I’m testing three themes:

• How to fight nature’s pesky way of inhibiting our ability to make clean causal statements.

• The importance of imagination in identifying independent variables.

• The role of evidence in decision making.

Simplification of a message is not pandering. However, many pandering statements are deliberate simplifications.

If your optimization objective is to gain followers:

• Post often.

• Post simply.

• Post what people want to hear.

I’m choosing simplification while avoiding pandering.

Let’s see how that unfolds over the next 60 days.

Consider the following deliberately simple scenario:

March 2012 Impressions:

• Total Digital Impressions Delivered: 100,000,000

• Total Impressions with Chicken Creative: 25,000,000

• Total Impressions with Beef Creative: 50,000,000

• Total Impressions with Pork Creative: 75,000,000

Something doesn’t make sense. I’m telling you that 100,000,000 impressions were delivered in total, but each component of that figure: 25 million, 50 million, and 75 million, don’t actually add up.

That’s because creative can have multiple attributes. An ad may feature Chicken alone, Beef alone, or Pork alone. An ad may feature Beef with Pork. An ad may feature Chicken with Beef. An ad may feature Chicken with Pork. In a crazy twist, perhaps some creative features all three! (The madness!). Attributes can cause such complexity when it’s possible for a single thing to have multiple attributes.

The next scenario demonstrates complications that arise because of instrumentation:

March 2012 Impressions:

• Total Digital Impressions Delivered: 100,000,000

• Total Impressions served to Males: 60,000,000

• Total Impressions served to Females: 10,000,000

• Total Impressions likely served to 35 to 50 year olds: 1,000,000

All people have attributes, but not all people have attributes that can be measured.

It might very well be that for the XBOX Live component, Microsoft can report with greater certainty, owing to profile information, that the content was served to more males. And, because that particular app was geared towards males, there’s greater certainty on that end. It also might be the case that another component was on mommy blogger ad networks, however, the knowledge of the ad targeter was really ethical, and wasn’t uniquely tracking everybody, so, the ‘missing 40 million impressions’ aren’t missing.

The same goes for the age component. We may hypothesize because of Quantcast data that those impressions served on mommy blog networks were heavily 35 to 50 year old females, but, there’s nothing in the instrumentation itself that confirms that hypothesis.

Just because it may be measurable doesn’t guarantee that it will be measured.

Finally, consider the complexity imposed by time:

March 2012 Impressions:

• Total Digital Impressions Delivered: 100,000,000

• Total Impressions from Affiliate Program: 10,000,000

• Total Impressions from the RayRayHayHay campaign: 8,000,000

• Total Impressions from the A campaign: 1,000,000

• Total Impressions from the Eh campaign: 1,000,000

Well, CLEARLY the A campaign and the Eh campaign failed – since the affiliates didn’t use those creative treatments much at all. What we don’t know is time.

• Date the RayRayHayHay campaign creative was posted: January 5, 2012

• Date the A campaign creative was posted: March 1, 2012

• Date the Eh campaign creative was posted: March 28, 2012

That’s 1 million impressions served in 3 days for the Eh campaign. That’s 1 million impressions served in 31 days for the A campaign.

Such component analysis is made particularly tricky when we’re trying to do it using a monthly report or some other arbitrary unit of time.

In sum:

Channel performance analysis is not channel component analysis. These are two distinct types of analytics, aimed at answering two different classes questions. For the reasons listed above, attribute overlap, instrumentation limitations, and time, the sum of the components may not add up to the total. This is not a devastating realization if you understand the differences and how to think of them.

There’s a general optimistic sense that drillability, the ability to drill into any metric and see its components, is possible in all contexts. It is possible in some contexts. It is not possible in all contexts. Privacy and technical disruption impose long run constraints in ever being able to achieve that.

It’s not likely to be perfect any time soon, and, in some cases, the components won’t ever add up.

***

(Note to fellow analysts: I chose impressions to keep it really simple. On-site and post-click analysis is required. Statistical analysis exists for a reason, so, even armed with impression and CTR data, you may analyze performance across multiple attributes. Moreover,you ought to be aware of the biases that exist in your data set – is it the case that males really did respond better, or, is it the case that the instrumentation is just better at identifying males?)

But first – three notes:

• You may be familiar with Reddit. If you’re not – you can read this explanation about what Reddit is.

• To answer that question, I downloaded a dataset that was built in early 2011 or very late 2010. The dataset is a 29MB gzip compressed and contains 7,405,561 votes from 31,927 users over 2,046,401 links. You can read about the methodology here.
• The file contains three columns – a vote, a userid, and a link. Only people who had their privacy settings set to open had that data read by an API. There is no meta-data about who these people are in real life (IRL) or even what was the nature of the content they were upvoting and downvoting.

So, who’s downvoting you on reddit?

To find out, I took that huge file transformed it into another one – boiling it down into a single user name, how many times that username vote (numberofvotes), and the average of all their votes.

You can see below that _mike voted 26 times, and, if you take the average of all his votes, +1 for an upvote and -1 for a downvote, it turns out to be -.92. Basically, _mike didn’t like a lot of what he saw. In fact, _mike upvoted once (+1) and downvoted 25 times (-25). So (- 25) + (+1) is -24, and -24/26 is -.92.

There are over 30,000 usernames here – and that’s a lot of data. It’s really important to visualize the data before you really get into any analysis. One way to do that is to run a histogram.

To read the histogram below, remember:

• Frequency means ‘the number of usernames that fall into this category or range’.

• Numberofvotes means ‘the number of times a username voted.’

• Mean is another word for average.

There are three takeaways from the histogram above:

• The average number of votes by a username was 234.

• A large number of usernames didn’t vote very many times at all.

• There are bumps at 1000 and 2000 votes. (If you’re interested as to why – see the Methodological notes. Incidentally – this is why you should always visualize your data.)

A histogram is built from a Frequency Table, which we’ll see below.

The way to read a frequency table is:

• The ‘Valid’ column means ‘how many times a username voted’.

• Frequency means ‘the number of usernames that falls into this category’.

• Percent means ‘the percentage of all the usernames that those in this category represents’.

There are three takeaways from the Frequency Table above:

• 4877 of the usernames only voted one time (It’s likely they submitted a single link and never returned).

• Note how both the percentages and number of usernames in each category decrease.

• 50.1% of all the usernames voted 20 times or less. (Look at the cumulative percent column and make sure that makes sense to you. We’re going to use this column later.)

You may have heard the term ‘long tail’ many times before. This is a demonstration of what that means. The bars on the histogram falls away to right.

Recall that the average of all the votes a username made is called ‘averagevote’. If somebody was persistently downvoting links, they’d have a negative number. If they upvoted everything they saw, they’d have an averagevote of +1.

Read the histogram below.  The three takeaways are:

• Negativity follows a reverse long tail. (It really happens – see how the figures fall away to left)
• On average, usernames upvoted what they saw (average 0.79).
• There are bumps at 0 (related to a methodological note) and at -1.

By now, two of my good friends in London are screaming at the screen. Means are a horrible way to explain long tail distributions. You can see that now too. Means are giving us a pretty skewed view of the world.

The table below is a byproduct of our Frequency table. It’s aptly labeled ‘Statistics’, and compares these two variables, numberofvotes, and averagevote, side by side. I’ve thrown a yellow box around ‘percentiles’. Recall the cumulative column from previous frequency table.

• 22.8% of all usernames voted 2 times or less.
• 40.8% voted 9 times or less.

The program I’m using is giving me ‘break points’ for those percentiles.

Two takeaways:

• The median gives a better summary of what’s going on here – half of the usernames voted 20 times or less, and, another set of usernames always upvoted what they saw.
• If I know that roughly 80% of all usernames posted 325 times or less, then I know that 20% of the usernames in my sample posted 325 times or more.

We’re going to use those percentile cutoff points to inform a segmentation, next.

Segmentation

A segmentation is a grouping of records, usually people, into categories. There is not prescription for how to do this. If you talk to a modeller, they’ll tell you about their clustering algorithms. If you talk to a machine learning scientist, they’ll tell you about bump-hunting or unsupervised machine learning clustering. Those are all very good algorithms. I use them myself.

I’m going for simplicity here. I have these four percentile cut-off points that evenly cut people into five categories. And, for further simplicity, instead of referring to a group of people who posted between 9 and 48 times as ‘those who posted between 9 and 48 times’, I’m going to call them Average-Andy’s. And I’ll just keep on calling them that.

At this point, I don’t know if they’re male or female. (And we won’t in this thread). And it’s controversial to use alliteration. But it’s done.

So, mapping the percentiles against a segmentation, based on how many times a username voted, we have:

• 1 time: One-Time-Oliver

• 2 to 9 times: Vanity-Vanessa

• 9 to 48 times: Average-Andy

• 48 to 325 times: Frequent-Fred

• More than 325 times: Power-Pauline

Take a look at the result below – a variable I’m calling ‘equalseg’ – short for ‘equal segmentation’.

Takeaways:

• There are 4877 One-Time-Olivers, representing 15.5% of the usernames in the sample.

• Vanity-Vanessa’s represent 23.9% of the usernames.

• The last three segments are pretty equally divided – the first two are more lopsided.

Even though I aimed to have five groups of people with equal numbers in each, you can see the division between One-Time-Olivers and Vanity-Vanessa’s are off. This happens very often when segmenting a long tail into equal groups. And, while not ideal, it’s okay for our purposes.

Next, we’re going to examine each segment individually.

One-Time-Olivers

There are very efficient ways that statisticians quickly summarize and understand the relationship among variables. The aim here isn’t to be efficient – but to be clear. In that spirit, I give you the histogram below.

Takeaways:

• All 4877 One-Time-Olivers voted exactly one time.

You should lol. It makes sense though, right? And, the segment name should make a lot more sense.

The histogram below summarizes how, on average, One-Time-Olivers voted – positive or negative. Since they only voted one time, it’s either an upvote, or a downvote. A +1 or -1 average.

 Takeaways:

• One-Time-Oliver’s tend to upvote once, and are never heard from again.
• In answering the question – “Who’s downvoting you on Reddit”, it isn’t One-Time-Olivers.

 Vanity Vanessa

Vanity accounts frequently enter Reddit, they flicker, and they go out. They get discouraged. They never really commit to the bit. That’s what happens to them. The histogram below takes on that familiar long-tail curve.

Takeaways:

• There are lot of Vanity-Vanessa’s, some 7,527 of them.

• Most of them posted only 2, 3, or 4 times.

So, how did they vote?

The histogram below summarizes the story:

Takeaways:

• Vanity-Vanessa’s upvoted nearly everything they saw, with very few exceptions.

• Very few persistently downvoted everything they saw.

• They’re not the ones downvoting you on Reddit.

Average-Andy’s

Recall that the average username votes 326 times, and yet, I still labeled Average-Andy, ranging between 9 and 48 votes, as average andy. That’s because the mean number of votes that Average-Andy’s cast is 22.25 – which is close to the median of 20 for the entire set.

This mixing and abstraction of median, mean, and segmentation isn’t something that I expect most people to consider or think about, but I can foresee some getting hung up on it. When you think about an equal segmentation though, it makes sense that the mean of your middle category should be close to the median of the entire set.

For everybody else – just know that you’re you’re looking at the “average joe redditor” here.

Takeaways:

• Average number of votes is 22.25, close to the median of 20 for the whole set.
• Familiar long tail.

How do they vote?

Takeaways:

• A majority of Average Andy’s liked everything they saw – they upovoted everything.

• They downvote more often than Vanity-Vanessa’s or One-Time-Oliver’s, but not massively.

• They aren’t downvoting in such a huge way to say that these are the ones downvoting you on reddit.

Frequent Fred

By now you’re pretty much a pro at reading these histograms. Frequent Fred’s vote frequently. Look at the histogram below.

Takeaways:

• Classic long-tail continues.

• Averaging 139.3 votes.

• The unusual bump at the beginning of the series is just magnified by the scale from the previous vote frequency histogram. (It’s fine).

How do they vote?

Takeaways:

• Far fewer of them are likely to upvote absolutely everything they see.

• There’s significant flattening of the long tail – the average is .74.

• More of them, on average, are disposed to downvoting.

Power Paulines

Power Paulines are the most difficult group to analyze, but the easiest to summarize and understand. Take a look at the histogram below.

Takeaways:

• The long tail is holding – there’s significant clustering at 1000 and 2000.

• The cause is related to rate limiting within the Reddit API.

• The longest part of the long tail – those power users with thousands and thousands of votes, are all bundled and clustered together at 2000.

• There are around 500 of such power users, representing some 1.5% of the total usernames.

So how do they vote?

 Takeaways:

• The bump at 0 is caused by 1000 upvotes getting averaged out by 1000 upvotes.

• 0′s aside, which are tugging on the mean, Pauline’s are on average more prone to downvoting.

• Power Paulines are downvoting you on Reddit.

Putting a bow on it

The chart below summarizes the relationship between segment and their average vote. You can see a clear negative direction. The more one uses Reddit, the more one downvotes – even if the mean is exaggerated in the Power Pauline segment.

To really hammer the point home about the origin of downovotes, take a look a the table below. It’s broken out by the segments you understand. It also contains two new variables – upvotes and downvotes. That is the total count of the number of upvotes and downvotes made by each segment.

Takeaways:

• One-Time Olivers as a group were responsible for 175 of all the downvotes cast.

• Vanity-Vanessa’s as a group were responsible for 1781 of all the downvotes cast.

• Average-Andy’s as a group were responsible for 13,258 of all the downvotes cast.

• Frequent-Fred as a group were responsible for 120,758 of all the downvotes cast.

• Power-Paulines as a group were responsible for 1,672,368 of all the OBSERVED downvotes cast – but are probably responsible for a lot more in aggregate across all of Reddit. (This sample contains a bias, but bias doesn’t mean I can’t say anything at all about anything.)

Note the differences in order of magnitude between each group. 1781 is roughly 10 times greater than 175. And so, a bit imperfectly on the way up to Frequent-Fred’s. There’s an order of magnitude difference here in terms of the amount of weight each group casts.

The greatest power users users of Reddit are the ones who are downvoting you – and it’s an exponential power.

But wait, there’s more.

Recall, however, that there over 7 million votes cast. 1.8 million were downvotes, and 5.5 million were upvotes. Read the statistics table below to verify that.

Takeaways:

• Upvotes outnumber downvotes.

• The interface of Reddit itself causes upvotes to accumulate.

• Reddit itself is a cause of a bias – probably by design.

The histogram below is by links – the content getting upvoted or downvoted. There were just over 2 million links submitted. On average, each link received 3.62 upvotes. Given everything you know about long tails, think about just how deceptive that 3.62 mean figure is. Note how you can’t even see the bumps in the tail. And be in awe of the efficiency of the collective Reddit behavior that causes popular content to disproportionately promoted while even ‘good’ or ‘average’ content gets relentlessly shifted to the left – all by a very small group of people.

Takeaways:

• The long tail is long and powerful.

• This small group Power-Paulines are far more likely to downvote because of a much higher frequency of use.

I’m thanking Reddit for making so many API’s publicly exposed and enabling this sort of analysis and exploration. Thank you.

Portions of this post appeared on Eyes On Analytics the week of February 5, 2012.

It’s also the first get together after eMetrics New York, which was a major, and had big time Canadian attendance. These tend to be among the more interesting evenings. It has also been some three months since the last WAWTO event, so there should be quite a few fresh stories.

Among other reasons:

• 43% said “My wall was becoming way too crowded with marketing posts and I needed to get rid of some of them”.

• 38% said “The content became repetitive or boring over time”.

• 19% said “The content wasn’t relevant to me from the start”,

• 17% “The company’s posts were too chit-chatty – not focused on real value”.

All of these reasons cited go directly to the concept of relevancy.

When does content become too much? When it ceases to be relevant.

When do you want to make some content go away? When it ceases to be relevant.

When does content become boring? When it ceases to be relevant.

This competition for relevance is one half of the defining challenge for social media marketers. To a certain extent, paying for the privileged of pushing an unwanted message into the yawning maw of consumers provided a specific degree of insurance. Relevancy was always theoretically important to marketing effectiveness. It’s just that it wasn’t a big enough factor to truly matter. Or rather, it didn’t matter to enough people.

It’s not a new problem. Rather, it’s an intensification of a latent one.

8. They deliberately use an odd sounding number for the length of a list. Round numbers like 10 sound engineered.

7. They know that there’s a high reading completion rate on such a list. That is to say, the probability of a person clicking through to another page, right below the list, is high, thereby increasing overall ad impressions on a single visit.

6. They know that some of the most effective list titles contain a promise of insider information.

5. They know that a small percentage of the population creates lists, but a large percentage of the population cares about them.

4. They know that some people love to be ranked by other people. More specifically, the adulation that goes with being top ranked.

3. They know that at least 200,000 US People (Quantcast definition) visited one of the top five sites dedicated entirely to top 10 lists. It’s an industry unto itself.

2. They know they are an effective layout to embed ad units.

1. They know that lists are an effective, engineered, design pattern designed to evoke a very specific reaction.

And they really don’t care if you know it.

And there’s your jolt below:

ICE is not the successor to Value of a Fan, abbreviated VOAF. We asked different questions.

Last year, in response to VOAF, many of my cohorts came forward with brilliant follow up questions, and the dialogue that ensued contributed to the subsequent study and model design. Work continues.

I welcome, in the spirit laid out by Tsang, engagement on the topic.

What do you think about Increasing Campaign Effectiveness using social media? What would you consider and explore?

The goal of target setting is to make a statement about a desired future state – with or without a forecast.

Targets are political artifacts. You can read all about such dynamics in public policy here.

Forecasts, ideally, are scientific artifacts.

The interplay between forecasts and targets is particularly interesting. Those who produce sophisticated forecasts should understand that the motivation of those probing models is to assess whether or not a future state is possible, or, in certain situations, just how probable a given scenario could be.

Don’t become trapped into the mindset that a trend is locked in permanently. Actively explore what needs to be true, in which circumstances, to produce a better outcome. Most importantly, and I’m learning this the very hard way, make every attempt to make affirmative recommendations.

My original hypotheses are rejected. A piece of the world doesn’t really work the way that I expected.

The great news is that I’m forced to look beyond the clean dataset and write new hypotheses. Even failures can be great. However, it doesn’t make for good commercial reading. Instead of having that nice, clean, nugget:

Brands that did x realized y.

There’s a much messier message:

Neither a, b, c, d, e, f, g, h, i , j, k, l, m, n, nor p had a significant impact on y.

That messier message works among marketing scientists. Usually a sound of surprise. Then acceptance when they see the summary tables.

It’s not commercially actionable.

It’s far more effect to give very clear ‘to do’ recommendations than clear ‘do not’ recommendations. Memory and recall is precious. It’s hard to get things to stick and even harder to fish it out. A laundry list of ‘not significants’ is not effective. Moreover, being unethical and pulling out a statistically insignificant term doesn’t quite settle it, either.

So instead, tomorrow, I’ll have to change the dependent variable. Y will be e. Or f. or i. It’s a lot more work, but there are actionable recommendations in there. It has to be commercially interesting- knowing full well that if I poke without hypothesis in mind the odds of being fooled by randomness increases. And, I’m energized by having more justification for a chosen paradigm of social media analytics.

In sum, it’s been rough. And I’m charging on.

It’s what we do.

I told the story about how different versions of ROI is rooted well before anybody in the room had been born. And it’s such a contentious issue because it goes directly to one’s being. ROI is the reflection of your own worth to an organization, and naturally, as such, it’s going to be contended.

The approach taken in the Syncapse Value of a Fan study was selected for a very specific reason – emphasizing a longer view of time and an emphasis on the monetary value of relationships. The approach taken with Earned Media Value is selected for being very direct, rapid, and comparable with other mediums. They both correspond to a personal concept of time and the value of relationships. Recurring LTV versus instant Impressions.

The biggest cleavage that emerged in the subsequent hour was around a different fundamental belief. For a marketing scientist, CEO, CFO, CMO – the dependent variable is always money. There are many independent variables, of which, one of them is relationships. Relationships can be an important source of sustainable competitive advantage. However, relationships are an independent variable, not the dependent one. In business, the reason why you build relationships with customers, suppliers, governments and employees is to realize sustained money. It’s not simply or purely out of altruism.

Other organizations seek good relationships to achieve sustainable competitive advantage. Typically though, it boils down to money, even for a non-profit or a not-for-profit.

I didn’t anticipate this difference in belief, and I’m happy to have discovered it.

I thank Joseph Thornley for the opportunity to come out and speak to a new audience, and look forward to carrying on the discussion.