Canva, the design company with nearly $ 250 million in funding, has today announced a variety of new features, including a video editing tool.
The company has also announced Canva Apps, which allows developers and customers alike to build on top of Canva. Thus far, Dropbox, Google Drive, PhotoMosh and Instagram are already in the Canva Apps suite, with a total of 30 apps available at launch.
The video editing tool allows for easy editing with no previous experience required, and also offers video templates, access to a stock content library with videos, music, etc. and easy-to-use animation tools.
Meanwhile, Canva is taking the approach of winning customers when they’re young, with the launch of Canva for Education. It’s a totally free product that has launched in beta with Australian schools, integrating with GSuite and Google Classroom to allow students to build out projects, and teachers to mark them up and review them.
Canva has also announced the launch of Canva for Desktop.
As design becomes more important to the way every organization functions and operates, one of the only barriers to the growth of the category is the pace at which new designers can emerge and enter the workforce.
Canva has positioned itself as the non-designer’s design tool, making it easy to create something beautiful with little to no design experience. The launch of the video editing tool and Canva for Education strengthen that stance, not only creating more users for the platform itself but fostering an environment for the maturation of new designers to join the ecosystem as a whole.
Alongside the announcement, Canva CEO Melanie Perkins has announced that Canva will join the 1% pledge, dedicating 1% of equity, profit, time and resources to making the world a better place.
Here’s what she had to say about it, in a prepared statement:
Companies have a huge role to play in helping to shape the world we live in and we feel like the 1% Pledge is an incredible program which will help us to use our company’s time, resources, product and equity to do just that. We believe the old adage ‘do no evil’ is no longer enough today and hope to live up to our value to ‘Be a Force for Good’.
Interestingly, Canva’s position at the top of the design funnel hasn’t slowed growth. Indeed, Canva recently launched Canva for Enterprise to let all the folks in the organization outside of the design department step up to bat and create their own decks, presentations, materials, etc., all within the parameter’s of the design system and brand aesthetic.
A billion designs have been created on Canva in 2019, with 2 billion designs created since the launch of the platform.
One thing that is usually uppermost in your mind as a marketer is how to ensure that you not only survive the competition but also become one of the market leaders.
And in order to become a market leader you are expected to work seriously on personalization but doing this at scale because you must focus on the global market, must require automation and that is where machine learning comes in.
You must create a digital presence that will help in better customer engagement, raise brand awareness, and reinforce business objectives. It’s expected that you must have been working on your web content and building out your CRM capabilities, you must also have it behind your mind that there is the absolute need to have various efforts underway to automate key marketing activities.
With the global market as your target, getting personal maybe a little difficult task to achieve but you can enhance this with a personalization engine. Your ultimate aim will be to target the content you deliver to your customers and prospects based on what you know about them and what you believe they might need.
Personalization or customization
Before embarking on machine learning integration, it’s essential that you refrain from mixing up personalization with customization. While personalization is carried out for the customer’s benefit, customization, on the other hand, is initiated by the customer in an effort to drill down to the desired content.
In the research by PWC titled ‘Financial Services Technology 2020 and Beyond: Embracing disruption’ it was observed that customer intelligence will be the most important predictor of revenue growth and profitability. Personalization is the amazing outcome of your customer intelligence that will ensure you’re able to control over-messaging customers with blanket promotions, this will also translate into a huge reduction in media buys.
Personalization is a critical mission your startup cannot afford to toy with in order to embark on effective marketing. Once you are able to personalize the journey of your potential customers you are on to increased customer engagement and long-term loyalty.
You can take a cue from the way Netflix does movie recommendations, music suggestions from Spotify and special promotions on Amazon to really comprehend the effect personalized content is having and that it is not only becoming the norm but a consumer expectation. All these big tech companies are able to accomplish this onerous task by integrating machine learning, which is quickly turning out to becoming an essential and must-have tool in content personalization.
Interestingly, there are quite a number of personalization engine vendors. Evergage, Monetate, Certona, and Dynamic Yield, are some of the vendors out there in the market that offer this service. Gartner’s “Magic Quadrant for Personalization Engines” 2019 report shows that personalization engine adoption is up 28% since 2016.
You must locate the essential points in your customer journey that are optimal for adding a personal touch. Context has always been the source of the differences between customers that usually trigger a need for specific content.
As personalization is predictive, machine learning has started playing a central role.
The following are three ways you can utilize machine learning to improve personalization.
1. Making use of secured demographic data
The basis of demographic data is to have access to your customers’ distinctive behaviors and preferences and this you can effect with machine learning. While it may be easy for you to lay your hands on this information, there is a cliche to it.
Your competitors, especially those who have access to large search engines can use these search engines to find out highly personal information about your customers, such as medical issues, employment status, financial information, political beliefs, and other private details. This data, of course, will be collected, stored, and linked to your data profile.
The only way to effectively “opt-out” of this, is to keep your data safe and out of the hands of data collectors. Cybercriminals also know that this information is a gold mine and are eager to lay their hands on it.
A comprehensive demographic data can often reveal an entire socioeconomic profile for customers — their distance from retail locations, average income, average age, ethnic ratios, youth or college student populations and sometimes even married versus single statistics.
While your competitors will make use of this data to train and improve their predictive model as well as simplifying the ultimate personalization data crunch just the same way you will, cybercriminals will use the information to launch attacks at your customers or even cripple your business.
It’s true that as a new startup founder, you may be considering the financial implications of having to secure your data but this will go a long way to save you from very bad experiences. Where you don’t have the funds for a paid VPN, nothing stops you from subscribing to the services of a free VPN.
What you end up achieving is the ability to mask your I.P. address and encrypt all traffic which will help with geo-blocks and contribute to your secured demographic data and ultimate online privacy.
2. Who makes up your social media audience?
Cross-channel personalization is a very beneficial source of information because a customer’s social media channel of choice is an avenue to discovering how friendly the customer is to mobile contact. It’s also a channel to accumulating demographic data for the mere fact that different age and social groups prefer different social media platforms.
For instance, Gen Z is known to have a preference for Instagram and Snapchat, while Gen X and millennials cling more to Facebook.
3. Catching in on your consumer’s online behaviors
Besides demographic data and who belongs to your social media audience, another source of information that enables your workable insight into the individual consumer in personalization is applying machine learning for a comprehensive knowledge of your consumer’s online behavior. The navigation path of your potential consumer can reveal a great deal about the person.
You will have very useful insight into your consumer’s preferences, the amount of time a consumer spends browsing pages on your site is a revealing clue to the degree of priority and a source of valuable data. While you may not be able to garner all this valuable information manually, machine learning can easily make sense of this somehow “erratic” behavior.
Machine learning is able to articulate the repeated site visits and come up with an in-depth and knowledgeable profile of the customer and what they care for.
It’s very important for you to know that in order for you to succeed in integrating machine learning into your effort at improving personalization, you must endeavor to personalize content across all channels. This will ensure that your customers feel personally engaged in real-time and wherever they are.
Product pages on your startup websites should be full of zest and tailored to each individual’s preferences. Deploy predictive advertising on the consumer’s social media platform of choice.
You just don’t stop at your efforts on your website, exploit the opportunity email offers as a dependable personalized content repository, the reason is that it’s easier to come up with optimized content in an email than it is to spiritedly work such wonders on a webpage. However, the integration of machine learning as an application of AI affords you the opportunity of improved personalization at scale.
John Ejiofor is the founder and editor in chief at Nature Torch. He can be found on Twitter @John02Ejiofor.
A network of scammers used a ring of established right-wing Facebook pages to stoke Islamophobia and make a quick buck in the process, a new report from the Guardian reveals. But it’s less a vast international conspiracy and more simply that Facebook is unable to police its platform to prevent even the most elementary scams — with serious consequences.
The Guardian’s multi-part report depicts the events like a scheme of grand proportions executed for the express purpose of harassing Representatives Ilhan Omar (D-MI), Rashida Tlaib (D-MN) and other prominent Muslims. But the facts it uncovered point towards this being a run-of-the-mill money-making operation that used tawdry, hateful clickbait and evaded Facebook’s apparently negligible protections against this kind of thing.
The scam basically went like this: an administrator of a popular right-wing Facebook page would get a message from a person claiming to share their values that asked if they could be made an editor. Once granted access, this person would publish clickbait stories — frequently targeting Muslims, and often Rep. Omar, since they reliably led to high engagement. The stories appeared on a handful of ad-saturated websites that were presumably owned by the scammers.
That appears to be the extent of the vast conspiracy, or at least its operations — duping credulous conservatives into clicking through to an ad farm.
Its human cost, however, whether incidental or deliberate, is something else entirely. Rep. Omar is already the target of many coordinated attacks, some from self-proclaimed patriots within this country; just last month, an Islamophobic Trump supporter pleaded guilty in federal court to making death threats against her.
Social media is asymmetric warfare in that a single person can be the focal point for the firepower — figurative but often with the threat of literal — of thousands or millions. That a Member of Congress can be the target of such continuous abuse makes one question the utility of the platform on which that abuse is enabled.
In a searing statement offered to the Guardian, Rep. Omar took Facebook to task:
I’ve said it before and I’ll say it again: Facebook’s complacency is a threat to our democracy. It has become clear that they do not take seriously the degree to which they provide a platform for white nationalist hate and dangerous misinformation in this country and around the world. And there is a clear reason for this: they profit off it. I believe their inaction is a grave threat to people’s lives, to our democracy and to democracy around the world.
Despite the scale of its effect on Rep. Omar and other targets, it’s possible and even likely that this entire thing was carried out by a handful of people. The operation was based in Israel, the report repeatedly mentions, but it isn’t a room of state-sponsored hackers feverishly tapping their keyboards — the guy they tracked down is a jewelry retailer and amateur SEO hustler living in a suburb of Tel Aviv who answered the door in sweatpants and nonchalantly denied all involvement.
The funny thing is that, in a way, this does amount to a vast international conspiracy. On one hand, it’s a guy in sweatpants worming his way into some trashy Facebook pages and mass-posting links to his bunk news sites. But on the other, it’s a coordinated effort to promote Islamophobic, right-wing content that produced millions of interactions and doubtless further fanned the flames of hatred.
Why not both? After all, they represent different ways that Facebook fails as a platform to protect its users. “We don’t allow people to misrepresent themselves on Facebook,” the company wrote in a statement to the Guardian. Obviously, that isn’t true. Or rather, perhaps it’s true in the way that running at the pool isn’t allowed. People just do it anyway, because the lifeguards and Facebook don’t do their job.
With Larry Page and Sergey Brin stepping back (again\!), Google CEO Sundar Pichai is now in charge of Alphabet—and its dysfunction.
Feed: All Latest
For years, “actionable insights” have been the Holy Grail for data analytics companies. Actionable insights, the thinking goes, are the end product of data collection, aggregation, analysis, and judgment. They enable a decision-maker to modify behavior and achieve desired outcomes.
The process begins with data collection, which can take many forms. There’s a big difference between collecting data and aggregating it in a meaningful way that can provide a picture of reality. That’s the “insights” part of the puzzle. First, you need high-quality data, then you need the technological prowess to clean and organize it.
With high-quality data that’s been cleaned and organized, the next step is to provide context. This is the realm of companies like Tableau, which provide tools that translate machine-friendly data points into human-friendly visualizations that strive to depict an objective picture of current conditions.
But whereas a snapshot of current conditions may, in fact, yield new and meaningful insights (for example, if I look ‘sales numbers’ across an organization I can see which channels are over- or under-performing), human judgment has always been paramount in choosing a particular action. A perfect picture of static conditions doesn’t by itself offer any suggestions as to how to achieve particular outcomes. We still rely on management to tweak sales incentives or redistribute resources.
Or at least we did, up until recently. Machine learning is now shifting the balance of institutional decision-making. Advances in processing and algorithmic self-improvement mean that computers can now anticipate future outcomes and take steps to maximize particular ones. Intelligent systems can now see the world in shades of gray and evaluate likelihoods from multitudes of variables far beyond human comprehension.
That’s the world we currently live in, and the evidence is all around us. Machine learning algorithms have swayed elections by stoking targeted outrage. Our clothes, food, and consumer products are designed according to data-driven analytics. Every design feature in your favorite app is being constantly optimized according to how computers anticipate your future behavior. It’s why YouTube is actually pretty good at showing you videos that keep you engaged.
The day is coming when we will no longer require “actionable insights,” because the action will have already been taken. Nobody at YouTube is looking at your viewing history to determine what to recommend next. Computers do that. The value of the stock market is now largely driven by automated trading algorithms, and as a consequence, there are fewer stock analysts than there used to be. Not only can computers process information far better than humans, but they’ve also demonstrated better financial judgment.
The day will soon arrive when “actionable insights” will seem like a quaint notion from a simpler time. Computers will be smart enough to act on insights by themselves. In doing so, they may, in fact, diminish the need for human oversight.
Until then, however, human enterprise is still structured around hierarchies of decision-making and judgment. The CEO of a company still needs to delegate day-to-day responsibilities to human actors whose knowledge and judgment have proven sound.
And so, for now, we still need actionable insights. Data analytics companies will continue to build better mousetraps, until the day when there are no longer mice.
Gil Rachlin, SVP of Products and Partnerships at Synup.
Google is not only tightening up existing policies but also rolling out new policies that affect multiple industries. Here are common reasons for ad disapprovals, steps to fix them, and what to do if your disapproval reason doesn’t fall in the common category.
Read more at PPCHero.com
Google Analytics (GA) is one of the most popular traffic analytics tools for websites, but it can have serious drawbacks for anyone looking to measure content performance.
The problem is systemic: Analytics was built to track traffic for ecommerce and content sites, with the structure of its reports built around pageviews. It can provide some sophisticated data around those views – what kinds of audience members are behind them, how they might have arrived, what they did next, and other such questions – but today’s content marketers need the ability to measure and understand much more than that.
How do people interact with your content when they’re viewing an individual landing page? How do they feel about your brand after having been exposed to it on other media channels? Where are they running into conversion roadblocks? What are the content assets across touchpoints that people are consuming most on their paths to conversion? What assets are most compelling to your most qualified individual leads?
GA can hint at some of the answers to these types of questions, but to truly understand these aspects of your content marketing performance, you’ll need to turn elsewhere.
Here are a few of the biggest ways that Google Analytics can’t measure your content performance properly, along with some tips for overcoming these shortcomings.
1. On-page behavior
Google Analytics only tracks page views and movement within your site. Unless you manually add layers of event tracking, it can’t reveal what people do within specific pages. You’ll never know if visitors get two lines into your content and then get distracted by an interesting link.
This is the value of heatmaps, which are remarkably effective at showing user behavior. They map out which areas of the page get the most view time and the most clicks, and where the mouse rests.
A heatmap shows areas that get the most attention in red, shading to blue for those that get the least. It reveals whether the visitor engaged and interacted with the page, or left it open and unread for hours. With a heatmap, you can discover the most popular parts of your pages, the navigation links people click on most, and whether key elements below the fold are going unseen.
To get started experimenting with heatmaps, you can try using Hotjar, Lucky Orange or CrazyEgg.
2. Brand sentiment lift
Google Analytics is limited to tracking page views on your own website. It can’t tell you anything about the impact of your content on earned or shared media channels, where you don’t have the ability to install its tracking pixel. And even if you could use it track content views on all channels, you still wouldn’t know much about the impact that the content has on brand sentiment, or your share of voice in the general market.
Instead, use a social listening tool to track what people think about your brand. Social listening tools track social media shares, comments, reactions and mentions. This information has many key use cases, one of which is gaining a holistic view of brand sentiment.
The better platforms track far more than the number of brand mentions on social media, using semantic text analysis to reveal the emotions behind the posts and comparing these signals to those of your competitors. Merge these trends with your timeline of content marketing achievements, and correlations will start to emerge.
To get started experimenting with social listening for brand sentiment tracking, you can try using Awario, Mention or Talkwalker.
3. Friction points on forms
If a visitor tries to complete an online form and gives up in frustration, Google Analytics will never let you know. The best it can do is to show you how much time all visitors spent on the page. (Even this information can be extremely misleading since GA measures page view durations starting from the moment given page loads to the moment the next internal page loads. If your visitor stays for 10 minutes, reads your article from top to bottom, shares it, and then closes the tab without browsing any further within your site, GA will register ‘zero’ time on page.)
When it comes to lead capture forms, contact forms, and sales checkout forms, it can be hard to tell how many fields you’re best off including. The fewer fields your forms have, the lesser friction people will have opting in, which makes for more conversions.
On the other hand, the more fields you include, the more data you’ll have to work with when people do complete and submit forms, which is useful for identifying personas when executing segmented nurture sequences. You’ll also learn more about your audience, and you’ll be in the best possible position for determining the relevance of your leads. And there’s something to be said for asking a lot of your audience, as it helps to filter out people who are “just curious” about your lead magnet and will never actually do business with you.
To really understand the extent to which form fields are serving as roadblocks on the path to conversion, turn to your form builder tool’s analytics. The better platforms will reveal partial submissions, and how far a user gets through a form before abandoning it, so you can see if any single field is too long or question too confusing.
To get started experimenting with form conversion optimization, I recommend Formstack, Formismo or Jotform.
4. The identity of every visitor
One of GA’s biggest weaknesses is its inability to give context to visitor behavior. It can’t show you much about the identity of your visitors – at best, you can segment data about your entire pool of visitors according to their physical locations, devices, referrers, rough demographics and points of entry to your site.
What’s more, Google Analytics only uses a sample of your visitors, so that even if you tinker with your report settings to reveal the IP addresses of individual sessions, you can’t rely on this information as a comprehensive source of individual user insights.
Instead of GA, use audience intelligence tools that provide information about the interests, behavior, personal data (in a GDPR-compliant manner, of course.) and historic activity of every user, so that you can gain a deeper understanding of your visitors. This allows you to fine-tune your content to appeal to your audience, and it also reveals opportunities for account-based marketing.
To get started with audience intelligence, try Albacross, LinkedIn Website Demographics or Visitor Queue.
5. Funnel analytics
It is possible to use Google Analytics to track users through your funnel and measure its effectiveness. However, setting this all up can be highly complicated. You have to build a confusing series of filters and a dedicated URL structure that allows GA to correlate content pages with each stage of the funnel.
It’s much better to use a single tool that follows users through your funnel. Pick one that logs abandonment points and the cumulative impact of your various key funnel touchpoints. You’ll also need a good way to track the activity of returning visitors, which is another weak point for GA, thanks to uncertainty about cookies, lack of reliability when tracking visitors across devices, and the aforementioned notorious data sampling issue.
And if you integrate a funnel analytics tool with your CRM, logging each lead’s engagement activity on your website, you’ll be in great shape to set up a smart lead scoring system for identifying sales-readiness levels.
To get started with funnel analytics, check out Kissmetrics, Woopra or Yandex Metrica.
6. Off-site interactions
Google Analytics only measures interactions with the content on your own site. It’s not something you can use to measure the impact of content on shared, paid or earned media. So that guest post you recently published on someone else’s blog, or your LinkedIn Publisher articles, for example, will be blind spots for you.
GA can show you information about some of the visits you acquired via clickthroughs from these media presences, but that’s about it.
You’ll get better results from a multi-channel dashboard tool that pulls together user analytics from all channels, including email marketing, advertising tools, and social media. This type of solution can’t show you how people found your content on these properties, nor where they went next if they didn’t end up on your website, but it will help you consolidate all your metrics into one centralized dashboard for a more holistic analysis.
What’s more, if you combine data relating to engagement on all touchpoints into one timeline, you’ll start to see correlations between spikes on certain channels and website conversions, which can point you in the right direction for further drill-downs
To get started with multi-channel dashboards, try Klipfolio, Databox or Geckoboard.
Google Analytics isn’t a magic button
Google Analytics is hugely popular, but it can’t do everything, especially if you’re concerned about content performance. Fortunately, there are other tools that fill the gaps GA leaves behind, giving you a much clearer understanding of your content marketing success.
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