present.

    A feed-based algorithm for social media is not unusual. Facebook and other social media platforms have done the same. 

    The reasons for this change to an algorithmic mix of timeline Tweets are pretty clear. A purely personal, chronological timeline composed of only the accounts one has followed is very siloed and therefore limited – while introducing posts from accounts beyond one’s direct connections has the potential to increase the time one spends on the platform, which in turn increases overall stickiness, which in turn increases the worth of the service to advertisers and data partners.

    Various interest classifications of users and interest topics associated with their accounts and tweets further enables potential for advertisement targeting based upon user demographics and content topics.

    Twitter power users may have developed some intuitions about various Tweet factors that can result in greater visibility within the algorithm.

    A reminder about patents

    Corporations register patents all the time for inventions that they do not actually use in live service. When I worked at Verizon, I personally wrote a number of patent drafts for various inventions that my colleagues and I developed in the course of our work – including things that we did not end up using in production.

    So, the fact that Twitter has patents that mention ideas for how things could work does not at all guarantee that that is how things do work.

    Also, patents typically contain multiple embodiments, which are essentially various ways in which an invention could be implemented – patents attempt to describe the key elements of an invention as broadly as possible in order to claim any possible use that could be attributed to it.

    Finally, just as with the famous PageRank algorithm patent that was the foundation of Google’s search engine, in instances where Twitter has used an embodiment from one of their patents, it is highly likely that they have changed and refined the simple, broad inventions described, and will continue to do so.

    Even despite all this typical vagueness and uncertainty, I found a number of very interesting concepts in the Twitter patent descriptions, many of which are highly likely to be incorporated within their system.

    Twitter and Deep Learning

    One additional caveat before I proceed involves how Twitter’s timeline algorithm has incorporated Deep Learning into its DNA, coupled with various levels of human supervision, making it a frequently, if not constantly, self-evolving beast.

    This means that both large changes and small, incremental changes, can and will be occurring in how it performs content ranking. Further, this machine learning approach can lead to conditions where Twitter’s own human engineers may not directly know precisely why some content is displayed or outranks other content due to the abstraction of ranking models produced, similar to what I described when writing about models produced by Google’s quality ranking through machine learning.

    Despite the complexity and sophistication of how Twitter’s algorithm is functioning, understanding the factors that likely go into the black box can still reveal what influences rankings.

    Twitter’s original timeline was simply composed of all the Tweets from the accounts one has followed since one’s last visit, which were collected and displayed in reverse-chronological order with the most recent Tweets shown first, and each earlier Tweet shown one after another as one scrolled downward. 

    The current algorithm is still largely composed of that same reverse-chronological listing of Tweets, but Twitter performs a re-ranking to try to display the most-interesting Tweets first and foremost out of recent Tweets.

    In the background, the Tweets have been assigned a ranking score by a relevance model that predicts how interesting each Tweet is likely to be to you, and this score value dictates the ranking order.

    The Tweets with highest scores are shown first in your timeline list, with the remainder of most-recent Tweets shown further down. It is notable that interspersed in your timeline are now also Tweets from accounts you are not following, as well as a few advertisement Tweets. 

    Twitter’s connection graph

    First of all, one of the most influential aspects of the Twitter timeline is how Twitter is now displaying Tweets based upon not only your direct connections at this point, but essentially what is your unique social graph, which Twitter refers to in patents as a “connection graph”.

    The connection graph represents accounts as nodes and relationships as lines (“edges”) connecting one or more nodes. A relationship may refer to associations between Twitter accounts.

    For example, following, subscribing (such as via Twitter’s Super Follows program or, potentially, for Twitter’s announced subscription feature for keyword queries), liking, tagging, etc. – all of these create relationships. 

    Relationships in one’s connection graph may be unidirectional (e.g., I follow you) or bidirectional (e.g., we both follow each other). If I follow you, but you do not follow me, I would have a greater expectation of seeing your Tweets and Retweets appearing in my timeline, but you would not necessarily expect to see mine.

    Simply based on the connection graph, you are likely to see Tweets and Retweets from those you have followed, as well as Tweets your connections have Liked or Replied to.

    The Twitter algorithm has expanded Tweets you may see beyond those accounts that you have directly interacted-with. The Tweets you may see in your timeline now also include Tweets from others who are posting about topics you have followed, Tweets similar in some ways to Tweets you have previously Liked, and Tweets based on topics that the algorithm predicts you might like.

    Even among these expanded types of Tweets you may get, the algorithm’s ranking system applies – you are not receiving all Tweets matching your topics, likes, and predicted interests – you are receiving a list curated through Twitter’s algorithm.

    Interestingness ranking

    Within the DNA of a number of Twitter’s patents and algorithm for ranking Tweets is the concept of “interestingness.”

    This was quite likely inspired by a patent granted to Yahoo In 2006 called “Interestingness ranking of media objects”, which described the ranking methods used in the algorithm for Flickr (the dominant social media photo-sharing service that has been subsequently eclipsed by Instagram and Pinterest).

    That earlier algorithm for Flickr bears a great many similarities to Twitter’s contemporary patents. It used similar and even identical factors for computing interestingness. These included:

    One could easily describe Twitter’s algorithm as taking the Flickr interestingness algorithm, expanding upon some of the factors involved, computing it through a more sophisticated machine learning process, interpreting content based upon natural language processing (NLP), and incorporating a number of additional variations to enable rapidity for presentation in near real-time for a gargantuan number of users simultaneously.

    Twitter ranking and spam

    It is also of interest to focus some on methods used by Twitter to detect spam, spam user accounts, and to demote or suppress spam Tweets from view.

    The policing for disinformation, other policy-violating content, and harassment is likewise intense, but that does not necessarily converge as much with ranking evaluations.

    Some of the spam detection patents are interesting because I see users frequently running aground of Twitter’s spam suppression processes quite unintentionally, and there are a number of things one may do that result in sandbagging efforts to promote and interact with Twitter’s audience. Twitter has had to build aggressive watchdog processes to police and remove spam, and even the most prominent users can run afoul of these processes from time to time. 

    Thus, an understanding of Twitter’s spam factors can be important as they can cause one’s Tweets to get deductions from interestingness they would otherwise have, and this loss in the relevancy scores can reduce the visibility and distribution power of your Tweets.

    Twitter ranking factors

    So, what are the factors mentioned in Twitter’s patents for assessing “interest”, and which influence how Twitter scores Tweets for rankings?

    Recency of the Tweet posting

    With more recent being generally much more preferred. Aside from specific keyword and other types of searches, most Tweets would be from the last few hours. Some “in case you missed it” Tweets may also be included, which appear to range primarily over the last day or two.

    Images or Video

    In general, in general, Google and other platforms have indicated that users tend to prefer images and video media more, so a Tweet containing either might get a higher score.

    Twitter specifically cites image and video cards, which refers to websites that have implemented Twitter Cards, which enables Twitter to easily display richer preview snippets when Tweets contain links to webpages with the card markup.

    Tweets with links that show images and video are generally more engaging to users, but there may be an additional advantage for Tweets linking to the pages with the card markup for displaying the card content

    Interactions with the Tweet

    Twitter cites Likes and Retweets, but additional metrics related to the Tweet would also potentially apply here. Interactions include:

    Impressions

    While most impressions come from the display of the Tweet in timelines, some impressions are derived when Tweets are shared through embedding in webpages. It is possible that those impressions numbers might also affect the interestingness score for the Tweet.

    Likelihood of Interactions

    One Twitter patent describes computing a score for a Tweet representing how likely it is that followers of the Tweet’s Author in the social messaging system will interact with the message, the score being based on the computed interaction level deviation between the observed interaction level of Followers of the Author and the expected interaction level of the Followers.

    Length of Tweet

    One type of classification is the length of the text contained in the Tweet, which could be classified as a numerical value (e.g. 103 characters), or it could be designated as one of a few categories (e.g., short, medium, or long).

    According to topics involved with a Tweet, it might be assessed to be more or less interesting – for some topics, short might be more beneficial, and for some other topics, medium or long length might make the Tweet more interesting.

    Previous Author Interactions

    Past interactions with the author of a Tweet will increase the likelihood (and ranking score in one’s timeline) that one will see other Tweets by that same author.

    These social graph interaction metrics can include scoring by the origin of the relationship.

    So, a past history of replying-to, liking, or Retweeting an author’s Tweets, even if one does not follow that account, can increase the likelihood one will see their latest Tweets.

    There is a likelihood that the recent of one’s interactions with a Tweet author may also factor into this, so if you have not interacted with one of their Tweets for a long time, potential visibility of their newer Tweets may decrease for you.

    In the context of the algorithm, “author” and “account” are essentially used to mean the same thing, so Tweets from a corporate account are treated the same as Tweets from an individual.

    Author Credibility Rating

    This score can be calculated by an author’s relationships and interactions with other users.

    The example given in the patent is that an author followed by multiple high profile or prolific accounts would have a high credibility score.

    While one rating value cited is “low”, “medium”, and “high”, the patent also suggests a scale of rating values from 1 to 10, and it can include a qualitative and/or quantitative factor.

    I would guess that a range like 1 to 10 is much more likely. It seems likely that some of the spam assessment values could be used to subtract from an Author Credibility Rating. More on potential spam assessment factors in the latter portion of this article.

    Author Relevancy

    It is possible that authors that are assessed to be more relevant for a particular topic may have a higher Author Relevancy value. Also, mentions of an Author may make them more relevant in the context of the Tweets mentioning them.

    The patents also speak about associating Authors with topics, so it is possible that Authors that Tweet involving specific topics on a frequent basis, along with good engagement rates, may be deemed to have higher relevancy when their Tweets involve that topic.

    Author Metrics

    Tweets may be classified based on properties of the Author. These metrics may influence the relative interestingness of the Author’s messages. Such Author Metrics include:

    Topics

    Tweets get classified according to the topics they involve. There are some very sophisticated algorithms involved in classifying the Tweets.

    Twitter users often have selected topics to be associated with their accounts, and you will obviously be shown popular Tweets from the topics you have selected. But, Twitter also automatically creates topics based off of keywords found in Tweets.

    Based on your interactions with Tweets and the accounts you follow, Twitter is also predicting topics that you would likely be interested in, and showing you some Tweets from those topics despite you not formally subscribing to the topics.

    Phrase Classification

    Twitter’s system is highly complex, and allows custom ranking models to potentially be applied to Tweets for particular topics and when particular phrases are present.

    Twitter has a large staff that works to develop models for particular “customer journeys”, and this would appear to coincide with patent descriptions of how editors could set rules on topic-oriented posts and keywords or phrases in posts.

    For instance, posts containing text about “hiring now” or “will be on TV” might be considered boring for a topic, while phrases like “fresh”, “on sale”, or “today only” might be given greater weight as they could be predicted to be more interesting.

    This could be quite difficult to cater to, as there is a huge field of potential topics and custom weightings that could be applied.

    One recent job posting at Twitter for a Staff Product Designer, Customer Journey described how the position would help:

    “Whether you’re looking for Ariana Grande fanart, #herpetology, or extreme unicycling, it’s all happening on Twitter. Our team is responsible for helping new members navigate the diverse array of public conversations happening on Twitter and quickly find a sense of belonging…”

    “Gather insights from data and qualitative research, develop hypotheses, sketch solutions with prototypes, and test ideas with our research team and in experiments.”

    “Document detailed interaction models and UI specifications.”

    “Experience designing for machine-learning, rich taxonomies, and / or interest graphs.”

    This description sounds very similar to what’s described in Twitter’s patent for “System and method for determining relevance of social content” where:

    “Editors might set rules on classifying certain phrases as more or less interesting…”

    “…an editor may decide that some phrases and attributes are interesting in all content, regardless of the category of place that authors the content. For instance, the phrase ‘on sale’ or ‘event’ may be interesting in all cases and a positive weight may be applied.”

    One patent describes how Tweets detected to have commercial language could be assigned a lower score than Tweets that did not have commercial language. (Contrarily, such weights could be flipped if the user was conducting searches indicating an interest in purchasing something, so that Tweets containing commercial language could be given a higher weight.)

    Time of Day

    Time of day can be used to impact relevancy. For instance, a rule could be implemented to lend more weight to Tweets mentioning “Coffee” between 8:00am to 10:00am, and/or to Tweets posted by coffee shops.

    Locations

    Patents describe how “place references” in Tweets could invoke greater weight for Tweets about a place, and/or to accounts associated with the place reference versus other accounts that merely mention the place. Also geographic proximity between the location of a user’s device and location associated with content items (the Tweet text, image, video, and/or Author) can increase or decrease potential relevancy.

    Language

    Language of the Tweet can be classified (e.g., English, French, etc.).

    The language may be determined automatically using various automated language assessment tools.

    A Tweet in a particular language would be of more interest to speakers of the language and of less interest to others.

    Reply Tweets

    Tweets can be classified based on whether they are replies to previous Tweets. A Tweet that is a reply to a previous Tweet may be deemed less interesting than a Tweet concerning a new topic.

    In one patent description, the topic of a Tweet could determine whether the Tweet will be designated to be displayed to another account or included in other accounts’ message streams.

    When you are viewing your timeline, there are instances where some of a Tweet’s replies are also displayed with the main Tweet – such as when the Reply Tweets are posted by accounts you follow. In most cases, the Reply Tweets will be only viewable when one clicks to view the thread, or click the Tweet to view all the Replies.

    “Blessed” Accounts

    This is an odd concept, that I believe might not be in production.

    Twitter describes Blessed Accounts as being identified within a particular conversation’s graph, where the original Author in a conversation would be deemed “blessed”, and out of the subsequent replies to the original post, any of the Replies that is subsequently replied-to by the blessed account becomes “blessed” as well.

    Those Tweets posted by Blessed Accounts in the conversation would be given increased relevance scores.

    Website Profile

    This is not mentioned in Twitter patents, but it makes too much sense in context of all the other factors they have mentioned to pass up.

    A lot of major content websites frequently have their links shared on Twitter, and Twitter could easily create a website profile reputation/popularity score that also could factor into the rankings of Tweets when links to content on the websites is posted.

    News sites, information resources, entertainment sites – all of these could have scores developed from the same factors used to assess Twitter accounts. Tweets from better-liked and better-engaged-with websites could be given greater weight than relatively unknown and less-interacted-with websites.

    Twitter Verified

    Yes, if you suspected the blue badge next to usernames conveys preferential treatment, there is specific verbiage in one of Twitter’s patents that confirms they have at least considered this.

    Since Verified accounts often already have various other popularity indicators associated with them, it is not readily apparent if this factor is in-use or not. Tweets posted by an account that is Verified may be given a higher relevance score, enabling them to appear more than unverified accounts’ Tweets.

    Here is the patent description:

    “In one or more embodiments of the invention, the conversation module (120) includes functionality to apply a relevance filter to increase the relevance scores of one or more authoring accounts of the conversation graph which are identified in a whitelist of verified accounts. For example, the whitelist of verified accounts can be a list of accounts which are high-profile accounts which are susceptible to impersonation. In this example, celebrity and business accounts would be verified by the messaging platform (100) in order to notify users of the messaging platform (100) that the accounts are authentic. In one or more embodiments of the invention, the conversation module (120) is configured to increase the relevance scores of verified authoring accounts by a predefined amount/percentage.”

    Has Trend

    This is a binary flag indicating whether the Tweet has been identified as containing a topic that was trending at the time the message was broadcasted.

    App Detected Gender, Sexual Orientation & Interests

    Twitter may be able to use an account holder’s mobile device information to infer Gender of the account holder, or infer interests in topics such as News, Sports, Weight Training, and other topics.

    Some mobile devices provide information upon other apps loaded on the phone for purposes of diagnosing potential application programming conflicts. Thus, some Tweets matching your Gender, Sexual Orientation, and Topical Interests could be given more interestingness points simply based upon inferences made from your phone’s apps. (See:  https://screenrant.com/android-apps-collecting-app-data/ )

    And more ranking factors

    Twitter states that:

    “Our list of considered features and their varied interactions keeps growing, informing our models of ever more nuanced behavior patterns.”

    So this list of factors is likely something of an underrepresentation of the factors they may be using, and their list may be expanding.

    Also imagine that a custom combination of some of the above factors may be applied as models for Tweets associated with particular topics, lending a large potential complexity to rankings through machine learning methods. (Again, the machine learning applied to create rank weighting models custom to particular queries or topics is very similar to methods that are likely in use with Google.)

    Twitter has stated that the scoring of Tweets happens each time one visits Twitter, and each time one refreshes their timeline. Considering some of the complex factors involved, that is very fast!

    Twitter uses A/B testing of weightings of ranking factors, and other algorithm alterations, and determines whether a proposed change is an improvement based on engagement and time viewing/interacting with a Tweet. This is used to train ranking models.

    The involvement of machine learning in this process suggests that ranking models could be produced for many specific scenarios, and potentially specific to particular topics and types of users. Once developed, the model can get tested, and if it improves engagement, it can get rapidly rolled-out to all users. 

    How marketers can use this information

    There are a lot of inferences that can be drawn from the list of potential ranking factors, and which can be used by marketers in order to improve their Tweeting tactics.

    A Twitter account that only posts announcements about its products and promotional information about its company will likely not have as much visibility as accounts that are more interactive with their community, because interactions produce more ranking signals and potential benefits.

    Social media experts have long recommended an approach of blending types of posts rather than merely publishing self-referential promotion – these strategies include “The Rule of Thirds”, “The 80/20 Rule”, and others.

    The Twitter ranking factors likely support these theories, as eliciting more interactions with numbers of Twitter users is likelier to increase an account’s visibility.

    For instance, a large company account with many followers could post an interesting poll to get advice on what features to add to its product. The votes and comments posted by users will make it such that the respondents will be much more likely to see the company’s next posting due to the recent interactions, and that next posting could be promoting or announcing something new. And, the respondents’ followers might also be more likely to see the company’s next posting, since Twitter appears to factor-in that users with similar interests may be more open to seeing content matching their interests. 

    Also, the factors suggest a number of potentially beneficial approaches.

    When posting a Tweet promoting a product or making an announcement, combining something to elicit a response from one’s followers could easily expand exposure on the platform as each respondent’s replies to your Tweet may increase the odds that their direct followers may see the original Tweet and their connection’s reply Tweet. 

    Leveraging the social graph aspect of Twitter’s algorithm can help to increase the interestingness of your Tweets, and can increase exposure of your Tweets for other users.

    Spam factors can negatively impact tweet rankings

    Spam detection algorithms can negatively impact Tweet ranking ability.

    For one thing, Twitter is very fast to suspend accounts that are blatantly spamming, and in cases where it is obvious and unequivocal, one can expect the account to get terminated abruptly, causing all of its Tweets to disappear from conversation graphs and timelines, and causing the account profile to be no longer available to view.

    In yet other instances where it is not as clear whether an account is spamming, the account’s Tweets could simply be demoted by application of negative rank weight scores, or the Tweets could get locked or suspended until or if the account holder takes a corrective action or verifies their identity.

    For example, a Twitter account with a long history of good Tweets might abruptly begin posting Viagra ads or links to malware, such as if an established account became hacked. Twitter might temporarily suspend the account until corrective actions were taken, such as passing a CAPTCHA verification, or receiving a verification code via cellphone and changing passwords. Another example could be a new user that accidentally passes over some threshold of following too many accounts within a short timeframe, or posting a little too frequently. 

    Twitter employs a number of methods for detecting spam and sidelining it so users see it less.

    Much of the automated detecting relies upon detecting a combination of account profile characteristics, account Tweeting behaviors, and content found in the account’s Tweets.

    Twitter has developed numbers of characteristic spam “fingerprints” in order to perform rapid pattern detection. One Twitter patent describes how:

    “Spam is determined by comparing characteristics of identified spam accounts, and building a ‘similarity graph’ that can be compared with other accounts suspected of spam.”

    Tweets identified as potentially containing spam could be flagged with a binary value like “yes” or “no”, and then Tweets that are flagged can get filtered out of timelines. 

    It is equally possible for there to be a scale of spamminess, computed from multiple factors, and once a Tweet or account surpasses a threshold, it then suffers demotion. I think it is worthwhile to include mention of these as Twitter users may not understand the implications of how the use the platform. For example, posting one overly-aggressive Tweet might negatively impact an account’s subsequent Tweets for some period of time. Repeated edgy behavior could result in worse, such as complete account deletion, with no opportunity to recover.

    I will add a few factors here that are not specifically mentioned in Twitter patents or blog posts because Twitter does not reveal all spam identification factors for obvious reasons. But, some spam and spam account characteristics seem so obvious that I am adding a few from personal observations or from well-regarded research sources to provide a wider understanding of what can incur spam demotions.

    Spam factors & other negative ranking factors

    Simply listing out spam identification factors sharply understates Twitter’s sophisticated systems used for spam identification and spam management.

    Major Silicon Valley tech companies have often fought spam for years now, and it has been described as a sort of arms race.

    The tech company will create a method to detect the spam, and the spammers then evolve their processes to elude detection, and then the cycle repeats again, and again. 

    In Conclusion

    Twitter’s patents illustrate a huge sophistication in terms of employing components of Artificial Intelligence, social graph analysis, and methods that combine synchronous and asynchronous processing in order to deliver content extremely rapidly.

    The AI components include:

    As the ranking determinations can be based upon unique, abstracted, machine learning models according to specific phrases, topics, and interest profiling, what works for one area of interest may work a little differently for other areas of interest. 

    Even so, I think that looking at these many potential ranking factors that have been described in Twitter patents can be useful for marketers who want to attain greater exposure on Twitter’s platform.

    Author’s disclosure

    I served this year as an expert witness in arbitration between a company that sued Twitter for unfair trade practices, and the case was amicably settled recently.

    As an expert witness, I am often privy to secret information, including private communications such as employee emails within major corporations, as well as other key documents that can include data, reports, presentations, employee depositions and other information.

    In such cases, I am bound by legal protective orders and agreements not to disclose information that was revealed to me in order to be sufficiently informed on the matters I am asked to opine upon, and this was no exception.

    I have not disclosed any information covered by the protective order in this article from my recently-resolved case.

    I have gained a greater understanding and insights into some aspects of how Twitter functions from context, observations of Twitter in public use, logical projections based on their various algorithm descriptions and from reading Twitter’s patents and other public disclosures subsequent to the resolution of the case I served upon, including the following sources:

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    What’s the biggest hidden secret in Google Ads?

    Written on July 1, 2022 at 8:10 am, by admin

    Marketers are increasingly turning to automation to unlock new growth opportunities. Automated ads are quick, easy and simple to use. However, in a world of smart and automated strategies, optimizing advertising campaigns that outperform the competition can be challenging.

    Can marketers influence a smart or automated strategy? The answer may surprise you.

    A “secret” or not well-known tactic when it comes to Google Ads is that audience bid modifiers that are manually set can be used to help prioritize which audience list is served if a user is on multiple lists. For example, a higher bid modifier means your preferred list will be prioritized but not be used in the bid modifier in the auction itself.

    This is important for prioritizing high-value targets.

    Learn more by joining Torkel Öhman, CTO at Amanda AI, who explains how to optimize automated Google campaigns in his informative SMX Advanced session.

    After watching his presentation, you’ll be able to optimize your automated advertising campaigns to out-perform your competition on Google, change both the targeting and attribution of audience lists with bid modifiers and get insights into other methods of how to advertise effectively and drive better results on Google.

    Watch the session and become your marketing team’s rockstar by finding specific audiences you never thought you could find and understanding how channels, users, impressions, clicks and audiences impact your performance.

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    Google rich results guidelines now prohibits weapons, recreational drugs, tobacco & vaping products and gambling-related products

    Written on July 1, 2022 at 8:10 am, by admin

    Google has updated its rich results content guidelines to align better with the overall Google merchant guidelines thus disallowing rich results for products that are widely prohibited or regulated, or that can facilitate serious harm to self or others.

    These include, but are not limited to, weapons, recreational drugs, tobacco & vaping products and gambling-related products.

    New rich results guidelines. Google has updated the rich results product content guidelines to add this section:

    Don’t mark up content that promotes widely prohibited or regulated goods, services, or information that may facilitate serious and/or immediate or long term harm to self or others. This includes content related to firearms & weapons, recreational drugs, tobacco & vaping products and gambling-related products.

    Application. Google said this policy applies to all forms of rich result markup, including star ratings, prices, or availability information and more. This can impact products with rich result structured data markup that are widely prohibited or regulated, or that can facilitate serious harm to self or others. “This could include goods like fireworks, recreational drugs, and other products that can pose acute threats of physical harm,” a Google spokesperson told Search Engine Land.

    Effective immediately. Google told us this policy goes into affect today, at the time this story has published.

    Why we care. If you sell any of these types of products, you will probably want to remove the structured data markup from those pages. If you do not remove the markup, either way, Google will not show rich results for these product categories.

    We did ask Google if you will receive some sort of manual action within Google Search Console and Google said it will not issue manual actions, instead Google will simply just remove the rich results for these types of search results. So no manual action will be given but technically the removal of these rich results is the penalty one would receive for violating the rich results content guidelines.

    The post Google rich results guidelines now prohibits weapons, recreational drugs, tobacco & vaping products and gambling-related products appeared first on Search Engine Land.

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    3 ways to dominate with Google Auction Insights and search intelligence

    Written on June 30, 2022 at 4:59 am, by admin

    While marketers have overcome many challenges in recent years, sadly, the second half of 2022 is poised to be very different from the first. Unprecedented macroeconomic factors such as high inflation, food and energy costs and the war in Ukraine are impacting our business.

    Hiring is starting to slow down, and the pressure is on. There is a good chance that you’ll probably be asked to do more with less, as budgets may be prioritized and cut in certain areas. 

    On the flip side, Google Search ad spend continues to rise. It’s the channel that is always on, the channel with the highest conversion rate and the channel that won’t go away.

    This part of the marketing mix is reliable and constant, but are the campaigns delivering success? Are they contributing to sales? Contributing to leads?

    Auction Insights is a powerful tool we’ve all come to use for understanding campaign performance against competitors. Search intelligence adds another layer of granularity to ensure you’re one step ahead of your competition.  

    Join Ashley Fletcher, VP of Marketing at Adthena, in his informative SMX Advanced session to explore three easy search intelligence tactics that will help you dominate your competitive landscape. He also shares use-cases from L’Oreal and Avanti West Coast trains.  

    After this session, you’ll be able to save time with competitive monitoring, track performance over time and see your competitor’s spend and ad copy. The presentation will help you use data to make better ad campaign decisions and dig into search intelligence to understand why certain ads are successful to ultimately dominate the competition.

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    Webinar: Work smarter, not harder, to give customers what they want

    Written on June 30, 2022 at 4:59 am, by admin

    Personalizing your marketing campaigns for one customer is easy, but how about one hundred or thousands of customers across multiple marketing channels?

    Work smarter, not harder, by using artificial intelligence (AI) as part of your martech stack and giving your customers the unique experiences they crave.

    Register today for “Use Data to Create Next-Level Customer Experiences at Scale,” presented by MoEngage.

    The post Webinar: Work smarter, not harder, to give customers what they want appeared first on Search Engine Land.

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    Warby Parker dodges 1-800 Contacts lawsuit over search results, website

    Written on June 30, 2022 at 4:59 am, by admin

    A lawsuit filed by 1-800 Contacts against online retailer Warby Parker has been dismissed by a federal judge.

    At issue was Warby Parker’s use of 1-800 Contacts’ branded keywords to redirect searchers to the Warby Parker online store, thereby “confusing” potential customers.

    The decision. Kevin Castel, senior judge of the U.S. District Court for the Southern District of New York, ruled against 1-800 Contacts, saying that customers are unlikely to think that they’re buying from 1-800 Contacts when they click on a Warby Parker ad.

    Castel also said the companies’ trademarks were too dissimilar to confuse contact-lens buyers, who are likely to pay close attention to what they are purchasing and noted that Warby Parker’s name is clearly displayed in the search results and on its website.

    Castel added that prospective customers will take the time to figure out that the search results link to Warby Parker’s website, and will therefore discern that they are buying from contacts from Warby Parker’s website. 

    In my own Google search, I was unable to mimic the results that this suit was based on and didn’t find any Warby Parker ads initially.

    1-800 Contacts response. A spokesperson for 1-800 Contacts said after the ruling that the decision by the judge was “inconsistent with several well-established legal principles,” and that the company is “evaluating appropriate next steps, including whether to appeal.”

    You can read more about the ruling from Reuters here.

    Sounds familiar. Competitive keyword advertising lawsuits rarely turn out well for the trademark owners in court. Earlier this year we reported on an attempt by Edible Arrangements to sue Google over theft, conversion and racketeering. Edible Arrangements lost.  

    Why we care. This is the latest reminder that, in general, using a competitor’s trademarks in PPC ads is not trademark infringement from a legal standpoint. You can visit the Google trademark help document if you find yourself facing a similar issue. Still, tread carefully if you use another brand’s keywords in your ad strategy if you want to steer clear of fighting lawsuits.

    The post Warby Parker dodges 1-800 Contacts lawsuit over search results, website appeared first on Search Engine Land.

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