How this innovation can be a competitive advantage for any business, including yours.
Demand for machine learning is skyrocketing. This growth is driven not only by “middle adopters” recognizing the vast potential of machine learning after watching early adopters benefit from its use, but by steady improvements in machine-learning technology itself. It may be too early to say with certainty that machine learning develops according to a predictable framework like Moore’s Law, the famous precept about computing power that has borne out for nearly 50 years and only recently began to show signs of strain. But the industry is clearly on a fast track.
As machine-learning algorithms grow smarter and more organizations come around to the idea of integrating this powerful technology into their processes, it’s high time your enterprise thought about putting machine learning to work, too.
First, consider the benefits and costs. It’s quite likely that your business could leverage at least one of these five reasons to employ machine learning, whether it’s taming apparently infinite amounts of unstructured data or finally personalizing your marketing campaigns.
1. Taming vast unstructured data with limited resources
One of the best-known use cases for machine learning is processing data sets too large for traditional data crunching methods to handle. This is increasingly important as data becomes easier to generate, collect and access, especially for smaller B2C enterprises that often deal with more transaction and customer data than they can manage with limited resources.
How you use machine learning to process and “tame” your data will depend on what you hope to get from that data. Do you want help making more informed product development decisions? To better market to your customers? To acquire new customers? To analyse internal processes that could be improved? Machine learning can help with all these problems and more.
2. Automating routine tasks
The original promise of machine learning was efficiency. Even as its uses have expanded beyond mere automation, this remains a core function and one of the most commercially viable use cases. Using machine learning to automate routine tasks, save time and manage resources more effectively has a very attractive paid of side effects for enterprises that do it effectively: reducing expenses and boosting net income.
The list of tasks that machine learning can automate is long. As with data processing, how you use machine learning for process automation will depend on which functions exert the greatest drag on your time and resources.
Need ideas? Machine learning has shown encouraging real-world outcomes when used to automate data classification, report generation, IT threat monitoring, loss and fraud prevention and internal auditing. But the possibilities are truly endless.
3. Improving marketing personalization and efficiency
Machine learning is a powerful force multiplier in marketing campaigns, enabling virtually endless messaging and buyer-profile permutations, unlocking the gate to fully personalized marketing without demanding an army of copywriters or publicity agents.
What’s especially encouraging for smaller businesses without much marketing expertise is that machine learning’s potential is baked into the top everyday digital-advertising platforms, namely Facebook and Google. You don’t have to train your own algorithms to use this technology in your next microtargeting campaign.
4. Addressing business trends
Machine learning has also proven its worth in detecting trends in large data sets. These trends are often too subtle for humans to tease out, or perhaps the data sets are simply too large for “dumb” programs to process effectively.
Whatever the reason for machine learning’s success in this space, the potential benefits are clear as day. For example, many small and midsize enterprises use machine learning technology to predict and reduce customer churn, looking for signs that customers are considering competitors and trigger retention processes with higher probabilities of success.
Elsewhere, companies of all sizes are getting more comfortable integrating machine learning into their hiring processes. By reinforcing existing biases in human-led hiring and promotion, earlier-generation algorithms did more harm than good, but newer models are able to counteract implicit bias and increase the chances of equitable outcomes.
5. Accelerating research cycles
A machine-learning algorithm unleashed in an R&D department is like an army of super-smart lab assistants. As more and more enterprises discover just what machine learning is capable of in and out of the lab, they’re feeling more confident about using it to eliminate some of the frustrating trial-and-error that lengthens research cycles and increases development costs. Machine learning won’t replace R&D experts anytime soon, but it does appear to empower them to use their time more effectively. More and better innovations could result.
If the experience of competitor businesses that have already deployed machine learning to great effect is any guide for your own experience, the answer to this question is a resounding yes.
The more interesting question is how you choose to make machine learning work for your businesses. This prompts another question, around what operational and structural changes your machine learning processes will bring. These changes, up to and including reducing headcounts in redundant roles or winding up entire lines of business, could be painful in the short run even as they strengthen your enterprise for the long haul.
Like all great innovations that increase operational efficiency and eliminate low-value work, machine learning does not benefit everyone equally. It’s up to the humans in charge of these algorithms to make the transition as orderly and painless as possible. It seems there are some things machine learning can’t yet do … yet.
Last November, Moz VP Product, Rob Ousbey, gave a presentation at Web Con 2020 on the evolution of SEO, and we’re sharing it with you today! Rob draws on his years of research experience in the industry to discuss how SEO has changed, and what that means for your strategies.
Editor’s Note: Rob mentions a promo in the video that has since expired, but you can still get a free month of Moz Pro + free walkthrough here!
Video Transcription
Hello, everyone. Thank you for that introduction. I very much appreciate it, and it’s wonderful to be with all of you here today. I’m Rob Ousbey from Moz.
Real quick, I was going to share my screen here and say that my gift to you for coming to the session today is this link. This won’t just get you a free month of Moz Pro, but everybody who signs up can get a free walkthrough with an SEO expert to help you get started. I’ll put this link up again at the end of the session. But if you’re interested in SEO or using a tool suite to help you, then Moz might be the toolset that can help.
Also, if you want to learn more about SEO, come join me on Twitter. I am @RobOusbey, and it would be wonderful to chat to you over there.
One reason I put my bio up here is because I’ve not been at Moz for all that long. I just started about a year ago. Before that, I was at Distilled, which is an international digital marketing agency, and I ran the Seattle office there for over a decade. I mention that because I want to share with you today examples of what I discovered when I was doing my client work. I want to share the research that my team members did when we were in your shoes.
A troubling story
So I wanted to kick off with an experience that stuck in my mind. Like I say, I’ve been doing this professionally for about 12 or 13 years, and back when I started, SEO was certainly more straightforward, if not getting easier.
People like my friend Rand Fishkin, the founder of Moz, used to do correlation studies that would discover what factors seem to correlate with rankings, and we’d publish these kinds of reports. This was the top ranking factors for 2005. And back then, they were broadly split between factors that assessed whether a page was relevant for a particular term and those that asked whether a site was authoritative. A lot of that relevance came from the use of keywords on a page, and the authority was judged by the number of links to the site. So we would help companies by doing good SEO. We’d put keywords on a page and build a bunch of links.
And I want to tell you a story about one of our clients. This is from just a couple of years ago, but it definitely stuck in my head. We were doing a lot of content creation for this client. We created some really informative pages and some really fun pages that would go viral and take over the Internet, and all of this earned them a lot of links. And this was the result of our efforts — a consistent, steady growth in the number of domains linking to that site. We had an incredible impact for them.
And here’s the graph of how many keywords they had when they ranked on the first page. This is fantastic. They ranked for a lot of keywords. And finally, here’s the graph of organic traffic to the site. Amazing.
But if you looked a little closer, you notice something that is a bit troubling. We never stopped acquiring links. In fact, a lot of the content we produced is so evergreen that even content built two or three years ago is still gathering new links every single week. But the number of keywords we have ranking in the top 10 went up and up and then stopped growing. And not surprisingly, the same trend is there in organic search traffic as well. What appears to have happened here is that we got strong enough to get on the front page with these keywords, to be a player in the industry, but after that, just building more links to the site didn’t help it rank for more keywords and it didn’t help it get any more search traffic.
SEO fundamentals
It seems like all the SEO fundamentals that we’ve learned about, keywords and links and technical SEO still apply and they’re still necessary to help you become a player in a particular industry. But after that, there are other factors that you need to focus on.
Now this evolution of SEO into new factors has been an accelerating process. My colleague at Moz, Dr. Pete Meyers has been tracking and collecting a lot of data about this. Last year, Google made close to 4,000 improvements to their results, and that’s the result of running something like 45,000 different experiments.
Pete has also been tracking how much the search results change every day. Blue is really stable results. Orange is a lot of changes. And so if you felt like your rankings for your site are getting more volatile than ever, you’re not wrong. When we hit 2017, we saw more changes to the results every day than we ever had before.
Now the way that Google’s algorithms used to be updated was by a bunch of people in a room making decisions. In fact, it was this bunch of people in this room. They decided what factors to dial up or down to create the best results.
Google’s goal: portal to the Internet
But what does this mean? What does it mean to make the best results? Well, we should think about what Google’s real goal is. They want to be your portal to the Internet. They want your web experience to begin with a Google search, and you’ll continue to do that if they make you satisfied with the results you see and the pages you click on. If they send you to the perfect web page for your query, that’s a satisfying experience that reflects well on Google. If they send you to page that’s a bad experience, it reflects poorly on them.
So it’s interesting to ask, “How would Google avoid doing that, and what would be a bad user experience?” Well, there are some obvious things, like if you arrive on a page that installs malware or a virus on your computer, or you arrive at a product page where everything is out of stock, or you go to a website that’s really slow or full of adverts. These are the pages Google does not want to include in their results.
And they’ve always been good at measuring these things pretty directly. More than 10 years ago they were testing how fast sites are and then using that to inform their rankings. If they spot malware or viruses on a site, they’ll temporarily remove it from the search results.
But they also tried more opinion-based measures. For a while, they were running surveys to ask people: Are you satisfied with these results? This was how they knew if their algorithm was working to get people what they wanted, to give them a good experience.
But the Google way of doing this is to try and do it at massive scale and hopefully to do it in the background, where users don’t have to answer a survey pop-up like this. And doing this in the background, doing it at huge scale has been more and more possible, firstly because of how much data Google has.
Click through rates
So I want to take a look at some of the kinds of things they might be looking at. Here’s an example of something they may want to do. Let’s consider the average click-through rate for every ranking position in the search results. Imagine that Google knows that 30% of people click on the first result and 22% click on number two and 5% click on number six and so on. They have a good understanding of these averages. But then for a particular keyword, let’s say they notice number six is getting 12% of the clicks. Something is going on there. What is happening? Well, whatever the reason why this is, Google could be better satisfying its users if that result was higher up in the rankings. Whoever is ranking at number six is what people want. Maybe they should rank higher.
“Pogo sticking”
Here’s another example. This is what we call pogo sticking. A user does a search and then clicks on a result, and then after a couple seconds looking at the page, they realize they don’t like it, so they click the back button and they select a different result. But let’s say they don’t like that one either, so they click back and they select a third result, and now they stay here and they use that site. Imagine a lot of people did the same thing. Well, if we were Google, when we saw this happening, it would be a pretty strong indicator that the third result is what’s actually satisfying users. That’s actually a good result for this query, and it probably deserves to be ranking much higher up.
User satisfaction: refinement
There’s even an extension of this where users pogo stick around the SERPs, and then they decide they can’t find anything to do with what they wanted. So they refine their search. They try typing something else, and then they find what they want on a different query. If too many people are not satisfied by any of the results on the first page, it’s probably a sign to make a pretty serious change to that SERP or to nudge people to do this other query instead.
Google’s evolution with Machine Learning
And doing this kind of huge analysis on a massive scale is something that was made much easier with the advent of machine learning. Now for a long time the folks in charge of the search results at Google were very reluctant to incorporate any machine learning into their work. It was something they did not want to do. But then Google appointed a new head of search, and they chose someone who had spent their career at Google promoting machine learning and its opportunities. So now they’ve moved towards doing that. In fact, Wired magazine described Google as remaking themselves as a “machine learning first” company.
What we’re seeing now
So this is where I want to move from my conjecture about what they could do into giving some examples and evidence of all of this for you. And I want to talk about two particular modern ranking factors that we have evidence for and that if you’re doing SEO or digital marketing or working on a website you can start considering today.
User signals
Firstly, I talked about the way that users interact with the results, what are they clicking on, how are they engaging with pages they find. So let’s dive into that.
A lot of this research comes from my former colleague, Tom Capper. We worked at Distilled together, but he’s also a Moz Associate, and a lot of this has been published on the Moz Blog.
User engagement
Let’s imagine you start on Google. You type in your query, and here’s the results. Here’s page one of results. Here’s page two of results. Not going to worry much about what happens after that because no one tends to click through further than page two.
Now let’s think about how much data Google has about the way people interact with those search results. On the front page, they see lots going on. There are lots of clicks. They can see patterns. They can see trends. They can see what people spend time on or what they pogo stick back from. On the second page and beyond, there’s very little user engagement happening. No one is going there, so there’s not many clicks and not much data that Google can use.
So when we look at what factors seem to correlate with rankings, here’s what we see. On page two, there is some correlation between the number of links a site has and where it ranks. That’s kind of what we expected. That’s what SEOs have been preaching for the last decade or more. But when we get to the bottom of page one, there’s a weaker correlation with links. And at the top of page 1, there’s almost no correlation between the number of links you have and the position you rank in.
Now we do see that the folks on page one have more links than the sites on page two. You do need the SEO basics to get you ranking on the first page in the first place. We talk about this as the consideration set. Google will consider you for the first page of results if you have good enough SEO and if you have enough links.
But what we can take away from this is that when all that user data exists, when Google know where you’re clicking, how people are engaging with sites, they will use those user metrics as a ranking factor. And then in situations where there isn’t much user data, the rankings might be more determined by link metrics, and that’s why deeper in the results we see links being a more highly correlated factor.
In a similar way, we can look at the whole keyword space, from the very popular head terms in green to the long tail terms in red that are very rarely searched for. Head terms have a lot of people searching for them, so Google has a lot of user data to make an assessment about where people are clicking. For long tail terms, they might only get a couple of searches every month, they just don’t have that much data.
And again, what we see is that the popular, competitive terms, where there’s lots of searching happening, Google seems to be giving better rankings to sites with better engagement. For long tail terms, where they don’t have that data, the rankings are more based on link strength. And there have been plenty of studies that bear this out.
Larry Kim found a relationship between high click-through rates and better rankings. Brian Dean found a relationship between more engagement with a page and better rankings. And Searchmetrics found that time on site correlated with rankings better than any on-page factor.
Contemporary SEO
And even though Google keeps a tight lid on this, they won’t admit to exactly what they’re doing, and they don’t describe their algorithms in detail, there are occasionally insights that we get to see.
A couple of years ago, journalists from CNBC had the chance to sit in on a Google meeting where they were discussing changes to the algorithm. One interesting part of this article was when Googlers talked about the things they were optimizing for when they were designing a new feature on the results page. They were looking at this new type of result they’d added, and they were testing how many people clicked on it but then bounced back to the results, which they considered a bad sign. So this idea of pogo sticking came up once again.
If that was something that they were monitoring in the SERPs, we should be able to see examples of it. We should be able to see the sites where people pogo stick don’t do so well in SEO, which is why I’m always interested when I find a page that has, for whatever reason, it has a bad experience.
User metrics as a ranking factor
So here’s a site that lists movie trivia for any movie you might be interested in. It’s so full of ads and pop-ups that you can barely see any of the content on the page. It’s completely overrun with adverts. So if my hypothesis was correct, we’d see this site losing search visibility, and in fact that’s exactly what happened to them. Since their peak in 2014, the search visibility for the site has gone down and down and down.
Here’s another example. This is a weird search. It’s for a particular chemical that you buy if you were making face creams and lotions and that kind of thing. So let’s have a look at some of the results here. I think this first result is the manufacturer’s page with information about the chemical. The second is an industrial chemical research site. It has all the data sheets, all the safety sheets on it. The third is a site where you can buy the chemical itself.
And then here’s another result from a marketplace site. I’ve blurred out their name because I don’t want to be unfair to them. But when you click through on the result, this is what you get, an immediate blocker. It’s asking you to either log in or register, and there’s no way I want to complete this form. I’m going to hit the back button right away. Google had listed nine other pages that I’m going to look at before I even consider handing over all my data and creating an account here.
Now if my theory is right, as soon as they put this registration wall up, visitors would have started bouncing. Google would have noticed, and their search visibility would have suffered.
And that’s exactly what we see. This was a fast-growing startup, getting lots of press coverage, earning lots of links. But their search traffic responded very poorly and very quickly once that registration wall was in place. The bottom graph is organic traffic, and it just drops precipitously.
Here’s my final example of this, Forbes. It’s a 100-year-old publishing brand. They’ve been online for over 20 years. And when you land on a page, this is the kind of thing you see for an article. Now I don’t begrudge advertising on a page. They need to make some money. And there’s only one banner ad here. I was actually pleasantly surprised by that.
But I’m baffled by their decision to include a video documentary in the corner about a totally different topic. Like I came to read this article and you gave me this unrelated video.
And then suddenly this slides into view to make absolutely sure that I didn’t miss the other ad that it had in the sidebar. And then the video, that I didn’t want any way about an unrelated topic, starts playing a pre-roll ad. Meanwhile their browser alert thing pops up, and then the video — about the unrelated topic that I didn’t want in the first place — starts playing. So I’m trying to read and I scroll away from all this clutter on the page. But then the video — about an unrelated topic that I didn’t want in the first place — pins itself down here and follows me down the page. What is going on? And then there’s more sidebar ads for good measure.
And I want to say that if my theory is right, people will be bouncing away from Forbes. People will avoid clicking on Forbes in the first place, and they will be losing search traffic. But I also know that they are a powerhouse. So let’s have a look at what the data said.
I grabbed their link profile, and people will not stop linking to Forbes. They’re earning links from 700 new domains every single day. This is unstoppable. But here’s their organic search visibility. Forbes is down 35% year-on-year. I think this is pretty validating.
At this point, I’m confident saying that Google has too much data about how people engage with the search results and with websites for you to ignore this. If your site is a bad experience, why would Google let you in the top results to begin with and why would they keep you there?
What can you do?
So what can you do about this? Where can you start? Well, you can go to Google Search Console and take a look through the click-through rates for your pages when they appear in search. And in your analytics package, GA or whatever else, you can see the bounce rate for visitors landing on your pages, particularly those coming from search. So look for themes, look for trends. Find out if there are pages or sections of your site that people don’t like clicking on when they appear in the results. Find out if there are pages that when people land on them, they bounce right away. Either of those are bad signs and it could be letting you down in the results.
You can also qualitatively take a critical look at your site or get a third party or someone else to do this. Think about the experience that people have when they arrive. Are there too many adverts? Is there a frustrating registration wall? These things can hurt you, and they might need a closer look.
Brand signals
Okay, so we talked about those user signals. But the other area I want to look at is what I talk about as brand signals. Brand can apply to a company or a person. And when I think about the idea being a brand, I think about how well-known the company is and how well-liked they are. These are some questions that signal you have a strong brand, that people have heard of you, people are looking for you, people would recommend you.
And this second one sounds like something SEOs know how to research. When we say people are looking for you, it sounds like we’re just talking about search volume. How many times every month are people typing your brand name into Google?
Again, my colleague, Tom Capper did some research about this that’s published on the Moz Blog. He looked at this problem and said, “Okay. Well, then let’s see if the number of people searching for a brand has any correlation to how well they rank.” And then there’s a load of math and a long story that led to this conclusion, that branded search volume did correlate with rankings. This is in blue. In fact, it correlated more strongly with rankings than Domain Authority does, so that’s the measure that shows you the link strength of a website.
So think about this. We’ve worried about links for two decades, but actually something around brand strength and maybe branded search volume seems to correlate better.
For data geeks, here’s a way of using the R-squared calculation to answer the question, “How much does this explain the rankings?” Again, what you need to know here is that branded search volume explained more of the rankings than anything else.
So we’ve been preaching about this for a while, and then literally two days ago I saw this tweet. A team in the UK was asking about controversial SEO opinions. And the SEO manager for Ticketmaster came out and said this. He believes that when Google sees people searching for your brand name alongside a query, they start ranking you higher for the non-branded terms. And I don’t think this is controversial. And in fact, one of the replies to this was from Rand Fishkin, the founder of Moz. He also now believes that the brand signals are more powerful than what links and keywords can do.
What can you do?
So what can you do about this? Well, first you have to realize that any investment you make in brand building, whether that’s through PR activities or through like traditional advertising, is good business to do anyway. But it now has twice the value because of its impact on SEO, because those activities will get people looking for you, following you, sharing your brand. If you work for a billion-dollar company, you should make sure that your SEO and PR teams are well-connected and well-aligned and talking together. If you don’t work for a billion-dollar company, I’ve got two small, interesting examples for you.
Example: AdaFruit
First I want to call out this site, AdaFruit.com. They sell electronic components. There are many, many sites on the web that sell similar products. Not only do they have great product pages with good quality images and helpful descriptions, but I can also look at a product like this and then I can click through to get ideas for things I can build with it. This is some LED lights that you can chain together. And here’s an idea for a paper craft glowing crystal you can build with them. Here’s the wiring diagram I’d need for that project plus some code I can use to make it more interactive. It’s only an $8 product, but I know that this site will make it easy for me to get started and to get value from making this purchase.
They go even further and have a pretty impressive AdaFruit channel on YouTube. They’ve got 350,000 subscribers. Here’s the videos, for instance, that they publish every week walking you through all the new products that they’ve recently added to the site.
The CEO does a hands-on demo telling you about everything they have in stock. And then they have other collections of videos, like their women in hardware series that reaches an audience that’s been typically underserved in this space.
AdaFruit made a significant investment in content for their own channels, and it paid off with some brand authority, but brand trust and brand engagement as well.
Example: Investor Junkie
But I want to show you one other example here from arguably a much less exciting industry and someone who couldn’t invest so much in content. This is InvestorJunkie.com, a site that does reviews of financial services and products. And when I was working at the agency, we worked with this site and specifically with its founder, Larry. Larry was an expert in personal finance and particularly in personal investments. And this was his solo project. He blogged on the site and used his expertise. But as the site grew, he hired some contractors as well as our agency, and they created a lot of great content for the site, which really helped with SEO. But to make a significant impact on brand strength, we had to get the word out in front of loads of people who didn’t already know about him.
So we took Larry’s expertise and we offered him as a guest to podcasts, a lot of podcasts, and they loved having him on as a guest. Suddenly Larry was able to provide his expertise to huge new audiences, and he was able to get the Investor Junkie brand and their message in front of lots of people who had never heard of the site before.
But better still, this had a compounding effect, because people who are interested in these topics typically don’t just subscribe to one of these podcasts. They subscribe to a bunch of them. And so if they hear about Larry and Investor Junkie once, they might never think about it again. But if he shows up in their feed two or three or four times over the course of a few months, they’ll start to form a new association with the brand, maybe trusting him more, maybe seeking out the site.
And as an aside, there’s one other thing I love about podcasts, which is that if you’re creating a blog post, that can take hours and hours of work. If you’re creating a conference presentation, it can take days or weeks of work. If you’re a guest on a 30-minute podcast, it literally takes you about 30 minutes. You log on, you talk to a host, and then your part of the work is done.
So this can get you in front of a new audience. It gets people looking for you, which Google will notice. But it has even more SEO value as well, because every podcast typically has a page like this with show notes. It’s a page that Google can index, a page that Google can understand. And Google can see the signals of trust. It can see your brand being mentioned. It can see the links back to your site as well. I obviously can’t speak highly enough of podcasts for PR, for brand awareness, and even for SEO.
Did this help Larry and the Investor Junkie team? Yeah. This obviously wasn’t the extent of their SEO strategy. But everything they did contributed to them getting great rankings for a variety of competitive terms, and it helped them rank up against much bigger sites with much bigger teams and much bigger budgets. And that story actually came to an end just about two years ago, because the site was finally acquired for $6 million, which is not bad for a solo founder who was just busy building his own brand.
In summary
All right. I’ll wrap up with some of these thoughts. Google has been evolving. They’ve now been able to collect so much more data about the way people interact with the search results and other pages, and they’re now using machine learning to process all of that so they can better assess: Are we giving people a good user experience? Are the sites that we’re ranking the ones that satisfy people’s queries? The game of SEO has changed.
Now when you’re starting out, all the basics still apply. Come to Moz, read the Beginner’s Guide, do great technical SEO, do great keyword research, do great link building. Those are still necessary to be considered to become a player in your industry to help get you near the first page for any terms you want to target.
But when you’re trying to move up the front page, when you’re trying to establish yourself much further and become a much bigger brand, we’re not seeing a lot of correlation between things like links and getting into the very top rankings for any particular term. Instead, think about the good game that Google is playing. They want to make sure that when someone clicks on a result, they stay there. They don’t want to see this pogo sticking. They don’t want to see the link and the title that people want to click on sitting down at number six. So target their KPIs. Think about how you can help Google by making sure that your results are the ones people want to click on. Make sure that when people click on your results, that’s the page that they stay on.
But ultimately, you will never lose out if you improve your brand authority and engagement with your content. These are just good things to do for business. A stronger brand, content, and a website that people want to spend time on is hugely important and pays dividends. But now it’s all doubly important because it also has this massive impact on your SEO.
Today we navigate our way across cities, pull up electronic tickets, purchase items, monitor our health, and, of course, stay connected with friends and family on our smartphones. The smartphone is one of those innovations that make us think, “how did I ever function without it?” Smartphones revolutionized our personal lives, but there’s a megatrend set to disrupt the business world; it’s called augmented analytics.
Augmented analytics is on the cusp of becoming the business world’s next significant evolution.
Gartner identified augmented analytics as to the number 1 top trend for data and analytics technology in 2019, and market leaders are already starting to invest in this burgeoning industry.
SAP recently acquired augmented people analytics company Qualtrics for $8 billion, shelling out a price equivalent to over 20x the company’s current revenue. A newcomer to the game, Denver based startup Nodin raised $5 million in funding this past March, a month before even launching its platform.
The global market for augmented analytics is forecasted to reach $29.86 billion by 2025. But just what is augmented analytics, and what makes it such a hot new trend?
Data or die
According to a recent study by Forbes Insights and Treasure Data, only 13% of companies can be considered “leaders” in leveraging the full potential of their customer data. The full potential of the customer data is significant, as 55% of executives think these insights to be valuable in achieving disruptive innovation.
Companies must now collect, clean, and translate their raw data into insights they can use to build better products and reach target audiences.
In today’s fast-paced business world, data-driven decisions are no longer a nice to have; they’re a necessity to stay competitive and on top of market volatility. To get ahead, significant players from Booking.com to PepsiCo are relying on teams of data analysts to collect, clean, and analyze the surge of data now being generated.
SME’s are also leveraging their data to gain a competitive advantage in a sea of new competitors popping up every day. The problem is that data analysts are not only scarce in number; they’re also costly, especially for SMEs.
Augmented analytics harnesses the power of AI and machine learning to automate these tasks and generate insights.
Let’s say you’re an ecommerce store that’s seen a sudden decrease in sales on your Shopify account. To find out why you’d have to comb through your company’s data and find insights by:
Logging in to Google Analytics to analyze patterns in your website traffic.
Instead, augmented analytics tools collect and analyze all your data together to identify potential causes and automatically generate reports with actionable insights.
Here are three significant ways augmented analytics will disrupt the business world:
We’re in a data race – the winner takes the money.
With most businesses adopting artificial decision-making capabilities, we’re now in a race to see who can make the faster, better business decisions. Our businesses are like data-guzzling V12 engines that need data to fuel growth. Automating this process, and using augmented analytics to spot growth opportunities in your data, before your competitors, means you win the race.
Gartner believes that by 2020, over 40% of data science tasks will be automated. The automation will allow data scientists to spend less time on repetitive tasks and more time on strategic analysis and decision-making. Not only does it take the manual labor out of their job, but it also does it faster and eliminates the potential for human error.
Bring together the whole picture.
At the moment, most company’s data lives on several different platforms – isolated. Only 34% of executives agreed they have one aggregated view of all their customer data points. Not only is this inefficient, but it also blocks businesses from making informed decisions. We shouldn’t be looking at how each part of the engine works separately but how it all works together.
Having data points integrated into a rapid reporting system, such as Aerialscoop or DataBox, allows you to track the entire customer journey on one platform, all the way from lead generation until earning your first Dollar from the client. It also provides for better cohesion and collaboration across the organization. It’s not just ‘how is my marketing team doing on their KPIs?’ — but how are the marketing team’s results directly impacting my revenue growth and retention rates?
Democratize your data analytics.
Meanwhile, for smaller companies that don’t have the means to hire a team of data scientists (currently the global average salary is $90k), augmented analytics will make data-driven insights accessible to the masses. The accessibility is expected to be a major wave of development for the next five years.
According to Gartner, through 2020, the number of citizen data scientists will grow five times faster than professional data scientists. This means everyone from executives to marketeers will have the power to make data-driven decisions, without having to rely on data science professionals to provide the information they need.
Having the information easily accessible to all opens doors for SME’s to accelerate their growth at an exponential rate across departments. If there was ever a time that smaller, more nimble start-ups were able to pose a real threat to major companies, the democratization of data analytics ought to be the catalyst.
Much like smartphones have become the tool we can’t imagine our lives without, augmented analytics will set a new standard for business growth.
ata Science is such a broad field that includes several subdivisions like data preparation and exploration; data representation and transformation; data visualization and presentation; predictive analytics; machine learning, etc. For beginners, it’s only natural to raise the following question: What skills do I need to become a data scientist?
This article will discuss 10 essential skills that are necessary for practicing data scientists. These skills could be grouped into 2 categories, namely, technological skills (Math & Statistics, Coding Skills, Data Wrangling & Preprocessing Skills, Data Visualization Skills, Machine Learning Skills,and Real World Project Skills) and soft skills (Communication Skills, Lifelong Learning Skills, Team Player Skills and Ethical Skills).
Data science is a field that is ever-evolving, however mastering the foundations of data science will provide you with the necessary background that you need to pursue advance concepts such as deep learning, artificial intelligence, etc. This article will discuss 10 essential skills for practicing data scientists.
10 Essential Skills You Need to Know to Start Doing Data Science
1. Mathematics and Statistics Skills
(I) Statistics and Probability
Statistics and Probability is used for visualization of features, data preprocessing, feature transformation, data imputation, dimensionality reduction, feature engineering, model evaluation, etc. Here are the topics you need to be familiar with:
a) Mean
b) Median
c) Mode
d) Standard deviation/variance
e) Correlation coefficient and the covariance matrix
f) Probability distributions (Binomial, Poisson, Normal)
Most machine learning models are built with a data set having several features or predictors. Hence familiarity with multivariable calculus is extremely important for building a machine learning model. Here are the topics you need to be familiar with:
a) Functions of several variables
b) Derivatives and gradients
c) Step function, Sigmoid function, Logit function, ReLU (Rectified Linear Unit) function
d) Cost function
e) Plotting of functions
f) Minimum and Maximum values of a function
(III) Linear Algebra
Linear algebra is the most important math skill in machine learning. A data set is represented as a matrix. Linear algebra is used in data preprocessing, data transformation, and model evaluation. Here are the topics you need to be familiar with:
a) Vectors
b) Matrices
c) Transpose of a matrix
d) The inverse of a matrix
e) The determinant of a matrix
f) Dot product
g) Eigenvalues
h) Eigenvectors
(IV) Optimization Methods
Most machine learning algorithms perform predictive modeling by minimizing an objective function, thereby learning the weights that must be applied to the testing data in order to obtain the predicted labels. Here are the topics you need to be familiar with:
a) Cost function/Objective function
b) Likelihood function
c) Error function
d) Gradient Descent Algorithm and its variants (e.g. Stochastic Gradient Descent Algorithm)
Programming skills are essential in data science. Since Python and R are considered the 2 most popular programming languages in data science, essential knowledge in both languages are crucial. Some organizations may only require skills in either R or Python, not both.
(I) Skills in Python
Be familiar with basic programming skills in python. Here are the most important packages that you should master how to use:
a) Numpy
b) Pandas
c) Matplotlib
d) Seaborn
e) Scikit-learn
f) PyTorch
(ii) Skills in R
a) Tidyverse
b) Dplyr
c) Ggplot2
d) Caret
e) Stringr
(iii) Skills in Other Programming Languages
Skills in the following programming languages may be required by some organizations or industries:
a) Excel
b) Tableau
c) Hadoop
d) SQL
e) Spark
3. Data Wrangling and Proprocessing Skills
Data is key for any analysis in data science, be it inferential analysis, predictive analysis, or prescriptive analysis. The predictive power of a model depends on the quality of the data that was used in building the model. Data comes in different forms such as text, table, image, voice or video. Most often, data that is used for analysis has to be mined, processed and transformed to render it to a form suitable for further analysis.
i) Data Wrangling: The process of data wrangling is a critical step for any data scientist. Very rarely is data easily accessible in a data science project for analysis. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. Knowing how to wrangle and clean data will enable you to derive critical insights from your data that would otherwise be hidden.
ii) Data Preprocessing: Knowledge about data preprocessing is very important and include topics such as:
a) Dealing with missing data
b) Data imputation
c) Handling categorical data
d) Encoding class labels for classification problems
e) Techniques of feature transformation and dimensionality reduction such as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
4. Data Visualization Skills
Understand the essential components of a good data visualization.
a) Data Component: An important first step in deciding how to visualize data is to know what type of data it is, e.g. categorical data, discrete data, continuous data, time series data, etc.
b) Geometric Component: Here is where you decide what kind of visualization is suitable for your data, e.g. scatter plot, line graphs, barplots, histograms, qqplots, smooth densities, boxplots, pairplots, heatmaps, etc.
c) Mapping Component: Here you need to decide what variable to use as your x-variable and what to use as your y-variable. This is important especially when your dataset is multi-dimensional with several features.
d) Scale Component: Here you decide what kind of scales to use, e.g. linear scale, log scale, etc.
e) Labels Component: This include things like axes labels, titles, legends, font size to use, etc.
f) Ethical Component: Here, you want to make sure your visualization tells the true story. You need to be aware of your actions when cleaning, summarizing, manipulating and producing a data visualization and ensure you aren’t using your visualization to mislead or manipulate your audience.
5. Basic Machine Learning Skills
Machine Learning is a very important branch of data science. It is important to understand the machine learning framework: Problem Framing; Data Analysis; Model Building, Testing &Evaluation; and Model Application. Find out more about the machine learning framework from here: The Machine Learning Process.
The following are important machine learning algorithms to be familiar with.
6. Skills from Real World Capstone Data Science Projects
Skills acquired from course work alone will not make your a data scientist. A qualified data scientist must be able to demonstrate evidence of successful completion of a real world data science project that includes every stages in data science and machine learning process such as problem framing, data acquisition and analysis, model building, model testing, model evaluation, and deploying model. Real world data science projects could be found in the following:
a) Kaggle Projects
b) Internships
c) From Interviews
7. Communication Skills
Data scientists need to be able communicate their ideas with other members of the team or with business administrators in their organizations. Good communication skills would play a key role here to be able to convey and present very technical information to people with little or no understanding of technical concepts in data science. Good communication skills will help foster an atmosphere of unity and togetherness with other team members such as data analysts, data engineers, field engineers, etc.
8. Be a Lifelong Learner
Data science is a field that is ever-evolving, so be prepared to embrace and learn new technologies. One way to keep in touch with developments in the field is to network with other data scientists. Some platforms that promote networking are LinkedIn, github, and medium (Towards Data Science and Towards AI publications). The platforms are very useful for up-to-date information about recent developments in the field.
9. Team Player Skills
As a data scientist, you will be working in a team of data analysts, engineers, administrators, so you need good communication skills. You need to be a good listener too, especially during early project development phases where you need to rely on engineers or other personnel to be able to design and frame a good data science project. Being a good team player world help you to thrive in a business environment and maintain good relationships with other members of your team as well as administrators or directors of your organization.
10. Ethical Skills in Data Science
Understand the implication of your project. Be truthful to yourself. Avoid manipulating data or using a method that will intentionally produce bias in results. Be ethical in all phases from data collection, to analysis, to model building, analysis, testing and application. Avoid fabricating results for the purpose of misleading or manipulating your audience. Be ethical in the way you interpret the findings from your data science project.
In summary, we’ve discussed 10 essential skills needed for practicing data scientists. Data science is a field that is ever-evolving, however mastering the foundations of data science will provide you with the necessary background that you need to pursue advance concepts such as deep learning, artificial intelligence, etc.
Understanding how artificial intelligence (AI) and machine learning (ML) can benefit your business may seem like a daunting task. But there is a myriad of applications for these technologies that you can implement to make your life easier.
Through AI and ML, your business will benefit as it becomes more efficient at its operations and eliminates those mundane tasks that seem to be slowing you down. Additionally, AI-powered tools and automated systems can help your company improve the use of its resources, with visible effects on your bottom line.
Fifteen members of Forbes Technology Council discuss some of the latest applications they’ve found for AI/ML at their companies. Here’s what they had to say:
1. Powering Infrastructure, Solutions and Services
We’re leveraging AI/ML in our collaboration solutions, security, services and network infrastructure. For example, we recently acquired an AI platform to build conversational interfaces to power the next generation of chat and voice assistants. We’re also adding AI/ML to new IT services and security, as well as hyper-converged infrastructure to balance the workloads of computing systems. – Maciej Kranz, Cisco Systems
2. Cybersecurity Defense
In addition to traditional security measures, we have adopted AI to assist with cybersecurity defense. The AI system constantly analyzes our network packets and maps out what is normal traffic. It is aware of over 102,000 patterns on our network. The AI wins over traditional firewall rules or AV data in that it works automatically without prior signature knowledge to find anomalies. – John Sanborn, RAA – Financial Advisors
3. Health Care Benefits
We are exploring AI/ML technology for health care. It can help doctors with diagnoses and tell when patients are deteriorating so medical intervention can occur sooner before the patient needs hospitalization. It’s a win-win for the healthcare industry, saving costs for both the hospitals and patients. The precision of machine learning can also detect diseases such as cancer sooner, thus saving lives. – Adam Bayaa, Heal
4. Recruiting Automation
With unemployment at historical lows, recruitment of qualified workers remains one of the most difficult challenges. By harnessing the power of recruiting automation, savvy employers are using AI-powered sourcing tools to find candidates who may not have been considered for roles in the past, not because they weren’t qualified, but because they weren’t surfaced in the first place. – Jon Bischke, Entelo
5. Intelligent Conversational Interfaces
We are using machine learning and AI to build intelligent conversational chatbots and voice skills. These AI-driven conversational interfaces are answering questions from frequently asked questions and answers, helping users with concierge services in hotels, and to provide information about products for shopping. Advancements in deep neural network or deep learning are making many of these AI and ML applications possible. – Mitul Tiwari, Passage AI
6. Reduced Energy Use And Costs
We have used AI to cut energy use and reduce energy costs for drilling, crude and natural gas transportation, storage and petroleum refining operations. Until recently the industry has been looking at historical data points. The AI application we run can now learn and predict future energy load at levels as granular as a single blending activity. This opens up an entire range of opportunities to reduce waste, reduce peak demand and cut costs. – Jane Ren, Atomiton, Inc.
7. Predicting Vulnerability Exploitation
We’ve recently started using machine learning to predict if a vulnerability in a piece of software will end up being used by attackers. This allows us to stay days or weeks ahead of new attacks. It’s a large scope problem, but by focusing on the simple classification of “will be attacked” or “won’t be attacked,” we’re able to train precise models with high recall. – Michael Roytman, Kenna Security
8. Becoming More Customer-Centric
We’re using AI to better analyze customer responses to surveys and activities over time. This enables us to understand not only the feedback they provide but whether or not there are specific qualities and attributes that correlate to their response rate and likelihood to engage. This information will allow our customers to alter their own client survey strategies. – Alan Price, visioncritical.com
9. Market Prediction
We are using AI in a number of traditional places like personalization, intuitive workflows, enhanced searching and product recommendations. More recently, we started baking AI into our go-to-market operations to be first to market by predicting the future. Or should I say, by “trying” to predict the future? –Tim Rendulic, Thomson Reuters
10. Accelerated Reading
AI is accelerating our understanding of written text. Simply put, humans cannot read fast enough, and cannot mentally mine and structure the vast quantity of data that is available. We have developed advanced AI that reads and understands life science articles, helping researchers to accelerate the discovery of cures for diseases and the development of new treatments and medications. – Daniel Levitt, Bioz
11. Cross-Layer Resilience Validation
We continually hear from our customers that existing testing methodologies fall short when relating to predicting misconfigurations in-between different IT layers. We invest significantly in research and development of cross-layer dependency mapping and cross-layer validation techniques, utilizing both knowledge-driven analytics and ML. Our validation technology goes beyond detecting what is broken now into predictive resilience risk detection. – Gil Hecht, Continuity Software
12. Accounting And Fintech
AI is affecting many industries. Accounting and fintech are no exceptions. After years of working closely with professional accountants, I’m noticing a growing trend — they’re utilizing AI to streamline their professional routines through practices like automated data entry and reporting. And it’s not just accountants, the entire financial services industry is embracing automation. – Nick Chandi, PayPie
13. Advanced Billing Rules
Our organization has added machine learning-powered billing rules to maximize our credit card processing success rates for recurring billing. By identifying trends in declined credit cards (for example, cards being declined more often on a Sunday evening compared to a Wednesday morning), and fraud patterns that lead to chargebacks, we’ve been able to raise revenue with little human interaction. – Jason Gill, The HOTH
14. Understanding Intentions And Behaviors
Bad actors follow specific communication patterns — for example, colleagues spreading malicious rumors tend to be pretty chatty. Advanced AI has the distinct ability to not just identify these patterns, but leverage behavioral analytics to understand the intention behind communication. Using AI to spot bad behavior is something we use to empower customers across various industries. – Brandon Carl, Digital Reasoning
15. Proposal Review
We found an exciting use of AI for our application that saves incredible time and improves quality for customers. When a facility manager receives a proposal from a contractor, machine learning analyzes the scope, the pricing, and the contractor’s historical performance, to determine if the proposal is the right cost and will be done at the right quality level. It’s a huge win for our clients. – Tom Buiocchi, ServiceChannel
Forbes Technology Council is an invitation-only, fee-based organization comprised of elite CIOs, CTOs and technology executives. Find out if you qualify at forbestechcouncil.com. Questions about an article? Email [email protected].
Machine learning and artificial intelligence (AI) will change the way search marketers do business. In the latest article in his multipart series on PPC and AI, columnist Frederick Vallaeys shares his strategies for keeping your agency successful in a world of AI-first PPC.
Artificial intelligence (AI) and machine learning have long been part of PPC — so why are AI and machine learning all of a sudden such hot topics? It is, in part, because exponential advances have now brought technology to the point where it can legitimately compete with the performance and precision of human account managers.
