We all want to feel connected, that we’re part of a brand bigger than ourselves. And we want the brands we support to have a similar feeling towards us. That’s part of the motivation for some retailers, who have switched their business models to better reflect how we shop and how they reach us in the first place.
Since Facebook ads are a seemingly ubiquitous and seamless way to reach a customer base, many companies take to Facebook to engage with their audiences. That’s nothing new. However, what is a growing trend is the number of companies investing their Facebook and LinkedIn ad dollars to “dark posts”. But what are they?
Think about ads for select demographics: whether a post with a status update, a video or photo, or a link to another spot. But these posts are only seen by their intended targets as an advertisement in their NewsFeed. They’re not seen on the brand’s Facebook timeline, allowing companies to try out new advertising concepts without pestering all of their followers with seemingly disjointed approaches.
Does your business need dark posts?
For companies, there are several advantages. For starters, you can hone advertising efforts to specific bands of potential customers without being visible to the world at large.
More importantly, companies can test message content without resorting to spam or looking desperate by over saturating those interested in your brand. This reduces the risk of potential customers unliking your page or blocking your ad.
Everyone wants to ensure dollars devoted to advertisement provide the biggest return on investment possible. Dark posts (or unpublished posts, as they’re sometimes known) allow brands to adjust their headlines and identify more effective times of publication and calls for customer response. This level of deployment customization allows companies to determine different levels of efficacy and utilize what works best. Companies can (and should) do so for each customer band that they’re after.
By targeting ads to customer-specific traits, tendencies, or behaviors, brands hope to increase customer engagement. It’s important to understand the appropriate strategy for their use. TrackMaven’s research into the different reaches of dark posts versus boosted posts on Facebook provides some insight.
They identified that boosted posts received slightly more interaction overall, but dark posts were more successful in generating page likes for the business. Dark posts are also deployed for longer lifespans. Firms use dark posts for an average of 42 days versus 27 for boosted posts.
Things to avoid
No one wants to feel like a company is stalking them online. Target the ad too closely to the demographics or the customer behaviors, and the super-cute approach that’s meant to persuade engagement feels creepy. Going further in an attempt to engage users by name risks not only a loss of engagement, but a full disavowal of the brand and your products.
Also, it’s important to be specific, but not exclusionary. Facebook’s recent change allowing advertisers to create targeted ads addressing a user’s preferred (and self-reported) “ethnic affinity” has been controversial. Their advertising algorithms allowed marketers to exclude potential customers by ethnic affinity. A smart business strategy would be to ensure targeted posts reach the intended audience without being too exclusive.
Facebook is taking steps to ensure their approaches to advertising provide marketers with a wide variety of search options while remaining within the law. Responding this week to concerns, Facebook stated those ethnic affinity tools would no longer be available for marketers placing credit, employment, or housing ads.
“There are many nondiscriminatory uses of our ethnic affinity solution in these areas, but we have decided that we can best guard against discrimination by suspending these types of ads,” Erin Egan, Facebook’s chief privacy officer, wrote in a recent blog post on the topic.
Marketers outside these three areas can still utilize ethnic affinity as one of their targeting features for creating dark posts, however. Egan also added that Facebook’s new advertising guidelines would “require advertisers to affirm that they will not engage in discriminatory advertising” on the site.
If used correctly, brands can create a whole host of advertisements that allow customers to feel a part of the brand and do so in an organic fashion, remaining true to your overall branding strategy, one segment at a time.
4 ways startups prove their investment in upcoming technology trends
(TECH NEWS) Want to see into the future? Just take a look at what technology the tech field is exploring and investing in today — that’s the stuff that will make up the world of tomorrow.
Big companies scout like for small ones that have proven ideas and prototypes, rather than take the initial risk on themselves. So startups have to stay ahead of technology by their very nature, in order to be stand-out candidates when selling their ideas to investors.
Innovation Leader, in partnership with KPMG LLP, recently conducted a study that sheds light onto the bleeding edge of tech: The technologies that the biggest companies are most interested in building right now.
The study asked its respondents to group 16 technologies into four categorical buckets, which Innovation Leader CEO Scott Kirsner refers to as “commitment level.”
The highest commitment level, “in-market or accelerating investment,” basically means that technology is already mainstream. For optimum tech-clairvoyance, keep your eyes on the technologies which land in the middle of the ranking.
“Investing or piloting” represents the second-highest commitment level – that means they have offerings that are approaching market-readiness.
The standout in this category is Advanced Analytics. That’s a pretty vague title, but it generally refers to the automated interpretation and prediction on data sets, and has overlap with Machine learning.
Wearables, on the other hand, are self explanatory. From smart watches to location trackers for children, these devices often pick up on input from the body, such heart rate.
The “Internet of Things” is finding new and improved ways to embed sensor and network capabilities into objects within the home, the workplace, and the world at large. (Hopefully that doesn’t mean anyone’s out there trying to reinvent Juicero, though.)
Collaboration tools and cloud computing also land on this list. That’s no shock, given the continuous pandemic.
The next tier is “learning and exploring”— that represents lower commitment, but a high level of curiosity. These technologies will take a longer time to become common, but only because they have an abundance of unexplored potential.
Blockchain was the highest ranked under this category. Not surprising, considering it’s the OG of making people go “wait, what?”
Augmented & virtual reality has been hyped up particularly hard recently and is in high demand (again, due to the pandemic forcing us to seek new ways to interact without human contact.)
And notably, AI & machine learning appears on rankings for both second and third commitment levels, indicating it’s possibly in transition between these categories.
The lowest level is “not exploring or investing,” which represents little to no interest.
Quantum computing is the standout selection for this category of technology. But there’s reason to believe that it, too, is just waiting for the right breakthroughs to happen.
Internet of Things and deep learning: How your devices are getting smarter
(TECH NEWS) The latest neural network from Massachusetts Institute of Technology shows a great bound forward for deep learning and the “Internet of Things.”
The deep learning that modifies your social media and gives you Google search results is coming to your thermostat.
Researchers at the Massachusetts Institute of Technology (MIT) have developed a deep learning system of neural networks that can be used in the “Internet of Things” (IoT). Named MCUNet, the system designs small neural networks that allow for previously unseen speed and accuracy for deep learning on IoT devices. Benefits of the system include energy savings and improved data security for devices.
Created in the early 1980s, the IoT is essentially a large group of everyday household objects that have become increasingly connected through the internet. They include smart fridges, wearable heart monitors, thermostats, and other “smart” devices. These gadgets run on microcontrollers, or computer chips with no processing system, that have very little processing power and memory. This has traditionally made it hard for deep learning to occur on IoT devices.
“How do we deploy neural nets directly on these tiny devices? It’s a new research area that’s getting very hot,” said Song Han, Assistant Professor of Computer Science at MIT who is a part of the project, “Companies like Google and ARM are all working in this direction.”
In order to achieve deep learning for IoT connected machines, Han’s group designed two specific components. The first is TinyEngine, an inference engine that directs resource management similar to an operating system would. The other is Tiny NAS, a neural architecture search algorithm. For those not well-versed in such technical terms, think of these things like a mini Windows 10 and machine learning for that smart fridge you own.
The results of these new components are promising. According to Han, MCUNet could become the new industry standard, stating that “It has huge potential.” He envisions the system has one that could help smartwatches not just monitor heartbeat and blood pressure but help analyze and explain to users what that means. It could also lead to making IoT devices far more secure than they are currently.
“A key advantage is preserving privacy,” says Han. “You don’t need to transmit the data to the cloud.”
It will still be a while until we see smart devices with deep learning capabilities, but it is all but inevitable at this point—the future we’ve all heard about is definitely on the horizon.
Google is giving back some privacy control? (You read that right)
(TECH NEWS) In a bizarre twist, Google is giving you the option to opt out of data collection – for real this time.
It’s strange to hear “Google” and “privacy” in the same sentence without “concerns” following along, yet here we are. In a twist that’s definitely not related to various controversies involving the tech company, Google is giving back some control over data sharing—even if it isn’t much.
Starting soon, you will be able to opt out of Google’s data-reliant “smart” features (Smart Compose and Smart Reply) across the G-Suite of pertinent products: Gmail, Chat, and Meet. Opting out would, in this case, prevent Google from using your data to formulate responses based on your previous activity; it would also turn off the “smart” features.
One might observe that users have had the option to turn off “smart” features before, but doing so didn’t disable Google’s data collection—just the features themselves. For Google to include the option to opt out of data collection completely is relatively unprecedented—and perhaps exactly what people have been clamoring for on the heels of recent lawsuits against the tech giant.
In addition to being able to close off “smart” features, Google will also allow you to opt out of data collection for things like the Google Assistant, Google Maps, and other Google-related services that lean into your Gmail Inbox, Meet, and Chat activity. Since Google knowing what your favorite restaurant is or when to recommend tickets to you can be unnerving, this is a welcome change of pace.
Keep in mind that opting out of data collection for “smart” features will automatically disable other “smart” options from Google, including those Assistant reminders and customized Maps. At the time of this writing, Google has made it clear that you can’t opt out of one and keep the other—while you can go back and toggle on data collection again, you won’t be able to use these features without Google analyzing your Meet, Chat, and Gmail contents and behavior.
It will be interesting to see what the short-term ramifications of this decision are. If Google stops collecting data for a small period of time at your request and then you turn back on the “smart” features that use said data, will the predictive text and suggestions suffer? Only time will tell. For now, keep an eye out for this updated privacy option—it should be rolling out in the next few weeks.
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