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Blogging

5 Ways to Become a Lean, Mean, Blogging Machine

by Loknath Das
written by Loknath Das

Many business owners recoil at the notion of regular blogging—and their concern is a perfectly fair one: They simply feel as though they can’t afford the time investment required for consistent, high-quality blogging.

To be sure, maintaining a robust business blog is going to require some man hours. With that said, there are ways to make your blogging endeavors more efficient—requiring less time, but still getting good results.

Indeed, with the right tweaks to your blogging strategy, you can become a lean, mean, content-creating machine—and we can show you how!

Recommended for YouWebcast, August 1st: SEO: How to Outrank Your Competitors on Google (Our Step-by-Step Process)

Become Ruthlessly Efficient in Your Blogging

Here are our five tips:

  1. Always start with an outline. One of the most time-consuming parts of blogging is going back through a post to review, to add or subtract points, and to bring shape and focus to your argument. One way you can cut back on revisions is to have a clear blueprint of all your primary points before you start writing. A simple outline will take a couple of minutes to put together, but it could save you a lot of time on the back end.
  2. Know your call to action in advance. A lot of time can be spent trying to land the plane—figuring out how you want your blog post to end and what results you hope to achieve. Those are things you should know before you start writing. Have a goal in mind—a specific call to action—and allow that to guide your writing. Again, this is a small investment of time on the front end that can make your process more efficient overall.
  3. Keep buyer personas handy. You should always know who you are writing for. Once more, this is imperative for keeping your writing focused and on-point. Start each blog post with a clear sense of who’s in your audience, and which problems or pain points you need to address on their behalf.
  4. Block off time for content marketing. Schedule an hour each week (or more) for content marketing activities, and treat it just like an appointment with a client—that is to say, don’t blow it off! Use this time to write a blog post, but also to put together the accompanying social media posts you’ll use to share that blog. Get all of these like tasks done at the same time.
  5. Keep a running list of blog ideas. Always be ready to write down a topic for some future post—meaning that, when you sit down to write, you shouldn’t feel stuck or have to spend too much of your time brainstorming.

[“Source-business2community”]

 

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Online Marketing

Google Brings Machine Learning to Online Marketing Assessment

by Loknath Das
written by Loknath Das

google-attribution-online-marketing-assessment

Google earlier this week unveiled Google Attribution, a tool that uses machine learning to measure the effectiveness of online marketing campaigns across a variety of devices and channels.

Part of a series of new measurement tools introduced at Google Marketing Next, Google Attribution aims to help marketers determine what is driving consumers to make their online purchasing decisions.

Google is at an “inflection point,” noted Sridhar Ramaswamy, senior vice president of ads and commerce, when it comes to the use of technology to measure online marketing effectiveness.

“What’s exciting is that things that were really hard we’re going to be able to make easy, because technology is doing the heavy lifting,” he told conference attendees.

Seconds Matter

Page-loading time is an important factor in terms of how long advertisers have to hook a consumer online, Ramaswamy said.

As load time increased from one to seven seconds, the probability of a consumer bouncing more than doubled, found a recent study that analyzed 900,000 mobile landing pages in more than 100 countries. That translates to conversions falling by up to 20 percent for every one second delay in page-load time.

Most existing attribution measurement tools have three main shortcomings, according to Ramaswamy. First, they lose track of the customer journey when they move between tools; second, they are hard to set up; and third, they are not integrated with ad tools.

Because of those shortcomings, marketers typically are stuck with using last-click attribution, which misses the impact of most marketing touchpoints.

The new Google Attribution tool incorporates data from AdWords, Google Analytics and DoubleClick Search, which provides a more comprehensive picture of data from all marketing channels, according to Google.

Google Attribution also makes it easy to switch over to data-driven attribution, which uses ads, keywords and campaigns that have the greatest impact on a company’s goals, Google said. Using the data-driven attribution model typically requires a minimum set of data, which is 15,000 clicks, and at least 600 conversions within a 30-day period.

Google will offer an enterprise version, Google Attribution 360, to meet the needs of larger advertisers, noted Bill Kee, group product manager at the company.

Google Attribution currently is in beta and will roll out to additional customers over the next few months.

Not Quite Ready

Google Attribution is an “aspirational sell” for advertisers, particularly for small and medium-sized businesses, suggested Paul Teich, principal analyst at Tirias Research.

“Google intends to make it free, so it will see a lot of uptake, but many users will have zero comparative experience to judge how much better they are doing with Attribution,” he told the E-Commerce Times.

The major benefit will be providing more-accessible feedback for designing online user experiences, Teich said, noting that it’s not clear how artificial intelligence can make a contribution — at least until Attribution has been able to analyze millions of Web pages.

Google is ahead of the curve compared to other Internet companies, which are only average at measuring the efficacy of online marketing, observed Michael Jude, a program manager at Stratecast/Frost & Sullivan.

“Machine learning does give it an edge in making sense of the relationship between key clicks and online purchases,” he told the E-Commerce Times.

“While simple algorithms can provide some insight, especially as applied to last click models, machine learning can understand such things as click back, where the last key click is not the purchase, but rather an artifact of comparison shopping,” Jude explained.

Data Points

Google made several other announcements related to providing additional marketing data.

It launched a new AdWords beta to let advertisers use fast-loading AMP pages as landing pages for search ads and to speed up ads across the Google Display Network. This follows Google’s introduction about 18 months ago of the Accelerated Mobile Pages project to speed up the Web with faster browsing.

Google also announced a new tool to bring in-market audiences to Search, in order to help advertisers reach users who are ready to buy their products. Using machine learning, the tool analyzes trillions of search queries and millions of websites to determine when searchers are close to making a buying decision on a product.

The company unveiled plans to roll out store-sales measurement at the device and campaign levels, which will allow advertisers to measure in-store revenue and store visits delivered by search and shopping ads.

If advertisers collect email information at the point of sale through loyalty programs, Google said, store transactions can be imported directly into Adwords by the advertiser or through a third-party data partner.

[“Source-ndtv”]

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Online Marketing

AI. Machine Learning. What’s the Impact on Digital Marketing Today? [PODCAST]

by Loknath Das
written by Loknath Das

AI. Machine Learning. What’s the Impact on Digital Marketing Today? [PODCAST]

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Artificial intelligence and machine learning have been highly predicted trends in marketing and SEO. But how are they changing the industry now? Read on to find out more about the influence of AI on the marketing world in this Search Engine Nerds episode.

Marc Poirier, CEO and co-founder of Acquisio, joins SEJ’s Brent Csutoras to talk about how artificial intelligence and machine learning impacts online marketing.

Poirier also gives us a primer on how AI is affecting local search, and shares his verdict on the battle between AI and humans in search and marketing.

Artificial Intelligence

We’re starting to see AI integrate itself into a lot of marketing campaigns. Is that the direction we’re headed in?

Marc: I think this is the direction most software companies need to start steering towards over the next five years or so. We have all this data we’re looking at; no matter what field of marketing you’re in, there’s just so much data coming at us now. Being able to leverage that data and make good use of it through machine learning and data mining is something that’s happening today, but it’s going to accelerate over the next few years.

How do you see the transition of machine learning and AI really start to interact with marketing over the next five years?

Marc: There are various models of algorithms that are designed to solve specific problems, and they exist out there. So you can just use some of the work that has been done by academics before and apply it to the problem you’re trying to solve to parse through a ton of data and come out with a better answer really rapidly. I think a lot of companies do that today, but it hasn’t necessarily been labeled as such.

The reality is not so much about the data and having tools to visualize it. That’s great, but what are you going to do with it? How do you analyze it? How do you make decisions based on that data, and how do you automate that decision making process? How do you do that in marketing?

Brent: I think we see that even with small data. You can go in and show somebody Google Analytics at a very small scale and show the data.

Marc: Yeah, exactly.

Brent: And people aren’t really willing to implement that data as it is, let alone start getting into large-scale data collection and analysis. I think one of the big things for marketers is this sense of disconnect because it might not be overly new. It might not be overly complicated at the rudimentary level, but when it comes down to it, if I wanted to do AI, if I wanted to incorporate some of this stuff into my marketing campaigns, there’s a disconnect in the path on how I do that.

When you look at an AI solution versus an expert, do you feel that AI provides an equal or better outcome? Or is it still just a time versus money type of scenario?

AI vs expert: man arm wrestling with robot

Marc: No, it’s a better outcome, but it depends on the problem you’re solving. Some of the things we do are just research, so the outcomes are not better.

What we’ve been doing for five or six years is controlling money, and that’s not something humans can do very well, especially at scale. So we build software to help the small business, but we don’t sell it directly to them. We work with large resellers, companies that have tens of thousands of small businesses they need to serve. They need to make sure that the results are on point, that they’re spending the budgets, and they’re maximizing phone calls and clicks and things like that. This is where technology will do a better job than humans 100% of the time.

Brent: Is this similar to the stock market where you really have no chance if you’re not utilizing these tools? Has it gotten to that point, you feel?

Marc: Yeah, it has. We’re making decisions and making changes on budgets and bids every 30 minutes now across tens of millions of ad groups and hundreds of thousands of accounts. So you can’t imagine humans doing that.

First of all, it’s redundant. It’s error-prone and there’s a lot of accurate decision making going on, a lot of machine learning applied that always tries to hone in on the target. We’re trying to spend a $500 budget, or a $200 budget — it’s very little money, so how do we hit it? How do we get really close to it and not go over? How do we learn about that specific business, about their trend? So there are a few levers there that are really important to model.

I mean you could do it if you have just one account to manage and you apply a lot of time and energy. I’m sure humans can do a pretty good job at it, but you can’t do that at scale for small accounts. It just doesn’t make any sense, and it doesn’t work. It’s not possible to make the right decision all the time. That’s what algorithms and AI do.

What would be the steps and tools to start incorporating AI into paid search? What would be the end-point solution for somebody right now?

Marc: We have a product called Promote where any local business can sign up and we’ll create a local presence for them. We’ll create a Google AdWords account for them and all of the keywords they need, the ads, everything. It’s all automated. Then we’ll assign a small budget and control it really well.

If you look at what Google’s doing, or Marin Software, Kenshoo, Adobe, Ignition One, SearchForce, all the companies in this space, all the bid management solutions have some kind of algorithms or rule engines. So there is technology there to help make the right decisions on large data sets, and they do very well for different situations.

There are products out there in the search world that do that, but they’re limited, typically, to bid management. I think the areas of focus for us are how do we go beyond that in the future and automate more and do a better job at writing ad copy, for example, or testing ad copy.

What is it about AI or machine learning that’s really impacting local search? How does it affect what people are doing locally?

Marc: It revolves a lot around delivering value for small budgets, so it’s very difficult to control. If you have a lot of money to spend on AdWords, you’ll be able to see results, and there’s going to be a lot of waste. But you can’t afford any waste when you have a $200 budget for the month, right? You want to get some clicks. You want to get some calls, so it’s really important that we go out there and get those clicks that will cost the least amount of money. They need to be valid clicks and they need to be relevant.

It’s understanding the auction, understanding the profile for each business — the peaks and valleys, time of day, day of week — to see how the data has flowed historically when we have that. This is something the software will pick up, not only per vertical but also by business. You just gain more certainty if you can have many layers.

So you can say, “Okay, I’m looking at the law services category in general, all of the different attorneys and legal services that are available. Do I see patterns on a day of week basis? Yes. I see some kind of trend where Monday seems to be maybe 20% busier than all the other days.”

Then let’s look at by city for example, or by DMA. Do we see trends there as well? Yeah, larger cities seem to have more of that. Then per business, like for this specific business, if I look back three months, do I see a pattern? Do all these things come together? And does that help me make a better decision? How sure am I that this decision of changing that bid, lowering it by five cents will have an impact in the next 30 minutes?

In a nutshell, it’s a lot of complexities, a lot of data points to look at, but it’s to make that small decision. They’re always small changes that we make, and then validate the next 30 minutes, 60 minutes, 90 minutes. Did I see pick up on impressions? Because when there’s not a lot of click-flow, do I at least see more impression flow hour per hour? Those decisions will be tweaked as the function of the results we observe.

How do you feel about where people spend their money on paid ads right now? Do you feel that Google AdWords is still a major place to spend budget? Are you seeing certain social areas better than others?

Marc: Facebook ads is the place where small businesses are going to start because Facebook is making it easy for anyone to start and they’re making it part of your everyday experience. If you have a business page and you wrote a post, we’ll suggest you boost it for 10 bucks or something. Facebook’s been really good at garnering small span from small advertisers, small SMBs. Most SMBs today have realized that before they graduate to do some paid search investments, they’ll go to Facebook first then introduce Google AdWords.

Brent: Is that just because of the ease of use? It’s just something they can easily engage with, and then once they see some kind of traction, they start looking at other areas to run ads?

Marc: Well, yes, it’s easy and there are many ad products on Facebook. It’s a Swiss army knife of advertising. And there are different ways to build your audience as well, so it’s not only the ad products, it’s how you target. It’s quite complex, despite how easy they make it for a small advertiser to start. They have products like local awareness ads that are that simple. But local awareness helps retailers, restaurants and bars, the food industry in general.

But maybe it’s not that helpful for an emergency plumbing service or the landscaper. When you need a roofer, you’re going to look for it. It’s not going to be an awareness play so it may not be the first place you go for those. Depending on the type of business you are, it may be best to start on Facebook when you’re building local awareness. When it’s something that’s more search-driven intent, search ads would be more appropriate to start with.

Do you feel that you can take learnings from one platform to the other? Or do you feel that they really are individual and need to be viewed as individual?

Marc: We’re not finding any learnings from Facebook can apply to search or vice versa. However, the one thing we’re doing there in terms of bringing things together is budget allocation. How do we devise or change the way our product works to automatically allocate the money correctly across search — AdWords, Bing Ads, Facebook? We didn’t crack that nut yet. This is something that’s really interesting to a lot of companies.

But we’re in the middle of exploring if it does make sense, if it adds value. We’re hearing a lot of “yes”. So we’ll continue that investigation, but the idea of having a marketing budget for advertising online, like a digital advertising budget, and to have one place where you can just sort of put your money in there and trust that the algorithm will do the right thing, seems to be appealing to marketers today.

To listen to this Search Engine Nerds Podcast with Marc Poirier:

  • Listen to the full episode at the top of this post
  • Subscribe via iTunes
  • Sign up on IFTTT to receive an email whenever the Search Engine Nerds podcast RSS feed has a new episode
  • Listen on Stitcher, Overcast, or Pocket Casts

Think you have what it takes to be a Search Engine Nerd? If so, message Loren Baker on Twitter, or email him at loren [at] searchenginejournal.com. You can also email Brent Csutoras at brent [at] alphabrandmedia.com.

Visit our Search Engine Nerds archive to listen to other Search Engine Nerds podcasts!

[“Source-ndtv”]

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Internet

Internet Archive’s Wayback Machine Adds Keyword Search as a Beta Feature

by Rajdeep
written by Rajdeep

Internet Archive's Wayback Machine Adds Keyword Search as a Beta Feature

It can be hard to find sites that have disappeared from the Internet. But the Internet Archive’s Wayback Machine is on the verge of rolling out a feature that will make tracking down dead websites much easier, according to Internet Archive founder Brewster Kahle.

The Wayback Machine has been helping people see past Internet sites over the past 15 years, but searchers always needed to know the URL of a website to find the archived copies. Soon, however, you’ll be able to use keyword searches to find old websites – in fact, you can already test it out through a public beta.

The new search feature is not quite like Google, where all the text on each page on a website is indexed to help with searches. The Wayback Machine feature lets you search for an archived website’s main page, although it does not have the capacity to enable searches for specific webpages on that site. But once you’re there, you’re able to navigate around the old websites.

But the feature is still a big step in terms of usability. Kahle said that he, too, has struggled with the lack of keyword search availability in the Wayback Machine. “I use the Wayback Machine mostly by going to Google, finding a URL and then going to the Wayback to find previous versions of it,” said. “But what if the site is completely gone?”

The ever-changing online ecosystem is partly to blame for all of that hoop jumping: The Internet’s history is filled with the digital corpses of entire sites that are hard, if not impossible, to find through Google.

Take Geocities. It was once a hugely popular hosting service that was acquired by Yahoo but is now defunct everywhere except Japan. Today, the first Google search result for “geocities” is a Wikipedia page about the service, followed by stories about its demise, but not the actual geocities.com URL, which currently redirects to a page about Yahoo services for small businesses.

But if you search for “geocities” in the test version of the Wayback Machine, the first result is geocities.com. That result shows the tool captured over 200 million copies of some 37 million pages that were once hosted on the domain.

And that’s the real power of the Wayback Machine’s new search: It makes it easier for us to see how the Web used to look.

[Source:- gadgets.ndtv]

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Internet

Google Aims to Take Machine Learning Mainstream With New Cloud-Based Tools

by Rajdeep
written by Rajdeep

Google Aims to Take Machine Learning Mainstream With New Cloud-Based Tools

At the GCP Next 2016 conference in San Francisco this week, Google announced the launch of Cloud Machine Learning, a suite of cloud-based tools, with which the company hopes to make sophisticated machine learning tools available to developers across the globe.

“Cloud Machine Learning will take machine learning mainstream, giving data scientists and developers a way to build a new class of intelligent applications. It provides access to the same technologies that power Google Now, Google Photos and voice recognition in Google Search as easy to use REST APIs,” Fausto Ibarra, Director, Product Management, Google said in a blog post announcing the launch of the new feature.

Unlike traditional learning techniques that involve programming a machine with certain set of instructions for each scenario, machine learning (typically) involves feeding a computer with a large set of data – called training the machine – and then letting it figure a reaction for different situations, or come up with answers for new scenarios. The likes of Amazon and Microsoft, Google’s main competitors in the public cloud space, offer cloud-based machine learning tools as well.

sundar_pichai_gcp_next_2016_google.jpg

Google Cloud Platform (GCP) features a couple of pre-trained machine learning APIs called Google Translate API and Cloud Vision API that have been available to the public for a while. While the former is self explanatory, the latter identifies the contents of an image with a scarily good level of accuracy.

Given an image of a concert, for example, the Cloud Vision API can not only identify that it has people in it, but it can also say that there’s a stage, and that it’s likely a performance or a concert. The API is also capable of identifying specific landmarks, like the Golden Gate Bridge. Google has trained the Cloud Vision API by feeding it millions of images, and the power of this trained API has been available to all since its launch.

Joining the Google Translate API and Cloud Vision API this week is the Google Cloud Speech API, which makes the power of Google’s amazing speech-to-text conversion capabilities available to any developer. Anyone who’s used speech-to-text on an Android phone has no doubt been impressed by its accuracy, and now anyone can leverage these capabilities. The API can operate in batch mode – i.e. given a bunch of audio files to transcribe – or in streaming mode, where it can transcribe audio in real time. Google says the API supports over 80 languages.

“Cloud Speech API brings the same advanced neural network technology that powers voice search in the Google app and voice typing in Google Keyboard,” Ibarra explains. “It demonstrates speech-to-text conversion in 80+ languages with unparalleled accuracy, especially in noisy environments, and is blazingly fast. The technology that has already empowered developers to add speech to their Chrome and Android is now available for any application in real-time streaming or batch mode.”

In addition, Google Cloud Platform customers can build their own machine learning models using the open-source TensorFlow machine learning library, which is most popular machine learning project on GitHub. Google said TensorFlow Serving will be able talk to Kubernetes, another one of its open-source projects.

[Source:- NDTV]

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Online Marketing

Is machine learning the future of marketing?

by Loknath Das
written by Loknath Das

machine learning

What will the future of marketing hold? Will it be entirely automated by smart AIs that fully understand human nuance? Probably not. Will it be entirely manual and managed only by individual people without the aid of technology? Definitely not.

As we’ll discuss shortly, the top influencers in marketing put their heads together on this very topic, and the results may surprise you. In any case, it’s safe to say that marketing probably isn’t going back to the old days of billboards, newspapers, and radio spots. The numbers don’t lie: the future of marketing is definitely digital.

The digital marketing space continues to grow: Online sales have doubled in the past five years, and more than two-third of adults in the U.S. shop online monthly. Revenue for online advertising recently surpassed that of television for the first time in history, and growth of traditional advertising such as TV and print are projected to stay flat while newspaper advertising continues to decline.

What all of this means is that marketers need to develop effective strategies to realize the opportunities digital provides. However, there are several factors that can either help or hinder your digital marketing efforts, and they all revolve around data and the customer journey.

Too much data, too few answers

Marketers must always strive to understand the needs of their customers, so digital marketing is driven by customer data. The good news is that there’s a wealth of data in every online search and click that can reveal insights about consumer behavior and demographics. Ideally, marketers would use this information to drive customer engagement that leads to increased revenues.

The problem is that data isn’t an easy-to-use solution to marketing challenges in and of itself; it needs to be collected, managed, and interpreted correctly for it to have any value at all. Even valuable data can go to waste when it isn’t integrated properly, and most marketing leaders spend up to half their budget on data analytics that ultimately lead to no company-wide improvements.

Data is also often mismanaged because of a lack of central ownership. This leads to significant digital skills gaps that are projected to cost businesses $3.3 billion by 2020 considering that much of the data marketers collect gets mismanaged and thus becomes irrelevant, redundant, or obsolete.

It’s always a bad investment when businesses spend money on data that goes nowhere. There needs to be a method to solve data management problems so marketers can realize the benefits of using data for digital marketing.

Customers are in control of their own journey

 Back in the day, advertising was a one-way street: Businesses sent advertising messages to customers to drive sales through one-way channels such as billboards, print, and broadcast. There was no dialogue between companies and consumers. People didn’t have the internet to allow them to find the best possible deal, nor did they have powerful social networks to let them know about others’ brand experiences. Nowadays, most of the buying process is customer-controlled.

In addition, consumers are growing intolerant of annoying and irrelevant advertising, and pop-ups and other unwelcome messages are now getting blocked with increasing frequency. In fact, 11 percent of global consumers now use ad blocking software, up 30 percent since last year. Google Chrome will soon have a built-in ad filter feature, as well. The use of ineffective online marketing communications isn’t just wasteful, it can also be toxic to your brand as consumers become annoyed and shut you out completely.

As a result, marketers now need to ensure that they’re reaching the right customer at the right time with the right message to drive brand engagement. Otherwise, they risk annoying or upsetting their customers, thus leading to brand abandonment.

Machine learning helps you use data to serve empowered consumers

In a perfect world, marketers would use insights from data to engage with empowered consumers and help them make smart purchasing decisions through digital marketing. However, the challenge marketers face is managing massive amounts of information to serve a fickle market that will punish them harshly if they make a mistake.

As brilliant as marketers are – and must be in these challenging times – it’s difficult to downright impossible for individuals to put their arms around the millions of data points that constantly stream in from their digital marketing campaigns every nanosecond. Thus, an automated process called machine learning is necessary to take all the data you collect, organize it, manage it, and use it to predict the best ways to reach people with messages to galvanize buying behavior most effectively.

Machine learning means using predictive analytics and intelligent automation to formulate data-driven predictions. It allows marketers to identify the likelihood of future outcomes based on historical data. In a recent survey of top marketing influencers, 97 percent said that the future of marketing will actually be a combination smart people armed with machine learning – in other words, that machine learning is the future of marketing. Some of the use cases for machine learning include:

  • Chatbots and Voice Assistants – The increasing use of conversational chatbots and voice assistants from Google, Amazon and Facebook highlights the importance of being able to intelligently create a relevant and engagingly conversational user experience based on empirical data.
  • User Engagement – The use of machine learning to build predictive analytics models, such as those created by Urban Airship and Microsoft Azure, can determine potential points of customer churn and proactively help businesses retain customers.
  • Natural Language Processing – Machine learning can be used to optimize bidding in digital advertising, mitigating data scarcity with highly accurate predictive models based on semantically similar keyword groups, such as the model used by QuanticMind.

The rise of digital marketing presents many challenges and opportunities for marketers. Those who manage data effectively to communicate with consumers appropriately will have an advantage over those who don’t. Machine learning offers marketers the ability to fully realize the benefits of digital marketing, build real user engagement rather than being filtered out…and ultimately grow their business and revenues by always being where their customers are.

[“Source-thenextweb”]

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