How to Be a Better Brand Spokesperson Than Mark Zuckerberg

Recently, during a live stream of Facebook’s weekly internal Q&A meeting, Mark Zuckerberg shared, “I do such a bad job at interviews.” When the CEO of a company with a market cap of over $500 billion admits that he does a poor job at press interviews, it makes you wonder: What makes for a good brand spokesperson?

Recently, during a live stream of Facebook’s weekly internal Q&A meeting, Mark Zuckerberg shared, “I do such a bad job at interviews.” When the CEO of a company with a market cap of over $500 billion admits that he does a poor job at press interviews, it makes you wonder: What makes for a good interview?

For starters, the quality of an interview should be judged from both the perspective of the interview subject (and their company) as well as the reporter (and their publication). I’ve worked with dozens of spokespeople over the years and have facilitated good, bad and, quite frankly, ugly interviews.

The likelihood of a good interview increases greatly if you identify spokespeople with certain innate qualities. Good interviews are also the result of proper preparation and training.

4 Requirements of Good Brand Spokespeople

Whether a company is big or small, there should be an arsenal of spokespeople to cover a variety of topics that will boost the corporate reputation. The best place to begin selecting your spokespeople is with these four requirements: interest, availability, knowledge, and title.

Interest

If a spokesperson isn’t interested in participating in interviews or doesn’t see the value, then guess what? They won’t be good at them. Public relations teams should educate their spokespeople on PR goals and share examples and results, especially over time, to maintain interest.

Availability

In the digital and social media age, the news cycle is rapid. If a spokesperson can’t respond within minutes or hours, they will miss out on opportunities.

Don’t assume travel means a spokesperson isn’t available. I’ve worked with colleagues who are road warriors, but make the time for interviews from airports, hotels, and cars. I’ve even done chat and email interviews with spokespeople who are in-flight.

Knowledge

An interview is an opportunity to share knowledge specific to a story topic. If a spokesperson doesn’t know what they’re talking about, they make themselves and their company look bad. A brand spokesperson should be a subject matter expert and, in partnership with the PR team, the interviewee should do additional research ahead of time.

Title

Not all brand spokespeople are created equal in the eyes of the press. Certain roles and titles garner more media interest than others. More often than not, reporters prefer the opportunity to speak to a CEO or other member of the C-suite. It’s very difficult to get a reporter excited about speaking to a sales leader.

Going From Good to Great

Now that you’ve got someone who is a willing participant and knows what they’re talking about, there are a number of other factors that will make for an engaging and valuable interview, for both the company and the publication.

Unique and Timely POV

Contrarian and provocative points of view make for more interesting stories and help reporters provide a balance of ideas. A brand interviewee should be able to speak to relevant and timely matters, and provide perspective on what’s to come.

Sincerity

A good brand spokesperson, much like a good politician, is likeable, genuine, and sincere. When Mark Zuckerberg sat down with CNN in March 2018, following Facebook’s Cambridge Analytica scandal, he was robotic and dodged a lot of the issues, as noted by the BBC.

Clarity

Interviews can last a few minutes or hours. However, that doesn’t mean a brand spokesperson should ramble. It’s important to be clear and concise. The best spokespeople repeat their key points and pause periodically to allow reporters to ask follow-up questions. Training and preparation provide an opportunity to pinpoint key messages and practice concise delivery.

Good Judgment

Even with extensive preparation and a public relations representative facilitating an interview, there’s onus on the brand spokesperson to exercise good judgment when asked a tough question, or in general. Elon Musk’s erratic behavior in interviews and with the media makes him a liability, not an asset, when it comes to interviews.

Open to Feedback

There’s always room for improvement, when it comes to interviews. Feedback, during the pre-interview prep work and post-interview, is critically important for a successful partnership between the brand spokesperson and the PR team.

Empathy

Journalism has transformed in the last two decades. Many publications have shifted to digital platforms, while numerous publications have folded. Reporter deadlines are tight and workdays are long. Spokespeople who can empathize with the position a reporter is in will be better interview subjects.

To help my spokespeople understand the reporters they speak with, I’ve not only focused on general media training (i.e. message development, interview tactics) but have also shared “a day in the life” of a reporter.

Ready for Prime Time

Rarely, will you find a brand spokesperson who has all of the skills and characteristics outlined above. However, with the right partnership between PR and spokespeople, companies can be well-represented in press interviews and can forge relationships that will help tell their story and improve their reputation.

Flash (Sale) AAAHHH!!

Part of me feels like, since I revealed my obsession with song lyrics in my first entry, I can’t keep using it anymore—like that old magician’s rule. But oh well, I found myself way too amusing when I came up with this title so I’m going to get past that.

Part of me feels like, since I revealed my obsession with song lyrics in my first entry, I can’t keep using it anymore—like that old magician’s rule. But oh well, I found myself way too amused when I came up with this title so I’m going to get past that.

Today I’ve got just a quick A/B test result from a (wait for it … you’ll be shocked …) flash sale (gasp!) I did this past summer for our Direct Marketing IQ Bookstore.

We wanted to offer a 24-hour flash sale on some of our popular titles, but the question was how to get the most out of the offer. Would we get a better response by offering a discount on specific titles, or would it work better to simply toss out the discount code and give recipients free reign on what to use it for?

When in doubt, hit the split. We created two very similar HTML promotions, both promoting the flash sale for the same 24-hour period. We gave both the same subject line: “24-HOUR FLASH SALE—The countdown is on!” And of course, each version was deployed at the same time.

Email A offered the discount for three specific titles belonging to the same Boosting Direct Mail Response series. Email B gave a code which could be used on any title in the store produced by Direct Marketing IQ. The coupon codes themselves were slightly different for easy tracking of which email had prompted the purchase.

Any guesses as to which would be the bigger hit?

Get your guesses in now …

Drumroll, please …

  • Email A’s click rate was 1.2 percent; Email B’s was 0.7 percent.
  • Email A earned more than double the number of items sold than Email B.
  • Those sales amounted to Email A bringing in a grand total of $380.77 more than Email B over the 24 hour period.

So, there you have it. Based on these results, folks are much more likely to act immediately on a sale if the options are narrowed down and laid out for them plainly. It’s a test I’d like to try a few more times, but the difference in numbers was pretty significant this time around.

Look for future posts talking wedding emails, memes in email marketing, more fun with subject lines, or whatever else happens to poke me in the side along the way.

How to Be a Good Data Scientist

I guess no one wants to be a plain “Analyst” anymore; now “Data Scientist” is the title of the day. Then again, I never thought that there was anything wrong with titles like “Secretary,” “Stewardess” or “Janitor,” either. But somehow, someone decided “Administrative Assistant” should replace “Secretary” completely, and that someone was very successful in that endeavor. So much so that, people actually get offended when they are called “Secretaries.” The same goes for “Flight Attendants.” If you want an extra bag of peanuts or the whole can of soda with ice on the side, do not dare to call any service personnel by the outdated title. The verdict is still out for the title “Janitor,” as it could be replaced by “Custodial Engineer,” “Sanitary Engineer,” “Maintenance Technician,” or anything that gives an impression that the job requirement includes a degree in engineering. No matter. When the inflation-adjusted income of salaried workers is decreasing, I guess the number of words in the job title should go up instead. Something’s got to give, right?

I guess no one wants to be a plain “Analyst” anymore; now “Data Scientist” is the title of the day. Then again, I never thought that there was anything wrong with titles like “Secretary,” “Stewardess” or “Janitor,” either. But somehow, someone decided “Administrative Assistant” should replace “Secretary” completely, and that someone was very successful in that endeavor. So much so that, people actually get offended when they are called “Secretaries.” The same goes for “Flight Attendants.” If you want an extra bag of peanuts or the whole can of soda with ice on the side, do not dare to call any service personnel by the outdated title. The verdict is still out for the title “Janitor,” as it could be replaced by “Custodial Engineer,” “Sanitary Engineer,” “Maintenance Technician,” or anything that gives an impression that the job requirement includes a degree in engineering. No matter. When the inflation-adjusted income of salaried workers is decreasing, I guess the number of words in the job title should go up instead. Something’s got to give, right?

Please do not ask me to be politically correct here. As an openly Asian person in America, I am not even sure why I should be offended when someone addresses me as an “Oriental.” Someone explained it to me a long time ago. The word is reserved for “things,” not for people. OK, then. I will be offended when someone knowingly addresses me as an Oriental, now that the memo has been out for a while. So, do me this favor and do not call me an Oriental (at least in front of my face), and I promise that I will not call anyone an “Occidental” in return.

In any case, anyone who touches data for living now wants to be called a Data Scientist. Well, the title is longer than one word, and that is a good start. Did anyone get a raise along with that title inflation? I highly doubt it. But I’ve noticed the qualifications got much longer and more complicated.

I have seen some job requirements for data scientists that call for “all” of the following qualifications:

  • A master’s degree in statistics or mathematics; able to build statistical models proficiently using R or SAS
  • Strong analytical and storytelling skills
  • Hands-on knowledge in technologies such as Hadoop, Java, Python, C++, NoSQL, etc., being able to manipulate the data any which way, independently
  • Deep knowledge in ETL (extract, transform and load) to handle data from all sources
  • Proven experience in data modeling and database design
  • Data visualization skills using whatever tools that are considered to be cool this month
  • Deep business/industry/domain knowledge
  • Superb written and verbal communication skills, being able to explain complex technical concepts in plain English
  • Etc. etc…

I actually cut this list short, as it is already becoming ridiculous. I just want to see the face of a recruiter who got the order to find super-duper candidates based on this list—at the same salary level as a Senior Statistician (another fine title). Heck, while we’re at it, why don’t we add that the candidate must look like Brad Pitt and be able to tap-dance, too? The long and the short of it is maybe some executive wanted to hire just “1” data scientist with all these skillsets, hoping to God that this mad scientist will be able to make sense out of mounds of unstructured and unorganized data all on her own, and provide business answers without even knowing what the question was in the first place.

Over the years, I have worked with many statisticians, analysts and programmers (notice that they are all one-word titles), dealing with large, small, clean, dirty and, at times, really dirty data (hence the title of this series, “Big Data, Small Data, Clean Data, Messy Data”). And navigating through all those data has always been a team effort.

Yes, there are some exceptional musicians who can write music and lyrics, sing really well, play all instruments, program sequencers, record, mix, produce and sell music—all on their own. But if you insist that only such geniuses can produce music, there won’t be much to listen to in this world. Even Stevie Wonder, who can write and sing, and play keyboards, drums and harmonicas, had close to 100 names on the album credits in his heyday. Yes, the digital revolution changed the music scene as much as the data industry in terms of team sizes, but both aren’t and shouldn’t be one-man shows.

So, if being a “Data Scientist” means being a super businessman/analyst/statistician who can program, build models, write, present and sell, we should all just give up searching for one in the near future within your budget. Literally, we may be able to find a few qualified candidates in the job market on a national level. Too bad that every industry report says we need tens of thousands of them, right now.

Conversely, if it is just a bloated new title for good old data analysts with some knowledge in statistical applications and the ability to understand business needs—yeah, sure. Why not? I know plenty of those people, and we can groom more of them. And I don’t even mind giving them new long-winded titles that are suitable for the modern business world and peer groups.

I have been in the data business for a long time. And even before the datasets became really large, I have always maintained the following division of labor when dealing with complex data projects involving advanced analytics:

  • Business Analysts
  • Programmers/Developers
  • Statistical Analysts

The reason is very simple: It is extremely difficult to be a master-level expert in just one of these areas. Out of hundreds of statisticians who I’ve worked with, I can count only a handful of people who even “tried” to venture into the business side. Of those, even fewer successfully transformed themselves into businesspeople, and they are now business owners of consulting practices or in positions with “Chief” in their titles (Chief Data Officer or Chief Analytics Officer being the title du jour).

On the other side of the spectrum, less than a 10th of decent statisticians are also good at coding to manipulate complex data. But even they are mostly not good enough to be completely independent from professional programmers or developers. The reality is, most statisticians are not very good at setting up workable samples out of really messy data. Simply put, handling data and developing analytical frameworks or models call for different mindsets on a professional level.

The Business Analysts, I think, are the closest to the modern-day Data Scientists; albeit that the ones in the past were less so technicians, due to available toolsets back then. Nevertheless, granted that it is much easier to teach business aspects to statisticians or developers than to convert businesspeople or marketers into coders (no offense, but true), many of these “in-between” people—between the marketing world and technology world, for example—are rooted in the technology world (myself included) or at least have a deep understanding of it.

At times labeled as Research Analysts, they are the folks who would:

  • Understand the business requirements and issues at hand
  • Prescribe suitable solutions
  • Develop tangible analytical projects
  • Perform data audits
  • Procure data from various sources
  • Translate business requirements into technical specifications
  • Oversee the progress as project managers
  • Create reports and visual presentations
  • Interpret the results and create “stories”
  • And present the findings and recommended next steps to decision-makers

Sounds complex? You bet it is. And I didn’t even list all the job functions here. And to do this job effectively, these Business/Research Analysts (or Data Scientists) must understand the technical limitations of all related areas, including database, statistics, and general analytics, as well as industry verticals, uniqueness of business models and campaign/transaction channels. But they do not have to be full-blown statisticians or coders; they just have to know what they want and how to ask for it clearly. If they know how to code as well, great. All the more power to them. But that would be like a cherry on top, as the business mindset should be in front of everything.

So, now that the data are bigger and more complex than ever in human history, are we about to combine all aspects of data and analytics business and find people who are good at absolutely everything? Yes, various toolsets made some aspects of analysts’ lives easier and simpler, but not enough to get rid of the partitions between positions completely. Some third basemen may be able to pitch, too. But they wouldn’t go on the mound as starting pitchers—not on a professional level. And yes, analysts who advance up through the corporate and socioeconomic ladder are the ones who successfully crossed the boundaries. But we shouldn’t wait for the ones who are masters of everything. Like I said, even Stevie Wonder needs great sound engineers.

Then, what would be a good path to find Data Scientists in the existing pool of talent? I have been using the following four evaluation criteria to identify individuals with upward mobility in the technology world for a long time. Like I said, it is a lot simpler and easier to teach business aspects to people with technical backgrounds than the other way around.

So let’s start with the techies. These are the qualities we need to look for:

1. Skills: When it comes to the technical aspect of it, the skillset is the most important criterion. Generally a person has it, or doesn’t have it. If we are talking about a developer, how good is he? Can he develop a database without wasting time? A good coder is not just a little faster than mediocre ones; he can be 10 to 20 times faster. I am talking about the ones who don’t have to look through some manual or the Internet every five minutes, but the ones who just know all the shortcuts and options. The same goes for statistical analysts. How well is she versed in all the statistical techniques? Or is she a one-trick pony? How is her track record? Are her models performing in the market for a prolonged time? The thing about statistical work is that time is the ultimate test; we eventually get to find out how well the prediction holds up in the real world.

2. Attitude: This is a very important aspect, as many techies are locked up in their own little world. Many are socially awkward, like characters in Dilbert or “Big Bang Theory,” and most much prefer to deal with the machines (where things are clean-cut binary) than people (well, humans can be really annoying). Some do not work well with others and do not know how to compromise at all, as they do not know how to look at the world from a different perspective. And there are a lot of lazy ones. Yes, lazy programmers are the ones who are more motivated to automate processes (primarily to support their laissez faire lifestyle), but the ones who blow the deadlines all the time are just too much trouble for the team. In short, a genius with a really bad attitude won’t be able to move to the business or the management side, regardless of the IQ score.

3. Communication: Many technical folks are not good at written or verbal communications. I am not talking about just the ones who are foreign-born (like me), even though most technically oriented departments are full of them. The issue is many technical people (yes, even the ones who were born and raised in the U.S., speaking English) do not communicate with the rest of the world very well. Many can’t explain anything without using technical jargon, nor can they summarize messages to decision-makers. Businesspeople don’t need to hear the life story about how complex the project was or how messy the data sets were. Conversely, many techies do not understand marketers or businesspeople who speak plain English. Some fail to grasp the concept that human beings are not robots, and most mortals often fail to communicate every sentence as a logical expression. When a marketer says “Omit customers in New York and New Jersey from the next campaign,” the coder on the receiving end shouldn’t take that as a proper Boolean logic. Yes, obviously a state cannot be New York “and” New Jersey at the same time. But most humans don’t (or can’t) distinguish such differences. Seriously, I’ve seen some developers who refuse to work with people whose command of logical expressions aren’t at the level of Mr. Spock. That’s the primary reason we need business analysts or project managers who work as translators between these two worlds. And obviously, the translators should be able to speak both languages fluently.

4. Business Understanding: Granted, the candidates in question are qualified in terms of criteria one through three. Their eagerness to understand the ultimate business goals behind analytical projects would truly set them apart from the rest on the path to become a data scientist. As I mentioned previously, many technically oriented people do not really care much about the business side of the deal, or even have slight curiosity about it. What is the business model of the company for which they are working? How do they make money? What are the major business concerns? What are the long- and short-term business goals of their clients? Why do they lose sleep at night? Before complaining about incomplete data, why are the databases so messy? How are the data being collected? What does all this data mean for their bottom line? Can you bring up the “So what?” question after a great scientific finding? And ultimately, how will we make our clients look good in front of “their” bosses? When we deal with technical issues, we often find ourselves at a crossroad. Picking the right path (or a path with the least amount of downsides) is not just an IT decision, but more of a business decision. The person who has a more holistic view of the world, without a doubt, would make a better decision—even for a minor difference in a small feature, in terms of programming. Unfortunately, it is very difficult to find such IT people who have a balanced view.

And that is the punchline. We want data scientists who have the right balance of business and technical acumen—not just jacks of all trades who can do all the IT and analytical work all by themselves. Just like business strategy isn’t solely set by a data strategist, data projects aren’t done by one super techie. What we need are business analysts or data scientists who truly “get” the business goals and who will be able to translate them into functional technical specifications, with an understanding of all the limitations of each technology piece that is to be employed—which is quite different from being able to do it all.

If the career path for a data scientist ultimately leads to Chief Data Officer or Chief Analytics Officer, it is important for the candidates to understand that such “chief” titles are all about the business, not the IT. As soon as a CDO, CAO or CTO start representing technology before business, that organization is doomed. They should be executives who understand the technology and employ it to increase profit and efficiency for the whole company. Movie directors don’t necessarily write scripts, hold the cameras, develop special effects or act out scenes. But they understand all aspects of the movie-making process and put all the resources together to create films that they envision. As soon as a director falls too deep into just one aspect, such as special effects, the resultant movie quickly becomes an unwatchable bore. Data business is the same way.

So what is my advice for young and upcoming data scientists? Master the basics and be a specialist first. Pick a field that fits your aptitude, whether it be programming, software development, mathematics or statistics, and try to be really good at it. But remain curious about other related IT fields.

Then travel the world. Watch lots of movies. Read a variety of books. Not just technical books, but books about psychology, sociology, philosophy, science, economics and marketing, as well. This data business is inevitably related to activities that generate revenue for some organization. Try to understand the business ecosystem, not just technical systems. As marketing will always be a big part of the Big Data phenomenon, be an educated consumer first. Then look at advertisements and marketing campaigns from the promotor’s point of view, not just from an annoyed consumer’s view. Be an informed buyer through all available channels, online or offline. Then imagine how the world will be different in the future, and how a simple concept of a monetary transaction will transform along with other technical advances, which will certainly not stop at ApplePay. All of those changes will turn into business opportunities for people who understand data. If you see some real opportunities, try to imagine how you would create a startup company around them. You will quickly realize answering technical challenges is not even the half of building a viable business model.

If you are already one of those data scientists, live up to that title and be solution-oriented, not technology-oriented. Don’t be a slave to technologies, or be whom we sometimes address as a “data plumber” (who just moves data from one place to another). Be a master who wields data and technology to provide useful answers. And most importantly, don’t be evil (like Google says), and never do things just because you can. Always think about the social consequences, as actions based on data and technology affect real people, often negatively (more on this subject in future article). If you want to ride this Big Data wave for the foreseeable future, try not to annoy people who may not understand all the ins and outs of the data business. Don’t be the guy who spoils it for everyone else in the industry.

A while back, I started to see the unemployment rate as a rate of people who are being left behind during the progress (if we consider technical innovations as progress). Every evolutionary stage since the Industrial Revolution created gaps between supply and demand of new skillsets required for the new world. And this wave is not going to be an exception. It is unfortunate that, in this age of a high unemployment rate, we have such hard times finding good candidates for high tech positions. On one side, there are too many people who were educated under the old paradigm. And on the other side, there are too few people who can wield new technologies and apply them to satisfy business needs. If this new title “Data Scientist” means the latter, then yes. We need more of them, for sure. But we all need to be more realistic about how to groom them, as it would take a village to do so. And if we can’t even agree on what the job description for a data scientist should be, we will need lots of luck developing armies of them.

Google Authorship Image Not Showing? Here’s What to Do Next.

Are your Google Authorship images not showing in search results? Are you seeing a drop in site visitor traffic or leads? Google recently pulled the plug. The results are in: Lower traffic for some social sellers, while others aren’t much affected. So what should you do?

Are your Google Authorship images not showing in search results? Are you seeing a drop in site visitor traffic or leads? Google recently pulled the plug. The results are in: Lower traffic for some social sellers, while others aren’t much affected. So what should you do?

Why Your Google Authorship Images Are Not Showing
Well, because Google says so. It decided not to anymore! It was just an experiment.

“In the early days of Google Authorship, almost anyone could get the coveted face photo in search by correctly setting up Authorship markup on their content and linking to that content from their Google+ profile,” says Google+ expert, Mark Traphagen in a recent SEOmoz blog.

“As time went on, Google became pickier about showing the rich snippet, and some sort of quality criteria seemed to come into play.”

In October 2013, Google announced a reduction in the number of photo images it displayed. In late June 2014 it pulled the plug completely on photo images in search results. Poof!

Says Traphagen, “It appears that the net result is no overall change in the amount of Authorship (appearing) in search, just an elimination of a ‘first class’ status for some authors.”

Author Images Actually Did Not Drive More Traffic
Everyone knows Authorship links with photos drove more traffic and leads to Web pages of authors, right? Eh, maybe.

“We never really knew for sure, and we never knew how much. Most importantly, there was never any proof that any CTR boost was universal,” says Traphagen, who’s done the research.

Many “studies” were conducted supporting the theory of Authorship links grabbing more eyes—and holding more perceived authority—than a “text only” link. But none of them hold much water.

Myself, I am running a handful of blogs for lead generation. After my author images were removed, I am apparently experiencing a drop in traffic and leads. But it’s not huge by any means. Why?

I’ve copywritten my Web page titles, blog post headlines, lead sentences and posts.

What You Should Do Next
Learn to copywrite. Already know how? Practice more. Most importantly, be sure you have the ability to have FULL control over Web or blog page titles.

To draw maximum attention from Google and prospective buyers make sure your Web page titles are balanced. Make sure they:

  1. are written to display a keyword phrase you’re targeting and
  2. create curiosity in the reader using copywriting.

Warning: Your blog platform may not allow you to control the Web page title freely. It’s common for blog software to take your blog (article) headline (that readers see) and place it in your Web page title (that Google and readers see in search engine results).

This is not optimal. You’ll have more ability to copywrite freely by having control over URL structure and Web page title.

For example, the structure of my blog post here is focused on the keyword phrase “Google authorship image not showing.” However, I do not have control over my URL structure or Web page title. The blog software takes my article headline and places it in the URL structure and Web page title.

It’s not optimal but I don’t cry much about it to the good folks at Target Marketing!

It would be better to have the option of editing the URL to “google-authorship-image-not-showing” and separately copywrite my Web page title to create curiosity in the reader.

Don’t Give Up (I’m Not)
“I’m done! Trying to please Google a waste of time. I’m going back to cold calling!”

I understand those who feel this way. Especially after discovering all your Google authorship images not showing. Whether you’re just starting to use B-to-B content marketing or have been investing for years Google can frustrate us.

But that’s precisely the point. It doesn’t need to be this way.

As someone who continues to generate leads online I can tell you definitively: You don’t want to depend on Google for lead generation. However, you do need to be online—capturing leads your competition will otherwise capture.

So what can you do today? The best starting point is to elevate social media copywriting as a priority. For example, what are posts to Google+, YouTube video or blog posts structured to provoke curiosity in buyers?

Creating curiosity that lures customers seems obvious. But are you doing it?

Manhandle Google With Good Copywriting
There is no silver bullet for generating B-to-B leads online. However, there is one habit that consistently brings my students, clients and by business more leads.

Giving customers a reason (in writing!) to click and take action—resolve or improve something important to them. It starts with Google and your Web page titles.

Once you take this simple idea and turn it into a habit you will continue to generate leads no matter what Google does next! You’ll forget about your Google Authorship image not showing. Won’t that feel good?

Let me know how you feel in comments.