Monthly Archives: October 2020
- Nearly half of all consumers consume plenty of content before deciding on a purchase, so brands should focus on crafting compelling, useful reads.
- If you position your brand as a trusted source, people are five times likelier to look to you for pre-purchase information.
- RAPP copywriter Jack Schuleman shares three tips for encouraging a team to use personal passions to write richer content.
Content is still one of the best ways to engage consumers. Create meaningful content, and you offer like-minded customers more reason to get involved and invested with your brand. Whether information is coming from peers, family, or brands, people like the feeling of being understood. That’s what meaningful content does. It makes the individual feel seen and heard.
Besides, nearly half of all consumers engage with copious amounts of content before arriving at a purchase decision. This is the perfect opportunity to persuade with a compelling, useful read and move the ultimate choice in your favor. It may also help position your brand as a trusted source, which has benefits of its own. Individuals will be five times more likely to look to you for information prior to a purchase, giving you yet another opportunity to persuade.
The question then is, how do you go about crafting a meaningful piece of content?
The power behind a passion
It all comes down to one two-syllable word: passion. Personal passion makes all the difference in the creation of meaningful content. It brings deeper insights into an intended audience. You already know what that community likes, engages with, and finds compelling. If you’ve spent a life immersed in a given subject, you know these people on an intimate level.
I’m a car guy. Anybody who knows me knows that. Working for an automobile client now, I’m able to incorporate my wealth of industry knowledge into the work — and get a little return on the years of magazine subscriptions. It’s allowed me to tap into not only my passion for cars but my understanding of the people who own and love them.
Take an SUV, for instance. One buyer’s interest stems from a desire to go off-roading regularly, while another may only use it to go to the mall. Other than the obvious, what’s the meaningful difference between the two? Where might their interests coincide? How can you speak to both effectively? My passion affords me a better understanding of how to write to either one of these customers, helping to craft more compelling and engaging content.
Unleashing the full enthusiasm
Using a passion to inform content is straightforward, but instilling this idea throughout a team can take some time. There’s a comfort level that varies from one person to the next. But there are few steps to make the process easier, and it goes something like this:
1. Find opportunities to utilize your passion
Integrating your passions into your work can certainly have a positive impact on your job performance. I can attest to that. It simply comes through in the work — and, best of all, consumers can feel it. When customers understand that the people behind the brand are passionate about the products, it sets an expectation: You can trust us to deliver quality goods. In fact, studies show that communicating passion in your advertising influences everything from purchase behaviors to brand attitudes. Look for the opportunities in the workplace to best utilize your passions. Ask to take part in that work.
2. Bring more of yourself to work
My previous team knew I was into cars, so they were more than willing to keep an ear to the ground should something on the automotive front open up. Had I decided to leave that part of myself at home, who knows whether I’d be working on that client today? Not that you need to divulge your entire personal life to co-workers, but sharing more of your “self” in the workplace allows you to bring your passions with you each day. You can more easily lean on your enthusiasm and do your best, most innovative work. There’s a lot of potential in that.
3. Give credit where credit is due
Whether ideas come from trade publications or industry events, lived experiences advance the work. So you should feel comfortable sharing its origin; it won’t make the idea any less valuable or worthwhile. And while on the topic, look for suggestions outside the confines of your department. Someone from customer service, for example, could provide valuable insights for your next marketing campaign. Ask for ideas. Challenge teams to bring new concepts to the table, and provide feedback on what you like most about it. The constant exchange can create momentum throughout your company and encourage everyone to think outside the box.
Speaking from a place of knowledge will always be more compelling. It simply provides an air of expertise that consumers respond to. Of course, each individual has only so many interests, which is why building a team with an eclectic mix of hobbies, passions, and lifestyles is essential to an agency or marketing department. The more backgrounds you can get, the better off your team will be — and you’ll see it in your content.
Jack Schuleman is a writer who never learned the meaning of the phrase “slow down”. After a lifetime of drag shows, car meets, and all sorts of misadventures, he’s been able to apply his unique point of view and improv-honed creativity into engaging copy across nonprofits, automotive brands, and tech companies. Now writing for Toyota, he’s pursuing the most elusive target yet: a 100% click-through rate.
The post How to use personal passions to create meaningful content appeared first on Search Engine Watch.
Instagram is today introducing a new way for creators to make money. The company is now rolling out badges in Instagram Live to an initial group of over 50,000 creators, who will be able to offer their fans the ability to purchase badges during their live videos to stand out in the comments and show their support.
The idea to monetize using fan badges is not unique to Instagram. Other live streaming platforms, including Twitch and YouTube, have similar systems. Facebook Live also allows fans to purchase stars on live videos, as a virtual tipping mechanism.
Instagram users will see three options to purchase a badge during live videos: badges that cost $ 0.99, $ 1.99, or $ 4.99.
On Instagram Live, badges will not only call attention to the fans’ comments, they also unlock special features, Instagram says. This includes a placement on a creator’s list of badge holders and access to a special heart badge.
The badges and list make it easier for creators to quickly see which fans are supporting their efforts, and give them a shout-out, if desired.
To kick off the roll out of badges, Instagram says it will also temporarily match creator earnings from badge purchases during live videos, starting in November. Creators @ronnebrown and @youngezee are among those who are testing badges.
The company says it’s not taking a revenue share at launch, but as it expands its test of badges it will explore revenue share in the future.
“Creators push culture forward. Many of them dedicate their life to this, and it’s so important to us that they have easy ways to make money from their content,” said Instagram COO Justin Osofsky, in a statement. “These are additional steps in our work to make Instagram the single best place for creators to tell their story, grow their audience, and make a living,” she added.
Additionally, Instagram today is expanding access to its IGTV ads test to more creators. This program, introduced this spring, allows creators to earn money by including ads alongside their videos. Today, creators keep at least 55% of that revenue, Instagram says.
The introduction of badges and IGTV ads were previously announced, with Instagram saying it would test the former with a small group of creators earlier this year.
The changes follow what’s been a period of rapid growth on Instagram’s live video platform, as creators and fans sheltered at home during the coronavirus pandemic, which had cancelled live events, large meetups, concerts, and more.
During the pandemic’s start, for example, Instagram said Live creators saw a 70% increase in video views from Feb. to March, 2020. In Q2, Facebook also reported monthly active user growth (from 2.99B to 3.14B in Q1) that it said reflected increased engagement from consumers who were spending more time at home.
Hello and welcome back to Equity, TechCrunch’s venture-capital-focused podcast where we unpack the numbers behind the headlines.
It’s a big day in tech because the U.S. federal government is going after Google on anti-competitive grounds. Sure, the timing appears crassly political and the case is not picking up huge plaudits thus far for its air-tightness, but that doesn’t mean we can ignore it.
So Danny and I got on the horn to chat it up for about 10 minutes to fill you in. For reference, you can read the full filing here, in case you want to get your nails in. It’s not a complicated read. Get in there.
As a pair we dug into what stood out from the suit, what we think about the historical context and also noodled at the end about what the whole situation could mean for startups; it’s not all good news, but adding lots of competitive space to the market would be a net-good for upstart tech companies in the long-run.
And consumers. Competition is good.
- Hyperlocal SEO will help struggling communities salvage their local businesses.
- Moz surveyed over 1,400 local business marketers and more than half said they plan to implement Google’s new features to support COVID-19 affected businesses.
- Five under-rated yet crucial parameters marketers need to stay on top of.
- Sarah Bird’s special tips to optimize audience engagement at various marketing touchpoints.
- The best things you can do for landing pages is….?
- Dive in for these golden nuggets and a lot more.
2020 has hit the reset button for the world in many ways adding more wheels to digital marketers’ and brands’ “car of struggles” for success. SEO is somewhat looked at as a game of Russian roulette where you win some and you lose some, and COVID-19 hasn’t made this any easier. To help you hit bull’s eye and add an extra push to your digital strategies, we caught up with Moz’s CEO, Sarah Bird to uncover emerging trends in the search scape, SEO, audience behaviors, and more!
Q. What technologies, tools, and audience behaviors do you see shape up as 2020 progresses. If you were to draw a line between the temporary and ones that are here to stay, what would it be?
Sarah Bird: Hyperlocal search has been important for years. 2020 has only increased its merit.
COVID-19 has made active local business listings management more vital than ever before. Communities struggling to keep themselves supplied and cared for in changed conditions must depend on the internet as a crucial resource, and when business listings can quickly communicate to them what’s available, where, when, and how, that’s truly important.
With Google rolling out new features that allow business owners to share updates about curbside pickup, home delivery, or special hours for vulnerable populations directly on their listings, customers can access convenient information with a simple search. We surveyed over 1,400 local business marketers and more than half said they plan to implement such services permanently. Aside from being absolutely necessary this year, businesses recognize that the investment in ecommerce should not simply be for the short-term, but should be able to accommodate their business and customers in the long-term.
Q. If you were to pick the hero of Moz’s local and international SEO strategy for the rest of 2020, what would it be?
Sarah Bird: Reputation management will be crucial for local SEO strategy during 2020. We offer reputation management features through Moz Local that we urge users to leverage.
Some of the most valuable features of Moz Local at this time are review alerts that allow you to quickly facilitate complaint resolution and response rating for quality control. During hectic times, customers are more emotional — this can either work for or against you. Should you receive a poor review during this time, it’s imperative that you respond quickly and empathetically.
Moz Local also offers a sentiment analysis feature that shows the most commonly used words for each of your star ratings. This can be useful in deciphering exactly what customers are finding important during this time.
Q. What five under-rated yet crucial parameters do marketers need to stay on top of to ensure that their brand has positively influenced their customers/target audiences?
- Keywords: Understanding your own keywords and those of your competitors ensures marketers have a plan in place to secure visibility on a brand’s offerings or content.
- External links: These are an important source of ranking power in a SERP.
- Differentiation: Framing content correctly is key to reaching target audiences. Sometimes that means presenting contrarian ideas, as described by Caroline Forsey of HubSpot.
- Omnichannel communication: Not all of your readers are going to read and engage via laptop or mobile, but be sure to consider how SEO is involved in your social media strategy.
- Outcome alignment: SEO goals don’t always have to focus on clicks. Ensure your marketing team is aligned on how content or a topic should be engaged, as it could mean that your ideal outcome is answering your customer’s question directly within the SERP.
Q. What are the best ways to use entities that can leverage BERT, add more dimensions to keyword strategy, content, and the overall digital presence?
Sarah Bird: I don’t encourage SEOs or marketers to optimize for BERT. There are too many variables to develop an effective strategy toward this model.
Instead, marketers should continue the focus on the overarching goal of creating excellent content that holistically understands and meets the intent of users. This is no small feat and requires an intense understanding of your business, your audience, and how the two intertwine. Creating world-class content that’s data-driven, timely, and empathetic to the audience will prove to be far more effective than focusing on this specific component of an algorithmic change from Google.
Q. Tips to optimize audience engagement at marketing touchpoints like emails, landing pages, and social media?
Sarah Bird: Each of these touchpoints are important for a business’s SEO strategy. These aren’t tactics that can be tacked on — they all have a powerful impact.
Email marketing delivers some of the highest ROI, generating $ 38 for every $ 1 spent. When it comes to emails, call-to-actions must be clear. Consider which landing pages you’re sending people to and whether they’re appropriate to improve bounce rates.
Social shares of a brand’s content have a high correlation to ranking (as described by our own Cyrus Shepard.) As with everything in SEO, a focus should be put on the keywords used as well as the medium of the content being put out and whether or not it’s optimized.
High-converting landing pages may lead to high bounce rates, which could negatively impact SEO. Rand Fishkin actually addressed this exact issue in a Whiteboard Friday. The best things you can do for landing pages is – focus on high-conversion long-tail keywords and to provide keyword-based content.
Feel free to share your thoughts on our interview and the emerging trends, drop a comment!
The post CEO’s take on emerging industry trends and strategies: Q&A with Moz’s Sarah Bird appeared first on Search Engine Watch.
It was an active week in the technology world broadly, with big news from Facebook and Twitter and Apple. But past the headline-grabbing noise, there was a steady drumbeat of bullish news for unicorns, or private companies worth $ 1 billion or more.
A bullish week for unicorns
The Exchange spent a good chunk of the week looking into different stories from unicorns, or companies that will soon fit the bill, and it’s surprising to see how much positive financial news there was on tap even past what we got to write about.
Databricks, for example, disclosed a grip of financial data to TechCrunch ahead of regular publication, including the fact that it grew its annual run rate (not ARR) to $ 350 million by the end of Q3 2020, up from $ 200 million in Q2 2019. It’s essentially IPO ready, but is not hurrying to the public markets.
Sticking to our theme, Calm wants more money for a huge new valuation, perhaps as high as $ 2.2 billion which is not a surprise. That’s more good unicorn news. As was the report that “India’s Razorpay [became a] unicorn after its new $ 100 million funding round” that came out this week.
Razorpay is only one of a number of Indian startups that have become unicorns during COVID-19. (And here’s another digest out this week concerning a half-dozen startups that became unicorns “amidst the pandemic.”)
There was enough good unicorn news lately that we’ve lost track of it all. Things like Seismic raising $ 92 million, pushing its valuation up to $ 1.6 billion from a few weeks ago. How did that get lost in the mix?
All this matters because while the IPO market has captured much attention in the last quarter or so, the unicorn world has not sat still. Indeed, it feels that unicorn VC activity is the highest we’ve seen since 2019.
And, as we’ll see in just a moment, the grist for the unicorn mill is getting refilled as we speak. So, expect more of the same until something material breaks our current investing and exit pattern.
What do unicorns eat? Cash. And many, many VCs raised cash in the last seven days.
A partial list follows. It could be that investors are looking to lock in new funds before the election and whatever chaos may ensue. So, in no particular order, here’s who is newly flush:
- $ 450 million for OpenView, $ 800 million for Canaan, $ 840 million for True Ventures, $ 950 million for Lead Edge Capital
- Something called Benson Capital Partners has put together a $ 50 million fund. Gayle Benson, for whom the firm is named, owns several New Orleans sports teams, per Forbes.
- Plus Venture Capital, built by two former 500 Startups Mena investors according to fundsglobalMENA, has raised $ 60 million.
- First Round is looking for $ 220 million, former Google exec Kai-Fu Lee’s Sinovation Ventures is looking for a billion, while Khosla wants a bit more.
All that capital needs to go to work, which means lots more rounds for many, many startups. The Exchange also caught up with a somewhat new firm this week: Race Capital. Helmed by Alfred Chuang, formerly or BEA who is an angel investor now in charge of his own fund, the firm has $ 50 million to invest.
Sticking to private investments into startups for the moment, quite a lot happened this week that we need to know more about. Like API-powered Argyle raising $ 20 million from Bain Capital Ventures for what FinLedger calls “unlocking and democratizing access to employment records.” TechCrunch is currently tracking the progress of API-led startups.
On the fintech side of things, M1 Finance raised $ 45 million for its consumer fintech platform in a Series C, while another roboadvisor, Wealthsimple, raised $ 87 million, becoming a unicorn at the same time. And while we’re in the fintech bucket, Stripe dropped $ 200 million this week for Nigerian startup Paystack. We need to pay more attention to the African startup scene. On the smaller end of fintech, Alpaca raised $ 10 million more to help other companies become Robinhood.
A few other notes before we change tack. Kahoot raised $ 215 million due to a boom in remote education, another trend that is inescapable in 2020 as part of the larger edtech boom (our own Natasha Mascarenhas has more).
Turning from the private market to the public, we have to touch on SPACs for just a moment. The Exchange got on the phone this week with Toby Russell from Shift, which is now a public company, trading after it merged with a SPAC, namely Insurance Acquisition Corp. Early trading is only going so well, but the CEO outlined for us precisely why he pursued a SPAC, which was actually interesting:
- Shift could have gone public via an IPO, Russell said, but prioritized a SPAC-led debut because his firm wanted to optimize for a capital raise to keep the company growing.
- How so? The private investment in public equity (PIPE) that the SPAC option came with ensured that Shift would have hundreds of millions in cash.
- Shift also wanted to minimize what the CEO described as market risk. A SPAC deal could happen regardless of what the broader markets were up to. And as the company made the choice to debut via a SPAC in April, some caution, we reckon, may have made some sense.
So now Shift is public and newly capitalized. Let’s see what happens to its shares as it gets into the groove of reporting quarterly. (Obviously, if it flounders, it’s a bad mark for SPACs, but, conversely, successful trading could lead to a bit more momentum to SPAC-mageddon.)
A few more things and we’re done. Unicorn exits had a good week. First, Datto’s IPO continues to move forward. It set an initial price this week, which could value it above $ 4 billion. Also this week, Roblox announced that it has filed to go public, albeit privately. It’s worth billions as well. And finally, DoubleVerify is looking to go public for as much as $ 5 billion early next year.
Not all liquidity comes via the public markets, as we saw this week’s Twilio purchase of Segment, a deal that The Exchange dug into to find out if it was well-priced or not.
Various and Sundry
We’re running long naturally, so here are just a few quick things to add to your weekend mental tea-and-coffee reading!
- This Operator Collective + @BLCKVC + @SalesforceVC mashup caught our attention.
- Accel has notes here on the EU startup scene, especially its later stages.
- Here’s a TechCrunch piece we helped put together that digs into the current state of media startups. It’s fun!
- Bytedance charts for your education and entertainment.
- Here’s where you can track the growth of DuckDuckGo as it takes on Google and Bing.
- Equity was a bundle of fun this week, so make sure to tune in if you have 30 minutes of free time.
How Are Featured Snippet Answers Decided Upon?
I recently wrote about Featured Snippet Answer Scores Ranking Signals. In that post, I described how Google was likely using query dependent and query independent ranking signals to create answer scores for queries that were looking like they wanted answers.
One of the inventors of that patent from that post was Steven Baker. I looked at other patents that he had written, and noticed that one of those was about context as part of query independent ranking signals for answers.
Remembering that patent about question-answering and context, I felt it was worth reviewing that patent and writing about it.
This patent is about processing question queries that want textual answers and how those answers may be decided upon.
it is a complicated patent, and at one point the description behind it seems to get a bit murky, but I wrote about when that happened in the patent, and I think the other details provide a lot of insight into how Google is scoring featured snippet answers. There is an additional related patent that I will be following up with after this post, and I will link to it from here as well.
This patent starts by telling us that a search system can identify resources in response to queries submitted by users and provide information about the resources in a manner that is useful to the users.
How Context Scoring Adjustments for Featured Snippet Answers Works
Users of search systems are often searching for an answer to a specific question, rather than a listing of resources, like in this drawing from the patent, showing featured snippet answers:
For example, users may want to know what the weather is in a particular location, a current quote for a stock, the capital of a state, etc.
When queries that are in the form of a question are received, some search engines may perform specialized search operations in response to the question format of the query.
For example, some search engines may provide information responsive to such queries in the form of an “answer,” such as information provided in the form of a “one box” to a question, which is often a featured snippet answer.
Some question queries are better served by explanatory answers, which are also referred to as “long answers” or “answer passages.”
For example, for the question query [why is the sky blue], an answer explaining light as waves is helpful.
Such answer passages can be selected from resources that include text, such as paragraphs, that are relevant to the question and the answer.
Sections of the text are scored, and the section with the best score is selected as an answer.
In general, the patent tells us about one aspect of what it covers in the following process:
- Receiving a query that is a question query seeking an answer response
- Receiving candidate answer passages, each passage made of text selected from a text section subordinate to a heading on a resource, with a corresponding answer score
- Determining a hierarchy of headings on a page, with two or more heading levels hierarchically arranged in parent-child relationships, where each heading level has one or more headings, a subheading of a respective heading is a child heading in a parent-child relationship and the respective heading is a parent heading in that relationship, and the heading hierarchy includes a root level corresponding to a root heading (for each candidate answer passage)
- Determining a heading vector describing a path in the hierarchy of headings from the root heading to the respective heading to which the candidate answer passage is subordinate, determining a context score based, at least in part, on the heading vector, adjusting the answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score
- Selecting an answer passage from the candidate answer passages based on the adjusted answer scores
Advantages of the process in the patent
- Long query answers can be selected, based partially on context signals indicating answers relevant to a question
- The context signals may be, in part, query-independent (i.e., scored independently of their relatedness to terms of the query
- This part of the scoring process considers the context of the document (“resource”) in which the answer text is located, accounting for relevancy signals that may not otherwise be accounted for during query-dependent scoring
- Following this approach, long answers that are more likely to satisfy a searcher’s informational need are more likely to appear as answers
This patent can be found at:
Context scoring adjustments for answer passages
Inventors: Nitin Gupta, Srinivasan Venkatachary , Lingkun Chu, and Steven D. Baker
US Patent: 9,959,315
Granted: May 1, 2018
Appl. No.: 14/169,960
Filed: January 31, 2014
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for context scoring adjustments for candidate answer passages.
In one aspect, a method includes scoring candidate answer passages. For each candidate answer passage, the system determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading to which the candidate answer passage is subordinate; determines a context score based, at least in part, on the heading vector; and adjusts answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score.
The system then selects an answer passage from the candidate answer passages based on the adjusted answer scores.
Using Context Scores to Adjust Answer Scores for Featured Snippets
A drawing from the patent shows different hierarchical headings that may be used to determine the context of answer passages that may be used to adjust answer scores for featured snippets:
I discuss these headings and their hierarchy below. Note that the headings include the Page title as a heading (About the Moon), and the headings within heading elements on the page as well. And those headings give those answers context.
This context scoring process starts with receiving candidate answer passages and a score for each of the passages.
Those candidate answer passages and their respective scores are provided to a search engine that receives a query determined to be a question.
Each of those candidate answer passages is text selected from a text section under a particular heading from a specific resource (page) that has a certain answer score.
For each resource where a candidate answer passage has been selected, a context scoring process determines a heading hierarchy in the resource.
A heading is text or other data corresponding to a particular passage in the resource.
As an example, a heading can be text summarizing a section of text that immediately follows the heading (the heading describes what the text is about that follows it, or is contained within it.)
Headings may be indicated, for example, by specific formatting data, such as heading elements using HTML.
This next section from the patent reminded me of an observation that Cindy Krum of Mobile Moxie has about named anchors on a page, and how Google might index those to answer a question, to lead to an answer or a featured snippet. She wrote about those in What the Heck are Fraggles?
A heading could also be anchor text for an internal link (within the same page) that links to an anchor and corresponding text at some other position on the page.
A heading hierarchy could have two or more heading levels that are hierarchically arranged in parent-child relationships.
The first level, or the root heading, could be the title of the resource.
Each of the heading levels may have one or more headings, and a subheading of a respective heading is a child heading and the respective heading is a parent heading in the parent-child relationship.
For each candidate passage, a context scoring process may determine a context score based, at least in part, on the relationship between the root heading and the respective heading to which the candidate answer passage is subordinate.
The context scoring process could be used to determine the context score and determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading.
The context score could be based, at least in part, on the heading vector.
The context scoring process can then adjust the answer score of the candidate answer passage at least in part by the context score to form an adjusted answer score.
The context scoring process can then select an answer passage from the candidate answer passages based on adjusted answer scores.
This flowchart from the patent shows the context scoring adjustment process:
Identifying Question Queries And Answer Passages
I’ve written about understanding the context of answer passages. The patent tells us more about question queries and answer passages worth going over in more detail.
Some queries are in the form of a question or an implicit question.
For example, the query [distance of the earth from the moon] is in the form of an implicit question “What is the distance of the earth from the moon?”
Likewise, a question may be specific, as in the query [How far away is the moon].
The search engine includes a query question processor that uses processes that determine if a query is a query question (implicit or specific) and if it is, whether there are answers that are responsive to the question.
The query question processor can use several different algorithms to determine whether a query is a question and whether there are particular answers responsive to the question.
For example, it may use to determine question queries and answers:
- Language models
- Machine learned processes
- Knowledge graphs
- Combinations of those
The query question processor may choose candidate answer passages in addition to or instead of answer facts. For example, for the query [how far away is the moon], an answer fact is 238,900 miles. And the search engine may just show that factual information since that is the average distance of the Earth from the moon.
But, the query question processor may choose to identify passages that are to be very relevant to the question query.
These passages are called candidate answer passages.
The answer passages are scored, and one passage is selected based on these scores and provided in response to the query.
An answer passage may be scored, and that score may be adjusted based on a context, which is the point behind this patent.
Often Google will identify several candidate answer passages that could be used as featured snippet answers.
Google may look at the information on the pages where those answers come from to better understand the context of the answers such as the title of the page, and the headings about the content that the answer was found within.
Contextual Scoring Adjustments for Featured Snippet Answers
The query question processor sends to a context scoring processor some candidate answer passages, information about the resources from which each answer passages was from, and a score for each of the featured snippet answers.
The scores of the candidate answer passages could be based on the following considerations:
- Matching a query term to the text of the candidate answer passage
- Matching answer terms to the text of the candidate answer passages
- The quality of the underlying resource from which the candidate answer passage was selected
I recently wrote about featured snippet answer scores, and how a combination of query dependent and query independent scoring signals might be used to generate answer scores for answer passages.
The patent tells us that the query question processor may also take into account other factors when scoring candidate answer passages.
Candidate answer passages can be selected from the text of a particular section of the resource. And the query question processor could choose more than one candidate answer passage from a text section.
We are given the following examples of different answer passages from the same page
(These example answer passages are referred to in a few places in the remainder of the post.)
- (1) It takes about 27 days (27 days, 7 hours, 43 minutes, and 11.6 seconds) for the Moon to orbit the Earth at its orbital distance
- (2) Why is the distance changing? The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles
- (3) The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles
Each of those answers could be good ones for Google to use. We are told that:
More than three candidate answers can be selected from the resource, and more than one resource can be processed for candidate answers.
How would Google choose between those three possible answers?
Google might decide based on the number of sentences and a selection of up to a maximum number of characters.
The patent tells us this about choosing between those answers:
Each candidate answer has a corresponding score. For this example, assume that candidate answer passage (2) has the highest score, followed by candidate answer passage (3), and then by candidate answer passage (1). Thus, without the context scoring processor, candidate answer passage (2) would have been provided in the answer box of FIG. 2. However, the context scoring processor takes into account the context of the answer passages and adjusts the scores provided by the query question processor.
So, we see that what might be chosen based on featured snippet answer scores could be adjusted based on the context of that answer from the page that it appears on.
Contextually Scoring Featured Snippet Answers
This process starts which begins with a query determined to be a question query seeking an answer response.
This process next receives candidate answer passages, each candidate answer passage chosen from the text of a resource.
Each of the candidate answer passages are text chosen from a text section that is subordinate to a respective heading (under a heading) in the resource and has a corresponding answer score.
For example, the query question processor provides the candidate answer passages, and their corresponding scores, to the context scoring processor.
A Heading Hierarchy to Determine Context
This process then determines a heading hierarchy from the resource.
The heading hierarchy would have two or more heading levels hierarchically arranged in parent-child relationships (Such as a page title, and an HTML heading element.)
Each heading level has one or more headings.
A subheading of a respective heading is a child heading (an (h2) heading might be a subheading of a (title)) in the parent-child relationship and the respective heading is a parent heading in the relationship.
The heading hierarchy includes a root level corresponding to a root heading.
The context scoring processor can process heading tags in a DOM tree to determine a heading hierarchy.
For example, concerning the drawing about the distance to the moon just above, the heading hierarchy for the resource may be:
The ROOT Heading (title) is: About The Moon (310)
The main heading (H1) on the page
H1: The Moon’s Orbit (330)
A secondary heading (h2) on the page:
H2: How long does it take for the Moon to orbit Earth? (334)
Another secondary heading (h2) on the page is:
H2: The distance from the Earth to the Moon (338)
Another Main heading (h1) on the page
H1: The Moon (360)
Another secondary Heading (h2) on the page:
H2: Age of the Moon (364)
Another secondary heading (h2) on the page:
H2: Life on the Moon (368)
Here is how the patent describes this heading hierarchy:
In this heading hierarchy, The title is the root heading at the root level; headings 330 and 360 are child headings of the heading, and are at a first level below the root level; headings 334 and 338 are child headings of the heading 330, and are at a second level that is one level below the first level, and two levels below the root level; and headings 364 and 368 are child headings of the heading 360 and are at a second level that is one level below the first level, and two levels below the root level.
The process from the patent determines a context score based, at least in part, on the relationship between the root heading and the respective heading to which the candidate answer passage is subordinate.
This score may be is based on a heading vector.
The patent says that the process, for each of the candidate answer passages, determines a heading vector that describes a path in the heading hierarchy from the root heading to the respective heading.
The heading vector would include the text of the headings for the candidate answer passage.
For the example candidate answer passages (1)-(3) above about how long it takes the moon to orbit the earch, the respectively corresponding heading vectors V1, V2 and V3 are:
- V1=<[Root: About The Moon], [H1: The Moon's Orbit], [H2: How long does it take for the Moon to orbit the Earth?]>
- V2=<[Root: About The Moon], [H1: The Moon's Orbit], [H2: The distance from the Earth to the Moon]>
- V3=<[Root: About The Moon], [H1: The Moon's Orbit], [H2: The distance from the Earth to the Moon]>
We are also told that because candidate answer passages (2) and (3) are selected from the same text section 340, their respective heading vectors V2 and V3 are the same (they are both in the content under the same (H2) heading.)
The process of adjusting a score, for each answer passage, uses a context score based, at least in part, on the heading vector (410).
That context score can be a single score used to scale the candidate answer passage score or can be a series of discrete scores/boosts that can be used to adjust the score of the candidate answer passage.
Where things Get Murky in This Patent
There do seem to be several related patents involving featured snippet answers, and this one which targets learning more about answers from their context based on where they fit in a heading hierarchy makes sense.
But, I’m confused by how the patent tells us that one answer based on the context would be adjusted over another one.
The first issue I have is that the answers they are comparing in the same contextual area have some overlap. Here those two are:
- (2) Why is the distance changing? The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles
- (3) The moon’s distance from Earth varies because the moon travels in a slightly elliptical orbit. Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles
Note that the second answer and the third answer both include the same line: “Thus, the moon’s distance from the Earth varies from 225,700 miles to 252,000 miles.” I find myself a little surprised that the second answer includes a couple of sentences that aren’t in the third answer, and skips a couple of lines from the third answer, and then includes the last sentence, which answers the question.
Since they both appear in the same heading and subheading section of the page they are from, it is difficult to imagine that there is a different adjustment based on context. But, the patent tells us differently:
The candidate answer score with the highest adjusted answer score (based on context from the headings) is selected, and the answer passage.
Recall that in the example above, the candidate answer passage (2) had the highest score, followed by candidate answer passage (3), and then by candidate answer passage (1).
However, after adjustments, candidate answer passage (3) has the highest score, followed by candidate answer passage (2), and then-candidate answer passage (1).
Accordingly, candidate answer passage (3) is selected and provided as the answer passage of FIG. 2.
Boosting Scores Based on Passage Coverage Ratio
A query question processor may limit the candidate answers to a maximum length.
The context scoring processor determines a coverage ratio which is a measure indicative of the coverage of the candidate answer passage from the text from which it was selected.
The patent describes alternative question answers:
Alternatively, the text block may include text sections subordinate to respective headings that include a first heading for which the text section from which the candidate answer passage was selected is subordinate, and sibling headings that have an immediate parent heading in common with the first heading. For example, for the candidate answer passage, the text block may include all the text in the portion 380 of the hierarchy; or may include only the text of the sections, of some other portion of text within the portion of the hierarchy. A similar block may be used for the portion of the hierarchy for candidate answer passages selected from that portion.
A small coverage ratio may indicate a candidate answer passage is incomplete. A high coverage ratio may indicate the candidate answer passage captures more of the content of the text passage from which it was selected. A candidate answer passage may receive a context adjustment, depending on this coverage ratio.
A passage coverage ratio is a ratio of the total number of characters in the candidate answer passage to the ratio of the total number of characters in the passage from which the candidate answer passage was selected.
The passage cover ratio could also be a ratio of the total number of sentences (or words) in the candidate answer passage to the ratio of the total number of sentences (or words) in the passage from which the candidate answer passage was selected.
We are told that other ratios can also be used.
From the three example candidate answer passages about the distance to the moon above (1)-(3) above, passage (1) has the highest ratio, passage (2) has the second-highest, and passage (3) has the lowest.
This process determines whether the coverage ratio is less than a threshold value. That threshold value can be, for example, 0.3, 0.35 or 0.4, or some other fraction. In our “distance to the moon” example, each coverage passage ratio meets or exceeds the threshold value.
If the coverage ratio is less than a threshold value, then the process would select a first answer boost factor. The first answer boost factor might be proportional to the coverage ratio according to a first relation, or maybe a fixed value, or maybe a non-boosting value (e.g., 1.0.)
But if the coverage ratio is not less than the threshold value, the process may select a second answer boost factor. The second answer boost factor may be proportional to the coverage ratio according to a second relation, or maybe fixed value, or maybe a value greater than the non-boosting value (e.g., 1.1.)
Scoring Based on Other Features
The context scoring process can also check for the presence of features in addition to those described above.
Three example features for contextually scoring an answer passage can be based on the additional features of the distinctive text, a preceding question, and a list format.
Distinctive text is the text that may stand out because it is formatted differently than other text, like using bolding.
A Preceeding Question
A preceding question is a question in the text that precedes the candidate answer question.
The search engine may process various amounts of text to detect for the question.
Only the passage from which the candidate answer passage is extracted is detected.
A text window that can include header text and other text from other sections may be checked.
A boost score that is inversely proportional to the text distance from a question to the candidate answer passage is calculated, and the check is terminated at the occurrence of a first question.
That text distance may be measured in characters, words, or sentences, or by some other metric.
If the question is anchor text for a section of text and there is intervening text, such as in the case of a navigation list, then the question is determined to only precede the text passage to which it links, not precede intervening text.
In the drawing above about the moon, there are two questions in the resource: “How long does it take for the Moon to orbit Earth?” and “Why is the distance changing?”
The first question–“How long does it take for the Moon to orbit Earth?”– precedes the first candidate answer passage by a text distance of zero sentences, and it precedes the second candidate answer passage by a text distance of five sentences.
And the second question–“Why is the distance changing?”– precedes the third candidate answer by zero sentences.
If a preceding question is detected, then the process selects a question boost factor.
This boost factor may be proportional to the text distance, whether the text is in a text passage subordinate to a header or whether the question is a header, and, if the question is in a header, whether the candidate answer passage is subordinate to the header.
Considering these factors, the third candidate answer passage receives the highest boost factor, the first candidate answer receives the second-highest boost factor, and the second candidate answer receives the smallest boost factor.
Conversely, if the preceding text is not detected, or after the question boost factor is detected, then the process detects for the presence of a list.
The Presence of a List
A list is an indication of several steps usually instructive or informative. The detection of a list may be subject to the query question being a step modal query.
A step modal query is a query where a list-based answer is likely to a good answer. Examples of step model queries are queries like:
- [How to . . . ]
- [How do I . . . ]
- [How to install a door knob]
- [How do I change a tire]
The context scoring process may detect lists formed with:
- HTML tags
- Micro formats
- Semantic meaning
- Consecutive headings at the same level with the same or similar phrases (e.g., Step 1, Step 2; or First; Second; Third; etc.)
The context scoring process may also score a list for quality.
It would look at things such as:
- A list in the center of a page, which does not include multiple links to other pages (indicative of reference lists)
- HREF link text that does not occupy a large portion of the text of the list will be of higher quality than a list at the side of a page, and which does include multiple links to other pages (which are indicative of reference lists), and/are has HREF link text that does occupy a large portion of the text of the list
If a list is detected, then the process selects a list boost factor.
That list boost factor may be fixed or may be proportional to the quality score of the list.
If a list is not detected, or after the list boost factor is selected, the process ends.
In some implementations, the list boost factor may also be dependent on other feature scores.
If other features, such as coverage ratio, distinctive text, etc., have relatively high scores, then the list boot factor may be increased.
The patent tells us that this is because “the combination of these scores in the presence of a list is a strong signal of a high-quality answer passage.”
Adjustment of Featured Snippet Answers Scores
Answer scores for candidate answer passages are adjusted by scoring components based on heading vectors, passage coverage ratio, and other features described above.
The scoring process can select the largest boost value from those determined above or can select a combination of the boost values.
Once the answer scores are adjusted, the candidate answer passage with the highest adjusted answer score is selected as the featured snippet answer and is displayed to a searcher.
More to Come
I will be reviewing the first patent in this series of patents about candidate answer scores because it does have some additional elements to it that haven’t been covered in this post, and the post about query dependent/independent ranking signals for answer scores. If you have been paying attention to how Google has been answering queries that appear to be seeking answers, you have likely seen those improving in many cases. Some answers have been really bad though. It will be nice to have as complete an idea as we can of how Google decides what might be a good answer to a query, based on information available to them on the Web.
Added October 14, 2020 – I have written about another Google patent on Answer Scores, and it’s worth reading about all of the patents on this topic. The new post is at Weighted Answer Terms for Scoring Answer Passages, and is about the patent Weighted answer terms for scoring answer passages.
It is about identifying questions in resources, and answers for those questions, and describes using term weights as a way to score answer passages (along with the scoring approaches identified in the other related patents, including this one.)
Added October 15, 2020 – I have written a few other posts about answer passages that are worth reading if you are interested in how Google finds questions on pages and answers to those, and scores answer passages to determine which ones to show as featured snippets. I’ve linked to some of those in the body of this post, but here is another one of hose posts:
- January 24, 2019 – Does Google Use Schema to Write Answer Passages for Featured Snippets?
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- Old point and shoot methods of SEO are unsustainable.
- Agile marketers are paving the path forward combining technology and talent.
- Machine learning is helping search marketers remove repetitive and mundane tasks.
- COVID-19 has accelerated digital transformation that was underway well before.
- Combining business intelligence and search intelligence is a must.
- Jim Yu, Founder and CEO of BrightEdge discusses the essentials of being an agile marketer.
“Seeking marketing and search optimization expert with demonstrated abilities to understand search ranking process, lead strategically and collaborate with other teams and departments, bring forth new processes to streamline tasks and create efficiencies, drive brand storytelling strategy across channels…” – Being a digital marketer on the job market, you would see a mix of descriptions like this and the ones below which hint on the search for agile marketers.
“Must have experience in HTML, CSS, PHP, and web standards… own and execute digital strategy including content ideation and creation…”
“Perform A/B testing, own the marketing database, carry out full SEO audits… contribute to thought leadership by authoring blog posts and speaking at events…”
It’s not your imagination, clients and recruiters alike are in fact searching for digital hybrids—for equal parts technical expertise, strategic insight, and creativity all wrapped up in one neat package.
Once upon a time, these seemingly conflicting qualities existed inside of very different roles. Those days are long over. SEO is no longer a tactic and a siloed team, but an integral and foundational part of a holistic digital marketing strategy. Search insights reveal consumer behaviors and trends critical to marketing’s performance and thanks to recent developments in AI, we can monitor and make sense of more of this data than ever before.
Today, if you want to succeed as a leader in the marketing space, you must be agile in its most literal sense: able to move quickly and easily. Across teams and departments, between campaigns and tools, through various channels and market segments—the Agile Marketer has the analytical know-how and emotional intelligence to navigate and lead others through the sprawling digital marketing landscape with ease.
Here are two specific areas in which modern marketers can focus to build agility and value:
A. The technology and insights at your disposal
Monitoring, evaluating, and activating search insights at any sort of scale has proven challenging for SEOs still trying to cobble a workflow together out of disparate tools. Last year, BrightEdge research showed that the average search marketer relies on four to six SEO tools and data sources to execute their strategy.
These tools are responsive by nature, as they tend not to “speak” to one another. Data must be manipulated, reformatted, and evaluated before any recommendations can be deployed. Using point solutions leaves SEOs scrambling to answer consumers’ needs as they were expressed days, weeks, or months ago. To measure the impact of a search update, formulate a new strategy, and only then be able to respond.
Both search technology and consumer behavior have outpaced this approach by far. Data silos and point solutions limit the reach and efficacy of your every digital marketing effort, hindering your ability to drive traffic, leads, and revenue. Companies that make their decisions based on data are 58% more likely to beat their revenue goals, but the quality of that data is imperative.
There’s just no point anymore in creating a massive data warehouse jammed with prospective use cases. The path to operationalizing that data is too long, cumbersome, and far away from its actual utility in marketing.
If you haven’t already, it’s time to graduate beyond this time-consuming and labor-intensive approach. Find a platform that gathers data from all relevant sources, automates repetitive tasks, employs AI and deep learning to make meaningful recommendations, automates to assist, and keeps pace with changes in search all within a single interface.
Intelligent automation and this level enable agile marketers to get in front of consumer demand—to meet site visitors in their moments of need with personalized content that is relevant, timely, and speaks directly to their unique behavioral characteristics. Your tools and technology need to empower your marketing team, not frustrate them, or create more work. They must free up time for more creative, impactful pursuits.
B. The way you put these insights and tools to work
Building agility into your marketing strategy isn’t a matter of picking up a few new skills or switching tools. It’s a mindset, a culture that transforms your marketing strategy from start to finish, from SEO to CMO.
According to a recent survey from Aprimo, 95% of marketers who have agile on their mind plan to adopt the approach within the next 12 months. The same respondents told us that 74% of Agile marketers are satisfied with their team’s performance and that Agile teams feel more capable of handling fast-paced work than their peers.
Top tips to help you and your team become Agile marketers
1. Take a page from our friends in software development
Familiarize yourself with the core tenets of Agile as it’s been used successfully by tech and development teams for years. The Agile Manifesto of Software Development, the Agile bible produced by 17 people in 2001, is a great starting point.
This mindset was founded on four key values:
- Individuals and interactions over processes and tools
- Working software over comprehensive documentation
- Customer collaboration over contract negotiation
- Responding to change over following a plan
Agile began as a method of developing software but has evolved into an ideology applicable to all manner of digitally transformative projects such as the Agile Marketing Manifesto.
The four key values when applied specifically to marketing become seven, and they are:
- Validated learning over opinions and conventions
- Customer-focused collaboration over silos and hierarchy
- Adaptive and iterative campaigns over Big-Bang campaigns
- The process of customer discovery over static prediction
- Flexible vs. rigid planning
- Responding to change over following a plan
- Many small experiments over a few large bets
2. Decide what you want to achieve with your Agile approach
Understand the benefits of Agile and decide how each will apply in your own organization.
Accelerating time to market, enhancing one’s ability to manage changing priorities, increasing productivity, and improving alignment between IT and business objectives are among the top reasons firms adopt Agile methods. But what do you expect it to do for yours—and how will you accurately measure outcomes?
3. Understand the characteristics that make agile team members
As I’ve said, agile is a mindset. As you’re putting your teams in place and making new hires, keep your eye on building that culture you want to achieve. Marketers who exhibit qualities of collaboration, openness, creative thinking, and resilience are good choices. Rigidity, total ownership of processes and ideas, unwillingness to change once a plan is in place, and protectionism are all red flags.
Being an agile marketer doesn’t mean you stop planning. It means you build the ability to change and pivot quickly into your plans. At the leadership level, agility requires that you have a big picture view of the tools, data, and people in play but more importantly that you possess the emotional intelligence to understand the motivations and needs of each stakeholder. An agile marketer can “read the room” quickly and on an ongoing basis to inform incremental decisions and make adjustments as the plan is implemented.
Constantly learning. Constantly testing. The Coronavirus was a stark reminder of just how quickly things can change. Consumer behaviors, service delivery models, market segments, and entire organizational models had to change overnight. Companies have been forced to rethink entire product and service lines, in some cases shifting into new verticals.
Agile Marketers are best positioned to succeed in whatever comes next, having built the solid foundation of skills, technology, and talent it takes to constantly process and activate new information.
Is that you?
Jim Yu is the founder and CEO of BrightEdge, the leading enterprise SEO and content performance platform.
The post The agile marketer: Building agility with technology and talent appeared first on Search Engine Watch.
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