5 Things You Should Say ‘Yes’ To

Having just come back from a trade show, I am still reeling from all of the positive vibes, great contacts and inspiration that I experienced. It got me thinking about why people choose not to go to shows. I know that time and money are usually the two biggest considerations when it comes to going to something like this. And I really want you to look at this a different way.

Having just come back from three glorious days in Chicago at PRINT 17 I am still reeling from all of the positive vibes, great contacts and inspiration that I experienced. It got me thinking about why people choose not to go to shows. I know that time and money are usually the two biggest considerations when it comes to going to something like this. So, I wondered what other things people are probably regularly missing out on just because they don’t give themselves the opportunity to do something that would be considered outside the norm (for them).

And I really want you to look at this a different way. I want you to consider yourself personally and your company as something to invest in in ways other than equipment and software. These might seem like luxuries, but I want to argue that they are NOT luxuries. These kinds of things are vital to your development as a member and maybe even a leader – or THE leader — of your team. Saying a loud and enthusiastic YES to these things will make you better, stronger, happier and could just take you places you never thought you would go (literally and figuratively).

Here are 5 things you should say “yes” to, if you get the opportunity:

  1. Going somewhere new — Maybe a vendor has asked you to come and tour their facility. Maybe a networking group you belong to is having a happy hour next Friday at a new place across town. DO IT! Extending yourself out of your comfort zone is good for you. You will meet new people, find out about a service or product you didn’t know was available to you, or try some new kind of food you never knew you’d been missing all your life. New = adventure. New = Good.
  2. Going to a trade show — You might have to fly. You might have to pop for a hotel room. You will have to be off the shop floor for a day or two. You will never see that money or time again. Do it anyway. Being gone is a great time to see what your team is made of in your absence. Being around your peers, competitors, vendors and industry heroes is good for you. I shouldn’t have to explain why. This is your industry and you should be an active participant in it.
  3. Learning something — Whether it is how to start a Facebook page for your company or being able to explain the fundamentals behind direct marketing, if there is something going on in your business that you are not comfortable with, now is the time to raise your hand, say you need help and get to learning. There is no downside here. The more you know is not just an NBC jingle from the 90’s. It is the truth.
  4. A project that makes you A LITTLE uncomfortable — Let’s say a client asks you to do something you’ve never done before. It will not require you to go out and buy new equipment or software, but you have not tried something like it before. Before you say no, think about the implications of being able to add that aspect of your offering to all of your clients. Hearing the dollar signs ringing in yet? It is also empowering to your team to give them some latitude to figure out the best way to do it. Yes. You might lose money on the first one. But on each subsequent project you will get better and better and more profitable. Just say yes.
  5. A new hire that “gets it” — There are at least two whole generations of people who have grown up as technology natives, but who may not look and sound like the kinds of people you typically hire. Talk to them anyway. It is time to start reverse engineering some of the roles within your team. You can look at the person, decide that you admire their energy, vision and manner, and decide that you will find a place for him/her on your team. It may be a title you’ve never had before. You might INVENT a title for him/her. But new blood Is what our industry needs. It’s what YOU need. Fresh perspectives. People who are not afraid to challenge and even defy what you think you know. This industry needs more shaking up. Shake.

I hope I have convinced you that discomfort is the new growth. Change is the new steady. Give it a go.

Say yes.

Why ‘Adjacent Possibilities’ Are More Profitable Than Bright Shiny Objects

Identifying “adjacent possibilities” in your organization’s products and services has the potential to create something new, without the risk of chasing far-flung shiny object ideas with questionable ROI. I was recently introduced to the power of adjacent possibilities by …

Illustration of cloud network with multiple nodes and connectionsIdentifying “adjacent possibilities” in your organization’s products and services has the potential to create something new, without the risk of chasing far-flung shiny object ideas with questionable ROI. I was recently introduced to the power of adjacent possibilities by long-time friend and colleague, Nick Usborne, at an American Writers and Artists Web Intensive workshop where we were both speakers.

The premise of an adjacent possibility is that something new can be created from two existing and adjacent ideas. For example: chocolate and peanut butter. Separated for years, then combined to become a hot seller in Reece’s peanut butter cups.

Another example: laptops and smartphones. An adjacent possibility was the creation of the tablet — larger than a smartphone, but smaller than a laptop. Now tablets are everywhere.

For background about adjacent possibilities, it’s useful to quote “Finding Your Next Big (Adjacent) Idea” from the Harvard Business Review that says:

The idea of adjacent possibilities started with evolutionary biologist Stuart Kauffman, who used it to explain how such powerful biological innovations as sight and flight came into being. More recently, Steven Johnson, in “Where Good Ideas Come From,” showed that it’s also applicable to science, culture, and technology. The core of the idea: People arrive at the best new ideas when they combine prior (adjacent) ideas in new ways. Most combinations fail; a few succeed spectacularly.

Many organizations are obsessed with seeking the newest big product innovation. And that’s good. Disruptive technologies and products have power.

But a singular focus on completely new products or services, without considering adjacent possibilities of existing products, is also a risk. Why? Because a competitor may swoop in by identifying an adjacent possibility that’s been overlooked, and succeed with a new product by stealing smart.

Adjacent possibility tips that Nick suggested include:

  • Look inside your organization to see where you may have adjacent possibilities in current products where an outgrowth won’t involve a risky leap forward.
  • You don’t have to be the best at any one thing. Just be pretty good at two or three things you can combine.
  • If you don’t have two or three things to combine, connect with one or two other people (or organizations) who have adjacent skills.

In a world of adjacent possibilities, you can take the pressure off, and create big successes.

Smart Data – Not Big Data

As a concerned data professional, I am already plotting an exit strategy from this Big Data hype. Because like any bubble, it will surely burst. That inevitable doomsday could be a couple of years away, but I can feel it coming. At the risk of sounding too much like Yoda the Jedi Grand Master, all hypes lead to over-investments, all over-investments lead to disappointments, and all disappointments lead to blames. Yes, in a few years, lots of blames will go around, and lots of heads will roll.

As a concerned data professional, I am already plotting an exit strategy from this Big Data hype. Because like any bubble, it will surely burst. That inevitable doomsday could be a couple of years away, but I can feel it coming. At the risk of sounding too much like Yoda the Jedi Grand Master, all hypes lead to over-investments, all over-investments lead to disappointments, and all disappointments lead to blames. Yes, in a few years, lots of blames will go around, and lots of heads will roll.

So, why would I stay on the troubled side? Well, because, for now, this Big Data thing is creating lots of opportunities, too. I am writing this on my way back from Seoul, Korea, where I presented this Big Data idea nine times in just two short weeks, trotting from large venues to small gatherings. Just a few years back, I used to have a hard time explaining what I do for living. Now, I just have to say “Hey, I do this Big Data thing,” and the doors start to open. In my experience, this is the best “Open Sesame” moment for all data specialists. But it will last only if we play it right.

Nonetheless, I also know that I will somehow continue to make living setting data strategies, fixing bad data, designing databases and leading analytical activities, even after the hype cools down. Just with a different title, under a different banner. I’ve seen buzzwords come and go, and this data business has been carried on by the people who cut through each hype (and gargantuan amount of BS along with it) and create real revenue-generating opportunities. At the end of the day (I apologize for using this cliché), it is all about the bottom line, whether it comes from a revenue increase or cost reduction. It is never about the buzzwords that may have created the business opportunities in the first place; it has always been more about the substance that turned those opportunities into money-making machines. And substance needs no fancy title or buzzwords attached to it.

Have you heard Google or Amazon calling themselves a “Big Data” companies? They are the ones with sick amounts of data, but they also know that it is not about the sheer amount of data, but it is all about the user experience. “Wannabes” who are not able to understand the core values often hang onto buzzwords and hypes. As if Big Data, Cloud Computing or coding language du jour will come and save the day. But they are just words.

Even the name “Big Data” is all wrong, as it implies that bigger is always better. The 3 Vs of Big Data—volume, velocity and variety—are also misleading. That could be a meaningful distinction for existing data players, but for decision-makers, it gives a notion that size and speed are the ultimate quest. But for the users, small is better. They don’t have time to analyze big sets of data. They need small answers in fun size packages. Plus, why is big and fast new? Since the invention of modern computers, has there been any year when the processing speed did not get faster and storage capacity did not get bigger?

Lest we forget, it is the software industry that came up with this Big Data thing. It was created as a marketing tagline. We should have read it as, “Yes, we can now process really large amounts of data, too,” not as, “Big Data will make all your dreams come true.” If you are in the business of selling toolsets, of course, that is how you present your product. If guitar companies keep emphasizing how hard it is to be a decent guitar player, would that help their businesses? It is a lot more effective to say, “Hey, this is the same guitar that your guitar hero plays!” But you don’t become Jeff Beck just because you bought a white Fender Stratocaster with a rosewood neck. The real hard work begins “after” you purchase a decent guitar. However, this obvious connection is often lost in the data business. Toolsets never provide solutions on their own. They may make your life easier, but you’d still have to formulate the question in a logical fashion, and still have to make decisions based on provided data. And harnessing meanings out of mounds of data requires training of your mind, much like the way musicians practice incessantly.

So, before business people even consider venturing into this Big Data hype, they should ask themselves “Why data?” What are burning questions that you are trying to solve with the data? If you can’t answer this simple question, then don’t jump into it. Forget about it. Don’t get into it just because everyone else seems to be getting into it. Yeah, it’s a big party, but why are you going there? Besides, if you formulate the question properly, often you will find that you don’t need Big Data all the time. If fact, Big Data can be a terrible detour if your question can be answered by “small” data. But that happens all the time, because people approach their business questions through the processes set by the toolsets. Big Data should be about the business, not about the IT or data.

Smart Data, Not Big Data
So, how do we get over this hype? All too often, perception rules, and a replacement word becomes necessary to summarize the essence of the concept for the general public. In my opinion, “Big Data” should have been “Smart Data.” Piles of unorganized dumb data aren’t worth a damn thing. Imagine a warehouse full of boxes with no labels, collecting dust since 1943. Would you be impressed with the sheer size of the warehouse? Great, the ark that Indiana Jones procured (or did he?) may be stored in there somewhere. But if no one knows where it is—or even if it can be located, if no one knows what to do with it—who cares?

Then, how do data get smarter? Smart data are bite-sized answers to questions. A thousand variables could have been considered to provide the weather forecast that calls for a “70 percent chance of scattered showers in the afternoon,” but that one line that we hear is the smart piece of data. Not the list of all the variables that went into the formula that created that answer. Emphasizing the raw data would be like giving paints and brushes to a person who wants a picture on the wall. As in, “Hey, here are all the ingredients, so why don’t you paint the picture and hang it on the wall?” Unfortunately, that is how the Big Data movement looks now. And too often, even the ingredients aren’t all that great.

I visit many companies only to find that the databases in question are just messy piles of unorganized and unstructured data. And please do not assume that such disarrays are good for my business. I’d rather spend my time harnessing meanings out of data and creating values, not taking care of someone else’s mess all the time. Really smart data are small, concise, clean and organized. Big Data should only be seen in “Behind the Scenes” types of documentaries for manias, not for everyday decision-makers.

I have been already saying that Big Data must get smaller for some time (refer to “Big Data Must Get Smaller“) and I would repeat it until it becomes a movement on its own. The Big Data movement must be about:

  1. Cutting down the noise
  2. Providing the answers

There is too much noise in the data, and cutting it out is the first step toward making the data smaller and smarter. The trouble is that the definition of “noise” is not static. Rock music that I grew up with was certainly a noise to my parents’ generation. In turn, some music that my kids listen to is pure noise to me. Likewise, “product color,” which is essential for a database designed for an inventory management system, may or may not be noise if the goal is to sell more apparel items. In such cases, more important variables could be style, brand, price range, target gender, etc., but color could be just peripheral information at best, or even noise (as in, “Uh, she isn’t going to buy just red shoes all the time?”). How do we then determine the differences? First, set the clear goals (as in, “Why are we playing with the data to begin with?”), define the goals using logical expressions, and let mathematics take care of it. Now you can drop the noise with conviction (even if it may look important to human minds).

If we continue with that mathematical path, we would reach the second part, which is “providing answers to the question.” And the smart answers are in the forms of yes/no, probability figures or some type of scores. Like in the weather forecast example, the question would be “chance of rain on a certain day” and the answer would be “70 percent.” Statistical modeling is not easy or simple, but it is the essential part of making the data smarter, as models are the most effective way to summarize complex and abundant data into compact forms (refer to “Why Model?”).

Most people do not have degrees in mathematics or statistics, but they all know what to do with a piece of information such as “70 percent chance of rain” on the day of a company outing. Some may complain that it is not a definite yes/no answer, but all would agree that providing information in this form is more humane than dumping all the raw data onto users. Sales folks are not necessarily mathematicians, but they would certainly appreciate scores attached to each lead, as in “more or less likely to close.” No, that is not a definite answer, but now sales people can start calling the leads in the order of relative importance to them.

So, all the Big Data players and data scientists must try to “humanize” the data, instead of bragging about the size of the data, making things more complex, and providing irrelevant pieces of raw data to users. Make things simpler, not more complex. Some may think that complexity is their job security, but I strongly disagree. That is a sure way to bring down this Big Data movement to the ground. We are already living in a complex world, and we certainly do not need more complications around us (more on “How to be a good data scientist” in a future article).

It’s About the Users, Too
On the flip side, the decision-makers must change their attitude about the data, as well.

1. Define the goals first: The main theme of this series has been that the Big Data movement is about the business, not IT or data. But I’ve seen too many business folks who would so willingly take a hands-off approach to data. They just fund the database; do not define clear business goals to developers; and hope to God that someday, somehow, some genius will show up and clear up the mess for them. Guess what? That cavalry is never coming if you are not even praying properly. If you do not know what problems you want to solve with data, don’t even get started; you will get to nowhere really slowly, bleeding lots of money and time along the way.

2. Take the data seriously: You don’t have to be a scientist to have a scientific mind. It is not ideal if someone blindly subscribes anything computers spew out (there are lots of inaccurate information in databases; refer to “Not All Databases Are Created Equal.”). But too many people do not take data seriously and continue to follow their gut feelings. Even if your customer profile coming out of a serious analysis does not match with your preconceived notions, do not blindly reject it; instead, treat it as a newly found gold mine. Gut feelings are even more overrated than Big Data.

3. Be logical: Illogical questions do not lead anywhere. There is no toolset that reads minds—at least not yet. Even if we get to have such amazing computers—as seen on “Star Trek” or in other science fiction movies—you would still have to ask questions in a logical fashion for them to be effective. I am not asking decision-makers to learn how to code (or be like Mr. Spock or his loyal follower, Dr. Sheldon Cooper), but to have some basic understanding of logical expressions and try to learn how analysts communicate with computers. This is not data geek vs. non-geek world anymore; we all have to be a little geekier. Knowing Boolean expressions may not be as cool as being able to throw a curve ball, but it is necessary to survive in the age of information overload.

4. Shoot for small successes: Start with a small proof of concept before fully investing in large data initiatives. Even with a small project, one gets to touch all necessary steps to finish the job. Understanding the flow of information is as important as each specific step, as most breakdowns occur in between steps, due to lack of proper connections. There was Gemini program before Apollo missions. Learn how to dock spaceships in space before plotting the chart to the moon. Often, over-investments are committed when the discussion is led by IT. Outsource even major components in the beginning, as the initial goal should be mastering the flow of things.

5. Be buyer-centric: No customer is bound by the channel of the marketer’s choice, and yet, may businesses act exactly that way. No one is an online person just because she did not refuse your email promotions yet (refer to “The Future of Online is Offline“). No buyer is just one dimensional. So get out of brand-, division-, product- or channel-centric mindsets. Even well-designed, buyer-centric marketing databases become ineffective if users are trapped in their channel- or division-centric attitudes, as in “These email promotions must flow!” or “I own this product line!” The more data we collect, the more chances marketers will gain to impress their customers and prospects. Do not waste those opportunities by imposing your own myopic views on them. Big Data movement is not there to fortify marketers’ bad habits. Thanks to the size of the data and speed of machines, we are now capable of disappointing a lot of people really fast.

What Did This Hype Change?
So, what did this Big Data hype change? First off, it changed people’s attitudes about the data. Some are no longer afraid of large amounts of information being thrown at them, and some actually started using them in their decision-making processes. Many realized that we are surrounded by numbers everywhere, not just in marketing, but also in politics, media, national security, health care and the criminal justice system.

Conversely, some people became more afraid—often with good reasons. But even more often, people react based on pure fear that their personal information is being actively exploited without their consent. While data geeks are rejoicing in the age of open source and cloud computing, many more are looking at this hype with deep suspicions, and they boldly reject storing any personal data in those obscure “clouds.” There are some people who don’t even sign up for EZ Pass and voluntarily stay on the long lane to pay tolls in the old, but untraceable way.

Nevertheless, not all is lost in this hype. The data got really big, and types of data that were previously unavailable, such as mobile and social data, became available to many marketers. Focus groups are now the size of Twitter followers of the company or a subject matter. The collection rate of POS (point of service) data has been increasingly steady, and some data players became virtuosi in using such fresh and abundant data to impress their customers (though some crossed that “creepy” line inadvertently). Different types of data are being used together now, and such merging activities will compound the predictive power even further. Analysts are dealing with less missing data, though no dataset would ever be totally complete. Developers in open source environments are now able to move really fast with new toolsets that would just run on any device. Simply, things that our forefathers of direct marketing used to take six months to complete can be done in few hours, and in the near future, maybe within a few seconds.

And that may be a good thing and a bad thing. If we do this right, without creating too many angry consumers and without burning holes in our budgets, we are currently in a position to achieve great many things in terms of predicting the future and making everyone’s lives a little more convenient. If we screw it up badly, we will end up creating lots of angry customers by abusing sensitive data and, at the same time, wasting a whole lot of investors’ money. Then this Big Data thing will go down in history as a great money-eating hype.

We should never do things just because we can; data is a powerful tool that can hurt real people. Do not even get into it if you don’t have a clear goal in terms of what to do with the data; it is not some piece of furniture that you buy just because your neighbor bought it. Living with data is a lifestyle change, and it requires a long-term commitment; it is not some fad that you try once and give up. It is a continuous loop where people’s responses to marketer’s data-based activities create even more data to be analyzed. And that is the only way it keeps getting better.

There Is No Big Data
And all that has nothing to do with “Big.” If done right, small data can do plenty. And in fact, most companies’ transaction data for the past few years would easily fit in an iPhone. It is about what to do with the data, and that goal must be set from a business point of view. This is not just a new playground for data geeks, who may care more for new hip technologies that sound cool in their little circle.

I recently went to Brazil to speak at a data conference called QIBRAS, and I was pleasantly surprised that the main theme of it was the quality of the data, not the size of the data. Well, at least somewhere in the world, people are approaching this whole thing without the “Big” hype. And if you look around, you will not find any successful data players calling this thing “Big Data.” They just deal with small and large data as part of their businesses. There is no buzzword, fanfare or a big banner there. Because when something is just part of your everyday business, you don’t even care what you call it. You just do. And to those masters of data, there is no Big Data. If Google all of a sudden starts calling itself a Big Data company, it would be so uncool, as that word would seriously limit it. Think about that.

7 Email Marketing Mistakes Even Seasoned Marketers Make

Email marketing is so easy that it is tempting to use it as a set-and-forget marketing tool. Failure to optimize email marketing strategy and execution affects customer loyalty, sales and costs. Email provides a personal, one-to-one connection between customer and company. It’s a shame to lose opportunities to build relationships, increase revenue and reduce expenses by not committing the time and effort required to maximize email effectiveness.

Email marketing is so easy that it is tempting to use it as a set-and-forget marketing tool. After all, if the subscriber list is large enough, almost every send will generate revenue. Marketers dealing with constantly changing technology, platforms and channels have little time to commit to a channel that works with minimal effort.

Failure to optimize email marketing strategy and execution affects customer loyalty, sales and costs. Email provides a personal, one-to-one connection between customer and company. It’s a shame to lose opportunities to build relationships, increase revenue and reduce expenses by not committing the time and effort required to maximize email effectiveness.

Most of the mistakes made in email marketing have simple fixes with minimal costs. Here are seven common mistakes made by even the most experienced marketers:

1. Treating All Subscribers Alike
People choose to receive your emails for personal reasons. Some are trendsetters who want to see the latest and greatest items. Others are discount shoppers seeking the best deal. Nestled between the two are a variety of personalities looking for specific solutions to their problems. Failing to recognize the different types and create customized marketing messages for them speeds the email fatigue process and reduces sales opportunities.

2. Failing to Capitalize on Contact Opportunities
The email subscription process provides several opportunities to connect with people interested in knowing more about your business and products. Each step should be used to educate, entertain, and enlighten new subscribers. Poorly designed confirmation pages and welcome emails are lost opportunities.

3. Ignoring Deliverability Rules
The problem with this mistake is simple and obvious: Emails that don’t reach recipients won’t generate responses. Spam is a huge problem. According to a report by Symantec, 75 percent of global emails are spam (pdf). The tools designed to eliminate spam aren’t perfect. Encouraging subscribers to whitelist your emails increases deliverability but it doesn’t guarantee it. Ensuring that all emails follow deliverability rules improves chances that people will actually receive them.

4. Repeatedly Sending the Same Visual Email
Creating branded templates so that your emails are easily recognized is a good practice. Using the same one repeatedly isn’t. You have less than three seconds to capture the recipient’s attention before the delete button is pushed. People respond to visual information first. If all of your emails look alike, they trigger an “I’ve seen that already” response.

5. Presuming Recipients Recognize Icons and Know What You Want Them to Do
Icons are great visual add-ons, but they need a text call to action to encourage people to take the next step. People are trained from an early age to follow instructions. If you want them to connect with you on social platforms, visit your website, call your business, or get directions to your store, tell them. Icons without a call to action are tools for people who already know what they want. Icons with a call to action encourage people to do what you want.

6. Neglecting to Make Emails Mobile Friendly
According to a study by YesMail, over 41 percent of mobile device owners said that they have made either an online or in-store purchase as a direct result of an email promotion they viewed on their device. Are your emails easy to read on the small screen? Do all sections render properly for mobile devices? Some emails show a blank body when viewed on cell phones. Be sure to test your emails on Apple, Android and Blackberry devices to ensure recipients can read them.

7. Expecting HTML Emails to Automatically Convert to Readable Plain Text
The automated conversion tool provided by most email marketing services simply converts HTML to text. It does not make it readable. If your email is filled with links, the text version will look like a page of computer code instead of a message from a company that cares about customers and prospects. Always create HTML and text versions of every email to insure the message is appealing and readable for all recipients.

Paid Advertising Opportunities on Twitter

With 140 million registered users and 350,000 new sign-ups per day, it’s past time for marketers to think about taking advantage of the paid advertising opportunities on Twitter. Twitter will continue to monetize its site by rolling out new advertising products in the near future, and there are two opportunities that are currently live: Promoted Tweets and Promoted Trends. A third opportunity called Promoted Accounts is currently in testing for a select few advertisers.

With 140 million registered users and 350,000 new sign-ups per day, it’s past time for marketers to think about taking advantage of the paid advertising opportunities on Twitter. Twitter will continue to monetize its site by rolling out new advertising products in the near future, and there are two opportunities that are currently live: Promoted Tweets and Promoted Trends. A third opportunity called Promoted Accounts is currently in testing for a select few advertisers.

Promoted Tweets
Promoted tweets allow advertisers to bid on keywords on search results pages. The ad unit shows up at the top of the search results and looks like a regular tweet except that it’s labeled “promoted.” Similar to paid search, the advertiser pays when a searcher engages with the ad, which Twitter calls cost per engagement (CPE).

An engagement is classified as a click on a tweet, a retweet, a favorite or an @reply to the tweet. CPE is currently reasonable because of limited competition. Promoted Tweet advertisers mostly only bid on their brand terms and have little or no competition for those terms. Thus, a small budget can go a long way.

Links within Promoted Tweets can go anywhere — like to a brand’s native website, its Facebook fan page or a YouTube video. With Twitter’s new version being rolled out through September, advertisers can embed content within a Promoted Tweet. Promoted Tweet users will also have access to a dashboard that measures engagement metrics for their tweets.

Twitter users may be searching for product names to see what the Twitter universe is saying about a product they’re considering purchasing. Promoted Tweets give advertisers the ability to show up on top of the search results for their product names. Thus, a Promoted Tweet can do things like help manage a brand’s reputation, provide more information on certain products and offer coupons.

Promoted Trends
Promoted Trends allow advertisers to show up in the “trending topics” section on the right rail of Twitter. For Twitter’s redesign, the trends show above the fold. The first 10 trends are topics that are naturally trending on Twitter that day. Promoted Trends show as the 11th trending topic, and are labeled “promoted.” Promoted Trends run for a day at a fixed cost. When a user clicks on a Promoted Trend, they’re taken to the Twitter search results for that trend, where the advertiser’s Promoted Tweet ranks on top.

If you’re thinking about running a Promoted Trend, pick a topic that seems to fit with the day’s other trends. Keep in mind, the topic could have trended naturally. This makes Promoted Trends ideal for keywords around new product releases that will be generating some amount of buzz on Twitter.

Movie studios have embraced Promoted Trends for new releases. Twitter users are likely to be buzzing about topics related to a new movie release. A Promoted Trend will help create even more buzz around the movie. Promoted Trend advertisers thus garner more engagement — e.g., clicks, retweets, favorites and @replies — and followers.

Promoted Accounts
Promoted Accounts launched this week and is currently in testing. They allow advertisers to pay to be included in the “Who to Follow” feature, which is displayed on a user’s profile page. “Who to Follow” suggests accounts that users should follow based on their interests, as determined by other accounts they follow. Promoted Accounts should be a great way to gain more followers who are interested in a particular brand or service.

The Twitter phenomenon isn’t something that advertisers can ignore. All brands should be using Twitter to engage with their fans and critics naturally. And for some brands, paid opportunities like Promoted Tweets and Promoted Trends can help increase engagement, manage reputation, gain followers and sell products.