12/21/13

Permalink 11:30:30 am, by dano Email , 779 words, 1761 views   English (US)
Categories: Analytics, Mobility

Defining an enterprise social media strategy

A successful social media strategy begins with just that, a strategy. There is a non-trivial amount of technology at play here, and before wading into the deep end of social media, it is imperative that you have a clear understanding of your objectives. To begin:

1)Set a strategy. What are your long-term and short-term objectives for social media interaction? Are you looking to use a more nuanced social media capability to gain market share, expand against your current user base, or gain a stronger grasp of how to engage customers through a deeper understanding of what they are interested in based on their group dynamic? How are your customers likely to react to more focused attention from you, particularly in a public venue? Does this need socialization prior to implementation? What are your measures for success? How will you know when you’ve accomplished your goals? At what point do you begin adjusting variables, how will you adjust them, and which variables are likely to be most important?

2)Determine the stakeholders. Given the insight social media can deliver to your organization, the number of groups who are going to have an interest is likely to expand from your current operational model. This can include marketing, sales, customer support, merchandising (if applicable), operations, IT, etc. Each will have an interest in gaining a deeper perspective into how to use social media to interact meaningfully with their particular facet of customer engagement, and each is going to have a particular set of data requirements and reporting needs. Get your ducks lined up before your start moving, and once the process starts expect to adjust as you gain clarity into what works and what doesn’t.

3)Study the current level of performance of your existing social media initiatives. How much detail do you have on your existing social media initiatives? Are you able to measure beyond Likes and Retweets? What does the current data tell you? Are you finding a measureable level of success (or perceived success) with your existing initiatives? Helpful hint: before implementing an integrated social media strategy, create a starting frame of reference based on your existing social media initiatives. This will give you something to point back towards (“look how much we improved!”); a fully integrated social media strategy will always have a positive effect on your marketing performance; you want to be seen as the person responsible for making it happen.

4)Examine the current set of variables that can be used to drive segmentation. How many variables are you able to track across different social media groupings of information? The other issue to consider is that there are variables that may be in separate silos that could be incredibly useful for purposes of analysis. Questions you should be asking include:

a)What is the current marketing and/or CRM system of record, what relevant information is in there, and how easy is it to access the data?

b)Where do you track customer support requirements? Customers who are active in social media are already leaving a wealth of details in their wake which can be used to personalize customer support interactions.

c)Are you able to tie social media interaction driven by in-line posts into your customer’s profile? When they receive a communication from you and respond or engage, are you able to capture and track interaction information on the event, and can it be tracked as part of the user profile to driven content optimization?

d)What level of detail can you pick up from mobile social behavior? The beauty of mobile social is knowing when and where to reach out to people in the context of a peer group. A customized and well-timed social communication mapped to the user’s physical location can drive a serendipitous interaction. Don’t just satisfy your customers, delight them.

e)Are you able to assign attribution from social media and map that to your outbound customer engagement strategy?

f)What level of detail are you able to gain from your merchandising system? Do you know who bought what when and is there any corollary data in social media that ties to the purchase event? Look for longer term patterns that allow you to anticipate their next move with high confidence, which can subsequently be driven by social media interactions.

g)How can you integrate this initiative with existing Behavioral Targeting, or Collaborative Filtering or Predictive Modeling, etc. applications?

Social media strategy planning is not difficult, but it is complicated. While the variables that drive social media are constantly shifting, the core elements addressed here are going to be pretty constant, and should provide a consistent framework for execution.

08/26/13

Permalink 06:01:13 pm, by dano Email , 2139 words, 23634 views   English (US)
Categories: Analytics

Ad Ecosystem Primer

I’ve been doing some work in the Advertising and Retargeting spaces over the past few weeks. This is a very complex and dynamic domain; lots of moving parts, tons of technology, lots of acquisitions, etc. I thought it would be useful to walk through a definition of who the player are (categorically), since this type of information does not appear to be aggregated anywhere. So here we go:

Ad Exchange Ad exchanges are technology platforms that facilitate the bidded buying and selling of online media advertising inventory from multiple ad networks. The approach is technology-driven as opposed to the historical approach of negotiating price on media inventory. This represents a field beyond ad networks as defined by the IAB (Internet Advertising Bureau).

Ad Networks An online advertising network or ad network is a company that connects advertisers to web sites (also called publishers) that want to host advertisements. The core function of an ad network is aggregation of ad space supply from publishers and matching that space with advertiser demand. The fundamental difference between traditional media ad networks and online ad networks is that online ad networks use Ad Servers to deliver advertisements to consumers, which enables targeting, tracking and reporting of impressions in ways not possible with analog media alternatives.

Ad Operations Also referred to as “online ad operations", “online advertising operations", “online ad ops", “ad ops", and “ops", refers to processes and systems that support the sale and delivery of online advertising. These are the workflow processes and software systems that are used to sell, input, serve, target and report on the performance of online ads. Ad operations are typically a department within a digital content publisher, ad network, ad technology provider (such as a rich media vendor or an ad server) or ad agency. They may fall under sales organization, information technology, or may be a separate entity. The primary function of ad operations is fulfilling the order of sale (also called an “ad campaign” or “media ad buy”) purchased directly by or on behalf of an advertiser (also called a “direct marketer” or a “client”). Therefore ad operations is a group that directly responsible for revenue generation.

Ad Server An ad server is a computer server, specifically a web server that stores advertisements used in online marketing and delivers them to website visitors. The content of the webserver is constantly updated so that the website or webpage on which the ads are displayed contains new advertisements—e.g., banners (static images/animations) or text—when the site or page is visited or refreshed by a user. The purpose of ad serving is to deliver targeted ads that match the website visitor’s interest.

Agencies These are similar to traditional advertising agencies (think Mad Men), with the difference being that now they are purely focused on digital media as the delivery method. Most digital agencies can include delivering creative services for banner ads, as well as doing the media buys for larger brands. For example, Samsung introduces a new smartphone and wants to buy advertising across a broad range of publisher sites, targeting different consumer groups with different messages. The process is complex, with lots of rapidly changing variables. It requires specialized competency that a brand like Samsung lacks, but the agency specializes in.

Agency Trading Desk The agency trading desk is essentially a service that helps advertisers and agencies buy online advertising. It is also an advertising technology platform combined with human skills in advertising and technology that provides access to a wildly complex digital advertising marketplace. In terms of customer deliverables, the agency trading desk is a collection of organizational and technology capabilities focused on optimizing digital advertisers’ budgets through real-time bidding and ad exchanges. Within a trading desk, human resources and skills are mainly software engineers, algorithm specialists, analysts and digital media strategists, account manager and buyers. On the technology side, the tools used are essentially DSPs (Demand Side Platforms), APIs, DMPs (Digital Management Platforms) and Ad Servers. Normally the DSP within a trading desk is integrated with Ad Exchanges, SSPs (Supply Side Platforms)and networks. The primary objectives of a trading desk are to optimize buying and campaign deployment according to advertisers’ goals - based on CPM (cost per thousand impressions), CPC (cost per click), CPA (cost per action) and other branding metrics.

Content Delivery Network This (for some reason) is not included in the chart above, but it is an integral part of the advertising ecosystem. A content delivery network or content distribution network (CDN) is a large distributed system of servers deployed in multiple data centers in the Internet. The goal of a CDN is to serve content to end-users with high availability and high performance. CDNs serve a large fraction of the Internet content today, including web objects (text, graphics, URLs and scripts), downloadable objects (media files, software, documents), applications (e-commerce, portals), live streaming media, on-demand streaming media, and social networks. A CDN operator gets paid by content providers such as media companies and e-commerce vendors for delivering their content to their audience of end-users. In turn, a CDN pays ISPs, carriers, and network operators for hosting its servers in their data centers.

Creative Optimization Companies in the creative optimization space focus on businesses that are trying to solve issues related to scalability and measurability of targeted advertising. In most instances, companies are able to identifying their marketing segments, but creating unique ads for each segment would result in an unmanageable ad spend. In addition, these businesses would like to know which elements of their advertising are resonating with which customers, not just base metrics such as CTR. Creative optimization companies provide the tools and services needed to address these challenges, by allowing their customers to bifurcate online ads into their separate elements, and then customize those elements to the individual customer. A travel agency could look at a customer’s geographic location or flight history and suggest trips to locations that will appeal to the user, rather than using generic copy about saving money on flights. A retailer could use the customer’s IP address to identify the closest branch of its store and display the address and phone number in the ad. Creative optimization companies deliver the ability to create copy which changes according to customer data, which means their customers get tailored messages that makes them much more likely to buy.

Data Management Platform A data management platform is the backbone of data-driven marketing, and serves as a unifying platform to collect, organize, and activate first- and third-party audience data from any source, including online, offline, or mobile. A true Data Management Platform should have the ability to collect unstructured audience data from any source, including email, mobile web and app, web analytic tools, CRM, point of sale, social, online video, and other available offline data sources.

Data Suppliers Data suppliers provide consumer-centric purchase and consumption data to help improve and define online advertising targeting by delivering a more detailed and nuanced interpretation of consumer behaviors and habits. Businesses like grocery and clothing stores aggregate shopping behavior and then sell their point-of-sale data to these companies, which interpret and package it prior to supplying it to online retailers and advertisers, which helps them fine tune their product offers and promotions to suit consumer habits and taste. This domain is populated by very large companies, and is one of the core elements of what is commonly referred to as Big Data.

Demand Side Platform A demand-side platform (DSP) is a system that allows buyers of digital advertising to manage multiple ad exchange and data exchange accounts through one interface. Real-time bidding for displaying online ads takes place within the ad exchanges, and by using a DSP, marketers can manage their bids for the display ads and the pricing for the data that they are layering on top of basic consumer profile information to target their audiences. Much like Paid Search, using DSPs allows users to optimize based on set Key Performance Indicators such as effective Cost per Click (eCPC), and effective Cost per Action (eCPA).

Digital or online advertising is a subset of the advertising industry that references electronic communication promotions and marketing. This can include but is not limited to website display advertising (banner ads or rich media advertising), text advertising, search advertising (paid search results), online video advertising, mobile and device advertising (sms, wap display ads, video, application ads), email display ads and text advertising. These advertisements are a forum of revenue generation for content providers.

Measurement and Analytics Refers to companies that track and measure consumer behavior across individual website, networks such as Yahoo and MSN, and includes mobile measurement, social media analytics and a very broad and deep range of online behavior. The companies in this sector are large, very technical, and deeply integrated with their marketing execution cohorts. This sector is probably the closest to the core in terms of how retargeting and ad serving works, since the entire ecosystem depends on analysis of vast amounts of consumer data—this is the measurement and analysis of Big Data.

Media Management Systems (also referred to as Social Media Management) refers to companies that provide customers the ability to coordinate media campaign across multiple channels (such as Facebook, Twitter, LinkedIn, Flickr, YouTube, Google+, etc.) and provides coordination and dashboarding across functions such as publishing automation, ad management, page management, web analytics integration, platform analytics, support for mobile applications, etc. It is, as you can imagine, a very complex domain, and is populated by companies such as Hootsuite, Argyle, Shoutlet, Spredfast, etc.

Media Planning and Attribution Is similar to Media Management Systems, with the addition of a much heavier focus on attribution modeling. The premise they work off is that when brands execute campaigns across multiple channels (including off-line channels) there are influences at play between the channels, and it is important to assign the right attribution to the right advertising element within the sales funnel. As an example, a consumer may see a banner ad on a search return, then subsequently be retargeted as a result of site visit that did not convert, they may see an ad delivered through a set top box, etc. all of which are focused on the same product. Attribution modeling balances out the ad stream in terms of purchase influence. Does credit go to the last thing click prior to purchase, or to the first? If there are multiple stages (and there always are), does credit go equally to all, or are some stages more influential than others? Similar to other technologies in this domain, attribution modeling is complex, algorithm driven, still in an early stage of development, and is tightly coupled to media planning and management.

Retargeting Retargeting is an online advertising technology that serves customized ads to people who have indicated an interest in a brand by visiting a specific website. These users will then see related ads as they navigate to other web sites such as blogs, news sites, or sports pages. Technically speaking, an advertiser places a pixel, or small snippet of code, on their website to begin. This pixel identifies how potential customers interact with their website and allows for segmentation of those customers for later advertising targeting. This is primarily a conversion, rather than an acquisition technology.

Supply Side Platform A Supply-Side Platform or Sell-Side Platform (SSP) is a technology platform with the single mission of enabling publishers to manage their ad impression inventory and maximize revenue from digital media. As such they offer an efficient, automated and secure way to tap into the different sources of advertising income that are available, and provide insight into the various revenue streams and audiences. Many of the larger web publishers of the world use a Supply Side Platform to automate and optimize the selling of their online media space.

Verification and Privacy This covers two areas (hence the name) that are related. Verification focuses primarily on media verification, and includes things such as inappropriate content (don’t server adult themed ads on a page pushing back-to-school sales), as well as management of black lists (sites where ads should never be served), white lists (the opposite of black lists, partner lists, etc. This has also recently expanded to include restrictions on geo-targeting, ad placement above or below the fold, double-serving (same ad twice on a page), fraud detection (including malware, hidden ads, etc.). So verification is that the ad is running as the client intended and nothing that could be subject to misinterpretation is present. The corollary to this is privacy, which includes opt-in/opt-out capabilities, automatic filtering of third party tracking cookies (which is now a browser function), etc. The privacy aspect in particular is getting a lot of attention from Congress, and is starting to have a significant impact on how advertising is delivered.

07/01/13

Permalink 04:02:59 pm, by dano Email , 1009 words, 23333 views   English (US)
Categories: Analytics

Big Data Primer

“Everybody complains about the weather, but nobody does anything about it.” Charles Dudley Warner

Similar to Warner’s observation about the weather, enterprises (and the marketers who work in them) are aware of “Big Data” and how important it probably is, but very few enterprises and marketers can articulate a cohesive strategy to leverage big data into their operational initiatives. According to a recent McKinsey report, “Big data: What’s your plan?” the authors note that although “The payoff from joining the big-data and advanced-analytics management revolution is no longer in doubt, most companies don’t have a plan for what to do with that data”.

Given this, it probably makes sense to align on an easy-to-grasp definition of Big Data. Big Data is essentially a reflection of the digital life around us; over a billion users on Facebook pouring their lives on-line, over 5 billion mobile subscribers interacting with both each other and on-line content and services, and the much bigger M2M (machine to machine) data play which is starting to interact with the consumer data play via products such as Google Glass, healthcare informatics, telemetry systems, geo-location targeting, etc. Tracking and operationalizing data on this level requires a different approach to analytics and multichannel marketing.

The upside? Marketers will now have access to unlimited data about their customers.

The downside? Marketers will now have access to unlimited data about their customers.

The challenges associated with Big Data can be categorized along the following sequence:

• Capturing the data
• Storing the data
• Accessing the data in real time for analytic purposes
• Displaying the data in meaningful ways to a non-technical audience
• Operationalizing the data so relevant functional groups can make use of it
• Iterating the use of Big Data in a continuously improving customer engagement cycle

Capturing the data—we live in a multi-channel world. Think about your day-to-day and how you use technology: your mobile phone is probably your alarm clock, can be used to purchase a wide range of products and services, helps you get around (along with the navigation screen in your car), and it can also make phone calls. Your tablet has probably replaced newspapers, magazines, television and possibly books, and we still use laptops when we need to create anything significant. Billions of us move back and forth across these devices without giving it a second thought, and all of these elements are data capture points; anytime a user is interacting with any on-line resource there is data to be captured (and it is). There is also the angle of our moving through a grid network of sensors that track our movements (street level video surveillance being the most obvious example), Wi-Fi hotspots are now expected at nearly any location where people congregate, etc. All this information is streaming in at a stunning rate, all of it comes in via disparate formats, and it all needs to be captured, categorized, and integrated, which goes to the next step.

Storing the data—Facebook is currently uploading 500 terabytes of user data per day . Amazon has commercialized the concept of effectively infinite storage on-demand. Oracle has essentially ceded the mobile market in order to focus on Big Data. Some of the biggest, wealthiest companies in the world are moving aggressively to infrastructure around this trend. How do enterprises open themselves up to this volume of data in a controlled fashion, or more euphemistically, how do you fill a wine glass from a firehose? Technical issues such as data architectures and the budget impact of how to re-align your organization to absorb this much information become critical path decision points.

Real-time access to data-Now that you have zillions of bytes of data at your disposal, what makes you think you can get access to it? Or more importantly, how do you get access to the information you really want, that is, what is the customer doing right now, and how can you leverage this into a sale? How does the system know what data is relevant, and wouldn’t that depend on who is asking the question? Also keep in mind real-time means right this instant, not 10 minutes ago.

Displaying the data—A highly trained analyst can look at the matrix and recognize what’s there, but for the rest of us without advanced technical degrees, how do you visualize data on this level? What does 500 terabytes of data even look like, and how can you extract something meaningful from it? Also keep in mind you will have a wide range of data sets that need to be interconnected if they’re going to be meaningful, and how does that work? What tools do you invest in, and whose needs are going to be dealt with first? Finance, Marketing, and Sales may all be looking at the same data set, but like the six blind men and the elephant, each will interpret and therefor require something completely different.

Operationalizing the data—So you’ve captured it, stored it, accessed it, and looked at it in a way that makes sense. Now what? How can you take this hard-won knowledge and use it to drive marketing initiatives that enable you to provide a truly personalized experience to your customers and prospects? How do you integrate cross-functionally so that customer support and marketing are aligned as a new product rolls out? What tools and processes do you need in order to tie the data together? How do you make this data not only strategic, but more importantly, transactional?

Iterating the data—Then the final step, pulling this data bounty into a closed iterative loop that allows you to fine-tune your execution in real-time in order to optimize your customer’s experience. You, and your customer, and their customers, etc. are in a constant production cycle, whether its cars or cookies or on-line services. Presumably you want your customers coming back for more (as do they for their customers), and also presumably you’re not the only one pursuing your customer (don’t forget the competition, because they won ‘t forget you).

05/09/13

Permalink 02:41:18 pm, by dano Email , 690 words, 16258 views   English (US)
Categories: Analytics, Mobility

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.

05/08/13

Permalink 09:38:57 am, by dano Email , 362 words, 9565 views   English (US)
Categories: Analytics

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.

04/15/13

Permalink 06:05:50 pm, by dano Email , 541 words, 14994 views   English (US)
Categories: Analytics

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.

03/15/13

Permalink 03:58:52 pm, by dano Email , 473 words, 27723 views   English (US)
Categories: Analytics

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.

03/09/13

Permalink 10:39:10 am, by dano Email , 643 words, 32601 views   English (US)
Categories: Analytics

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.

02/27/13

Permalink 04:02:59 pm, by dano Email , 478 words, 47841 views   English (US)
Categories: Analytics

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.

01/17/13

Permalink 09:39:46 pm, by dano Email , 386 words, 18490 views   English (US)
Categories: Analytics

Here comes the data deluge

Whether they realize it or not, social media is already the most important technology channel for enterprises to engage their customers. The usage and adoption rates on social media may currently lag alternatives such as call centers or traditional media, but look at the trend lines. A recent survey by IBM shows social media moving from 16% to 57% usage as the primary customer interaction tool over the next three to five years, while call center usage drops from 40% to 31%, and traditional media goes from 39% to a paltry 15%.

While these numbers are interesting, they trigger a broader and potentially far more complex debate. In spite of all the noise and attention social media receives, most enterprises do not have even a remotely clear idea of how to manage social imperatives within their existing workflows. This integration will be the key driver for long-term success, and if it follows previous technology adoption patterns, a small % of companies will get it and thrive, a bigger percentage will fail, and everyone else will muddle through. Every business out there is dependent on a workflow, whether it’s processing a mortgage, developing software, or baking cookies. Anything that streamlines or improves the efficiency of the workflow is adopted; anything that slows it down in the slightest will at best get perfunctory attention, and then slowly die of neglect. This is further compounded by the fact that social media is amorphous, reactive, and often not even remotely logical—which is the exact opposite of how most enterprises would prefer to run their business.

Now here’s the scary part. The amount of data being generated by consumers, as vast as it is at the moment, is merely the first trickle that will turn into a massive tsunami as machine to machine (M2M) data starts to become more integrated with the consumer data wave. While there are billions of mobile devices and users, there are over a trillion wireless sensors, all gathering data continuously, and the boundary between these two sets are becoming increasingly blurred. If you think we have a challenge integrating social data into the enterprise workflow now, just wait until the consumer and M2M spaces align. As disruptive as this is going to be, it also means an even bigger opportunity for the right technology at the right time.

12/20/12

Permalink 02:39:06 pm, by dano Email , 393 words, 33037 views   English (US)
Categories: Analytics

COPPA and the Privatistas

The FTC has release another wave of regulations that aim to stymie online advertisers’ ability to create a compelling experience for children. By expanding COPPA (Children’s Online Privacy Protection Act) to include limitations driven by persistent cookies, mobile device identifiers, IP addresses and geolocation data, the FTC has added a thick layer of ambiguity to an already poorly defined regulatory framework.

The first problem is that the regulatory parameters they’ve chosen such as cookies, device identifiers, etc. don’t identify a person, they identify a device. You can claim the device is tied to the person; sometimes yes, but more often no. Simplest example? We have multiple iPhones, iPad, Macs and PCs at our home, and everyone uses whichever one happens to be closest. Any device identifier or cookie data is going to reflect device use across a very diverse set of demographics (me, my wife, and my son), so claiming that device specific technology will tell the network exactly what I’m up to is an inherently flawed approach.

The second problem, which the FTC and the Privatistas over at the Center for Digital Democracy still don’t seem to understand, is that Behavioral Targeting is a science done at an aggregated level. Most of this technology is not about going after a very specific person (child or otherwise), its about targeting cohorts of common interest in order to minimize spam and create a compelling experience.

The notion of greedy advertisers drooling over kids as they traverse a network is the core driving element of what the CDD harps on, and is completely off point. Aggregate data in terms of how apps are being used is critical to developing a product roadmap that maps to market requirements, and anonymized cookies are designed to do exactly that.

The third issue is defining the scope of what is gathered and what is done with it. The idea of capturing my child’s name and location does make me feel incredibly uncomfortable, but that is an extreme of what this technology can deliver, and it is up to industry groups such as the IAB to self-police to the point that this sort of thing doesn’t happen. Or they can continue to play the defensive role (which they seem to be good at) and watch the scope of their capabilities continue to shrink.

12/11/12

Permalink 03:40:26 pm, by dano Email , 590 words, 63888 views   English (US)
Categories: Analytics

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.

11/28/12

Permalink 12:48:55 pm, by dano Email , 582 words, 18492 views   English (US)
Categories: Analytics

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.

10/14/12

Permalink 08:42:22 pm, by dano Email , 647 words, 37882 views   English (US)
Categories: Analytics

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.

09/25/12

Permalink 06:54:27 pm, by dano Email , 596 words, 28023 views   English (US)
Categories: Analytics

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.

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