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Updated: 2017-03-25T08:53:40+00:00

 



The 6 Values (and 4 Benefits) of Agile Marketing - Whiteboard Friday

2017-03-24T00:02:00+00:00

Posted by AgileJimYou've probably heard of agile processes in regards to software development. But did you know those same key values can have a huge impact if applied to marketing, as well? Being adaptive, collaborative, and iterative are necessary skills when we live in a world where Google can pull the rug out from under us at a moment's notice.In today's Whiteboard Friday, we welcome guest host Jim Ewel, founder of AgileMarketing.net, as he describes what's important in the agile marketing process and why incorporating it into your own work is beneficial. src="https://fast.wistia.net/embed/iframe/em8z5sq2zn?videoFoam=true" title="Wistia video player" allowtransparency="true" frameborder="0" scrolling="no" class="wistia_embed" name="wistia_embed" allowfullscreen="" mozallowfullscreen="" webkitallowfullscreen="" oallowfullscreen="" msallowfullscreen="" width="100%" height="100%" style="background-color: initial;"> Click on the whiteboard image above to open a high-resolution version in a new tab! Video TranscriptionHey, Moz fans, this is Jim Ewel. I'm the blogger behind AgileMarketing.net, the leading blog on agile marketing, and I'm here to talk to you today about agile marketing. Agile marketing is an approach to marketing that takes its inspiration from agile software development. Like agile software development, it has a set of values and it has a set of benefits, and we're going to talk about those values and benefits today. 6 Values of Agile MarketingValue number one: Responding to change over following a plan. It's not that we don't plan. It's just that we don't write 30- to 40-page marketing plans. Instead, every quarter, we write a one-page plan that specifies our goals, our aspirations to get everybody on the same page, and then every two to four weeks, we reset our priorities. We say, "This is what we're going to get done during this two- to four-week period."Value number two: Rapid iterations over "big bang" campaigns. In traditional marketing, we get together in a room and we say, "We're going to run a campaign for three to six months to a year." We hash out the idea of what we're going to do for that campaign. Then we communicate to the agency. They come up with creative. They review it with us. We go back and forth, and eventually we'll run that campaign for three to six months. And you know what happens at the end of that campaign? We always declare victory because we've spent so much money and time on that campaign that every time we say, "It worked."Well, we take a very different approach in agile marketing. We take an iterative approach. We start out with a little strategy. We meet for half an hour or an hour to figure out what do we think might work. Then we figure out how to test it. We measure the results, and this is very important, we document the learning.If something doesn't work, we test it out and it doesn't work, it's okay because we've learned something. We've learned what doesn't work. So then we iterate again, and we try something else and we do that, we get that cycle going in a very effective way. Value number three: Testing and data over opinions and conventions Here, again, the importance is that we're not following the highest-paid person's opinion. No HiPPOs. It's all about: "Did we test it? Do we have data? Do we have the right metrics?" It's important to select the right metrics and not vanity metrics, which make us feel good, but don't really result in an improvement to the business. Value number four: Many small experiments over a few big bets And I like to talk about here the 70:20:10 rule. The idea behind the 70:20:10 rule is that we spend 70% of our budget and 50% of our time on the things that we know that work. We do it broadly across all our audiences. We then spend 20% of our budget and 25% of our time modifying the things that we know that work and trying to improve them. Maybe we distribute it in a little different way or we modify the content, we modify what the page looks like. But, anyways, we're trying to improve that content.And the last 10% of our budget and 2[...]



The State of Searcher Behavior Revealed Through 23 Remarkable Statistics

2017-03-14T00:00:00+00:00

Posted by randfishOne of the marketing world's greatest frustrations has long been the lack of data from Google and other search engines about the behavior of users on their platforms. Occasionally, Google will divulge a nugget of bland, hard-to-interpret information about how they process more than X billion queries, or how many videos were uploaded to YouTube, or how many people have found travel information on Google in the last year. But these numbers aren't specific enough, well-sourced enough, nor do they provide enough detail to be truly useful for all the applications we have. Marketers need to know things like: How many searches happen each month across various platforms? Is Google losing market share to Amazon? Are people really starting more searches on YouTube than Bing? Is Google Images more or less popular than Google News? What percent of queries are phrased as questions? How many words are in the average query? Is it more or less on mobile? These kinds of specifics help us know where to put our efforts, how to sell our managers, teams, and clients on SEO investments, and, when we have this data over time, we can truly understand how this industry that shapes our livelihoods is changing. Until now, this data has been somewhere between hard and impossible to estimate. But, thanks to clickstream data providers like Jumpshot (which helps power Moz's Keyword Explorer and many of our keyword-based metrics in Pro), we can get around Google's secrecy and see the data for ourselves! Over the last 6 months, Russ Jones and I have been working with Jumpshot's Randy Antin, who's been absolutely amazing — answering our questions late at night, digging in with his team to get the numbers, and patiently waiting while Russ runs fancy T-Distributions on large datasets to make sure our estimates are as accurate as possible. If you need clickstream data of any kind, I can't recommend them enough. If you're wondering, "Wait... I think I know what clickstream data is, but you should probably tell me, Rand, just so I know that you know," OK. :-) Clickstream monitoring means Jumpshot (and other companies like them — SimilarWeb, Clickstre.am, etc.) have software on the device that records all the pages visited in a browser session. They anonymize and aggregate this data (don't worry, your searches and visits are not tied to you or to your device), then make parts of it available for research or use in products or through APIs. They're not crawling Google or any other sites, but rather seeing the precise behavior of devices as people use them to surf or search the Internet. Clickstream data is awesomely powerful, but when it comes to estimating searcher behavior, we need scale. Thankfully, Jumpshot can deliver here, too. Their US panel of Internet users is in the millions (they don't disclose exact size, but it's between 2–10) so we can trust these numbers to reliably paint a representative picture. That said, there may still be biases in the data — it could be that certain demographics of Internet users are more or less likely to be in Jumpshot's panel, their mobile data is limited to Android (no iOS), and we know that some alternative kinds of searches aren't captured by their methodology**. Still, there's amazing stuff here, and it's vastly more than we've been able to get any other way, so let's dive in. 23 Search Behavior Stats Methodology: All of the data was collected from Jumpshot's multi-million user panel in October 2016. T-distribution scaling was applied to validate the estimates of overall searches across platforms. All other data is expressed as percentages. Jumpshot's panel includes mobile and desktop devices in similar proportions, though no devices are iOS, so users on Macs, iPhones, and iPads are not included. #1: How many searches are *really* performed on Google.com each month? On the devices and types of queries Jumpshot can analyze, there were an average of 3.4 searches/day/searcher. Using the T-Distribution scaling analysis on various sample set sizes of Jumpshot's data, R[...]