What vs. Why

A recent study indicated that only 14% of tablet experiences and 13% of smartphone experiences are personalized. Why are these numbers so low? The concept of personalization has been in play for quite a while, and some mobile websites do a great job of tracking interests and making recommendations, with Amazon probably being the best example. Although in fairness, they are in a nearly perfect position to drive personalization. They have a vast product offering and tons of data to work from; most of their recommendations are driven by a collaborative filtering engine (people like you bought stuff like this) that is continuously being refined via billions of transactions. They are arguably the market leader at addressing the “what” of marketing, perhaps less so at the more critical question: “why?”, which is what drives deep personalization. If they are the market leader for “what” personalization technology, and they’re struggling with “why”, you can well imagine what little has been done by other sites. What’s up with Why?

The “why” of mobile personalization requires a more nuanced interpretation of consumer behavior, and one of the potential benefits of mobility is that it can add that layer of nuance. Why? Because unlike desktops, the mobile device (specifically a smartphone) is always with the consumer, and always on. As mobile devices become more powerful and useful, we’ve come to rely on them almost continuously, and that heavy usage is where the subtleties that can address “why” come into play. I may shop at Amazon once or twice per week, but I am on my phone pretty much non-stop in one form or another.

So what is holding back personalization on a mobile device? Everyone (correctly) expects a rich and relevant experience when surfing from a desktop, but what happens when you move to that cool gadget in your pocket? There are several antecedent questions:

First, what kind of device? Tablet or smartphone? Which operating system and which release? Which browser and which release? What’s the screen size? Are your email messages and associated landing pages optimized for a mobile experience, or do you cram a PC site onto a mobile device (you’d be surprised how often this happens)?

Second, what data can you capture? Do you have a history of the user’ interaction with your brand? Have they opted in to having personal data collected? Have they bought from you before or are they a newbie? Are you able to track their movement through the funnel and map your messages to match their stage of interest?

Third, what do you do with the data? Are you able to tease out attribution? Assuming a multi-touch campaign (which applies to all non-impulse purchases), how do you know which ad exposure was the tipping point? Or does the last touch get all the credit? Knowing exactly what worked is incredibly valuable information for future initiatives designed to create those moments of serendipity that can delight your customers.

Fourth, how do you manage the complete customer lifecycle? Regardless of what you’re selling, customers will buy more that one of your product (exception: caskets). Marketing is not a process with a beginning and end, it’s a continuous loop of replacements and upgrades. Knowing how to cultivate a long term relationship can add multiple zeros to your bottom line.

So the why of mobility is not just about the device, it’s about the contextual use of the device, the contextual framework of underlying data and what is done with it that can lead to as rich an experience as you’d expect on a desktop, translated to a mobile device. It is the confluence of mobility and social media where “why” will really come into its own; consumers pouring the minutia of their lives online, then accessing it via an always on device. It is, as you can see, complex, subject to rapidly changing dynamics, and requires skills that are still beyond the grasp of most companies (particularly SMBs). However, the first company to figure out how to address “why” at scale is where the next crop of billionaires is likely come from.

The Rise of Serendipitous Analytics

For those of us who work with analytic data as part of our marketing efforts, being able to slice and dice user data to create targetable cohorts is a routine part of our day-to-day. Nearly all of this segmentation has focused on analyzing data to meet specific customer requirements; who is most likely to want to upgrade their existing service plan, who is interested in similar products, who is ready to replace an aging product, etc.

DataXu recently released a report that correlated user behavior across normally non-correlated variables to create a serendipitous profile of users, connecting dots that most people would never think to connect (e.g. what type of soda do free checking account customers prefer?). This type of information is gathered through a Programmatic Marketing Platform that tracks user interaction with advertisements and websites as people more through their digital day. Now, of course, the initial reaction to the earlier soda question is, who cares? And the answer to that question would be, soft drink companies, who just picked up an unexpected angle on their customers.

So while this is weird, interesting, and somewhat cool, think about applying the same type of correlative analysis to social media. Programmatic platforms are great at high-volume tracking of consumer behavior, but the data is very light, very brief. If a company like DataXu is able to extract that kind of serendipitous correlation from surface-level transactional behavior, imagine what they (or someone like them) could do with the vast, deep, and rich trove of user data that people upload into social media sites on a non-stop basis.

One of the consequences of social media is continuous bifurcation of people into groups of common interest. This happens routinely on Facebook, and the reason this matters is once you’re in a focused cohort, there tends to be a lot more depth of discussion, and the contextual frame of reference becomes a lot more focused. This level of focus and detail, the nuances of consumer interest (rather than transactional behavior), will be what drives serendipitous analytics to a whole new level of cool, opening up opportunities that would not have occurred to most of us.

Facebook and the Future of Social Media

So Facebook is continuing to grow on a global basis, they’ve found a way to monetize mobile, they’re continuously adding new features and data streams, and while all this is happening, pundits and others who’ve never created anything in their lives (other than an opinion), are harping about Facebook’s eventual demise.

So what is the most likely scenario for a juggernaut like Facebook? Maybe the best frame of reference is other juggernauts from history, and the most obvious example to me seems to be some of the preceding technology giants from the past. There was a period when you could not step in any direction within the tech space and not bump into IBM on some level. Same thing with Digital Equipment (and anyone under 30 says, “Who?”). These are companies that completely dominated their ecosystem at their peak, but everyone, no matter how compelling or efficient, cannot stay on top of the pile, that is not how the free-market system works. Remember AltaVista’s dominance? Then a little start-up with a funny name came along, and AltaVista is a long gone memory.

So is Facebook likely to face the same fate? There’s never been a website with this many users across so many countries, so from a domain perspective there isn’t really an antecedent frame of reference. They could shift their business model completely (like IBM) and morph into something almost unrecognizable. They could teeter and fall (like DEC), and in the process spawn a whole new ecosystem of startups and cottage industries, or they could get outsmarted by two ten year olds who in a couple of years figure out something much better, and the whole world changes again.

Based on what I’ve seen (and I’ve seen a lot), a high probability scenario is the Piranha effect. Facebook is like a giant cow slowly fording a stream filled with potential little predators, who individually can’t do much damage, but collectively could knock the company off balance. The real problem with Facebook in the long run is that at the surface it’s a one-size fits all model. They have created infrastructure that allows internal specialized groups to form (and they are, at high speed), which is effectively a form of bifurcation. But Facebook has also shown people the potential of what can be done with social media. There are tons of smart, motivated, and creative people out there busily developing permutations of Facebook that will be optimized to meet a specific niche that is not looking for a subset of a one-size-fits-all model. So a potentially higher probability scenario? If Zuckerberg is as smart as he seems to be, I’m guessing they could actually start spinning out cohorts with common interests (think of a voluntary version of the ATT breakup). Any slice of a pie with a billion+ members is still going to be a statistically significant media site, and if they do this correctly they can still continue to make a fortune. So will Facebook keeps its dominance? No, not in their present form. Will it be around in some form for the foreseeable future? Definitely, and it will be cool and interesting to see how this plays out.

The Alphabet Soup wakes up, too late.

It appears that after months/years of sitting quietly by and watching privacy advocates claim the moral high ground on consumer’s “right” to privacy, the IAB, DAA, and ANA have finally started to push back. I suppose technically that’s good news, but if you look at how they’re doing it, I’m guessing this is going to quickly turn to bad news.

The triggering event for this was Mozilla saying they are now officially going to start blocking third party cookies, where consumers who have not opted in are not going to be subjected to advertisements that are normally part of a retargeting effort. Keep in mind there was extensive foreshadowing of this; Gary Kovacs (CEO of Mozilla) addressed this very issue over a year ago (see my post dated 3.9.13—below), and there was zero ambiguity as to where he stood and where he was going. It has taken these organizations this long to craft a “coherent” response, despite knowing full well what was going on and what the potential consequences of inaction were likely to be.

So if they’re responding, what’s the bad news? Look at how they respond. They whine about the threat to their business model, how its not fair, blah, blah, blah. This is EXACTLY the wrong approach. The privacy advocates have done a textbook job of FUD marketing; their message is aimed at consumers—who do not understand the nuances and details of behavioral targeting and third party cookies, can’t be bothered to find out, and are easily stampeded. Now that they have that herd headed over a cliff, the privatistas are now going after easier and bigger prey: politicians. As I have said before, politicians are neither businessmen nor technologists, yet they determine policy and governance on the business of technology. They pander to the loudest voice, and right now the only ones yelling in terms the politicians understand are the privacy advocates.

So what needs to happen? These groups need to sell the benefits of targeted advertising to the consumers who are enjoying the benefits of unlimited information and entertainment. I mean, hell, this is the advertising business, are they not able to convince people of something? They do this all day every day for all sorts of products, then when it actually matters to their long term survival, they freeze up then react in the exact opposite way they should.

The current business model of the internet is built on advertising; Google, Facebook, Yahoo, etc. all make zillions based on advertising. This is a given. Again, the question is, do consumers want relevancy or spam? Because that is what its going to boil down to.

The IAB, ANA, DAA, etc. need to stop feeling sorry for themselves and start selling the value of behavioral targeting, and do it now.

Privacy and Collusion

I recently saw a recording of a presentation given last year by Gary Kovacs (currently CEO of Mozilla), on the rising concerns regarding consumer privacy and the growth of behavioral targeting applications. He raises two key issues which are still as valid now as when he gave this presentation, and in fact, have developed much further since. First, in order for the web to work the way it does, we all have to deliver some level of information about ourselves (Facebook is a perfect example, it’s completely based on sharing information), but the downside per Gary is the extent to which people are tracked by applications running on sites they may have never visited or even heard of. There is a difference between being tracked when you have self-identified, and being tracked when you have not. So for those sites who track you without your explicit buy in, who are these sites, and what are they doing with this information? He then provides a more sinister example of his daughter being monitored by Behavioral Targeting apps without her being aware of it—how, as a parent, would you react to someone following your child?

So lets look at the core questions. Who are they and what are they doing with the data? The likeliest answer for who would be an advertising network such as Yahoo or MSN, a network of sites that have a common reference framework of content creators, publishers, advertisers, and so on. This type of infrastructure is well suited to tracking movement across the network, since (as a network) is it optimized to know what’s going on where. As consumers we already provide a great deal of data as to where we are going, which is used to create cookies—little snippets of data that identify us as we traverse a closed network. So even if we don’t voluntarily enter information on our movements, the Behavioral Tracking algorithms and associated cookies are paying attention, and altering other parts of the network about our behavior.

So why all this attention and effort to track our movements? The short answer? Money. Behavioral Targeting is about serving up relevant ads to consumers as they move around the web. The more accurately they can match an ad to our predicted interest, the more that ad is worth because I am more likely to click on it, and that is how the money is made. I would also point out that individual data is always aggregated, no advertiser is going to want a cookie identifying a single user, there’s no money in it. But a pool of cookies with 150,000 targets who have shown a recent behavioral tendency to potentially purchase e.g. a barbeque grill? That is worth a lot to someone who sells grills. The whole point of Behavioral Targeting is to serve relevant ads to someone. That’s it. Relevant ads. Nothing sinister or creepy, just advertising that is consistent with your behavior.

I have been very vocal about the lack of pushback on the advertising side as to the benefits of behavioral targeting, right now all the noise is coming from the Privacy side, and they don’t make any reference to the consequences of not tracking. Advertising on-line is a given no matter where you go, and that is not going to change, ever. How do you think Google makes it’s billions? What’s Facebook’s objective, to connect everyone? Wrong. It’s to create advertising cohorts via self identification of membership in targetable groups. Advertising is a given, the choice consumers face is let the advertisers know what you’re looking for so you can have relevancy as you move around the web, or be subjected to endless spam, since your “right” to privacy keeps them from knowing who you are or what you want.

The Actual Implementation of Social Media

Following up on my last post, it makes a lot of sense to add the same sanity check filters to the deployment and integration of social media into the mainstream corporate workspace. Similar to mobility, social media is an area that gets outsized attention from the mainstream and business press, as well as the analyst communities. One billion + users on Facebook is not a number to be trivialized, and even a small percentage of such a large number is a statistically significant cohort, which explains the vast cottage industry orbiting around Facebook.

But similar to the preceding rise of mobility (and the rise of the consumer internet before that), this is another technology trend that has caught corporate America with their pants down. What? Social Media? One Billion? Holy Smokes! Let’s do something! And everyone goes rushing off the cliff like good little lemmings, without asking the core question…Why, exactly? How does this tie into or augment what we’re already doing? How does this add value to our existing value proposition, and what is the best way to ensure tight operational alignment?

What happens more often than not is companies jump all over this, without a clear commercial or operational imperative, then nothing really happens since expectations were never properly set to begin with, and the whole initiative starts to slow down. And similar to mobility, this is a very consumer-centric framework; as anyone who has tried to market to consumers knows, they are fickle, flaky, easily stampeded, and have the attention span of a two year old. Do companies really want to jump into this dynamic given the relative immaturity of the technology and the proclivities of the end target?

I am not suggesting that companies should not pursue a social media (or mobility) strategy, quite the contrary. These are pervasive technologies, but they are not geared towards the need of the corporate model. Facebook started out as a way to create communities in colleges, and became so large it was impossible to ignore. But the fact that it’s there doesn’t mean you have to jump in without thinking about it long and hard. I’ve seen tons of companies who beat their chest saying “Yeah! We have a Facebook page! We’re totally social!” And you go to the page, and it’s static. A one-way conversation with no one apparently listening on the other end. No interactions, no engagement. It is not enough to post, you have to interact, that’s what makes it social. And guess what? Those interactions may not go in your favor. If you get it right, and truly integrate social media into your core business model and align around it, it can work wonderfully, but if you rush in without thinking, you run the risk of either being ignored, or worse, getting a public spanking.

Privacy paranoia triggers FTC smack-down

The mobile ecosystem had better perk up and start sweating the details before the Privatistas over at the Center for Digital Democracy force them into a tight and useless corner. And I say the entire ecosystem, rather than just the apps developers, because if the CDD prevails, the filters for downloading and using apps, particularly for children, are going to become so draconian that it will kill innovation in this market before its had the opportunity to really take off.

Apps are the compelling event in driving the mobile experience; no one buys an iPhone to make a phone call, they buy it for the apps. Same thing with Android, the devices are a commodity delivery mechanism for the app, but if the Privatistas have their way, the compelling event will be severely stifled, to the point of insignificance, and the entire ecosystem goes into a tailspin.

What was the triggering event in this instance? The CDD jumped on its high horse and filed a complaint with the FTC about a company called Mobbles, who has developed a wildly popular mobile game targeting children. Their concern is that information is being gathered by the app (including geo-location data, which is an integral component of how this particular app works) without the kids (or parents) consent.

A couple of news flashes for the CDD:

1)Kids don’t care about privacy. Ever heard of a site called Facebook? A billion people disclosing unimaginable levels of information about themselves, on a non-stop basis.

2)Even if you explain it to them (and I tried explaining it to mine), they want to be identifiable on a network (using an alias), so their friends can find them. They want to have advertisers show them more cool games, they’ve grown up in this milieu, and they’re totally used to it. The only ones who are wound up about this are the curmudgeons at the CDD.

3)If you have a problem with what a company is doing, tell them. The CDD never bothered to contact Mobbles, just went straight to the FTC (why address the problem when you can grandstand?).

These elements aside, the CDD still misses the point. What are the primary objectives of gathering data on users accessing applications from a mobile device?

First, better targeting of ads within a network, and creating the opportunity to sell ancillary products that are likely to be of interest to the consumer. The primary point here is that people want to be advertised to when the information is timely and relevant, and the whole objective of ad targeting is to increase relevancy rates. This is effectively the exact opposite of spam.

Second, capturing and providing feedback on how the product is being used, on the assumption that the next release will be an upgrade since developers have a better sense of what users are actually doing. The primary point here is that software products are continuously evolving, and the best products are those that are driven by an end-users needs or wants. In-app analytics are an incredibly valuable source of information on end-user requirements, the more information we have, the better product we can build.

The Association for Competitive Technology, which is the mobile app equivalent of the Internet Advertising Bureau (the last industry association to get their asses kicked by the CDD) had better start taking these people seriously. Like it or not, the CDD has the ear of the legislators in Washington, who are neither businessmen or technologists, and will pander to the loudest voice.

Insight vs. Information vs. Data

I came across an interesting article written by Michael Wu over at Lithium that goes into detail (in a not overly geeky way) on why there is a significant need for an even greater amount of what is commonly known as “Big Data”. The basic argument is that companies are looking for Insight, which is a subset of Information, which is a subset of Big Data.

Big Data is the noise that now envelopes us; Tweets, Facebook posts, comments on User Forums, any activity on the myriad of “Social Networks”, which some vendors who track this space now claim has over 200 million separate sites (really?). This actually tracks fairly well to my last post (see below), where I blogged about the need for relevancy based on setting realistic expectations in terms of what Big Data can actually deliver. There are now over 1 billion users on Facebook generating a staggering amount of content on a non-stop basis, the amount of data (Tweets) generated by Twitter is absolutely vast (low signal to noise ratio), on a different but still large scale, you have mobile developer user groups with millions of members posting constantly (smaller volume, but very high signal to noise ratio). The list of sites generating “social data” is extensive, and growing at exponential rates.

What is particularly interesting (and Wu’s article doesn’t specifically call this out) is the shift of analytic focus from B2C to B2B. This was actually called out in a blog post by Twitter but seemed to fly under the radar at the time (except for those interested in specifics of Twitter’s API changes driven by the need for authentication). It feels like the enterprise side of this ecosystem is finally starting to sort itself out in terms of coming to grips with how Big Data can be used to more effectively engage with their customers. I’ve been in the technology domain my entire career, and have seen the enterprise space get caught off guard multiple times (the unexpected rise of the World Wide Web, then a few years later the sudden ascendance of mobility, now its social media and the analysis thereof). The enterprise market does eventually engage with the technology as it matures, and then it becomes embedded and institutionalized.

The step that is still missing is the logical application of social data to operationalization of how an enterprise interacts with their customers. Having loads of data is great, the more data presumably the more information, the more information presumably the more insight, but that is still one step short of the final goal. I’ve seen lots of enterprises try to claim the moral high ground on social media (“look how much we post on Facebook!”), but all they do is post. The whole point of social media is that it’s Social. You have to engage in a meaningful dialogue (not a monologue, which seems to be the standard at the moment). It’s easy to run detailed analysis on Big Data since most of it is algorithm driven, the challenge is how to create a value-add response to a consumer in the context of social media, which is something an algorithm is not necessarily going to deliver. That requires a CSR (customer service rep) that has access to timely social data that has been thoroughly scrubbed, contextualized, and operationalized. Once this process is defined and institutionalized, then you will start to see Big Data reach its full potential.

The reality of social media updates

We have been spending a lot of time lately working with customers who are trying to get a sense of what can be expected from systematically measuring social media. One of the challenges that comes up consistently is a realistic expectation of how often these metrics need to be updated. The current assumption is that social media commentary is streamed into the system, and the scoring algorithm is continuously updating consumer sentiment.

It’s a nice idea, but the reality is a bit different. One of the issues is having a correct set of expectations; people are familiar with social media as a consumer—there’s over a billion people on Facebook alone and that many users talking at the same time generates a staggering amount of content. So as a user, I am used to a non-stop stream of social data from my friends, their friends, advertisers, etc. And that is the first problem.

Once you apply a business filter to social data, it immediately becomes a non-stream. What Facebook (as the most obvious example) has is a highly imbalanced signal to noise ratio. A billion folks rattling around a site generate a lot of noise, and business is looking for signal. Now of course, the signal will come from a subset of the noise, and when you have a source of a billion, even a small percentage relevancy will be significant. So businesses are looking for information (signal) about what consumers are thinking about their brands, products, services, etc. and they expect it in a steady stream, because that is how they are used to dealing with social media.

That is not, however, how filtered social media works. You can’t expect data to come in on a regular basis (“we want daily updates on what consumers are thinking”), it doesn’t work that way. A good example is the airline industry, which is a very consumer-facing domain, and tends to have very vocal customers. People do not tweet or blog constantly about issues with the airlines, unless something bad has happened (e.g. stuck on a tarmac for seven hours), and then it’s a Tweetstorm. Tweetstorms are a great source of sentiment-laden data about business issues, but by their nature, Tweetstorms are non-predictable. It’s not like every Tuesday JetBlue makes the 2pm New York to Boston flight sit on the tarmac for seven hours. Tweetstorms and the associated media effects are arbitrary, unpredictable, and event driven, and social media analytics customers are looking for non-arbitrary, predictable data. Data comes in when it comes in, and rather than setting a data update schedule that has no real connection to what is going on in the social media space, we would be much better off setting thresholds and importing against that. If the threshold is triggered, you can assume something significant has happened, and you’ll be the first to see the bigger picture.

So the problem is this will work great for some industries such as consumer facing companies that manage to piss off their customers (banks and airlines are good at this), and perhaps less so for others (an ERP company looking for sentiment on a new operational feature). That kind of input needs to come from user groups, and no user group is anywhere near the scale of a Facebook.

This is an interesting area to be working in these days. There is definitely signal out in the social media space, but getting to it in a statistically meaningful way that can be understood by a non-technical audience is turning in to a bit of a challenge. The whole social media analytics space (at least those of use focused on trying to help businesses get a grip on this) needs to set the right level of expectation, and show the value that a well thought through solution can bring to bear.

Getting a grip on social media analytics

Social media has gone overnight from a limited use application to a pervasive technology with billions of participants. The implications for business are significant, and like most technologies, can be a threat or a benefit. Comments or opinions from anyone are instantly available, and while good news travels fast, bad news seems to travel faster. People with an axe to grind now have a global bully pulpit, and the network effect means isolated incidents can take on a life of their own.

Like the earlier rise of the internet, the rise of the social ecosystem has caught most businesses off guard. Scrambling to create a social media strategy, companies are in the early stages of understanding the impact of this technology. While you’re reading this, millions of conversations, posts, tweets, likes, etc. are going on all over the world, and you can safely assume a significant portion of them are talking about your company or products. How do you track and measure what’s being said, and more importantly, what can you do about it?

Currently Social media analytics have a core focus on transactional numbers. You have lots of quantity, limited quality and actionability, and nothing ties to the bottom line. It’s clear something important is going on, but exactly what this means, and what you can do about are the big questions.

Companies with a social media presence need to identify who is saying what, but more importantly, those comments and the people making them need to be categorized in an actionable framework. You need to know who is promoting your brand, who is criticizing your company or products, and who is most influential on the social web. Assuming you could get this information, then what?

The real challenge is being able to take action on comments. You need to be able to respond to that particular person, and you need to be sure the right person within your organization is following up.

The true value for business in social media is in controlling your brand presence not only at a strategic, but at a tactical level. This means understanding broad sentiment on your brand or product relative to your competitors, understanding brand dynamics at a very tactical level, and then quickly responding to changing conditions in the social media space.

A properly developed social media analytic application can precisely track consumer sentiment across a broad array of networks, separating signal from noise, and giving marketers the critical insight needed to act. Businesses are then able to measure dynamic, unstructured comments about their brands using a predictable customer loyalty metric and business practice that is focused on driving profitable growth and customer loyalty.

By delivering actionable insights into customer sentiment across social media, social analytics complement and extend the capabilities of traditional Voice of the Customer surveys. By tracking changes in customer sentiment, you can deliver competitive benchmarking based on social media dynamics contextualized by an actionable framework. Social media analytics cut through the noise to identify the people that matter, allowing you to focuses on those individuals by leveraging positive posts where customer feedback is most active.

A social media enabled business is one with a clear, firm grip on its strategy and execution. When a major event occurs, they can track who says what, segmented into promoters and detractors. They know who is influential in affecting brand or product perception, and they can ensure the right person is following up in a timely fashion. Most importantly, they will be able to directly correlate social media sentiment to their bottom line.