Monthly Archives: February 2019
Since its inception back in 2007, Facebook Ads has changed the way companies approach their online advertising strategies. Early on, many advertisers have tried and failed with Facebook Ads NOT because they were targeting the wrong audience, but because they did not fully understand the dynamics of this (non-search like) Ad platform. The confusion (still today) is due to the enormous traffic volume of users (which many of them disclosed their likes, interests, age, sex, race, political views, education, marital status, household income, etc…) that are skewing the overall performance which forces many advertisers into believing that Facebook is a scam. In this post, I will try to reinforce the notion that Facebook Ads can be successful for advertisers if they approach their strategies on a more micro-targeted level.
Over the years, marketers (like myself) started to change the methodologies of campaign structures just like we did with Google to obtain a good Quality Score. In March of 2014, Facebook rolled out a bunch of new features which seemed to model that of Google Adwords. Some of these updates included:
- Self-serve ad tool, Ad Sets, Ads Manager, Power Editor, 3rd party interfaces
Even before the adoption of Ad Sets, marketers started to realize that in order to “offset” the huge traffic volume and identify what was working and not working, they needed restructure everything at a Micro-level. This strategy of creating individual campaigns for each specific interest is what empowered many to re-think their expectations of what Facebook could do for them.
Below is a quick example of a standard Facebook Ads campaign that focuses on one specific audience. As you can see, we are focusing on Green Tea only (not Tea Drinkers in general). We are also segmenting Women-only as well as different Age Ranges which allows for a more granular understanding of interest and interaction.
#1 Why Micro-Targeting Works
In order to get the most out of your Ad dollars as well as identify winners and losers, micro-targeting is a must for every advertiser. Yes, it’s a lot of work and yes it requires many hours to set it up correctly. However, not investing in this time could cost you even more later down the line because all of the work that was done, can be utilized again in the future with little to no effort to update.
#2 Facebook Ads Creates Storytelling
Wouldn’t it be a great story to tell your CEO or client (Tea Company) that the majority of the FB conversions came from Single Women, 35-40, who live in Baltimore MD, and enjoy Pilates and Yoga. That specific piece of information was made possible by the micro-targeting created in Facebook Ads and quite possibly created a whole new level of both online and offline marketing strategies for years to come.
#3 Geo-Targeting Matters:
As mentioned in the storytelling example above, geography is a huge proponent of micro-targeting because of the different social behaviors that surround us. For example, advertisers that are interested in reaching a younger audience (25-35) that enjoy nightclubs and dancing, would be more likely to choose to target their ads in USA cities such as NYC, Miami, Las Vegas, LA, and Chicago instead of other locations that are not as likely to be interested.
#4 The Power of Indirect Targeting:
Lets assume that avid Tea Drinkers are also more likely to be fans of the Food Network and other TV cooking shows. With Facebook Ads, we have the ability to create individual campaigns targeting not only the Food Network, but also specific shows such as Man vs. Food, Barefoot Contessa and others… The fact that we can create TEST campaigns to see if those “in-direct” yet similar audiences could convert is a game-changer in all aspects of marketing.
#5 Why Timing Matters:
We are constantly being bombarded by news everyday coming from TV, radio and the internet. However, the one thing that is NOT constant is the “shelf-life” of the news story and that is where Facebook Ads (including all Social Media) provides a unique advantage for advertisers. For example, lets say the FDA (Food & Drug Administration) comes out with a study that says people who drink 2-3 cups of Green Tea everyday have a better chance to fight the symptoms of the common cold. This report obviously not only shines a positive light on the Tea Industry but it’s also fresh in everyone’s mind and when they see an ad for Green Tea in their Facebook Feed, they are likely to remember that news story about the health benefits and are more inclined to make an impulse buy.
Truth be told, Facebook Ads may not be a fit for everyone. While certain industries may thrive on having a social-friendly presence, many others will not find their target audience in that social environment. However, I implore that all advertisers/marketers to keep an open-mind when looking at Facebook Ads because there is more strategy potential than you think. In my opinion, FB Ads has become more a testing ground than a standard vehicle for website traffic. Facebook Ads may not be a GEM for everyone, but with an open-mind it could be a diamond in the rough.
Tonight an Israeli company will dispatch its spacecraft to the moon aboard a Falcon 9 rocket.
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If a picture is worth a thousand words, how many emails can you replace with a video? As offices fragment into remote teams, work becomes more visual and social media makes us more comfortable on camera, it’s time for collaboration to go beyond text. That’s the idea behind Loom, a fast-rising startup that equips enterprises with instant video messaging tools. In a click, you can film yourself or narrate a screenshare to get an idea across in a more vivid, personal way. Instead of scheduling a video call, employees can asynchronously discuss projects or give “stand-up” updates without massive disruptions to their workflow.
In the 2.5 years since launch, Loom has signed up 1.1 million users from 18,000 companies. And that was just as a Chrome extension. Today Loom launches its PC and Mac apps that give it a dedicated presence in your digital work space. Whether you’re communicating across the room or across the globe, “Loom is the next best thing to being there,” co-founder Shahed Khan tells me.
Now Loom is ready to spin up bigger sales and product teams thanks to an $ 11 million Series A led by Kleiner Perkins . The firm’s partner Ilya Fushman, formally Dropbox’s head of product and corporate development, will join Loom’s board. He’ll shepherd Loom through today’s launch of its $ 10 per month per user Pro version that offers HD recording, calls-to-action at the end of videos, clip editing, live annotation drawings and analytics to see who actually watched like they’re supposed to.
“We’re ditching the suits and ties and bringing our whole selves to work. We’re emailing and messaging like never before, but though we may be more connected, we’re further apart,” Khan tells me. “We want to make it very easy to bring the humanity back in.”
But back in 2016, Loom was just trying to survive. Khan had worked at Upfront Ventures after a stint as a product designer at website builder Weebly. He and two close friends, Joe Thomas and Vinay Hiremath, started Opentest to let app makers get usability feedback from experts via video. But after six months and going through the NFX accelerator, they were running out of bootstrapped money. That’s when they realized it was the video messaging that could be a business as teams sought to keep in touch with members working from home or remotely.
Together they launched Loom in mid-2016, raising a pre-seed and seed round amounting to $ 4 million. Part of its secret sauce is that Loom immediately starts uploading bytes of your video while you’re still recording so it’s ready to send the moment you’re finished. That makes sharing your face, voice and screen feel as seamless as firing off a Slack message, but with more emotion and nuance.
“Sales teams use it to close more deals by sending personalized messages to leads. Marketing teams use Loom to walk through internal presentations and social posts. Product teams use Loom to capture bugs, stand ups, etc.,” Khan explains.
Loom has grown to a 16-person team that will expand thanks to the new $ 11 million Series A from Kleiner, Slack, Cue founder Daniel Gross and actor Jared Leto that brings it to $ 15 million in funding. They predict the new desktop apps that open Loom to a larger market will see it spread from team to team for both internal collaboration and external discussions from focus groups to customer service.
Loom will have to hope that after becoming popular at a company, managers will pay for the Pro version that shows exactly how long each viewer watched. That could clue them in that they need to be more concise, or that someone is cutting corners on training and cooperation. It’s also a great way to onboard new employees. “Just watch this collection of videos and let us know what you don’t understand.” At $ 10 per month though, the same cost as Google’s entire GSuite, Loom could be priced too high.
Next Loom will have to figure out a mobile strategy — something that’s surprisingly absent. Khan imagines users being able to record quick clips from their phones to relay updates from travel and client meetings. Loom also plans to build out voice transcription to add automatic subtitles to videos and even divide clips into thematic sections you can fast-forward between. Loom will have to stay ahead of competitors like Vidyard’s GoVideo and Wistia’s Soapbox that have cropped up since its launch. But Khan says Loom looms largest in the space thanks to customers at Uber, Dropbox, Airbnb, Red Bull and 1,100 employees at HubSpot.
“The overall space of collaboration tools is becoming deeper than just email + docs,” says Fushman, citing Slack, Zoom, Dropbox Paper, Coda, Notion, Intercom, Productboard and Figma. To get things done the fastest, businesses are cobbling together B2B software so they can skip building it in-house and focus on their own product.
No piece of enterprise software has to solve everything. But Loom is dependent on apps like Slack, Google Docs, Convo and Asana. Because it lacks a social or identity layer, you’ll need to send the links to your videos through another service. Loom should really build its own video messaging system into its desktop app. But at least Slack is an investor, and Khan says “they’re trying to be the hub of text-based communication,” and the soon-to-be-public unicorn tells him anything it does in video will focus on real-time interaction.
Still, the biggest threat to Loom is apathy. People already feel overwhelmed with Slack and email, and if recording videos comes off as more of a chore than an efficiency, workers will stick to text. And without the skimability of an email, you can imagine a big queue of videos piling up that staffers don’t want to watch. But Khan thinks the ubiquity of Instagram Stories is making it seem natural to jump on camera briefly. And the advantage is that you don’t need a bunch of time-wasting pleasantries to ensure no one misinterprets your message as sarcastic or pissed off.
Khan concludes, “We believe instantly sharable video can foster more authentic communication between people at work, and convey complex scenarios and ideas with empathy.”
Resooma, the U.K. accommodation booking platform, is entering the fintech and utilities space with the launch of Resooma Bills, a new product to help “gen rent” manage household expenditure. The Cardiff, Wales-based company’s core offering is an accommodation marketplace primarily targeting students and other renters aged 18-30.
Previously trading under the brand name of University Cribs, Resooma was founded in 2014 by Jack Jenkins, Dan Jefferys and Christian Samuel as a solution aimed specifically at the student lettings market. The company has since broadened its remit to “fix the outdated methods” of renting a home and living together in shared accommodation.
“The existing processes, much of which [are] sitting offline, was a total mess and the numbers of people who have to experience it is climbing rapidly,” says co-founder Jenkins. “With more people living in shared accommodation post University life, we aim to appeal to a time constrained user base that want instant gratification from the products and services they use. We’re building a solution for generation rent”.
As a first step, Resooma set out to eradicate the viewing process, or at least make it digital, and help facilitate bookings online. This includes rolling out “VR tours” for homes, in a bid to gain the trust of renters booking online. “Student and young professionals are time sensitive, often nomadic in choosing where they work and live and as such our platform needs to cater for this,” says Jenkins. The startup also has plans to introduce rental guarantees and “Resooma Verified” stamps for rentals.
“Interestingly, we brand ourselves as a booking platform, a relatively unused term in the market we are in. People are used to booking directly on platforms for short-term accommodation, with the rise of Airbnb and Booking.com but our goal is to make this the norm for people renting medium or longer term homes,” adds the Resooma co-founder.
Jenkins says the next problem the company wants to solve is around utilities and the splitting of household bills. “We’ve all sat there in our new home, admiring the wall paper for the first 2 weeks while we wait for the internet or Sky TV to be set up. It’s brainless really, and we’re fixing it,” he says.
“Our product journey will put utilities as part of the rental transaction, allowing users to set up their household bills directly through the platform at the time of booking. What’s more, we’ll allow you to split these bills evenly between all tenants. No more arguments because Tom didn’t pay for his share of the internet bill. Our solution will track utility payments, aim to source the cheapest deals for our customers and then automatically issue each of the housemates one single bill each month for their share of the total house bills.
“While part of the full product vision of Resooma, Resooma Bills will sit as a standalone product as well to allow users the flexibility to use the service for homes found away from the Resooma platform”.
Asked to name Resooma’s competitors, Jenkins says the likes of Spotahome or Uniplaces are probably its most direct competition from a product perspective. “We differentiate ourselves through our adaptation of the utilities, as well as our focus on working with letting agents rather than directly with Landlords,” he says.
With regards to utilities and bill splitting, London-based Acasa could also been considered a very direct competitor.
Context Clusters and Query Suggestions at Google
A new patent application from Google tells us about how the search engine may use context to find query suggestions before a searcher has completed typing in a full query. After seeing this patent, I’ve been thinking about previous patents I’ve seen from Google that have similarities.
It’s not the first time I’ve written about a Google Patent involving query suggestions. I’ve written about a couple of other patents that were very informative, in the past:
- 6/10/2016 – Google Entity Search Suggestions Patent (Associating an entity with a search query)
- 5/26/2010How a Search Engine Might Identify Possible Query Suggestions (Generating query suggestions using contextual information)
In both of those, the inclusion of entities in a query impacted the suggestions that were returned. This patent takes a slightly different approach, by also looking at context.
Context Clusters in Query Suggestions
We’ve been seeing the word Context spring up in Google patents recently. Context terms from knowledge bases appearing on pages that focus on the same query term with different meanings, and we have also seen pages that are about specific people using a disambiguation approach. While these were recent, I did blog about a paper in 2007, which talks about query context with an author from Yahoo. The paper was Using Query Contexts in Information Retrieval. The abstract from the paper provides a good glimpse into what it covers:
User query is an element that specifies an information need, but it is not the only one. Studies in literature have found many contextual factors that strongly influence the interpretation of a query. Recent studies have tried to consider the user’s interests by creating a user profile. However, a single profile for a user may not be sufficient for a variety of queries of the user. In this study, we propose to use query-specific contexts instead of user-centric ones, including context around query and context within query. The former specifies the environment of a query such as the domain of interest, while the latter refers to context words within the query, which is particularly useful for the selection of relevant term relations. In this paper, both types of context are integrated in an IR model based on language modeling. Our experiments on several TREC collections show that each of the context factors brings significant improvements in retrieval effectiveness.
The Google patent doesn’t take a user-based approach ether, but does look at some user contexts and interests. It sounds like searchers might be offered a chance to select a context cluster before showing query suggestions:
In some implementations, a set of queries (e.g., movie times, movie trailers) related to a particular topic (e.g., movies) may be grouped into context clusters. Given a context of a user device for a user, one or more context clusters may be presented to the user when the user is initiating a search operation, but prior to the user inputting one or more characters of the search query. For example, based on a user’s context (e.g., location, date and time, indicated user preferences and interests), when a user event occurs indicating the user is initiating a process of providing a search query (e.g., opening a web page associated with a search engine), one or more context clusters (e.g., “movies”) may be presented to the user for selection input prior to the user entering any query input. The user may select one of the context clusters that are presented and then a list of queries grouped into the context cluster may be presented as options for a query input selection.
I often look up the inventors of patents to get a sense of what else they may have written, and worked upon. I looked up Jakob D. Uszkoreit in LinkedIn, and his profile doesn’t surprise me. He tells us there of his experience at Google:
Previously I started and led a research team in Google Machine Intelligence, working on large-scale deep learning for natural language understanding, with applications in the Google Assistant and other products.
This passage reminded me of the search results being shown to me by the Google Assistant, which are based upon interests that I have shared with Google over time, and that Google allows me to update from time to time. If the inventor of this patent worked on Google Assistant, that doesn’t surprise me. I haven’t been offered context clusters yet (and wouldn’t know what those might look like if Google did offer them. I suspect if Google does start offering them, I will realize that I have found them at the time they are offered to me.)
Like many patents do, this one tells us what is “innovative” about it. It looks at:
…query data indicating query inputs received from user devices of a plurality of users, the query data also indicating an input context that describes, for each query input, an input context of the query input that is different from content described by the query input; grouping, by the data processing apparatus, the query inputs into context clusters based, in part, on the input context for each of the query inputs and the content described by each query input; determining, by the data processing apparatus, for each of the context clusters, a context cluster probability based on respective probabilities of entry of the query inputs that belong to the context cluster, the context cluster probability being indicative of a probability that at least one query input that belongs to the context cluster and provided for an input context of the context cluster will be selected by the user; and storing, in a data storage system accessible by the data processing apparatus, data describing the context clusters and the context cluster probabilities.
It also tells us that it will calculate probabilities that certain context clusters might be requested by a searcher. So how does Google know what to suggest as context clusters?
Each context cluster includes a group of one or more queries, the grouping being based on the input context (e.g., location, date and time, indicated user preferences and interests) for each of the query inputs, when the query input was provided, and the content described by each query input. One or more context clusters may be presented to the user for input selection based on a context cluster probability, which is based on the context of the user device and respective probabilities of entry of the query inputs that belong to the context cluster. The context cluster probability is indicative of a probability that at least one query input that belongs to the context cluster will be selected by the user. Upon selection of one of the context clusters that is presented to the user, a list of queries grouped into the context cluster may be presented as options for a query input selection. This advantageously results in individual query suggestions for query inputs that belong to the context cluster but that alone would not otherwise be provided due to their respectively low individual selection probabilities. Accordingly, users’ informational needs are more likely to be satisfied.
The Patent in this patent application is:
(US20190050450) Query Composition System
Publication Number: 20190050450
Publication Date: February 14, 2019
Applicants: Google LLC
Inventors: Jakob D. Uszkoreit
Methods, systems, and apparatus for generating data describing context clusters and context cluster probabilities, wherein each context cluster includes query inputs based on the input context for each of the query inputs and the content described by each query input, and each context cluster probability indicates a probability that at a query input that belongs to the context cluster will be selected by the user, receiving, from a user device, an indication of a user event that includes data indicating a context of the user device, selecting as a selected context cluster, based on the context cluster probabilities for each of the context clusters and the context of the user device, a context cluster for selection input by the user device, and providing, to the user device, data that causes the user device to display a context cluster selection input that indicates the selected context cluster for user selection.
What are Context Clusters as Query Suggestions?
The patent tells us that context clusters might be triggered when someone is starting a query on a web browser. I tried it out, starting a search for “movies” and got a number of suggestions that were combinations of queries, or what seem to be context clusters:
The patent says that context clusters would appear before someone began typing, based upon topics and user information such as location. So, if I were at a shopping mall that had a movie theatre, I might see Search suggestions for movies like the ones shown here:
One of those clusters involved “Movies about Business”, which I selected, and it showed me a carousel, and buttons with subcategories to also choose from. This seems to be a context cluster:
This seems to be a pretty new idea, and may be something that Google would announce as an availble option when it becomes available, if it does become available, much like they did with the Google Assistant. I usually check through the news from my Google Assistant at least once a day. If it starts offering search suggestions based upon things like my location, it could potentially be very interesting.
User Query Histories
The patent tells us that context clusters selected to be shown to a searcher might be based upon previous queries from a searcher, and provides the following example:
Further, a user query history may be provided by the user device (or stored in the log data) that includes queries and contexts previously provided by the user, and this information may also factor into the probability that a user may provide a particular query or a query within a particular context cluster. For example, if the user that initiates the user event provides a query for “movie show times” many Friday afternoons between 4 PM-6 PM, then when the user initiates the user event on a Friday afternoon in the future between these times, the probability associated with the user inputting “movie show times” may be boosted for that user. Consequentially, based on this example, the corresponding context cluster probability of the context cluster to which the query belongs may likewise be boosted with respect to that user.
It’s not easy to tell whether the examples I provided about movies above are related to this patent or if it is tied more closely to the search results that appear in Google Assistant results. It’s worth reading through and thinking about potential experimental searches to see if they might influence the results that you may see. It is interesting that Google may attempt to anticipate what is suggests to show to us as query suggestions, after showing us search results based upon what it believes are our interests based upon searches that we have performed or interests that we have identified for Google Assistant.
The contex cluster may be related to the location and time that someone accesses the search engine. The patent provides an example of what might be seen by the searcher like this:
In the current example, the user may be in the location of MegaPlex, which includes a department store, restaurants, and a movie theater. Additionally, the user context may indicate that the user event was initiated on a Friday evening at 6 PM. Upon the user initiating the user event, the search system and/or context cluster system may access the content cluster data 214 to determine whether one or more context clusters is to be provided to the user device as an input selection based at least in part on the context of the user. Based on the context of the user, the context cluster system and/or search system may determine, for each query in each context cluster, a probability that the user will provide that query and aggregate the probability for the context cluster to obtain a context cluster probability.
In the current example, there may be four queries grouped into the “Movies” cluster, four queries grouped into the “Restaurants” cluster, and three queries grouped into the “Dept. Store” cluster. Based on the analysis of the content cluster data, the context cluster system may determine that the aggregate probability of the queries in each of the “Movies” cluster, “Restaurant” cluster, and “Dept. Store” cluster have a high enough likelihood (e.g., meet a threshold probability) to be input by the user, based on the user context, that the context clusters are to be presented to the user for selection input in the search engine web site.
I could see running such a search at a shopping mall, to learn more about the location I was at, and what I could find there, from dining places to movies being shown. That sounds like it could be the start of an interesting adventure.
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Mark Zuckerberg recently revealed that commerce is a huge part of the 2019 roadmap for Facebook’s family of apps. But before people can easily buy things from Instagram etc, Facebook needs their credit card info on file. That’s a potentially lucrative side effect of Instagram’s plan to launch a Fundraiser sticker in 2019. Facebook’s own Donate buttons have raised $ 1 billion, and bringing them to Instagram’s 1 billion users could do a lot of good while furthering Facebook’s commerce strategy.
New code and imagery dug out of Instagram’s Android app reveals how the Fundraiser stickers will allow you to search for non-profits and add a Donate button for them to your Instagram Story. After you’ve donated to something once, Instagram could offer instant checkout on stuff you want to buy using the same payment details.
Back in 2013 when Facebook launched its Donate button, I suggested that it could add a “remove credit card after checkout” option to its fundraisers if it wanted to make it clear that the feature was purely altruistic. Facebook never did that. You still need to go into your payment settings or click through the See Receipt option after donating and then edit your account settings to remove your credit card. We’ll see if Instagram is any different. We’ve also asked whether Instagrammers will be able to raise money for personal causes, which would make it more of a competitor to GoFundMe — which has sadly become the social safety net for many facing healthcare crises.
Facebook mentioned at its Communities Summit earlier this month that it’d be building Instagram Fundraiser stickers, but the announcement was largely overshadowed by the company’s reveal of new Groups features. This week, TechCrunch tipster Ishan Agarwal found code in the Instagram Android app detailing how users will be able search for non-profits or browse collections of Suggested charities and ones they follow. They can then overlay a Donate button sticker on their Instagram Story that their followers can click through to contribute.
We then asked reverse engineering specialist Jane Manchun Wong to take a look, and she was able to generate the screenshots seen above that show a green heart icon for the Fundraiser sticker plus the non-profit search engine. A Facebook’s spokespeople tell me that “We are in early stages and working hard to bring this experience to our community . . . Instagram is all about bringing you closer to the people and things you love, and a big part of that is showing support for and bringing awareness to meaningful communities and causes. Later this year, people will be able to raise money and help support nonprofits that are important to them through a donation sticker in Instagram Stories. We’re excited to bring this experience to our community and will share more updates in the coming months.”
Zuckerbeg said during the Q4 2018 earnings call last month that “In Instagram, one of the areas I’m most excited about this year is commerce and shopping . . . there’s also a very big opportunity in basically enabling the transactions and making it so that the buying experience is good”. Streamlining those transactions through saved payment details means more people will complete their purchase rather than abandoning their cart. Facebook CFO David Wehner noted on the call that “Continuing to build good advertising products for our e-commerce clients on the advertising side will be a more important contributor to revenue in the foreseeable future”. Even though Facebook isn’t charging a fee on transactions, powering higher commerce conversion rates convinces merchants to buy more ads on the platform.
With all the talk of envy spiraling, phone addiction, bullying, and political propaganda, enabling donations is at least one way Instagram can prove it’s beneficial to the world. Snapchat lacks formal charity features, and Twitter appears to have ended its experiment allowing non-profits to tweet donate buttons. Despite all the flack Facebook rightfully takes, the company has shown a strong track record with philanthropy that mirrors Zuckerberg’s own $ 47 billion commitment through the Chan Zuckerberg Initiative. And if having some relatively benign secondary business benefit speeds companies towards assisting non-profits, that’s a trade-off we should be willing to embrace.
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