Category: analytics

  • Want to know your audience better? Try this test.

    When it comes to understanding your audience, few things provide as much insight as how your audience describe themselves. What are the words and phrases that they use to talk about themselves?

    Social media provides this answer to us in the form of profile biographies. Whether LinkedIn profile, Twitter bios, etc., we can learn quite a bit about our audiences if we dig into their words.

    Here’s a fun exercise to try. Using any common influence measurement tools such as Sysomos or Followerwonk, export the bios of your followers.

    Next, group them by whatever metric you choose; influence score, number of followers, engagement rate, etc.

    Feed their bios, their profiles, into any natural language processing software, from a simple word cloud maker like Tagxedo or Wordle, to sophisticated artificial intelligence programs. Whatever you’ve got on hand, feel free to use it.

    Here’s an example of the top quintile of my followers:

    bios_-_9.jpg

    Here’s an example of the middle quintile of my followers:

    bios_5.jpg

    Now, compare. What is the difference between higher influence groups and lower influence groups? What is the difference between people with less than 1000 followers and more than 1000 followers? What is the difference between people with low engagement versus high engagement?

    If you’re more quantatitively-minded, use any word frequency tool to break out the words by count:

    word_frequencies.jpg

    What can we learn from this exercise? If our most authoritative, influential followers are aligned with our target business audience, great. If not, we may want to change our definition of who constitutes an influencer. In turn, that changes who we reach out to, who we create content for, and who shares our stuff.


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  • Are you marketing to the same audience?

    Are you marketing to the same audience? If you aren’t, you’re missing both an opportunity and a problem.

    As marketers, our audience is our raw material. From audience, we grow evangelists, prospects, leads, revenue, and reputation. We need our audience to do even the most basic marketing work. If we were chefs in the kitchen, our audience would be our ingredients, from which we’d weave culinary magic.

    Suppose we set out to make an omelet, but we were fresh out of chicken eggs. We did, however, have a carton of liquid egg product in the refrigerator. Could we make the same omelet? We could certainly make something like an omelet, but it wouldn’t be exactly the same as with fresh-cracked eggs. Suppose that carton was liquid egg whites only. Could we make the same omelet? Not at all. We could still make something delicious, but it wouldn’t resemble our omelet at all.

    In the same way, if our audience varies from platform to platform, what we can make of that audience will vary significantly. Some audiences may be well-suited for lead generation. Others may be ideal for reputation and brand building.

    How would we determine if our audiences are similar or different? Here’s a simple way to test. First, ensure you have demographics and interests turned on in Google Analytics for your website:

    turn_on_demo_and_interest.jpg

    Next, take a look at your website audience’s basic demographics:

    My_Demographics__Overview_-_Google_Analytics.jpg

    And your website audience’s interests:

    My_Interests__Overview_-_Google_Analytics.jpg

    Make note of what interests your audience; this data comes from Google’s DoubleClick advertising network.

    Next, head to your Twitter analytics account at analytics.twitter.com. Check your audience’s demographics:

    My_Twitter_Audience_insights.jpg

    And check your audience’s interests:

    My_twitter_interests.jpg

    How different is your Twitter data from your Google Analytics data? Do you see significant variations between the two? Are they remarkably similar?

    Let’s next look at Facebook. Head over to Facebook Audience Insights:

    My_FB_Audience_Insights.jpg

    Again, compare your Facebook data to your Twitter and Google Analytics data. How do the audiences compare – are they similar or different?

    What do we do with this knowledge?

    If our audiences are substantially similar, our next step is to investigate our conversion data to see which of our channels our audiences finds the most value in, and increase our efforts there.

    If our audiences are substantially different, we must ask why. Why is Twitter or Facebook different from Google Analytics? Is there a sizable portion of our audience we’re leaving behind? Is there a part of our audience we’re not engaging?

    To return to our cooking analogy, if we’re not starting with the same ingredients, we should understand what we have before we try to cook with it. If we’re not starting with the same audiences, we should understand them better before we try one-size-fits-all marketing to them.


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  • The glaring flaw of influencer identification software

    brands_vs_influencers.jpg

    “It’s not just who you know, but who knows you.” – Mitch Joel

    Social media influencer identification software has one glaring flaw across many different analytics tools. Today’s tools focus too much on the what and not enough on the who.

    I was doing some client work the other night and found I needed to build a list of influencers for YouTube. I turned to the usual stalwart software tools for identifying influencers. What did I find? Lots of ways to identify top videos, most liked videos, videos with the highest number of views, etc. I found lots of information about the what, the media itself.

    What I didn’t find was the who. Who made these videos? What channels do they operate?

    Last year, I was working on a similar project on Pinterest. I found plenty of top pins, but very little information on who owned those pins in the various influencer marketing tools.

    Why don’t we focus on the creator, rather than the content? We still have too narrow a perspective as marketers. We focus on the biggest numbers – hey, this video got 1,000,000 views! – and not who can consistently create success, who we need to partner with for sustained growth.

    If we want our influencer programs to shine, to demonstrate the business-developing power our marketing and sales needs, we must adjust our focus from flash-in-the-pan “viral” hits to long-term talent identification.


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  • How do we measure influencers?

    Influence is a nebulous term. Its Latin roots hint at things flowing into something, the idea that ethereal energy flows into human destiny. Yet it might not be too bold to say that our ability to measure marketing influencers controls the destiny of our marketing today.

    Why does measuring influencers matter? Not all influencers are created equal. Some command enormous audiences; others inspire incredible engagement or compel purchase behaviors that we marketers need.

    In my previous book, Marketing Blue Belt, I outlined a basic social media funnel:

    blue_belt_slides_pptx.jpg

    Just as with any other marketing funnel, no one part of this funnel is more or less important than another; all are connected. When we talk about measuring influencers and what they can do for our brands, we can’t rely on just one “influence” number.

    We start by asking why we are engaging an influencer.

    Do we need more brand awareness?
    Do we need more engagement?
    Do we need increases in purchase consideration or behavior?

    Pick one.

    Once we know why, we can examine our influencers’ data through that lens. If our goal is to increase purchase intent, and our metric is clicks from the celebrity influencer’s Twitter feed to our website, what are we paying for?

    Let’s look at an example, Kim Kardashian West’s Twitter feed to determine if paying her $200K advertising fee per tweet is worth it.

    Here’s Mrs. West’s click data since January 1, 2016:

    Clicks per Tweet, by type.png
    (click for full size)

    Let’s dig into JUST the paid promotional tweets:

    Clicks per Tweet, Paid Only.png
    (click for full size)

    The median click per paid tweet is 5,351 clicks. That puts your average cost per click at $37.38. Is that reasonable? Is that high? Low? The answer depends on what you’re marketing. In Google’s AdWords, PPC management company Wordstream reports “insurance” as a keyword has a 54.91 cost per click. “Loans” costs44.28 per click.

    Depending on your product, conversion rates, etc. using Mrs. West’s paid ad platform may or may not be worthwhile. However, we can now make an apples-to-apples comparison for paying this influencer vs. other marketing methods at our disposal. We might seek an influencer with a lower cost-per-click price, or we might be willing to pay $37.38 per click.

    Before you start measuring influencers, understand what result you seek. Match your goals against the social media marketing funnel, then determine if the influencer’s audience reach, engagement, or purchase lift capabilities are a fit.


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  • Why marketers should care so much about influencers

    Why do marketers care so much about influencers? In the battle for attention between influencers and brands, influencers crush our brands.

    Influencer marketing matters more than ever. With Twitter’s new timelines, every major social network now offers some level of filtering based on engagement. We must elicit engagement from our audiences, or our social media marketing is for naught. We desperately need likes, comments, and shares just to be seen.

    How large is the gap between influencers and brands? I examined these top brands and influencers to compare engagement rates.

    Brands

    • Disney
    • The New York Times
    • Red Bull
    • The Wall Street Journal
    • BBC News
    • Facebook
    • Coca-Cola
    • Oreo
    • Ernst & Young
    • Microsoft
    • Walmart
    • Ford Motor Company
    Influencers

    • Kim Kardashian
    • Justin Bieber
    • Lady Gaga
    • Barack Obama
    • Vin Diesel
    • Selena Gomez
    • Taylor Swift
    • Will Smith
    • Dwayne “The Rock” Johnson
    • Megan Fox

    Let’s first look at the overall trends. How do influencers and brands compare, at least on Facebook, for likes, comments, and shares? I plotted the median engagements by week; brands are blue, influencers are green:

    Overall Engagement by Type.png

    Ouch. The brands – and these are major Fortune 50 brands and media powerhouses – are literally flatlined compared to individual influencers.

    How much of a difference is there between the influencer and the brand?

    Differences between Individuals and Brands.png

    Influencers are anywhere from 954% to 14,765% more influential than their brand peers. No wonder marketers are racing to court influencers as quickly and heavily as possible.

    What kinds of content are brands and influencers seeing success with?

    By Content Type - Brands.png

    For what gets shared? Video – native on Facebook, video in general, and YouTube links.

    If, at the highest tiers of marketing budgets and influence, brands are barely scratching engagement compared to similar top tier influencers, we can only imagine how bad engagement is at lower tiers versus influencers.

    Influencer marketing must be part of your digital marketing strategy if you want access to audiences, engagement, and social actions that matter.

    If you’d like a custom investigation of your industry or competitors, contact me through SHIFT and we’ll be happy to do a project with you.

    Methodology and disclosures: The above list of influencers was the sample pool; influencers and brands were selected based on total theoretical reach (number of Likes). The time period sampled was February 17, 2015 to February 16, 2016. SHIFT Communications underwrote this investigation because I used their software to generate the data and findings. Facebook was the only data source.


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  • What Twitter’s algorithm change means for marketers

    Past advice can be dangerous in digital marketing. What was effective even a day ago can suddenly become ineffective or counterproductive overnight.

    Why do things turn on a dime in digital marketing? Unlike human-based mental algorithms, machine algorithms change in a nanosecond and the change is absolute. Humans take a while to accept a new truth, such as the Earth being round or the planets revolving around the sun.

    As marketers, when we find a tactic that works on humans, we know we’ve got some time before that tactic decays in effectiveness. For example, the lost sheep email marketing tactic – “can you point me to the right person that handles X in your organization” – is still moderately effective.

    When a machine changes its algorithm, however, we lack the luxury of time. We must adapt to the new reality immediately. When Facebook changes its News Feed, when Google changes its search algorithm, we must change as quickly and completely as the machines do – and never go back to our old ways.

    Twitter announced a new algorithm – opt-in for now – in which more popular or relevant tweets will appear in our timelines first. This differs from the chronological order shown now. What does this mean for past advice about our Twitter strategy, tactics, and execution?

    Conventional wisdom says to tweet the same content over and over again to take advantage of the different times of day our audiences participate. Some popular social media consultants suggest repeating the same tweets every 8 hours.

    When social networks use algorithms to decide what content we should see first, they base the set of metrics they use in their algorithms on engagement. Facebook tracks how many people click on a link in our posts, how many people like, comment, and share.

    What might Twitter’s new algorithm use to make similar calculations? We don’t need to guess; Twitter tells us in their Twitter Analytics dashboard:

    Twitter Algorithm Analytics and Activity 2016 - cspenn.jpg

    Twitter pays attention to – and wants US to pay attention to – link clicks, retweets, likes, and replies. These four actions make up Twitter’s engagement formula.

    The strategy and tactic of putting your content on endless re-runs worked fine in a chronological timeline world. When the new stuff shows up first, the more new stuff we publish, the better we do.

    The repetition strategy breaks in an engagement-optimized world. We are better off publishing one tweet about our blog post which garners 5 Likes on Twitter than publishing 5 tweets about our blog post which garner 1 Like each. We must concentrate engagement.

    If you want to make the most of Twitter’s new algorithm, grow engagement on every tweet. Ask followers to share, to like, to respond to you. Create content worthy of engagement. If you’re unsure what drives Twitter’s four engagement metrics, I recommend watching this short video about how to use IBM Watson Analytics with social media data.

    Twitter’s new algorithm favors engagement. Disregard old advice about repeating yourself often if you want the new algorithm to work for you, not against you.


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  • Publishers will never stop ad blockers

    Publishers, in an attempt to recoup revenue losses, are trying to block the ad blockers. They are doomed to fail, not because ad blocking technology is superior, but because of content shock.

    Since the Internet became public, we’ve been in a technological arms race. For every new advertising tool developed by marketers, enterprising technologists develop an effective counter.

    • Bad marketers created spam; technologists developed very good spam filters.
    • Bad marketers tried to hijack search engines; search engine companies developed artificial intelligence to defeat them.
    • Bad marketers flooded the web with terrible, irrelevant ads; technologists developed ad blocking software.

    Today, publishers and advertisers try to block ad blockers:

    Forbes_Welcome.jpg

    Why is this doomed to fail? Ad blocking technology will eventually learn to detect the pixels detecting ad blockers and fool sites, but that’s not the reason publishers will fail.

    The reason publishers will fail to stop ad blockers is because of content shock, the phenomenon described by Mark Schaefer in which content creators flood the world with far more content than audiences can ever consume. Let’s look at an example.

    In 2013, Instagram users loaded 40 million photos per day to the service – 27,000 photos a minute. In late 2015, Instagram users loaded 90 million photos per day to the service – 62,500 photos a minute. Suppose, out of all the photos on Instagram, 5% are really great. 10% are good. Maybe 65% are mediocre. The last 20% are awful.

    Types of Instagram Photos.png

    In just three years, Instagram users are loading 2.25x more photos to the service. The number of great photos loaded in 2016 is greater than the number of good photos in 2013.

    What does that mean for us? It means audiences don’t have to tolerate anything less than great. Good isn’t good enough when our options for great content explode by 225% in just 3 years.

    For publishers and advertisers seeking to put up walls blocking ad blockers, the audience doesn’t have to tolerate the wall. Instead of complying, the audience will simply move onto a different source of great content. The publisher loses the ad revenue and the audience’s loyalty.

    What’s the solution for marketers and advertisers? Create great ads. We know for certain that audiences will watch great ads. According to the official YouTube blog, audiences have watched Super Bowl 50 ads – yes, even Puppy Monkey Baby – more than 330 million times. Create ads people want, ads that are as good as the great content consumers will choose, and we won’t need ad blockers.


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  • Small business digital advertising bakeoff

    What’s working today for small budget advertising? For my book, the biggest social network of all: Facebook.

    leading-innovation-cspenn-portrait-book-cover.pngOver the last week and a half, I’ve been busy launching my new book, Leading Innovation. In my normal duties at SHIFT Communications, I have access to budgets in the thousands of dollars or more for clients who have objectives other than pure direct-sales ROI.

    When I’m doing my own work, I pay as I go; pre-orders fund the first round of advertising, and I only add budget as I earn it. Why? This methodology keeps me laser-focused on ROI. Neutral or negative ROI gets the ax; like many small businesses, I can’t pay for more advertising with money I don’t have. This is a key point: my strategy is to sell as many books as possible at the highest margins achievable. Not every author has the same strategy or goals, nor should they.

    What did I do to launch my book? I had earned enough in pre-orders to sustain a week-long ad campaign on three different ad networks: Google’s AdWords, Twitter, and Facebook.

    • To maximize ROI, I focused my ad campaigns on all three networks to my existing audiences only.
    • I’ve had retargeting tracking bugs on my website for several years, tagging every visitor for inclusion in product launch campaigns.
    • I also used Customer Match on AdWords, Tailored Audiences on Twitter, and Custom Audiences on Facebook, using my email newsletter list as the data source.
    • I used the same copy and/or images for all three networks. Facebook’s campaign also included Instagram.
    • I also included email marketing for comparison, since I’m an avid user of WhatCounts Publicaster, still the greatest email marketing software on the planet.

    How did the testing go? Which service did the best? The results:

    leading_innovation_ad_stats.jpg

    Of the ad networks, Facebook thus far has done the best – but still has negative ROI. Twitter did the worst by far, with incredibly high costs and lackluster performance.

    Some caveats:

    All campaigns capped their budgets daily. It’s entirely possible that they could have performed better with additional upfront investment; whenever an ad campaign caps its budget, you’ve left audience on the table. However, like any other small business, I could afford what I could afford.

    Email isn’t an apples-to-apples comparison because it’s a monthly fee, rather than a media buy. Keep that in mind.

    AdWords was search plus display retargeting only.

    None of these campaigns did any kind of outreach or brand building to net new audiences. These campaigns only focused on monetizing existing audiences. For larger brands, net new audiences and brand building matters. For the small business / sole proprietor, we rely on organic methods to grow our audiences and paid methods to monetize them.

    What should you take away from my testing?

    The most important lesson you can take away is to run a similar test. My audience is unique to me. My results will be unique to me.

    Set up a similar test for your own marketing with the budget you have, with the audience you have, with the copy and creative you have.

    Find out what works best for you. Keep an eagle eye on ROI. Do more of what works, less of what doesn’t work.


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  • Is business slowing down?

    Is business slowing down? A handful of leading indicators may warrant concern.

    A theme I’ve heard repeated in many different business conversations is that business is slowing down. Customers aren’t buying as much. Executives aren’t signing contracts. Sales prospects are stalling in the pipeline. Are there economic indicators which could explain this phenomenon? Or are these just anecdotes without a basis in data?

    Three leading economic indicators worth paying attention to are the Baltic Dry Index (BDI), Initial Jobless Claims, and the Producer Price Index (PPI). These leading indicators can hint of troubles to come.

    BDI tells us the price of shipping containers. If the price goes up, more companies are competing for shipping space. In turn, that means companies are producing more. As a rule, companies don’t buy shipping space speculatively, only when needed. If the price goes down, companies are shipping less, which also means they’re making less.

    Initial Jobless Claims are a consumer leading indicator and a business leading indicator. More people laid off means more companies scaling back jobs.

    Finally, PPI tells us how much companies are paying for their raw materials. If prices are going up, companies are making more stuff (and thus competing for commodities needed to make stuff). Conversely, a decrease in PPI means companies are buying less stuff and therefore making less stuff.

    Combined, these indicators give a sense of the economy with regard to businesses. If all indicators are moving up, businesses are likely growing. If all indicators are moving down, businesses are uncertain or shrinking.

    When we examine these indicators, we look at two lines: resistance and support. These are stock market terms; resistance means the recent top levels of any metric, while support means the recent bottom levels. Technical stock traders use these guidelines to determine whether a given metric’s behavior is anomalous or not.

    Let’s take a look at the charts. First, BDI:

    Resistance_and_support_-_BDIY_Quote_-_Baltic_Dry_Index_-_Bloomberg_Markets.jpg

    Above, we see BDI has fallen through its support level. Already depressed, BDI has gone below support to a 5 year low. Companies are shipping less stuff.

    Next, Initial Jobless Claims:

    Resistance_and_Support_-_4-Week_Moving_Average_of_Initial_Claims_-_FRED_-_St__Louis_Fed.jpg

    We see Initial Jobless Claims have broken through their resistance level, signifying that the overall 5 year trend may be reversing. Companies might be paring back jobs.

    Finally, we look at PPI:

    Producer_Price_Index_for_All_Commodities_-_FRED_-_St__Louis_Fed.jpg

    PPI broke through a multiyear support level last year, but has declined below its 5 year support level at the end of 2015.

    Any one of these indicators could be due to interfering environmental conditions. All three indicators show business conditions eroding.

    Is business slowing down? In a nutshell: yes.

    We must prepare accordingly.

    Adjust our expectations for marketing’s ability to generate leads.
    Expect a decline sales’ ability to close in shorter-than-average sales cycles.
    Plan to increase spend on advertising just to maintain current levels of activity.

    Tougher economic conditions mean stepping up our game as marketers.


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  • Predicting the future of analytics with IBM Watson Analytics

    Last year I had the privilege to attend IBM’s Analytics for All event featuring one of my favorite products, Watson Analytics. As one of a dozen IBM Futurists, we were asked for our perspective on analytics trends. Here’s what Valdis Krebs and I shared:

    My prediction about machine to machine communication lacked one critical point. To cope with our new flood of data, we also need the help of machines. We can’t process the data we have now as humans, much less future volumes of data. Innovation in analytics will partly come from better analytics tools for humans, but also from better artificial intelligence-based analytics.

    Disclosure: I was invited to be an IBM Futurist and attend the Analytics for All without cost. IBM paid for my travel and expenses. IBM has not compensated me to write about Watson Analytics. I am a paying customer of Watson Analytics.


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