Category Archives: Twitter

Focus Features promotes ‘Insidious’ with an AI bot. Should this scare you?

Creator and Robot SXSW

Japanese roboticist Hiroshi Ishiguro unveils an android copy of himself at SXSW. Will marketing bots based on artificial intelligence help advertisers?

We love the idea of boosting a horror movie with an AI chatbot. Marketers looking to scale communications are watching Focus Features’ clever experiment in awe. Marketing bots are coming fast. But before we explain, let’s catch up on AI advances.

It’s been a whirlwind year so far for artificial intelligence. At SXSW Interactive in Austin this March, Japanese scientist Hiroshi Ishiguro presented an android that looked exactly like himself, capable of carrying on intelligent conversations in either English or Japanese thanks to Siri-like database matching, linguistic software and voice recognition. Days later, AlphaGo, an AI software developed by Google DeepMind, beat South Korean champion Lee Sedol at Go, a game multiple times more complicated than chess. And weeks later, Microsoft launched the silly Twitter bot Tay on the world, trying to demonstrate its AI could learn conversations by tweeting back and forth with users. Tay flamed out when online trolls taught it to say racist, violent things, forcing Microsoft to abort its Twitter experiment … but Tay did learn a human personality, albeit a mean one, quickly.

Artificial intelligence is no longer science fiction. It’s here to stay.

In marketing, AI algorithms and “bots” in recent years have earned a bad name, typically attributed to “black box” digital media buying systems that may opaquely distort ad campaigns with bad impressions, or bots that pretend to be human but are really designed to jack up clicks for inflating results. The media buyers who run today’s programmatic systems often invest a sizable portion of their time in monitoring digital ad campaigns for quality control—a war against bots, if you will. But now, some bots may be bringing benefits to marketing.

When marketing bots help promote a horror film

MIT Technology Review reports that some new mobile services such as Kik and Telegram have created “bot shops,” where AI virtual users provide everything from simple horoscopes (just fun) to helpful service and personal conversation. Focus Features used this AI-type system in Kik to promote the new film “Insidious: Chapter 3,” in a brilliant virtual conversation. In the movie, a girl named Quinn is stuck in bed, and needs to converse with the outside world. The Kik bot allowed you to do just that … with your personal conversation growing more and more intense.

Kik Insidious


Yikes. And well done. Just scanning those messages makes us feel like we’re inside the actual movie.

Marketers are watching this because one-on-one conversation agents could unlock value, in everything from explaining products to stamping out customer complaints. In call centers, human labor accounts for up to 85% of costs, while customers grow irate if hold times exceed a few minutes. Deploying bots in customer service could save companies millions while helping customers gain faster answers, in turn reducing customer churn.

When AI systems work well, they not only duplicate human intelligent conversation but do so at scale. Imagine a world where there was no more “on hold” time when you call a call center, and a friendly, Siri-type intelligence immediately took your complaint or order.

But can AI manage the real complexities of life?

But as the Tay debacle showed, AIs are still rough simulacrums at best, and prone to error, or worse, offense. The reason it took 20 years between IBM’s supercomputer Deep Blue beating Garry Kasparov in chess and AlphaGo whipping Sedol at Go this spring is chess, on average, has only 35 possible legal options per player move, while Go is far more complex, with approximately 250 game options to consider per player turn. AI can finally keep up with just 250 scenarios on a simple board. Real life has millions of possible turns in every human move. While marketers may rush soon to deploy AI bots to try to influence or serve customers more easily, they’ll need to tread carefully.

Microsoft CEO Satya Nadella told a conference of developers this spring, “We want to take the power of human language and apply it more pervasively to all of the computing interface and the computing interactions.” But to paraphrase Microsoft’s twitter AI-bot Tay, as she went off the deep end about Hillary Clinton, beware of “a lizard person hell-bent on destroying America.”

Twitter is so hungry for new users, it’s rebooting

twitter bad ads


Twitter is about to launch a major redesign of its central flowing cascade of tweets, code-named Project Lightning. Whether you call this an attempt to make live events an add-on (Mashable) or an attempt to kill Twitter to save Twitter (Wired), it is a huge shift. Soon, instead of relying just on friends to tell you what they are thinking or linking to, you can click a central button on Twitter to see what a bunch of … editors think you should see. Videos. Photos. News. All curated by experts and algorithms to help you easily learn about current events.

Twitter is about to become the Walter Cronkite of social media, an AP Wire feed of the world’s top topical buzz.

Why would Twitter make such a change? Basically, Twitter is in deep doodoo with Wall Street, despite growth that would delight most businesses. Quarterly revenue has soared from $18 million in Q1 2011 to $436 million just four years later … but investors are worried about user growth, not just dollars. In a shrinking social media bubble where startup valuation is still tied to (supposed) network effects of interconnected eyeballs, Twitter’s annual user growth has declined from 50% in 2012 to 18% in 2015 and is projected to dip below 10% soon. Twitter’s stock is down 50% from its high two years ago; investors punched it down 20% alone in April when missed earnings were announced; CEO Dick Costolo is so fed up with the pressure, he’s quit.

While Twitter now has 302 million monthly active users, that number is way below former company forecasts, which back in 2009 estimated Twitter would top 1 billion users by late 2013. Twitter missed that mark; Facebook cleared it with 1.4 billion users; and today, Twitter seems hopelessly lost behind its older brother Zuckerberg. 

7 ways Twitter can survive

Investors are worried because Twitter, in their view, must generate money; money is tied to eyeballs; and those eyeballs are growing sleepy. What can Twitter do to grow?

1. Get users. The first way is to grow its user base by making it easier for people to sign up, and stick, with Twitter.

2. Boost ad rates. The second way to boost revenue is to increase ad rates, difficult given the competition from Facebook’s CPC model and Google search, both of which perform extremely well in driving low cost response.

3. Increase ad volume. The third way is to increase the number of ad units within the stream — also problematic, because higher noise levels in social networks spur customer churn.

4. Sell media space outside Twitter. The fourth way would be to increase, somehow, the number of ads sold outside the Twitter ecosystem — quite possible, given Twitter’s April 2015 acquisition of TellApart, a tech firm specializing in cross-device and cross-platform ad retargeting. Of course, this would have to be supported by good data on Twitter users, and unfortunately Twitter has little of that. Facebook was smart enough to ask you about all your interests and get you to Like hundreds of brands … but Twitter, shy in the corner, forgot to ask.

5. Sell something else. The fifth path would be to expand to ancillary revenue streams, such as selling data on users (which Twitter has little of), taking a cut of e-commerce or Twitter-inspired offline transactions, or getting into actual mobile payments.

6. Paywall. The six option would be to charge a subscription for usage by consumers. Ha. In a world where information wants to be free, a Twitter consumer paywall would be death.

7. Charge media partners fees for access. The seventh would be charging other media platforms a fee for integration with Twitter promotions, sort of an extension of its current ad model.

Hm. That seems to be about it.

Twitter can only squeeze you so hard. So it wants more yous.

Most of our proposed solutions above — Nos. 2 through 7 — require Twitter to clearly push more ads or extract more data or money from current users. That’s a dangerous game, because boosting clutter in any network can kill it. (See: email marketing and telesales.) We are already seeing signs Twitter is at capacity in pushing ads on users. When the network missed revenue targets in April, analysts said that ad revenue per user was actually up (meaning each of you had more ads in your stream) while ad performance was down (new direct-response ad units didn’t perform to standards, meaning fewer of you clicked on Twitter ads). Too many ads with too little response suggests … Twitter is wringing its current users dry with too much marketing.

So solution No. 1 above — finding new users — is the best path forward. Here, Twitter’s challenge is it has become very difficult to use as more and more features are added.

Enter Project Lightning, with an easier learning curve

The service has always been complex with a steep learning curve. The Twitter UX now requires following people, hoping they follow you back, contributing to a tweet stream, a personal profile page parsed into tweets, tweets and replies, or photos and videos, lists, favorites, direct messages, and a core communication complexity involving short text but potential photo, video, URL links, @’s, notifications, and hashtag additions, all within a character limit. Someone new to the Twitter party may now find the once-simple text service so complex they think, WTF?

But Twitter’s Project Lightning circumvents all that learning. Soon, if you join Twitter, you won’t even need to connect with anyone to dive in. You can just click a big button in the center of your screen or mobile device and get a curated list of cool news/videos/images tied to the major hot topics of the day. Obama sings at a service. A sports team wins a championship. A hot movie star exposed on the red carpet. You’ll immediately get the warm buzz of real humans sharing these events, a verisimilitude of personal connection, all concocted by the algorithmic robots at Twitter’s HQ.

It’s a smart idea, making it easier for new users to join. But this experience will be a very different Twitter — sort of a virtual curated network of no friends inside a virtual network of somewhat fake friends. A news circle in a friend circle, growing the circle of users to attract the bigger circle of marketers.

Good luck with the new button, Twitter. We know your investors are counting on it. We just hope your constant expansion into the ancillary media world to attract less-sophisticated users doesn’t kill the fun we had trying to make 140 simple characters work.


5 things you can learn from Twitter’s new analytics

twitter dash 4

Whether you’re a brand curious about how your tweets are faring or an individual longing to gaze into your social-media navel, Twitter’s opening of its analytics dashboard this week is sure to excite you. Twitter first launched Google-esque analytics to its advertisers and verified users back in July, but now, anyone can go to the dashboard site to see how far and wide your tweets go.

For users new to Twitter analytics, the results are surprising:

  • First, a negative: Fewer people are exposed to your tweets than you might think. A Twitter account with about 10,000 followers will, on average, have each tweet seen by only 300 to 500 people. This intuitively makes sense, since obviously not everyone is using Twitter at all hours of the day, and your 9 a.m. missive about coffee is buried in the stream by the time another user logs in an hour later. Still, Twitter reach is less than expected.
  • Second, a positive: “Engagement rates” are through the roof. Twitter defines “engagement” as a user taking any action on your tweet, such as clicking on a link, favoriting it, retweeting it, or replying. While the standard interaction rate on normal banner ads is about 0.07%, Twitter interaction rates hover around 2.5%. This appears to be a decline from a few years ago (in 2011, Twitter boasted on its blog that brand interaction rates were 3-5%), but 2.5% is still amazing — users exposed to your tweet are basically 36 times more likely to take an action than if they saw a digital banner ad.
  • Third, click-through rates are also very high. Using my personal account @benkunz as an example, from Aug. 1-28 I sent out 109 tweets, of which 34 had links. The tweets with links generated a 1.70% click-through rate based on 330 clicks against 19,380 actual impressions. More than two-thirds of my Twitter interactions were clicks, and that CTR is 24 times higher than the average banner ad response rate. The caveat here, of course, is I’m not a brand trying to sell you anything, just a human trying to be interesting, but that response rate bodes well for any brand with an authentic, meaningful content stream on Twitter.
  • Fourth, if you want your tweets shared: News tied to the zeitgeist of Twitter chatter works. In our non-scientific-but-super-insightful study of what tweets have the highest interaction rates, missives related to current events usually have 2x or 3x the average Twitter interaction rate. In the past few weeks, our tweets about Matt Damon doing something innovative with the ice bucket challenge, Starbucks redesigning its stores, or Robin Williams being photographed as a mime in Central Park in 1975 had much higher interaction rates that our supposedly smart asides on life, liberty and the pursuit of advertising. News, on Twitter, sells.
  • Fifth, timing matters. We found the highest interaction rates on Wednesdays, Fridays and Saturdays, and the lowest on Sundays and Mondays. Apparently Twitter users back away from social media on Sundays and are too buried in work on Mondays to spend much time interacting.

Wisely, or perhaps sadly, Twitter only allows you to see analytics on your own  tweets. As of now, you can’t view the Twitter activity of Pepsi or Robert Scoble to parse how big brands or social-media celebrities are faring with their own Twitter messages. But if you want to examine your own tweets to see what makes people respond, the dashboard is an excellent resource.

Of course, the other option is you could continue to just be yourself online and share real, authentic insights and news not worrying about what other people think. Brands and humans, we’ll leave that up to you.

Why can’t advertisers get personalization right?

girl heart eye


Deep near the bottom of today’s NYTimes story on Facebook — “How Facebook Sold You Krill Oil” — a marketing manager for Reckitt Benckiser, a company that sells fish-oil pills, says “Facebook is a fantastic tool for doing personalized marketing at scale.” The NYT case study goes on to explain how the advertiser in question is able to pinpoint-target different female demographics, users of other fish-oil products, and even “lookalike” profiles of individuals with similar interests. The Facebook campaign worked and fish-oil sales went up.

Yet all this is outbound targeting from the marketer’s perspective, similar to really good archers being able to fire arrows into the chests of different consumers with different needs in a crowd. It’s not personalization.

True one-to-one personalization was described by Don Peppers in the 1990s as an iterative process in which consumers are identified, differentiated on both their financial value to a company and need from a company, interacted with, and then given customized services or communications. The most crucial step was to make all of this a feedback loop, a “learning relationship” in which a marketer grows ever more sophisticated about anticipating individual needs over time.

It is this anticipation of needs that creates loyalty, Peppers said, because once a consumer has trained one organization to anticipate her wants, she would face switching costs going somewhere else. The classic example of marketers who actually pull this off are local coffee shops who see you walk in and immediately warm your specific version of coffee and breakfast muffin without you asking. You skip the line, you save 3 minutes, you’re individually recognized, and so you don’t want to go anywhere else. One-to-one personalization, evolved as a learning relationship, becomes almost like a strong marriage — where all the prior history of learning enhances the bond between consumer and brand.

Now, let’s revisit advertising.

Today, I checked my ads for Facebook, and saw this: retargeting ads from a hotel chain I searched two weeks ago; a technology course apparently inspired by an online friend who works in Web services; a tiny chip-wafer thing I can attached to objects around the house to “never lose them again”; and an ad for a local plumber. Personalization score, from 1 to 100: About a 5. At one point, I was interested in the hotel.

Twitter was even worse. I got two ads for a Red Bull music festival; a local Applebee’s ad for a frozen fruit drink; and a promotion for small-business insurance. Personalization score: 3. Maybe, just maybe, we’ll go out tonight to a restaurant, but I don’t like frozen fruit drinks.

All of these ads express a knowledge of my personal needs about as sophisticated as the  direct mail lists that trigger our Pottery Barn catalogs. I am a target with some general color descriptors: So, go ahead, fire your arrow into me.

Product-focus creates a personalization failure

The problem that personalization faces is most marketing engines base it from the marketer’s point of view (since the guy with the ad budget is calling the shots). This creates a data collection model centered on a product, which inherently has vast gaps. A home services company will build a CRM system to include lots of information about an individual prospect’s interactions and a customer’s service records, but that data is only germane to the product. Jane Smith may have a forecast lifetime value of $20,000 in utility bills over 10 years … but the data systems don’t recognize that she is a mother with three daughters interested in mountain climbing and kayaking. The utility frankly doesn’t care.

All of this is driven by economics: individual companies want to store and analyze only data related to their product sales; social networks want to release only the data needed to target a product to a customer; and a truly customer-centric personalization campaign would require coordinating millions of potential product offers, likely from competing brands who have little incentive to sell services outside their own scope.

In simple terms, because the varied needs of a customer would require unified data and services that cut across brands, until an ecosystem of brands has an incentive to share data and revenue, personalization will not happen. What this gives us is ads on Facebook for fish-oil pills based on a rudimentary understanding that you are friends with someone who takes vitamins, but no anticipatory personalization that informs you of where to take your wife to dinner on the anniversary of your first college date.

The customer loses out

What would it take for true personalization to arrive? A few companies may come close. Amazon and Walmart, which house millions of product SKUs, have incentive to use data to anticipate your needs and the service offerings to potentially benefit from a vast range in sales. Twitter, which conceivably could parse your real conversations to build better profiles of your mind than simple Facebook stated interests, could truly personalize its #discover newsfeed to create content germane to your interests.

Facebook might do true personalization, if it could somehow depress ad offers that had nothing to do with your interests. Unfortunately, Facebook wants the billions from the unexpected plumber ad or computer technology course offering that sprays you despite your disinterest. Most Facebook ads are priced on a cost per click, rewarding Facebook if a user clicks on the ad, but not worrying about the 99.97% of users exposed who don’t want to respond. The economic model focuses on what advertisers catch, and ignores the adverse impact of what they spill.

Television is in the same boat. The typical U.S. consumer receives 6,600 spots per month, based on 4 hours and 34 minutes of TV viewing per day. You may want 3 of those products, putting your “response rate” in the 0.05% range, about the same as responses to online banner ads. If TV truly personalized ads, it might have to forego 99.95% of its advertising inventory — and lose billions of dollars a year by removing all that unwanted communications bloat.

Truth is, personalization will never arrive until a giant platform is able to match transparent data on individual needs with a vast consortium of products and services that can be personalized in promotions without friction from marketers demanding that their product appear next in line. Marketing would have to become a true marketplace, and product-makers would have to cede their product focus to a willingness to give consumers what they really want — even if that means the brand down the street wins instead of them.

Which is why, tomorrow, you’ll see more unpersonalized ads on Facebook for fish oil. Today’s economic incentives decree that unpersonalized ads are the way to go.

Photo credit: Ruurmo

The sad case of Twitter’s missing personalization

Way back in the 1990s I had the fortune to work with Don Peppers, who created the concept of “1to1 marketing” strategy. Don wrote a series of best-selling books describing a future world where information would allow nearly every service to be personalized — the idea being such customization, newly empowered by cheap databases, would build unbreakable switching costs. LIke a marriage where your spouse remembers you like chocolate in your coffee on Sunday mornings, you’d never leave a business that remembered so much for you that it became inconvenient to go somewhere else. Don’s 1to1 idea was adopted by software companies, renamed CRM for “customer relationship management,” and ended up a sales bullet point for The rise of social networks in which consumers talk to each other also took some steam out of the idea, since the business-to-customer 1to1 dynamic became less the focus of marketing strategy when CMOs were scared that the surrounding customer networks had grown out of control.

Which is sad. Here’s a case in point: Twitter. Go to and click on the “# Discover” link at top and you’ll get hit with stories that are popular right now among everybody — but not tailored to you. Imagine all the data Twitter has on us: Our tweets express all our interests, politics, purchases, hope and fears, and our links match up to homophily friendships that could further define exactly what we want. Twitter could build an unmatched predictive newsfeed for every individual, blowing The New York Times or Fox News out of the water with tales customized to our whims.

Except Twitter doesn’t. One top “Stories” recommendation for me today is a link to a Kentucky-Baylor basketball game. I don’t watch basketball. I have never mentioned basketball in any of my 24,410 tweets. Twitter, the game bores me to death, and a cursory analysis of my stream should show I’d much prefer a link to artificial intelligence advances, science fiction films, or chocolate recipes. (Don’t judge. That’s me.)

Perhaps for Twitter the cost of mining tweet data is too high (really?), or the small Twitter team is still underwater keeping the servers running (probable), or Twitter would rather go after very large advertisers such as Pepsi and AmEx who can spray everyone with the same message (ding! we have a winner!). So your news feed inside Twitter is completely off base.

Maybe Twitter just can’t see beyond the initial investment. Treating everyone the same is the easiest form of marketing, and 1to1 requires an upfront investment hurdle far before services reap the rewards of loyalty or increased use. The only companies really doing personalization are and Netflix, which must offer recommendations right before each sale, and so 1to1 prediction is a competitive requirement. Twitter is still just learning to accept ad payments from mass marketers, so the personalization concept may be too sophisticated at its early monetization stage.

Sad. Because, Twitter, this unpersonalized “# Discover” news stream stinks, and makes me spend yet more time on Google+. Which is exactly the point: If you treat everyone the same, customers don’t feel bad about leaving you.

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

Originally posted on Google+.

Why Google+ should carry advertising

It’s curious how gun-shy digital networks are about running advertising. Facebook famously held off on traditional banners, inventing its own not-so-intrusive tiny promos at the side, and now Google+ insists it will skip ads. The most amazing restraint I see is the Facebook mobile app — imagine, with 350 million active FB users staring into smartphones, and Facebook holds off on monetizing that audience.

All of this indicates that consumers now hate advertising — why skip ads unless you’re worried it will degrade your networked product? Advertising works, of course (we plan it for clients), and traditional television media that carries advertising still remains king, with consumers watching 4 hours and 44 minutes of television a day on average in the U.S. But “watching” is an overstatement; studies by Nielsen and Pew show consumers actually do two or three things at once with TV or radio on in the background. In-home observations show that when TV spots appear, consumers pick up laptops, handsets or magazines, and attentiveness slides. Put another way, the typical consumer is exposed to about 160 30-second TV spots a day, and of course none of us really “see” or recall most of them. The radio industry has the same problem; data from new Portable People Meters, which replace the old diary journals to tabulate radio ratings by picking up signals embedded in broadcasts, show people tend to switch the radio dial as soon as radio spots intrude.

So new communication networks, trying to gain mindshare in this cluttered space of media options, are very careful not to diminish UX with advertising — almost comically so. Twitter could easily push ads into its stream (and is just starting to roll this out), but has been scared to death that degrading the Twitterer experience might chase users out of its network. G+ could easily provide personalized sponsored links at the right of its pages, but for now, says it will hold off.

Why the fear?
Advertising works; it educates consumers and drives billions into the economy. But at heart, consumers find it a pain in the ass and are migrating to new channels that avoid it. The danger I see is if marketers cannot influence you by clearly putting their messages in an ad box, they will try more nefarious routes of embedding the message into other content — sponsored tweets, paid posts, advertorial — that degrades the actual content we hunger for itself. You’re starting to see this with top bloggers bragging about Kmart shopping experiences or GPS gadgets that actually pay them for mention, and the result is confusion. Is the message true? Doe someone I respect like that product? Or is someone just putting their self interest ahead of mine, giving me a message that may not have meaning? The value of advertising is it clarifies the source of the message, allowing consumers to clearly judge the content by knowing it is meant to influence. A catalog is selling you; you know that; so you look and judge the material by its source merits. Alas, if marketers and people can no longer be clear about their intent to influence you, they may resort to trickery, subterfuge, embedded lies, and that form of pollution may degrade our experience far more than little ad boxes at the side of the stream.

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

Originally posted on G+. Image by Jesse S.

The moral ethos, tantric lust and fakery of Instagram

The Ten Commandments are imperatives followed by the Jews, Catholics and Protestants for happy life — don’t steal, cheat, lie — set down by Moses and immortalized by the tanned Charlton Heston in the 1956 film by Cecil B. DeMille. The idea, of course, is that delayed consequences typically outweigh the pleasure of the moment; sleep with the hottie next door and oh, what a tantric afternoon, but when you lose your beloved later to a lawsuit you won’t feel so fine. Of these laws, the most interesting perhaps is “You shall not make for yourself an idol, whether in the form of anything that is in heaven above, or that is on the earth beneath, or that is in the water under the earth.”

Idols. We wonder, of course, at the meaning — no, this isn’t a blog about God, but instead of communications value. So why would a multi-thousand-yearlong meme about spirituality suggest that “idols” are as bad for your soul as sleeping with your neighbor’s spouse?

We live in an age of idolatry, from the obvious American Idol and reality-TV-flavored New Jersey housewives with plumed bosoms to iPods that cocoon us in artificial music and laptops that pull us to friends we have never met. Instagram is the latest invention, a seven-month-old social network of photo sharing that allows a snapshot of lackluster scenery to become a minor work of art.

Boring reality

For example, see the photo above. This was shot quickly on a cycling trip as the image of a tree before a cloudy sky caught our eye. The actual image wasn’t impressive, but we thought, “we can do something with that.” A crop, alignment, filter, and viola — colors bloom.

The expression of the human soul and hunger for connections have been with us for generations, but we are evolving to the point where technology can distort the world around us. Instagram is the latest addiction; a wonderfully clever service that ties quickly into Twitter to give your existing social graph a new overlay, photos you can edit into almost any creation. So we feel more connection to the world around us that doesn’t quite exist. The pastels of unreality are wonderful. What happens when technology embeds filters in our contacts, and artificial overlays make any image appear more what we want than what it is? Eventually, we can turn anything into a beautiful construct. Are fake idols good for us, too?

Until we figure it out, we’ll play on Instagram. It has more than 2 million users already, and is attracting 130,000 idolaters each week.

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

Twitter finally unveils ads in the stream

It’s a sign of the times how cautious Twitter has been about pushing ads into its network, but starting sometime this March, the ads will come. “Promoted tweets” will begin flowing into the stream of comments from each person you follow, putting ad messages front, center, and in a place you can’t ignore — sort of like most traditional advertising.

Twitter says the promoted tweets will provide four types of customer response, which it calls “engagement”: clicks on a link, similar to web banner ads; retweets, in which other users mention the tweet; “@” replies, in which a user replies to the company tweeting; and “favorites,” where a user bookmarks the tweet for later reference. Twitter also provides an analytics dashboard to help marketers optimize how their messages are promoted inside Twitter. The service has been tested since Nov. 1 on the HootSuite social media dashboard, and Twitter says adverse reaction from users there was minimal.

The video above is worth a view not just to see how the advertising works, but also for Twitter’s recommendations on how to be a compelling voice inside the tweetstream. Be humanly funny. Be an active participant in the community. Let others see a real legitimate two-way presence. In other words, try not to sound like an ad.

Ben Kunz is vice president of strategic planning at Mediassociates, an advertising media planning and buying agency, and co-founder of its digital trading desk eEffective.

The math behind influence and fame

Advertising exists to seed memes, ideas that spread through society like viruses in your head on a cold winter’s day. The fundamental hope is that a product or service concept becomes so desirable that, like Razor Scooters in the late 1990s or glass-tablet gadgets today, suddenly everyone will want one and share the news with their neighbors, with no incremental marketing cost. This is why the lure of social media is powerful with its promise of free, scalable connections …

Yet a new whitepaper by Cornell and HP Labs suggests building fame in social media is tougher than you think. Daniel M. Romero and colleagues processed more than 22 million tweets from 12 days in September 2009, looked at how often people clicked on web links inside the tweets, and then compared how those people were connected — and found that pure number of followers does not equal influence. Instead, as you might guess, there are some entities in social media with many followers whom no one listens to, and conversely some with few connections who tend to have their ideas shared everywhere. It’s an important dynamic to understand if you try to spread messages in Twitter, because only 1 in 318 tweets with URLs is ever retweeted, meaning the vast majority of Twitter missives trying lure people to click links hit a brick wall.

The Cornell/HP report found four types of entities on Twitter:

1. The superbly influential — users whose posted links are likely to be passed along (“A-listers” such as @mashable, @aplusk).

2. The passive — users who follow many, but rarely share things (“lurkers” or “automatons” such as @redscarebot).

3. Those not influential with many followers (“all show, no go,” alas, like @newsweek).

4. The highly influential with comparatively few followers (“beacons” such as @twitdraw).

The Economist suggests new analytics services such as Gnip will profit from helping marketers identify the human nodes inside social media that fall in buckets 1 or 4 above. Not everyone is an influencer; in new media, the money may flow to those who act most like old broadcasters.

Predicting where the social dice will fall

When digital shop Organic hired economist Jason Harper to evaluate ad campaigns, Harper had a brilliant idea — rather than use social media to push sales messages or monitor fuzzy “sentiment,” how about crunching social media mentions to predict whether current ad campaigns will hit their lead targets? In other words, every campaign that penetrates consumer word of mouth tools such as Twitter’s tweets or Facebook’s Likes has an ebb and flow — a baseline of buzz before the campaign, a spike in mentions as the campaign message scales, and then a radical falloff of chatter. By measuring the velocity and acceleration of the curves, Harper came up with predictive models showing whether in-market advertising was on track.

MIT’s Technology Review reports Harper has successfully done this for auto companies and brands such as Kotex. The article mentions only a few case studies, and one client, Chrysler, eventually moved on to another agency, but we love the concept. Social media may not be about pushing sales or pulling mentions; instead, it could be about monitoring what consumers are saying about your ads in traditional channels, and then adjusting your course accordingly.

Image: Rob W.