What does the marketing analytics stack look like?

Does Google’s recent announcement of the Google Analytics 360 Suite change our understanding of marketing analytics? No. It’s more of the same – and that’s a good thing for marketers.

Let’s consider what the new Google Analytics 360 Suite promises for marketers, a bundle of six different technologies. What’s in the suite?

  • Google Tag Manager 360
  • Google Analytics 360 (formerly Google Analytics Premium)
  • Google Attribution 360 (formerly Adometry)
  • Google Optimize 360
  • Google Audience Center 360
  • Google Data Studio 360

When organized, Google showcases the suite as a marketing analytics stack:


Look carefully. Google’s Analytics 360 Suite focuses on data collection, analysis, and insight derivation. Sound familiar? It should:


The new Google Analytics 360 Suite fulfills the first three steps of the MAISTER cycle, from Marketing Blue Belt. Google Tag Manager 360 (and regular Tag Manager) helps us measure and gather good data. The analytics layer helps us to analyze what happened, from what (Analytics) to who (Audience Center) to how (Attribution and Optimize). The presentation layer – Data Studio 360 – gives us the tools we need to turn analysis into insight.

Google Analytics 360 Suite promises to be a powerful tool for larger businesses who can afford the suite; since it’s a drop-in replacement for Google Analytics Premium, I would expect pricing to be the same.

What if you’re not a business who can afford the thousands of dollars for the premium option? Is there anything left for small and midsize businesses?

Yes! Consider the structure above, the basic marketing analytics stack: Measurement. Analysis. Insight. We won’t have the luxury of a fully integrated, one-stop shopping solution that a large business can enjoy, but the individual pieces are available to us now.

  • Every business can afford Google Tag Manager and other data gathering tools.
  • Every business can afford Google Analytics, social media audience analytics, and rudimentary testing facilities.
  • Every business can afford data visualization tools like IBM Watson Analytics.

This is the basic marketing analytics stack, within reach of every business. Once we grow big enough, we can afford the Google Analytics 360 Suite, but until then, we can apply the same conceptual structure to the tools and strategies we have now. Organize what you have now into the MAISTER cycle, and start profiting from your marketing analytics stack today, no matter what solution you employ!

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Why is Twitter over-represented for influence?

One of the recurring questions people asked yesterday about my post on influence was why agencies and marketers focus so much on Twitter, when other channels like Instagram, YouTube, and Facebook are equally, if not more impactful. The answer comes down to data. We manage what we can measure, and we give preference to what we can manage most easily. Twitter provides more usable data on a per-post basis.

What are the kinds of data we care about as marketers? At a post level, meaning on any individual piece of content, we care about:

  • Dates things happened, to measure over time
  • Usernames, to know who we’re examining
  • Relationships, to learn who talks to who
  • Content, to know what our audiences said
  • Likes/Votes, to discover what’s popular
  • Comments, to know what our audiences said to us
  • Shares, to judge how worthwhile the content is
  • Views, to uncover our reach
  • Follower counts, to uncover our potential reach
  • Location, to discover where our audience is

We also care, as marketing technologists, how much data a social network will give us over time. How fast can we receive our data?

Look over this chart of post-level data. What do we get from each network?


We see that Twitter provides us the most data at a per-post level. Facebook appears to come a close second, except that Facebook’s data is limited to Pages for the most part; we can see Page post content, but not individual content. On Twitter, we can see both. Instagram comes in third, and YouTube comes in fourth.

We can’t manage what we can’t measure. We can measure Twitter especially well, even if it’s not the most robust or popular social network. The tools of the trade focus on Twitter because they can generate more measurement and analysis from the data – and that means an easier sale to companies and agencies.

Does this bias create distortions in our ability to identify influencers? Yes. Tom Webster, VP of Strategy at Edison Research, often points out that social media tools’ bias towards Twitter means bias in their reporting, especially of politics. Twitter is very bad, for example, at predicting election outcomes. Why? Twitter’s demographics are far from representative of the population as a whole according to Pew Research:

For example we see black and Hispanic users outnumber, as a percentage, white users, when we look at the Census Bureau’s data:


Twitter’s predictive power for elections is very poor because of the bias in its user base. Thus, when we examine influence, Twitter may or may not be the best choice, depending on what biases influence our influencers.

Should we, as marketers, examine more than one channel? Yes, if resources permit. The more data we can gather from every social network, the more complete and representative a picture we can paint, and the better our influence identification will be. Twitter will likely remain our bias until the other networks provide comparable quality of data, so we must account for its biases when we work with its data.

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Are professional influencers impactful?

Olivier Blanchard started a conversation yesterday on Facebook by positing that professional influencers may not move the needle for the brands that hire them, if we vet influencers only on audience reach/follower counts. He asked what the performance differences might be if we judged influencers on things like engagements or down funnel metrics like link clicks. Is there a difference? Is paying a ‘professional’ influencer worthwhile?

Using the advanced research facilities at SHIFT Communications, I took him up on the challenge. We pulled a list of 16 influencers on Twitter in the business and technology sector. 8 of them are brand-side influencers who generally do not accept money to promote content, other than from their employer. 8 of them are professional influencers who accept significant sponsorships above and beyond their employers, celebrities of sorts. Olivier provided the lists and classifications.

Let’s first look at the distribution of audience reach:


Our influence pool has an average following of 140,000 and a median following of 98,000. We see a reasonable distribution of unpaid (organic, in blue) and paid (in orange) influencers in our pool.

Let’s move beyond raw audience numbers to engagement. How much engagement do organic and paid influencers generate? We define engagement as the sum total of actions a person can take on an influencer’s status; on Twitter, that’s replies, likes (formerly Favorites), and retweets. We looked at the last year of data for these influencers and charted it out:

Average Engagements Per Post.png

What we see is a sizable difference in overall engagement levels; organic influencers generate up to 6x the engagement of paid influencers. Why? Part of the reason is volume of content. More content isn’t necessarily better; paid influencers generate significantly more content:

volume of content.png

Above, paid influencers generated a median of 10,050 posts per quarter; unpaid influencers generated an average of 1,770.

How impactful are influencers at engaging their audiences? We measured engagements divided by total audience size:

Engagements  Impressions.png

What we see above is neither organic nor paid influencers are especially good at engaging a significant portion of their audience. Organic influencers engage 0.015% – that’s 1.5 out of every 10,000 people – while paid influencers engage 1 out of every 10,000 people who follow them. Organic influencers do slightly better, but not by a significant margin.

Let’s talk about where the rubber meets the road: clicks and audience shares. For the links influencers share, how many shares do those links get from the overall audience? How many clicks do influencers generate? Here’s where the stark difference lies:

Link Performance.png

We see that links shared by organic influencers garner significantly more shares and clicks than links shared by paid influencers. Why such a difference? Most of what organic influencers share is news, content audiences are likely to share anyway. Because paid influencers generate so much more volume (due to promotion of sponsorships, etc.) we would expect their performance to be significantly lower. I invite you to download the anonymized raw data here.

What does all this data tell us? Be very careful how you vet influencers. The biggest megaphone in the room doesn’t necessarily mean spurring action we want as marketers. Take into account reach, of course. Take into account other factors, such as engagement, topical alignment, what else the influencer shares, and whether they generate any real action down the marketing funnel.

I encourage you to download a free eBook I wrote for SHIFT on cultivating influence for even more insight.

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