Monthly Archives: April 2019
Microsoft has never been shy about being acquisitive, and today it announced it’s buying Express Logic, a San Diego company that has developed a real-time operating system (RTOS) aimed at controlling the growing number of IoT devices in the world.
The companies did not share the purchase price.
Express Logic is not some wide-eyed, pie-in-the-sky startup. It has been around for 23 years, building (in its own words) “industrial-grade RTOS and middleware software solutions for embedded and IoT developers.” The company boasts some 6.2 billion (yes, billion) devices running its systems. That number did not escape Sam George, director of Azure IoT at Microsoft, but as he wrote in a blog post announcing the deal, there is a reason for this popularity.
“This widespread popularity is driven by demand for technology to support resource constrained environments, especially those that require safety and security,” George wrote.
Holger Mueller, an analyst with Constellation Research, says that market share also gives Microsoft instant platform credibility. “This is a key acquisition for Microsoft: on the strategy side Microsoft is showing it is serious with investing heavily into IoT, and on the product side it’s a key step to get into the operating system code of the popular RTOS,” Mueller told TechCrunch.
The beauty of Express Logic’s approach is that it can work in low-power and low-resource environments and offers a proven solution for a range or products. “Manufacturers building products across a range of categories — from low-capacity sensors like lightbulbs and temperature gauges to air conditioners, medical devices and network appliances — leverage the size, safety and security benefits of Express Logic solutions to achieve faster time to market,” George wrote.
Writing in a blog post to his customers announcing the deal, Express Logic CEO William E. Lamie, expressed optimism that the company can grow even further as part of the Microsoft family. “Effective immediately, our ThreadX RTOS and supporting software technology, as well as our talented engineering staff join Microsoft. This complements Microsoft’s existing premier security offering in the microcontroller space,” he wrote.
Microsoft is getting an established company with a proven product that can help it scale its Azure IoT business. The acquisition is part of a $ 5 billion investment in IoT the company announced last April that includes a number of Azure pieces, such as Azure Sphere, Azure Digital Twins, Azure IoT Edge, Azure Maps and Azure IoT Central.
“With this acquisition, we will unlock access to billions of new connected endpoints, grow the number of devices that can seamlessly connect to Azure and enable new intelligent capabilities. Express Logic’s ThreadX RTOS joins Microsoft’s growing support for IoT devices and is complementary with Azure Sphere, our premier security offering in the microcontroller space,” George wrote.
When you incorporate machine learning techniques to speed up SEO recovery, the results can be amazing.
This is the third and last installment from our series on using Python to speed SEO traffic recovery. In part one, I explained how our unique approach, that we call “winners vs losers” helps us quickly narrow down the pages losing traffic to find the main reason for the drop. In part two, we improved on our initial approach to manually group pages using regular expressions, which is very useful when you have sites with thousands or millions of pages, which is typically the case with ecommerce sites. In part three, we will learn something really exciting. We will learn to automatically group pages using machine learning.
As mentioned before, you can find the code used in part one, two and three in this Google Colab notebook.
Let’s get started.
URL matching vs content matching
When we grouped pages manually in part two, we benefited from the fact the URLs groups had clear patterns (collections, products, and the others) but it is often the case where there are no patterns in the URL. For example, Yahoo Stores’ sites use a flat URL structure with no directory paths. Our manual approach wouldn’t work in this case.
Fortunately, it is possible to group pages by their contents because most page templates have different content structures. They serve different user needs, so that needs to be the case.
How can we organize pages by their content? We can use DOM element selectors for this. We will specifically use XPaths.
For example, I can use the presence of a big product image to know the page is a product detail page. I can grab the product image address in the document (its XPath) by right-clicking on it in Chrome and choosing “Inspect,” then right-clicking to copy the XPath.
We can identify other page groups by finding page elements that are unique to them. However, note that while this would allow us to group Yahoo Store-type sites, it would still be a manual process to create the groups.
A scientist’s bottom-up approach
In order to group pages automatically, we need to use a statistical approach. In other words, we need to find patterns in the data that we can use to cluster similar pages together because they share similar statistics. This is a perfect problem for machine learning algorithms.
BloomReach, a digital experience platform vendor, shared their machine learning solution to this problem. To summarize it, they first manually selected cleaned features from the HTML tags like class IDs, CSS style sheet names, and the others. Then, they automatically grouped pages based on the presence and variability of these features. In their tests, they achieved around 90% accuracy, which is pretty good.
When you give problems like this to scientists and engineers with no domain expertise, they will generally come up with complicated, bottom-up solutions. The scientist will say, “Here is the data I have, let me try different computer science ideas I know until I find a good solution.”
One of the reasons I advocate practitioners learn programming is that you can start solving problems using your domain expertise and find shortcuts like the one I will share next.
Hamlet’s observation and a simpler solution
For most ecommerce sites, most page templates include images (and input elements), and those generally change in quantity and size.
I decided to test the quantity and size of images, and the number of input elements as my features set. We were able to achieve 97.5% accuracy in our tests. This is a much simpler and effective approach for this specific problem. All of this is possible because I didn’t start with the data I could access, but with a simpler domain-level observation.
I am not trying to say my approach is superior, as they have tested theirs in millions of pages and I’ve only tested this on a few thousand. My point is that as a practitioner you should learn this stuff so you can contribute your own expertise and creativity.
Now let’s get to the fun part and get to code some machine learning code in Python!
Collecting training data
We need training data to build a model. This training data needs to come pre-labeled with “correct” answers so that the model can learn from the correct answers and make its own predictions on unseen data.
In our case, as discussed above, we’ll use our intuition that most product pages have one or more large images on the page, and most category type pages have many smaller images on the page.
What’s more, product pages typically have more form elements than category pages (for filling in quantity, color, and more).
Unfortunately, crawling a web page for this data requires knowledge of web browser automation, and image manipulation, which are outside the scope of this post. Feel free to study this GitHub gist we put together to learn more.
Here we load the raw data already collected.
Each row of the form_counts data frame above corresponds to a single URL and provides a count of both form elements, and input elements contained on that page.
Meanwhile, in the img_counts data frame, each row corresponds to a single image from a particular page. Each image has an associated file size, height, and width. Pages are more than likely to have multiple images on each page, and so there are many rows corresponding to each URL.
It is often the case that HTML documents don’t include explicit image dimensions. We are using a little trick to compensate for this. We are capturing the size of the image files, which would be proportional to the multiplication of the width and the length of the images.
We want our image counts and image file sizes to be treated as categorical features, not numerical ones. When a numerical feature, say new visitors, increases it generally implies improvement, but we don’t want bigger images to imply improvement. A common technique to do this is called one-hot encoding.
Most site pages can have an arbitrary number of images. We are going to further process our dataset by bucketing images into 50 groups. This technique is called “binning”.
Here is what our processed data set looks like.
Adding ground truth labels
As we already have correct labels from our manual regex approach, we can use them to create the correct labels to feed the model.
We also need to split our dataset randomly into a training set and a test set. This allows us to train the machine learning model on one set of data, and test it on another set that it’s never seen before. We do this to prevent our model from simply “memorizing” the training data and doing terribly on new, unseen data. You can check it out at the link given below:
Model training and grid search
Finally, the good stuff!
All the steps above, the data collection and preparation, are generally the hardest part to code. The machine learning code is generally quite simple.
We’re using the well-known Scikitlearn python library to train a number of popular models using a bunch of standard hyperparameters (settings for fine-tuning a model). Scikitlearn will run through all of them to find the best one, we simply need to feed in the X variables (our feature engineering parameters above) and the Y variables (the correct labels) to each model, and perform the .fit() function and voila!
After running the grid search, we find our winning model to be the Linear SVM (0.974) and Logistic regression (0.968) coming at a close second. Even with such high accuracy, a machine learning model will make mistakes. If it doesn’t make any mistakes, then there is definitely something wrong with the code.
In order to understand where the model performs best and worst, we will use another useful machine learning tool, the confusion matrix.
When looking at a confusion matrix, focus on the diagonal squares. The counts there are correct predictions and the counts outside are failures. In the confusion matrix above we can quickly see that the model does really well-labeling products, but terribly labeling pages that are not product or categories. Intuitively, we can assume that such pages would not have consistent image usage.
Here is the code to put together the confusion matrix:
Finally, here is the code to plot the model evaluation:
Resources to learn more
You might be thinking that this is a lot of work to just tell page groups, and you are right!
Mirko Obkircher commented in my article for part two that there is a much simpler approach, which is to have your client set up a Google Analytics data layer with the page group type. Very smart recommendation, Mirko!
I am using this example for illustration purposes. What if the issue requires a deeper exploratory investigation? If you already started the analysis using Python, your creativity and knowledge are the only limits.
If you want to jump onto the machine learning bandwagon, here are some resources I recommend to learn more:
- Attend a Pydata event I got motivated to learn data science after attending the event they host in New York.
- Hands-On Introduction To Scikit-learn (sklearn)
- Scikit Learn Cheat Sheet
- Efficiently Searching Optimal Tuning Parameters
- If you are starting from scratch and want to learn fast, I’ve heard good things about Data Camp.
Got any tips or queries? Share it in the comments.
Hamlet Batista is the CEO and founder of RankSense, an agile SEO platform for online retailers and manufacturers. He can be found on Twitter @hamletbatista.
The post Using Python to recover SEO site traffic (Part three) appeared first on Search Engine Watch.
Editors Note: This article is part of a series that explores the world of growth marketing for founders. If you’ve worked with an amazing growth marketing agency, nominate them to be featured in our shortlist of top growth marketing agencies in tech.
Startups often set themselves back a year by hiring the wrong growth marketer.
This post shares a framework my marketing agency uses to source and vet high-potential growth candidates.
With it, early-stage startups can identify and attract a great first growth hire.
It’ll also help you avoid unintentionally hiring candidates who lack broad competency. Some marketers master 1-2 channels, but aren’t experts at much else. When hiring your first growth marketer, you should aim for a generalist.
This post covers two key areas:
- How I find growth candidates.
- How I identify which candidates are legitimately talented.
Great marketers are often founders
One interesting way to find great marketers is to look for great potential founders.
Let me explain. Privately, most great marketers admit that their motive for getting hired was to gain a couple years’ experience they could use to start their own company.
Don’t let that scare you. Leverage it: You can sidestep the competitive landscape for marketing talent by recruiting past founders whose startups have recently failed.
Why do this? Because great founders and great growth marketers are often one and the same. They’re multi-disciplinary executors, they take ownership and they’re passionate about product.
You see, a marketing role with sufficient autonomy mimics the role of a founder: In both, you hustle to acquire users and optimize your product to retain them. You’re working across growth, brand, product and data.
As a result, struggling founders wanting a break from the startup roller coaster often find transitioning to a growth marketing role to be a natural segue.
How do we find these high-potential candidates?
To find past founders, you could theoretically monitor the alumni lists of incubators like Y Combinator and Techstars to see which companies never succeeded. Then you can reach out to their first-time founders.
There are thousands of promising founders who’ve left a mark on the web. Their failure is not necessarily indicative of incompetence. My agency’s co-founders and directors, including myself, all failed at founding past companies.
How do I attract candidates?
To get potential founders interested in the day-to-day of your marketing role, offer them both breadth and autonomy:
- Let them be involved in many things.
- Let them be fully in charge of a few things.
Remember, recreate the experience of being a founder.
Further, vet their enthusiasm for your product, market and its product-channel fit:
- Product and market: Do their interests line up with how your product impacts its users? For example, do they care more about connecting people through social networks, or about solving productivity problems through SaaS? And which does your product line up with?
- Product-channel fit: Are they excited to run the acquisition channels that typically succeed in your market?
The latter is a little-understood but critically important requirement: Hire marketers who are interested in the channels your company actually needs.
Let’s illustrate this with a comparison between two hypothetical companies:
- A B2B enterprise SaaS app.
- An e-commerce company that sells mattresses.
Broadly speaking, the enterprise app will most likely succeed through the following customer acquisition channels: sales, offline networking, Facebook desktop ads and Google Search.
In contrast, the e-commerce company will most likely succeed through Instagram ads, Facebook mobile ads, Pinterest ads and Google Shopping ads.
We can narrow it even further: In practice, most companies only get one or two of their potential channels to work profitably and at scale.
Meaning, most companies have to develop deep expertise in just a couple of channels.
There are enterprise marketers who can run cold outreach campaigns on autopilot. But, many have neither the expertise nor the interest to run, say, Pinterest ads. So if you’ve determined Pinterest is a high-leverage ad channel for your business, you’d be mistaken to assume that an enterprise marketer’s cold outreach skills seamlessly translate to Pinterest ads.
Some channels take a year or longer to master. And mastering one channel doesn’t necessarily make you any better at the next. Pinterest, for example, relies on creative design. Cold email outreach relies on copywriting and account-based marketing.
(How do you identify which ad channels are most likely to work for your company? Read my Extra Crunch article for a breakdown.)
To summarize: To attract the right marketers, identify those who are interested in not only your product but also how your product is sold.
The founder-first approach I’ve shared is just one of many ways my agency recruits great marketers. The point is to remind you that great candidates are sometimes a small career pivot away from being your perfect hire. You don’t have to look in the typical places when your budget is tight and you want to hire someone with high, senior potential.
This is especially relevant for early-stage, bootstrapping startups.
If you have the foresight to recognize these high-potential candidates, you can hopefully hire both better and cheaper. Plus, you empower someone to level up their career.
Speaking of which, here are other ways to hire talent whose potential hasn’t been fully realized:
- Find deep specialists (e.g. Facebook Ads experts) and offer them an opportunity to learn complementary skills with a more open-ended, strategic role. (You can help train them with my growth guide.)
- Poach experienced junior marketers from a company in your space by offering senior roles.
- Hire candidates from top growth marketing schools.
Vetting growth marketers
If you don’t yet have a growth candidate to vet, you can stop reading here. Bookmark this and return when you do!
Now that you have a candidate, how do you assess whether they’re legitimately talented?
At Bell Curve, we ask our most promising leads to incrementally complete three projects:
- Create Facebook and Instagram ads to send traffic to our site. This showcases their low-level, tactical skills.
- Walk us through a methodology for optimizing our site’s conversion rate. This showcases their process-driven approach to generating growth ideas. Process is everything.
- Ideate and prioritize customer acquisition strategies for our company. This showcases their ability to prioritize high-leverage projects and see the big picture.
We allow a week to complete these projects. And we pay them market wage.
Here’s what we’re looking for when we assess their work.
Level 1: Basics
First — putting their work aside — we assess the dynamics of working with them. Are they:
- Competent: Can they follow instructions and understand nuance?
- Reliable: Will they hit deadlines without excuses?
- Communicative: Will they proactively clarify unclear things?
- Kind: Do they have social skills?
If they follow our instructions and do a decent job, they’re competent. If they hit our deadline, they’re probably reliable. If they ask good questions, they’re communicative.
And if we like talking to them, they’re kind.
Level 2: Capabilities
A level higher, we use these projects to assess their ability to contribute to the company:
- Do they have a process for generating and prioritizing good ideas?
- Did their process result in multiple worthwhile ad and landing page ideas? We’re assessing their process more so than their output. A great process leads to generating quality ideas forever.
- Resources are always limited. One of the most important jobs of a growth marketer is to ensure growth resources are focused on the right opportunities. I’m looking for a candidate that has a process for identifying, evaluating and prioritizing growth opportunities.
- Can they execute on those ideas?
- Did they create ads and propose A/B tests thoughtfully? Did they identify the most compelling value propositions, write copy enticingly and target audiences that make sense?
- Have they achieved mastery of 1-2 acquisition channels (ideally, the channels your company is dependent on to scale)? I don’t expect anyone to be an expert in all channels, but deep knowledge of at least a couple of channels is key for an early-stage startup making their first growth hire.
If you don’t have the in-house expertise to assess their growth skills, you can pay an experienced marketer to assess their work. It’ll cost you a couple hundred bucks, and give you peace of mind. Look on Upwork for someone, or ask a marketer at a friend’s company.
- If you’re an early-stage company with a tight budget, there are creative ways to source high-potential growth talent.
- Assess that talent on their product fit and market fit for your company. Do they actually want to work on the channels needed for your business to succeed?
- Give them a week-long sample project. Assess their ability to generate ideas and prioritize them.
“We want your followers to focus on what you share, not how many likes your posts get. During this test, only the person who shares a post will see the total number of likes it gets.” That’s how Instagram describes a seemingly small design change test with massive potential impact on users’ well-being.
Hiding Like counts could reduce herd mentality, where people just Like what’s already got tons of Likes. It could reduce the sense of competition on Instagram, since users won’t compare their own counts with those of more popular friends or superstar creators. And it could encourage creators to post what feels most authentic rather than trying to rack up Likes for everyone to see.
The design change test was spotted by Jane Manchun Wong, the prolific reverse-engineering expert and frequent TechCrunch tipster who has spotted tons of Instagram features before they’re officially confirmed or launched. Wong discovered the design change test in Instagram’s Android code and was able to generate the screenshots above.
You can see on the left that the Instagram feed post lacks a Like count, but still shows a few faces and a name of other people who’ve Liked it. Users are alerted that only they will see their post’s Like counts, and anyone else won’t. Many users delete posts that don’t immediately get “enough” Likes or post to their fake “Finstagram” accounts if they don’t think they’ll be proud of the hearts they collect. Hiding Like counts might get users posting more because they’ll be less self-conscious.
Instagram confirmed to TechCrunch that this design is an internal prototype that’s not visible to the public yet. A spokesperson told us: “We’re not testing this at the moment, but exploring ways to reduce pressure on Instagram is something we’re always thinking about.” Other features we’ve reported on in the same phase, such as video calling, soundtracks for Stories and the app’s time well spent dashboard, all went on to receive official launches.
Meanwhile, Wong has also recently spotted several other Instagram prototypes lurking in its Android code. Those include chat thread stickers for Direct messages, augmented reality filters for Direct Video calls, simultaneous co-watching of recommended videos through Direct, karaoke-style lyrics that appear synced to soundtracks in Stories, emoji reactions to feed posts and a shopping bag for commerce.
It appears there’s no plan to hide follower counts on user profiles, which are the true measure of popularity, but also serve a purpose of distinguishing great content creators and assessing their worth to marketers. Hiding Likes could just put more of a spotlight on follower and comment counts. And even if users don’t see Like counts, they still massively impact the feed’s ranking algorithm, so creators will still have to battle for them to be seen.
The change matches a growing belief that Like counts can be counter-productive or even harmful to users’ psyches. Instagram co-founder Kevin Systrom told me back in 2016 that getting away from the pressure of Like counts was one impetus for Instagram launching Stories. Last month, Twitter began testing a design that hides retweet counts behind an extra tap to similarly discourage inauthentic competition and herd mentality. And Snapchat has never shown Like counts or even follower counts, which has made it feel less stressful but also less useful for influencers.
Narcissism, envy spiraling and low self-image can all stem from staring at Like counts. They’re a constant reminder of the status hierarchies that have emerged from social networks. For many users, at some point it stopped being fun and started to feel more like working in the heart mines. If Instagram rolls out the feature, it could put the emphasis back on sharing art and self-expression, not trying to win some popularity contest.
Hero Conf Philly is Tuesday, April 23 – Thursday, April 25 and includes two full, action-packed days of sessions and a 3rd day of workshops. You’ll see a variety of speakers; top-rated veterans that have roamed the world offering up valuable tip and tricks, as well as the newest up-and-comers in the digital marketing landscape.
Read more at PPCHero.com
There isn’t much left to be done in online networking apps. We are all familiar with professional (LinkedIn), social (Facebook), real time (Twitter) and dating (Tinder, Bumble, etc). But profile photos of the people you’re interacting with only get you so far. And we’ve all known that person who looked smart in the photo and turned out to be not so amazing in real life. Photos don’t communicate a person’s energy, body language or their voice.
It’s now added swiping people, Tinder-style. Left for “later” and right for “favorite.” In addition, you can see who’s “Nearby” with a location feature, making it more likely you may even bump into this person. How’s that for making your day more…interesting?
Founder Hanna Aase says Wonderloop is not so much “LinkedIn with video” as much as it is “About.me with video.” Why? Well, because it also has a web-platform, allowing you to share your video profile outside the app, as well as message inside it.
I must admit, it’s fair to say that the impression you get from a person from watching them for 10 seconds on a video is pretty persuasive.
Aase says Wonderloop could end up being your personal “video ID,” providing each user with their unique video profile. She says Wonderloop’s aim is to create a search engine out of people on video.
“To see people on video creates trust. Wonderloop’s goal is that every person in the world should have a video identity. We want to help users get seen in this world. You use Wonderloop for the first step of turning a stranger into a potentially cool person in your life,” she added.
She thinks the app will be used by people to make new friends, connect influencers with fans, connect entrepreneurs, connect freelancers and travelers and of course a bit of dating here and there.
She’s also hoping the app will appeal to millennials and Generation Z who, as frequent travelers, are often into meeting people “nearby.” “We did research and were surprised to the extent the age group 16-20 wish to find new friends,” she says. For instance, apps like Jodel are used by young people to reach out to chat to complete strangers nearby (although with no names attached).
Right now the app is invite-only, but users can apply inside the app. Aase says: “We hope to do it in stages as the company grows and in a way where users feel the community is a place they feel safe and can share who they are on video. But being invite-only also makes us differentiated to all other services.”
Maybe you love the sound of your alarm clock blaring in the morning, heralding a new day full of joy and adventure. More likely, though, you don’t. If you prefer a more gentle wake-up (and have invested in some smart home technology), here’s some good news: Google Home now lets you use your Philips Hue lights to wake you up by slowly changing the light in your room.
Philips first announced this integration at CES earlier this year, with a planned rollout in March. Looks like that took a little while longer, as Google and Philips gently brought this feature to life.
Just like you can use your Home to turn on “Gentle Wake,” which starts changing your lights 30 minutes before your wake-up time to mimic a sunrise, you also can go the opposite way and have the lights mimic sunset as you get ready to go to bed. You can either trigger these light changes through an alarm or with a command that starts them immediately.
While the price of white Hue bulbs has come down in recent years, colored hue lights remain rather pricey, with single bulbs going for around $ 40. If that doesn’t hold you back, though, the Gentle Sleep and Wake features are now available in the U.S., U.K., Canada, Australia, Singapore and India in English only.
Ever since Google rolled out the Optimization Score, all accounts are full of notifications and recommended changes that may improve your account score. These recommendations range from keyword suggestions, targeting changes, ad suggestions, and automated smart bidding strategies. Should you apply or dismiss these recommended changes?
Read more at PPCHero.com
There have been an abundance of hand-wringing articles published that wonder if the era of the phone call is over, not to mention speculation that millennials would give up the option to make a phone call altogether if it meant unlimited data.
But actually, the rise of direct dialing through voice assistants and click to call buttons for mobile search means that calls are now totally intertwined with online activity.
Calling versus buying online is no longer an either/or proposition. When it comes to complicated purchases like insurance, healthcare, and mortgages, the need for human help is even more pronounced. Over half of consumers prefer to talk to an agent on the phone in these high-stakes situations.
In fact, 70% of consumers have used a click to call button. And three times as many people prefer speaking with a live human over a tedious web form. And calls aren’t just great for consumers either. A recent study by Invoca found that calls actually convert at ten times the rate of clicks.
However, if you’re finding that your business line isn’t ringing quite as often as you’d like it to, here are some surefire ways to optimize your search ads to drive more high-value phone calls.
Content produced in collaboration with Invoca.
Four ways to optimize your paid search ads for more phone calls
Let your audience know you’re ready to take their call — and that a real person will answer
If you’re waiting for the phone to ring, make sure your audiences know that you’re ready to take their call. In the days of landlines, if customers wanted a service, they simply took out the yellow pages and thumbed through the business listings until they found the service they were looking for. These days, your audience is much more likely to find you online, either through search engines or social media. But that doesn’t mean they aren’t looking for a human to answer their questions.
If you’re hoping to drive more calls, make sure your ads are getting that idea across clearly and directly. For example, if your business offers free estimates, make sure that message is prominent in the ad with impossible-to-miss text reading, “For a free estimate, call now,” with easy access to your number.
And to make sure customers stay on the line, let them know their call will be answered by a human rather than a robot reciting an endless list of options.
Cater to the more than half of users that will likely be on mobile
If your customer found your landing page via search, there’s a majority percent chance they’re on a mobile device.
While mobile accounted for just 27% of organic search engine visits in Q3 of 2013, its share increased to 57% as of Q4 2018.
That’s great news for businesses looking to boost calls, since mobile users obviously already have their phone in hand. However, forcing users to dig up a pen in order to write down your business number only to put it back into their phone adds an unnecessary extra step that could make some users think twice about calling.
Instead, make sure mobile landing pages offer a click to call button that lists your number in big, bold text. Usually, the best place for a click to call button is in the header of the page, near your form, but it’s best practice to A/B test button location and page layouts a few different ways in order to make sure your click to call button can’t be overlooked.
Use location-specific targeting
Since 2014, local search queries from mobile have skyrocketed in volume as compared to desktop.
In 2014, there were 66.5 billion search queries from mobile and 65.6 billion search queries from desktop.
Now in 2019, desktop has decreased slightly to 62.3 billion — while mobile has shot up to 141.9 billion — nearly a 250% increase in five years.
Mobile search is by nature local, and vice versa. If your customer is searching for businesses hoping to make a call and speak to a representative, chances are, they need some sort of local services. For example, if your car breaks down, you’ll probably search for local auto shops, click a few ads, and make a couple of calls. It would be incredibly frustrating if each of those calls ended up being to a business in another state.
Targeting your audience by region can ensure that you offer customers the most relevant information possible.
If your business only serves customers in Kansas, you definitely don’t want to waste perfectly good ad spend drumming up calls from California.
If you’re using Google Ads, make sure you set the location you want to target. That way, you can then modify your bids to make sure your call-focused ads appear in those regions.
Track calls made from ads and landing pages
Keeping up with where your calls are coming from in the physical world is important, but tracking where they’re coming from on the web is just as critical. Understanding which of your calls are coming from ads as well as which are coming from landing pages is an important part of optimizing paid search. Using a call tracking and analytics solution alongside Google Ads can help give a more complete picture of your call data.
And the more information you can track, the better. At a minimum, you should make sure your analytics solution captures data around the keyword, campaign/ad group, and the landing page that led to the call. But solutions like Invoca also allow you to capture demographic details, previous engagement history, and the call outcome to offer a total picture of not just your audience, but your ad performance.
For more information on how to use paid search to drive calls, check out Invoca’s white paper, “11 Paid Search Tactics That Drive Quality Inbound Calls.”
On the same day that she became a Pulitzer Prize finalist for her work bringing the Cambridge Analytica scandal to light, journalist Carole Cadwalladr took the stage at TED to “address you directly, the gods of Silicon Valley.”
Cadwalladr began her talk by recounting a trip she took after the Brexit referendum, back to her hometown in South Wales.
She recalled feeling “a weird sense of unreality” walking around a town filled with new infrastructure funded by the European Union, while being told by residents that the EU had done nothing for them. Similarly, she said they told her about the dangers of immigration, even though they lived in a town with “one of the lowest rates of immigration in the country.”
Cadwalladr said she began to understand where those sentiments were coming from after her story ran, and someone contacted her about seeing scary, misleading ads about Turkey and Turkish immigration on Facebook . Cadwalladr, however, couldn’t see those ads, because she wasn’t targeted, and Facebook offered no general archive of all ads that had run on the platform.
Eventually, Facebook began building that archive of ads. And the pro-Brexit campaign was found guilty of breaking British election laws by breaching campaign spending limits to fund campaigns on Facebook.
Meanwhile, Cadwalladr said her interest in these issues led her to Christopher Wylie, whose whistleblowing about Cambridge Analytica’s use of Facebook user data helped prompt broader scrutiny of the social network’s privacy practices.
Cadwalladr described Wylie as “extraordinarily brave,” particularly since Cambridge Analytica repeatedly threatened them with legal action. The final threat, she said, came a day before publication, and it came from Facebook itself.
“It said that if we published, they would sue us,” Cadwalladr said. “We did it anyway. Facebook, you were on the wrong side of history on that, and you are on the wrong side of history in this.”
The “this” in question is what she characterized as a failure by the social media platforms to fully reckon with the extent to which they’ve become tools for the spread of lies and misinformation. For example, she pointed to CEO Mark Zuckerberg’s refusal thus far to appear before parliaments around the world that have asked him to testify.
Calling out executives like Facebook’s Sheryl Sandberg, Alphabet/Google’s Larry Page and Sergey Brin and Twitter’s Jack Dorsey (who’s scheduled to take the stage tomorrow morning), Cadwalladr insisted that the stakes could not be higher.
“This technology you have invented has been amazing, but now it’s a crime scene, and you have the evidence,” she said. “It is not enough to say that you will do better in the future, because to have any hope of stopping this from happening again, we have to know the past.”
She went on to declare that the Brexit vote demonstrates that “liberal democracy is broken.”
“This is not democracy,” Cadwalladr said. “Spreading lies in darkness, paid for with illegal cash from God knows where — it’s subversion, and you are accessories to it.”
And for those of us who don’t run giant technology platforms, she added, “My question to everybody else is: Is this what we want? To let them get away with it, and to sit back and play with our phones as this darkness falls?”
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