Category Archives: Marketing Science

Jul 25 0 comments
Marketing Science, Social Analytics

Changing Customer Behaviour

Certain technologies bring about changes in customer behaviour. I’ll state that while not every behaviour-changing technology is profitable (from the beginning or ever), aiming to change a behaviour is more likely to result in a profitable technology.

It’s relatively easy for me think of such technologies. Bronze, printing press, and internet are the three that come to mind most easily.

The incremental evidence of benefits is what caused them to be adopted. That adoption, for those benefits, resulted in changes in their behaviour. We generally like to believe for the long-term good, though, for every social action there is a reaction. The environment didn’t benefit from bronze wielding humans too much. Certain factions certainly didn’t benefit from the press. And, ask the RIAA what they think of the internet.

Social internet technologies have enabled the mass expression of an already existing trait in people – the tendency for self-expression. The transference of word of mouth (WOM) from the analog world into the digital world is one of those changing customer behaviours.

As I wrote a few weeks ago on Topic Bearing WOM, a relatively small number of people are generating a large amount of content. The challenge has been to understand a relevant section of it. A very recent technology, twitter, empowers anybody to tell the world a few snippets of what they’re thinking. The result is a massive corpus of information that isn’t processable by a single human being in any meaningful amount of time.

The belief is that by enabling people to understand a large quantity of feedback, they’ll actually be enabled to respond meaningfully to the largest number of people with their limited resources. This would constitute a change in their own behaviour.

Bringing it back – Twitter is a technology which has resulted in a change in customer behaviour. It is not profitable as of yet. It could be in the future. (Lagging revenue S-curve is lagging).

It would be great for the current nest of innovators to think about which behaiours they want to change using technology upfront, and then tailor their technologies and monetization models to that end. Profit isn’t guaranteed, but at least it solves some of the ???? problem.

Jun 11 4 comments
Marketing Science, Social Analytics, Social Media Analytics, Social Media Measurement

Calculating the Value of a Facebook Fan

I’ve been heads down with the team for awhile pounding out a study examining the value of a Facebook Fan.

The results of that study were presented at Internet Week on Friday morning and can be downloaded here.

I have hopes.

I hope it throws some wind into the sails of people who are doing good social media marketing strategy. Absolution is frequently sought in simple numbers. The importance of activation strategy should be very clear in the charts and text of the paper.

The second is for the lack of misquotes. It would be really nice if it wasn’t misquoted.

The third is that I hope you’ll find it useful.

In sum, take a look, and feed on back.

May 20 1 comments
Marketing Science, Simplicity Analytics, Social Analytics, Social Media Analytics, Social Media Measurement

Product Development and Evidence Based Marketing

So just what have I been up to?

shark

I’ve been dividing my time between a major initiative and product development. Much of my involvement revolves around Evidence Based Marketing – and it’s literally that deadly. It’s that level of sustainable competitive advantage. It’s like a Philosoraptor armed with an RPG, riding a shark. Yeaaaaaaaaaaah.

The most interesting aspect has been the integration of measurement science with information architecture with development with creative with product development. There are continuous collisions between the desire for intuitive simplicity with utility with robust functionality with elegant design with data accuracy – all within budget and a desired launch date of yesterday.

The best business models are those which you solve a problem for a group of customers and they give you money in exchange for doing that for them. If the problems were were trying to solve were easy to solve and the integration of multiple considerations were easy – well – we’d either be doing it wrong or we wouldn’t be successful. I know that based on the quality of the discourse, the attention to detail, and a disposition towards evidence that we’re doing well. The market is voting and we’re winning.

Many of us come from a very orthodox user-centered design thinking school. Many of us come from a very orthodox product development lifecycle. Much work and time is spent doing aggressive inquiry – asking why somebody has come to a particular conclusion with a desire to understand. And when people not only come from very different professional backgrounds – but actually use different languages that within themselves have very specific meanings and biases – well – it’s all the more challenging. Much to the credit of the teams – there’s a lot more meaningful discourse aimed at solving very specific (and frequently wicked) problems.

Within socialTALK, a product that helps you manage and measure your social media presence and impact, we have an evidence based marketing experience. The initial version of that tab was designed to be very simple and laid out in an intuitive cause/effect, count and ratio, format. The initial dashboard communicated, clearly, that this is what you’re doing – and this is how people are responding. The evidence is right there. Subsequent versions of socialTALK are looking more robust – with the same attention to detail. When you put a lot of thought into it – it just naturally looks easy. (That doesn’t mean that it’s easy to actually make it that way!).

The ability to actually optimize the experience for your communities through a single interface is particularly exciting. The unification of reaction-action-reaction-action is coming together. I’m working extremely hard to make the experience of doing and learning and doing better again as elegant and clear as possible. In effect – working hard to solve your problem so you don’t have to.

The second reason why post activity has been reduced was eMetrics London. It was a whirlwind 48 hours – 18 of which were spent in airplanes and preparing for it. I went over. I listened. I said my piece. I was heard. I got some very good feedback.

In sum, I’ve been spending a lot of time feeding the shark and making sure the RPG is ready to go. Philosoraptor is always on my side.

Apr 19 3 comments
Marketing Science, Social Analytics, Social Media Analytics, Social Media Measurement

Topic Bearing WOM

I’m increasingly disturbed by the accuracy of Topic Bearing Word of Mouth (WOM) algorithms.

A previous study, published in this space, expressed dissatisfaction with standard sentiment analysis. My mind has since turned to the difficulty in expressing massive amounts of WOM into simple metrics that are actionable and decomposable.

So let’s just go beyond the realm of evidence based pre-optimization of marketing messages, and set the entire area of sentiment-bearing word polarity aside for awhile. It’s relevant and important. Just not the focus tonight.

Let’s turn to topic bearing WOM.

Imagine you could listen to the world, and assume that Burke’s reality is now…a reality.

If you haven’t seen the video from my ‘about’ section – here it is again. It literally is what I’m going on about:

How would you be able to make sense of the world? How would you, as a person, listen and understand all of that material? If the world is constantly changing and is what you say it is – just say.

Well indeed. So what are people saying? How do you aggregate all of that information into a format that’s understandable to mere mortals?

How could you possibly? To use a web analytics analogy – it’s akin to reading server log-files manually, one at a time, for want of a log-file reader. Or at least, a log-file reader that you don’t really trust.

The initial reaction is to do what marketing statisticians have been trained to prior to 2004: use sample statistics. I have got to ask: why use sample statistics when you have the whole data mine right there? Isn’t the only reason for sample statistics existing is for want of the database? (And nobody truly knows the overall sample size that they’re trying to project against. In the case of many topics, the n is extremely small. In others, it’s effectively undefined until semweb comes along.)

We have a massive database.

The idea of taking 1000 log files and reading them manually – and then saying that those 1000 log files are representative of the whole isn’t psychologically acceptable to most marketers. That +/- 3.1% sampling error is reinforcing your 15 to 20% interpretation error and you’re looking at a pretty dense ROE. ROE is generally not psychologically acceptable. Shows are canceled on the basis of statistical error for want of understanding to this day (and we’re 80 years into that methodology (consider radio, yup, it goes back that far)). And yet, even if you were to pitch that sampling approach and the ROE was acceptable, that really doesn’t gel because of the expectation of drillability and a broader expectation about the granularity of the data. That drillability expectation is also vital to solving the Integral Problem. If you’re a web analyst reading this, it’s just implicit within your paradigm – the way you’ve been brought up with the data – to expect that you’re able to drill into anything. It’s a bias that’s always been there.

If you’re a digital marketer or a UX strategist – you probably won’t even question that relative availability of incredibly granular data. It’s like a can opener. You just assume it. Take that away and the beans just won’t taste the same.

The big n, the overwhelming amount of data, demands a data mining approach. It demands a machine algorithm. It also demands a statistical methodology that is scalable. This heads into a domain that lies at the intersection of data mining and computability. It’s just awesome. There are many solutions, but very few solutions that will actually produce timely intelligence.

Topic Bearing WOM and the categorization of it should be, on the surface, a much easier nut to crack than sentiment-polarity, which is intensely subjective. But it’s not. If you ask 100 marketers to write a one paragraph summary of a 600 word blog, you’ll get a diversity of opinion about what the blog was actually about. Unanimity on what the topic was is extremely difficult to achieve. Not convinced? Consider the diversity of opinion about what the topic of S.11 of the Canadian Charter of Rights and Freedoms. In fact, this is a very deep problem that has been struggled against for the better part of the last decade. It’s no easier.

In the coming days, many pixels will be spent writing about the categorization of topic bearing word of mouth. There’s just a confluence of news and opinion. We might see a resurgence of opinion-mining and, in an experiment I’m doing on you – the word-of-mouth/social nexus.

So I’ll say this:

People will write. I welcome that.

Many will claim that it’s so simple. It’s not. This 892 word post has been a hike for you.

Awesome minds have been working this problem for at least 31 years, and have been really serious about it for the past six. 100% accuracy is not probable (in your lifetime). Statistical sampling is not a panacea. And even with a unified corpus even the best analysts are going to have a tough time with it. (Though, unified corpus’ are great).

Topic Bearing WOM poses a huge opportunity, and a huge challenge. It should be tackled with same amount of care that we take at Syncapse.

So enjoy.

My point stands. I’m dissatisfied with the existing algorithms to summarize topic bearing WOM. And you should be too.

Dec 21 0 comments
Marketing Science, Social Analytics, eScience

eScience

I’ve just learned of eScience as a result of a book entitled “The Fourth Paradigm”.

While I don’t have that much to say about the essence of the Fourth Paradigm yet, I have to admit that I feel immediately at home with this group within eScience. One of the best quotes in the book is:

“Need driven versus curiosity driven. Basic science is question driven; in contrast, the new applications science is guided more by societal needs than scientific curiosity. Rather than seeking answers to questions, it focuses on creating the ability to seek courses of action and determine their consequences.”

Substitute ’societal needs’ with ‘business needs’, and I have myself a nice bridge between eScience and commercial eScience. I suppose that’s been one of the fundamental misunderstandings about the Scientist-Practitioner: that they were only poking about out of curiosity. Science for the sake of science.

What if we were transparent about the intent to use science for purely commercial gain? Sounds Edisonian I suppose?

Much of the literature seems to be about very huge computing problems, like analyzing the data from the LHC. I’m not necessarily as concerned with problems of that order of magnitude. In fact, most business problems are fairly modest by comparison. What will, however, hold back commercial eScience, are the same forces that will hold back eScience. That is to say, the lack of unification among the fundamental tools.

At any rate – this field looks attractive.

Dec 03 0 comments
Analytics, Marketing Science, Social Analytics

The Seven Axioms and Predictive Validity

I published seven axioms over the past week – in a not so humble fashion. I’m taking the James Burke line to heart and just putting it out there.

The Seven Axioms are:

1. The purpose of analytics is to derive competitive advantage for the organization / firm / entity.
2. Data alone does not yield competitive advantage.
3. A sequence of progressive hypothesis testing is the most efficient and effective method to derive competitive advantage from data.
4. Predicting the future requires an understanding of cause and effect.
5. Correlation is not always Causality.
6. Accuracy over Precision.
7. It is possible for there to be two optimal, equally true, answers to a problem. (And Sometimes More!) (X^2 = 4, x=-2, 2).

They might appear to be fairly straight-forward. And they are. In my opinion.

A statement like Accuracy over Precision was certain to cause problems. And it has.

If you look at the language around cause and effect, causality, and there being many correct right answers to the same problem: you get the point. It follows from the Axioms that, to derive competitive advantage, you need to be able to make predictions about the future, and the only way to really get there is through progressive hypothesis testing with accurate data, and understanding both complexity and causation.

Oct 02 0 comments
Marketing Science, Social Analytics

The Strength of Weak Ties

A tight group of friends will tend to overlap in terms of product adoption and preferences. Like people clump alike.

I hypothesize that the social graph is partially-fractal. I use the word ‘hypothesize’ because I don’t have the technology to prove it. Moreover, at this point, I don’t think I could write the proof to prove that it’s partially-fractal.

By fractal, I mean that at the most basic level, the individual with a circle of friends, they’re all alike. If you zoom out, treating each group as though it’s a person, they’re all linked together in a similar way, and if you zoom out again, treating each groups of groups…the structure is the same. In other words, the further you zoom out, the same essential pattern bears out. (I could see Maven’s clumping together in some way, even though Mavens might organize in groups of acquaintances – and it’s that pattern that replicates.)

There are times when ‘forward to a friend’ actions are important: intensity plays are one example. If a group of people enjoy wines, frequent talking about wine (and brands) will bring ideas to the front of mind, and I hypothesize that you’ll have a higher intensity of use.

There are times when ‘forward to acquaintance’ actions are important. It might very well be that you’ve achieved 90% penetration within one set of social groups, and you need to leap out.

In a way, the same rules that should apply at the micro-level should be possible at the macro-level. I suspect that there’s a law in there: perhaps a predictable step-function, that could be used to predict market penetration. I wonder if it’s really been embedded all along in our traditional S curves.

The takeaway from all this is that it’s worth considering which behaviours you want to encourage at which times in your customer lifecycle.

Sep 23 0 comments
Marketing Science, Social Analytics

Social Media Measurement

It’s been a busy week in the world of social media measurement, or social analytics, as I like to call it.

Anna O’Brien, Marketing Science analyst extraordinaire, wrote a very good post on the topic. Her primary point, enough with the phony people, is polarizing and necessary. The secondary point: social monitoring is not social measuring is also apt and important.

My interests like in the measurement side: content analytics and metric analytics. There’s a lot of utility there.

A few months ago Joseph Carrabis did a very interesting sentiment analysis on Zappos’ twitter stream. “Tone optimization” will no doubt end up being a major offering sooner rather than later. Let me explain.

Optimizing a web campaign can be very hard. It’s hard because our institutions make it hard. Social media marketing strategy can be built in such a way that it can be optimized. Every response can be improved. “Learning” is possible in an accelerated way.

To that end, tone is an important variable. It’s a vital variable in web copy just as it is in social media marketing. Language matters. (I’ve been reading “Language and Human Behavior” – sometimes the paragraphs drip with frustration). Sometimes what you say isn’t nearly as important as how you say it.

Knowing how many people are saying ‘positive’ and ‘negative’ things is one thing – but what about what your staff is putting out there. What’s the tone? What’s the effect? How can be it changed to improve the outcome and hit your goal?

This is the promise of social media measurement.

In sum, it’s been a busy week for social media measurement.

Sep 07 0 comments
Marketing Science

The Attention Economy and the Canadian Film and Television Industry

I had a great conversation with a producer of Canadian film and television.

Over the course of our discussion, which focused on the lack of money for the Canadian Film and Television Industry, I came to realize that there was a fundamental problem in monetization and a pretty hefty gap in motivators.

Success for a director or an artist is if a large audience sees their art and appreciates it. They don’t want commercialism to get in the way of their art – for instance – the mere notion that perhaps the protagonist could be drinking a Diet Doctor Pepper causes the blood to boil. Naturally, history is littered with studio and network executives actively messing with the creative arc of a series. Citizen Kane wouldn’t be 1/10th as good as it is if Orson didn’t keep the execs at bay. So, I can appreciate the fear.

Success for a broadcaster is the show making money. The show makes money if advertisers pay a lot money per ad unit. An advertiser makes money if the ads placed during that show generate high GRP / TRP AND the demand curve shifts to the right.

In film, anybody from Alliance Atlantis will tell you it’s about asses in theater seats and disks in home theaters. Again, more or less, it comes down to attention share.

Producers of Canadian content complain that networks don’t put any dollars behind getting eyeballs, and as a result, GRP / TRP from the advertisers is never forthcoming. Canadian networks complain that Canadian content doesn’t attract Canadian eyeballs. Without Canadian eyeballs, networks can’t make money.

And without proven advertiser dollars and track records – banks and private lenders won’t invest in Canadian productions.

Canadian productions are expensive. It costs around 300k to produce a single 44 minute episode of programming (easily), and economies of scale are fleeting. The very cheapest one could do a short 10 minute piece is around 56k, and that’s pushing the extreme lower limit. I’m talking about quality here – not webcam quality.

Where are all the Canadian Eyeballs at?

Enter the Attention Economy.

Producers are in a full scale war for attention. We consume more media than ever before, but it’s increasingly fragmented across multiple channels and screens. It’s harder to get a concentrated slice of a target market in a single medium. These days – you gotta go longitudinal: a mental shift that only the very young marketers are actively embracing.

Our conversation came to a head when the Producer said: “How do I get the content out there without commercial interference?” and I replied, “You’re competing with commercial interference for attention – and you can’t possibly outspend them.” In fact – commercial interests are wildly tearing at each to grab a slice of a fragmenting attention. The conversation, like any good Canadian conversation, resorts to regulation and citation of previous policy.

The back-stop is of course CanCon. CanCon is a regulation by the CRTC that mandates a minimum percentage of all broadcast content be Canadian. The common complaint, in Canadian Film and Television circles, is that the CRTC defines news and current affairs programming as being included under CanCon – something that is harder to get away with in broadcast radio.

The example in public policy they cite is the success of CanCon in developing the Canadian musical talent industry. Most local stations wouldn’t and couldn’t crowd their airwaves with Canadian ‘news talk’, and as a result, a plethora of great Canadian talent became supported and launched. The policy has been hailed as a wild success.

The common complaint is that Global and CTV count their 2 hour 5-7pm news broadcast as being Canadian content, along with the 11pm news broadcast, the 3 hours in the morning show news broadcast, and two current affairs programs at one hour a piece (W5 and 16 by something). They proceed to produce such shows as “Train 48″, “Da Kink”, “DeGrassi” and “Flashpoint”, and put them out to die at 8pm on Saturdays – reserving the best of prime time for American shows. Put simply, they’re incented to engage in that kind of behavior because when push comes to shove, Canadian eyeballs just aren’t on Canadian content.

The state of Canadian film is no better. Theater operators are fighting for attention too: and they need asses in seats – and the draw that goes along with it.

The problem is three-fold in my view.

The first is the classic blame game. Broadcasters blame Canadian content producers for not producing content that can compete directly with American content. And let’s face it – that is exactly what Canadian producers are up against. It’s hard to see how CanCon can be modified to avoid this un-escapable fact. To a certain extent, I invite the ultra-pro-market-anti-regulation people to advocate consistently on their position of no government interference. Taking that argument to the extreme would mean denying Canadian broadcasters exclusive re-broadcast rights as mandated by the CRTC. Trust me – it’s not something a network executive wants you to be advocating.

I believe that most network executives are good people who would absolutely love to have 80 hours of Canadian produced TV dramas and comedies broadcasting all the time. It would have to be every bit as good as what’s on the American networks, and frankly, how can Canadian producers compete with that?

Enter the next element of the blame game. Producers blame broadcasters for not getting behind them.

The first big problem is that everybody believes that they’re a victim of one sort or another. And when you’re a victim, you’re absolved of all responsibility for solving the problem.

Canadian expectations are the second problem. Canadians, and let’s really be honest here, have a massive element of “it’s not good enough for us” attitude with respect to Canadian produced content. It’s not surprising – they’ve been trained to think that way. Consider that the United States produces hundreds of pilots just looking for winners. In Canada, we hardly have the money to produce a handful of pilots. Less experimentation means lower actual quality, and those expectations have really become locked-in over the past forty years. Of course the material is mediocre by comparison. Canadian producers don’t have as much margin for error and as a result – commit loads of error. That error adds up into expectations of mediocrity.

The third is a lack of baseline innovation. 95% of the Canadian Film and Television Industry, I dare say, is just plain happy to complain about the lack of network and government support. 5% seem to be happy to try to innovate around the problem of economies of scale. I think that that’s sad.

Canadian producers continue to lose because they continue to lose attention market share. They can’t rely on the government or private enterprise to really solve their problems. CanCon won’t be modified because it benefits too many established groups. Broadcasters can’t step up to the plate because they’re subject to enhancing shareholder value. Producers  have to recognize who they’re competing against and adjust against it. They must take it on, head on, to have any chance.

The Attention Economy is ruthless.

Sep 01 1 comments
Analytics, Marketing Science

Of Personas and Market Segments: A reply to Hamel

David Hamel wrote:

The issue behind it all is that the web isn’t static, it is constently changing.  Ever heard of AOL?  Of course you have.  Know anyone still using it? Probably not.  What about MySpace?  Also there is the inevitable the march of time.  Your persona for Bob has his age at 52.   In five years time, will Bob still be useful?  Probably the difference between 52 and 57 isn’t that large.  But what if your target demographic is 22?  There is a much larger difference between the interestes of a 22 year old and a 27 year old…In conclusion, use personas but don’t let them get stagnate, your personas represent people and people change.

Hamel rightly points out that personas come into being and are sometimes printed out on huge boards and laid out in the office. And then they’re taken down after the redesign and we forget about them. They don’t evolve. They don’t breathe.

I’d argue that the typical market segment, “male, 56-65, rural” is incredibly bland and not adequate at all. I think somewhere along the line, perhaps over the decades, we forgot why market segments are supposed to be powerful: like people talk and they are self-referential. This was demonstrated with the original Word of Mouth marketing studies focused on seed distribution. Word of mouth is critical. No company can possibly afford to pay for every conversion.

The segment should describe, instead, where rural older males are talking and how do you get them to refer you along. A person is so much more than just an age, gender, income bracket, number of kids and general DMA. Quantitative marketing science is capable of real contributions and advancement in that field.

If personas impart empathy in design, then segments should impart empathy in marketing.

And directly to Hamel’s point: they should be periodically revisited.

That might not sit well with the common “rip’em down and reinvent them from scratch” mentality that dominates the world – but I’m very comfortable with the notion of optimizing segments and personas over time as a program of ‘learning’.

I’d like to see more of that.