3-Part Pre-Production Content Marketing Checklist

Here’s a three-part pre-production checklist of the questions your content needs to answer in order for it to succeed. Last time around, we talked about how long the content on your website pages should be if your goal is to attract, engage, and retain your audience through content marketing.

Last time around, we talked about how long the content on your website pages should be if your goal is to attract, engage, and retain your audience through content marketing.

This month, let’s look at a checklist of what your articles need, regardless of length, in order to succeed as content marketing. We’ve found that the best way to build a checklist that works for you is to identify the questions you must answer before you put pencil to paper — or fingertips to tapping.

Who Am I Trying to Reach?

Your first checklist item should focus on who you are trying to reach. You may be pro or con when it comes to the value of creating prospect personae, but they are an excellent way to draw a clear picture of who your target audience is. If you have another approach you prefer, that’s fine. Just as long as your profile includes data points on your prospects’ professional lives, as well as demographic information. Here are a few examples. The data points that are relevant to your marketing will vary.

Professional Profile

  • Title
  • Role
  • Department
  • Company size
  • Location

Demographic Profile

  • Age
  • Gender
  • Income
  • Level of education

What Is My Prospect’s Motivation?

Once you have a picture of who your prospect is, you need to understand what is driving them to seek the change that you could potentially provide. In other words, what are their pain points around this problem?

The key here is to dive into their pain points as deeply as possible. Your goal should be to not only know what their pain points are, but to understand why they are pain points, in the first place.

In most cases, that will require calculating what the value of solving the problem is to the prospect and his or her organization. That can help you determine your pricing and their sense of urgency.

As critically, you’ll want to identify what the costs will be of doing nothing. (That is often your biggest competitor, rather than another solution provider.)

As you identify the most critical benefits to your prospect, you may find your content beginning to take shape. Those benefits — or language alluding to them — are often best used as sub-headings in your article.

What Is My Goal for This Page?

Your goal is always the same: Get the prospect to take action.

What that action is will depend on all of the data we covered above, as well as where in the buying cycle your prospect is. That last piece will likely determine the nature of your offer: Asking someone who is just beginning their research to agree to an in-person meeting is likely a non-starter, while a prospect who is putting together her short list will be much more open to the idea.

What’s Next?

Regardless of the action you seek, be sure you are thinking a few moves ahead, as a good chess players does. Once they’ve taken this action, what action would you like them to take next? What content can help you move your prospects in that direction?

With luck, your pre-writing checklist can help you not only with the content piece in front of you, but with fitting what you create into a broader content library and content marketing strategy.

2 Emails You’re Sending That Rarely Work

Never say never? I try to not speak in absolutes and remain positive. But there are two flavors of cold emails you’re probably sending that do more harm than good.

Never say never? I try to not speak in absolutes and remain positive. But there are two flavors of cold emails you’re probably sending that do more harm than good. These are the cold:

  • “help me find the right person” request;
  • “show me how to sell to you” request.

Not sending these emails? I’ll be surprised if you haven’t sent one in past … or still consider them as valid options.

Beware. They are marks of amateurs.

Asking for a chance to learn about customers’ current pain points or challenges is common … and increasingly fails. Clients are deluged with these requests every day.

It’s not the client’s job to sort a way to sell your thing. Likewise, requesting a meeting in a cold email is too big an ask, too early.

Don’t Know? Find Out!

Let’s say you don’t know the right person to talk with — at your target organization. Fair enough.

Or in cases where you do know the contact, the pain or goal may be unclear. I respect that. But ya gotta find out. No excuses.

Please don’t do this:

Hi {name},

I’m trying to figure out who is in charge of [leading general statement] there at {company}.

Would you mind pointing me towards the right person please, and the best way I might get in touch with them?

Consider tools like LinkedIn, Google and countless others. Your ability to find the right decision-maker(s) is unprecedented. Not to mention innovators like Data.com and old-fashioned (yet, perfectly good) sources like InfoUSA and their like.

“Who’s the best person to get in touch about this?”

You must be kidding. This is NOT going to work for you.

Don’t get pegged as lazy, or worse!

‘Do My Work and Pity Me’

If you’re sending emails hoping someone will do the work for you … that’s pitiful. Especially if you’re starting at the top of an organization, looking to get handed-down. Your cold email signals: “help me do my work.” And that’s pitiful.

You might argue, “Jeff, people like to help people.” They do. I help people when I can. But consider this:

Would you call the CEO or top executive on the phone — looking to get handed down? I’d hope not but maybe you would! In a digital age, cold calling top executives (to discover who to talk to) is not effective. Instead, research the target online.

You may also argue, “Jeff, I do well discovering who decisionmakers are using the phone … by tapping into administrative assistants.”

I’m cool with that. In fact, we might be forced to. Decision-makers are starting to hide or disguise their authority on LinkedIn.

Also, gathering intelligence this way is worthwhile.

However, blasting “can you help direct me?” emails, trying to discover decision-maker names is mostly ineffective. It’s the sign of an unskilled sales person. Avoid it. Don’t encourage clients to pity you.

Let’s say you use email to discover who targets are at mid-management level. This is also a losing proposition. Any idea how many requests for help these people receive each day? More than you might imagine.

Think about your hectic day. If you received three to four messages per day asking for help from sales reps, wouldn’t it get annoying? And it might even get you in trouble. Forwarding people who you don’t know (selling products your colleagues may not need) could cost you embarrassment.

There is often a negative incentive for contacts to help guide you.

Go Direct, Go Informed or Go Home

Let’s say you were face-to-face with a new prospect at a networking event. They’ve identified themselves as the decision-maker. You wouldn’t ask a potential client, “Can I get some time with you … so you can help me understand a way to sell to you?”

The 2 Biggest Problems With Your Sales Communication

There are two of huge problems with sales communication techniques — they make you look weak, and like every other seller out there.

If you’ve ever written or spoken the words, “I just wanted to …” stop. If you’ve ever sent emails to clients pushing on pain points, stop that, too (because that’s exactly what your competition is doing).

These are two of the biggest problems with sales communication techniques — they make you look weak, and like every other seller out there.

Here’s how to understand if your mentality and pain-point-pushing are, in fact, causing you to start fewer conversations than you deserve. If so, we’ll get you on track with stronger written and voice-based digital messages.

Stop ‘Wanting To’

Subconsciously you may be on the defensive. We all are. In life and with our work. Defensiveness and uncertainty are part of the human experience. But it can destroy your ability to communicate effectively.

Case in point, “I just wanted to …”

Author and sales trainer, Jeb Blount, recently said, “You’re saying it on the phone, you’re saying it in emails and InMails, you’re saying it in person … ‘I just wanted to check-in’ … ‘I just wanted to set an appointment’ … ‘I just wanted to grab a few minutes of your time’ … ‘I just wanted to stop by’ … I just wanted to reach out.”

“Just wanted to” is poor grammar. I’ve taken heat from my students on this for a long time. But I feel empowered by Jeb to stand firm. Stop it.

Yes, we should strive to write as we speak. But when we speak weakly, we are average. And average in sales isn’t effective. Especially in digital communications — like voicemail and email.

“‘Just wanted to’ is yesterday … it is passive and weak. It makes you sound insecure,” says Blount.

Perhaps because you are insecure.

The cure? Well, be confident. But also shift to active tense. Take an active stance. Be confident. Don’t sound average!

“Say, ‘I want to.’ Say ‘I am.’ Be active. Be confident,” says Blount. “Because confidence transfers to your prospect. Stop saying, ‘I just wanted to.’ Just stop it.”

Are You Needy?

We all need. To need is human. But needing a reply, a conversation or a closed sale can set you up for communications failure. Just like when we date to find that perfect life partner: The more you communicate, subtly, you really need that second date, the less often you get it.

The more persuasive your tone (during the first date) the less you attract. Because persuading inherently puts you on the defense. It assumes you must convince. Instead, what if you confidently provoked your prospect to convince him/herself? Slowly.

Bottom line: A more confident mental attitude drives more productive behavior. Because confidence attracts, in personal and professional life. Word choice is everything.

“When I stop being needy, I can focus on my reader’s needs — like being respectfully short, factual, interesting … and ending with an implied choice,” says copywriter David Morrison.

“I think of this instruction as a prescription, and I think effective cold email is also a prescription for the reader: declarative, unambiguous, single action,” says Morrison.

Indeed, a cold call or email should be strong in tone. However, to be effective it should not be forceful. Instead, the message’s tone must be openly at peace with rejection.

“Doctor’s don’t beg. They tell you what to do and leave it up to you to follow instructions — and if you want to fix your pain/problem, you decide to take action. No one can persuade you or motivate you to do something. That desire comes from inside.”

Is what you sell prescriptive? Then David’s metaphor works.

Why ‘Pain Points’ Are Such a Pain

Marketers and sellers instinctively push on pain points. If a customer has a pain, tell them you can relieve it. But everyone is pushing information that touches on pains. If you want to blend in with the scenery, pushing on pains is an excellent way to get ignored/deleted.

Also, you cannot start near-term conversations with clients who don’t (yet) realize they have pain. Yet, sellers continue to turn to marketing prose for language that pushes on pains.

Donald Trump Gets the Why Behind the Buy

Ted Cruz still doesn’t know what hit him. Neither do most of the Republican party establishment, and large segments of the non-Republican electorate. But Carolyn Goodman has a pretty good idea: “Trump really understands the why behind the buy.”

Last night, a beleaguered Ted Cruz suspended his campaign after yet another loss to Donald Trump on the Republican primary campaign trail. After another drubbing in a state that was supposed to reject Trump’s big city conservative populism, Cruz said, “It appears that path has been foreclosed.”

Ted Cruz still doesn’t know what hit him. Neither do most of the Republican party establishment, and large segments of the non-Republican electorate. But Carolyn Goodman has a pretty good idea.

“Trump really understands the why behind the buy,” said Carolyn, president and creative director of Goodman Marketing Partners, during yesterday’s webinar on optimizing lead nurturing.

Pain Point Research > Persona Research

Carolyn’s answer was in response to an audience member’s question during the webinar Q&A: “Do Donald Trump and Bernie Sanders demonstrate that emotion drives more than facts?”

And it tied into something Carolyn said earlier in the webinar: Know the why behind the buy.

What that means is, for anyone asking people to choose their brand — whether it’s at the store, in an email or on the campaign trail — understanding why customers are in the market and why they choose your brand over another is the most important factor to turning a lead into a sale.

In fact, she said doing research on the pain points that lead customers to choose you, and marketing to those pain points, is far more important to successful lead nurturing and long-term sales than marketing to personas.

In effect, what you know about why they buy is more important than what you know about their demographics, niche and theoretical wants. And Donald Trump’s campaign is a perfect example of this, according to Goodman.

Donald Trump’s Marketing Epiphany

While the rest of the Republican field developed messaging around the grooved talking points of GOP politics today, Trump identified the why behind the buy (or vote).

This time, many Republican voters are making the buy based on frustration with what they see as stifling political correctness and a coddling bureaucracy that they don’t think can protect the country from a host of threats. And the only thing they want to vote for is change, to get “bought” career politicians out of office.

That’s the why behind their buy, and Donald Trump gets that.

If Trump hears voters saying the other candidates aren’t willing to tell what they see as a “truth” about immigrants, Muslims, tariffs or any other topic, he embraces that “truth” and speaks it as often as he can. If the other candidates say something might not be achievable, or affordable, Trump tells voters it is and he’ll make sure it’s paid for.

If voters are frustrated about politicians not doing something, Trump promises to do it. If they’re frustrated that something’s not being said, he says it.

Trump’s not over-analyzing the demographics or overthinking the personas of his voters. Instead he’s just listening to his likely voters’ pain points and addressing them.

Trump gets the why behind his customers’ buys.

Do you get the why behind yours?

Patients Aren’t Ready for Treatment?

The key is to an effective prescription is to listen to the client first. Why do they lose sleep at night? What are their key success metrics? What are the immediate pain points? What are their long-term goals? And how would we reach there within the limits of provided resources

In my job of being “a guy who finds money-making opportunities using data,” I get to meet all kinds of businesspeople in various industries. Thanks to the business trend around analytics (and to that infamous “Big Data” fad), I don’t have to spend a long time explaining what I do any more; I just say I am in the field of analytics, or to sound a bit fancier, I say data science. Then most marketers seem to understand where the conversation will go from there. Things are never that simple in real life, though, as there are many types of analytics — business intelligence, descriptive analytics, predictive analytics, optimization, forecasting, etc., even at a high level — but figuring what type of solutions should be prescribed is THE job for a consultant, anyway (refer to “Prescriptive Analytics at All Stages”).

The key is to an effective prescription is to listen to the client first. Why do they lose sleep at night? What are their key success metrics? What are the immediate pain points? What are their long-term goals? And how would we reach there within the limits of provided resources and put out the fire at the same time? Building a sound data and analytics roadmap is critical, as no one wants to have an “Oh dang, we should have done that a year ago!” moment after a complex data project is well on its way. Reconstruction in any line of business is costly, and unfortunately, it happens all of the time, as many marketers and decision-makers often jump into the data pool out of desperation under organizational pressure (or under false promises by toolset providers, as in “all your dreams will come true with this piece of technology”). It is a sad sight when users realize that they don’t know how to swim in it “after” they jumped into it.

Why does that happen all of the time? At the risk of sounding like a pompous doctor, I must say that it is quite often the patient’s fault, too; there are lots of bad patients. When it comes to the data and analytics business, not all marketers are experts in it, though some are. Most do have a mid-level understanding, and they actually know when to call in for help. And there are complete novices, too. Now, regardless of their understanding level, bad patients are the ones who show up with self-prescribed solutions, and wouldn’t hear about any other options or precautions. Once, I’ve even met a client who demanded a neural-net model right after we exchanged pleasantries. My response? “Whoa, hold your horses for a minute here, why do you think that you need one?” (Though I didn’t quite say it like that.) Maybe you just came back from some expensive analytics conference, but can we talk about your business case first? After that conversation, I could understand why doctors wouldn’t appreciate patients who would trust WebMD over living, breathing doctors who are in front of them.

Then there are opposite types of cases, too. Some marketers are so insecure about the state of their data assets (or their level of understanding) that they wouldn’t even want to hear about any solutions that sound even remotely complex or difficult, although they may be in desperate need of them. A typical response is something like “Our datasets are so messy that we can’t possibly entertain anything statistical.” You know what that sounds like? It sounds like a patient refusing any surgical treatment in an ER because “he” is not ready for it. No, doctors should be ready to perform the surgery, not the patient.

Messy datasets are surely no excuse for not taking the right path. If we had to wait for a perfect set of data all of the time, there wouldn’t be any need for statisticians or data scientists. In fact, we need such specialists precisely because most data sets are messy and incomplete, and they need to be enhanced by statistical techniques.

Analytics is about making the best of what we have. Cleaning dirty and messy data is part of the job, and should never be an excuse for not doing the right thing. If anyone assumes that simple reports don’t require data cleansing steps because the results look simple, nothing could be further from the truth. Most reporting errors stem from dirty data, and most datasets — big or small, new or old — are not ready to be just plugged into analytical engines.

Besides, different types of analytics are needed because there are so many variations of business challenges, and no analytics is supposed to happen in some preset order. In other words, we get into predictive modeling because the business calls for it, not because a marketer finished some basic Reporting 101 class and now wants to move onto an Analytics 202 course. I often argue that deriving insights out of a series of simple reports could be a lot more difficult than building models or complex data management. Conversely, regardless of the sophistication level, marketers are not supposed to get into advanced analytics just for intellectual curiosity. Every data and analytics activity must be justified with business purposes, carefully following the strategic data roadmap, not difficulty level of the task.

Chicken or the Egg? Data or Analytics?

I just saw an online discussion about the role of a chief data officer, whether it should be more about data or analytics. My initial response to that question is “neither.” A chief data officer must represent the business first.

I just saw an online discussion about the role of a chief data officer, whether it should be more about data or analytics. My initial response to that question is “neither.” A chief data officer must represent the business first. And I had the same answer when such a title didn’t even exist and CTOs or other types of executives covered that role in data-rich environments. As soon as an executive with a seemingly technical title starts representing the technology, that business is doomed. (Unless, of course, the business itself is about having fun with the technology. How nice!)

Nonetheless, if I really have to pick just one out of the two choices, I would definitely pick the analytics over data, as that is the key to providing answers to business questions. Data and databases must be supporting that critical role of analytics, not the other way around. Unfortunately, many organizations are completely backward about it, where analysts are confined within the limitations of database structures and affiliated technologies, and the business owners and decision-makers are dictated to by the analysts and analytical tool sets. It should be the business first, then the analytics. And all databases—especially marketing databases—should be optimized for analytical activities.

In my previous columns, I talked about the importance of marketing databases and statistical modeling in the age of Big Data; not all depositories of information are necessarily marketing databases, and statistical modeling is the best way to harness marketing answers out of mounds of accumulated data. That begs for the next question: Is your marketing database model-ready?

When I talk about the benefits of statistical modeling in data-rich environments (refer to my previous column titled “Why Model?”), I often encounter folks who list reasons why they do not employ modeling as part of their normal marketing activities. If I may share a few examples here:

  • Target universe is too small: Depending on the industry, the prospect universe and customer base are sometimes very small in size, so one may decide to engage everyone in the target group. But do you know what to offer to each of your prospects? Customized offers should be based on some serious analytics.
  • Predictive data not available: This may have been true years back, but not in this day and age. Either there is a major failure in data collection, or collected data are too unstructured to yield any meaningful answers. Aren’t we living in the age of Big Data? Surely we should all dig deeper.
  • 1-to-1 marketing channels not in plan: As I repeatedly said in my previous columns, “every” channel is, or soon will be, a 1-to-1 channel. Every audience is secretly screaming, “Entertain us!” And customized customer engagement efforts should be based on modeling, segmentation and profiling.
  • Budget doesn’t allow modeling: If the budget is too tight, a marketer may opt in for some software solution instead of hiring a team of statisticians. Remember that cookie-cutter models out of software packages are still better than someone’s intuitive selection rules (i.e., someone’s “gut” feeling).
  • The whole modeling process is just too painful: Hmm, I hear you. The whole process could be long and difficult. Now, why do you think it is so painful?

Like a good doctor, a consultant should be able to identify root causes based on pain points. So let’s hear some complaints:

  • It is not easy to find “best” customers for targeting
  • Modelers are fixing data all the time
  • Models end up relying on a few popular variables, anyway
  • Analysts are asking for more data all the time
  • It takes too long to develop and implement models
  • There are serious inconsistencies when models are applied to the database
  • Results are disappointing
  • Etc., etc…

I often get called in when model-based marketing efforts yield disappointing results. More often than not, the opening statement in such meetings is that “The model did not work.” Really? What is interesting is that in more than nine times out of 10 cases like that, the models are the only elements that seem to have been done properly. Everything else—from pre-modeling steps, such as data hygiene, conversion, categorization, and summarization; to post-modeling steps, such as score application and validation—often turns out to be the root cause of all the troubles, resulting in pain points listed here.

When I speak at marketing conferences, talking about this subject of this “model-ready” environment, I always ask if there are statisticians and analysts in the audience. Then I ask what percentage of their time goes into non-statistical activities, such as data preparation and remedying data errors. The absolute majority of them say they spend of 80 percent to 90 percent of their time fixing the data, devoting the rest to the model development work. You don’t need me to tell you that something is terribly wrong with this picture. And I am pretty sure that none of those analysts got their PhDs and master’s degrees in statistics to spend most of their waking hours fixing the data. Yeah, I know from experience that, in this data business, the last guy who happens to touch the dataset always ends up being responsible for all errors made to the file thus far, but still. No wonder it is often quoted that one of the key elements of being a successful data scientist is the programming skill.

When you provide datasets filled with unstructured, incomplete and/or missing data, diligent analysts will devote their time to remedying the situation and making the best out of what they have received. I myself often tell newcomers that analytics is really about making the best of what you’ve got. The trouble is that such data preparation work calls for a different set of skills that have nothing to do with statistics or analytics, and most analysts are not that great at programming, nor are they trained for it.

Even if they were able to create a set of sensible variables to play with, here comes the bigger trouble; what they have just fixed is just a “sample” of the database, when the models must be applied to the whole thing later. Modern databases often contain hundreds of millions of records, and no analyst in his or her right mind uses the whole base to develop any models. Even if the sample is as large as a few million records (an overkill, for sure) that would hardly be the entire picture. The real trouble is that no model is useful unless the resultant model scores are available on every record in the database. It is one thing to fix a sample of a few hundred thousand records. Now try to apply that model algorithm to 200 million entries. You see all those interesting variables that analysts created and fixed in the sample universe? All that should be redone in the real database with hundreds of millions of lines.

Sure, it is not impossible to include all the instructions of variable conversion, reformat, edit and summarization in the model-scoring program. But such a practice is the No. 1 cause of errors, inconsistencies and serious delays. Yes, it is not impossible to steer a car with your knees while texting with your hands, but I wouldn’t call that the best practice.

That is why marketing databases must be model-ready, where sampling and scoring become a routine with minimal data transformation. When I design a marketing database, I always put the analysts on top of the user list. Sure, non-statistical types will still be able to run queries and reports out of it, but those activities should be secondary as they are lower-level functions (i.e., simpler and easier) compared to being “model-ready.”

Here is list of prerequisites of being model-ready (which will be explained in detail in my future columns):

  • All tables linked or merged properly and consistently
  • Data summarized to consistent levels such as individuals, households, email entries or products (depending on the ranking priority by the users)
  • All numeric fields standardized, where missing data and zero values are separated
  • All categorical data edited and categorized according to preset business rules
  • Missing data imputed by standardized set of rules
  • All external data variables appended properly

Basically, the whole database should be as pristine as the sample datasets that analysts play with. That way, sampling should take only a few seconds, and applying the resultant model algorithms to the whole base would simply be the computer’s job, not some nerve-wrecking, nail-biting, all-night baby-sitting suspense for every update cycle.

In my co-op database days, we designed and implemented the core database with this model-ready philosophy, where all samples were presented to the analysts on silver platters, with absolutely no need for fixing the data any further. Analysts devoted their time to pondering target definitions and statistical methodologies. This way, each analyst was able to build about eight to 10 “custom” models—not cookie-cutter models—per “day,” and all models were applied to the entire database with more than 200 million individuals at the end of each day (I hear that they are even more efficient these days). Now, for the folks who are accustomed to 30-day model implementation cycle (I’ve seen as long as 6-month cycles), this may sound like a total science fiction. And I am not even saying that all companies need to build and implement that many models every day, as that would hardly be a core business for them, anyway.

In any case, this type of practice has been in use way before the words “Big Data” were even uttered by anyone, and I would say that such discipline is required even more desperately now. Everyone is screaming for immediate answers for their questions, and the questions should be answered in forms of model scores, which are the most effective and concise summations of all available data. This so-called “in-database” modeling and scoring practice starts with “model-ready” database structure. In the upcoming issues, I will share the detailed ways to get there.

So, here is the answer for the chicken-or-the-egg question. It is the business posing the questions first and foremost, then the analytics providing answers to those questions, where databases are optimized to support such analytical activities including predictive modeling. For the chicken example, with the ultimate goal of all living creatures being procreation of their species, I’d say eggs are just a means to that end. Therefore, for a business-minded chicken, yeah, definitely the chicken before the egg. Not that I’ve seen too many logical chickens.

7 Shopping Experience Tips to Make Holiday 2013 Your Best Ever

The holiday season is known as the time that makes or breaks companies dependent on seasonal sales. Competition is fierce. Already short attention spans are overstimulated with marketing messages, family demands and increased workloads. Breaking through the chaos requires more than super discounts and great copy. People expect a great shopping experience

The holiday season is known as the time that makes or breaks companies dependent on seasonal sales. Competition is fierce. Already short attention spans are overstimulated with marketing messages, family demands and increased workloads. Breaking through the chaos requires more than super discounts and great copy. People expect a great shopping experience.

Companies that want to win the holiday challenge start early, plan well and focus on the customer. They invest their resources in understanding what their customers want so they can deliver. Surprisingly, price is not the top priority when people choose brand loyalty. They care more about the experience than the discount.

This is really good news for companies that don’t have the negotiating power of big box stores. Instead of creating promotions that destroy profits, they can invest in programs that improve the shopping experience. There is one caveat: If your company has been participating in the “how low can we go” marketing strategy, you will have to retrain your customers. Once people have been trained to expect deep discounts, marketing that doesn’t include them won’t be as effective.

Marketing for the holiday season needs to start now to optimize your return. Connections have to be established between your company and the people who will buy your products or services. If you already have good customer relations, focus on making them better. If your relationships need improving, focus on fixing them. The things you do today make selling easier tomorrow. To get started:

  1. Think lifetime value when creating the shopping experience. Most marketing plans focus on sales for specific campaigns instead of looking at the long term value of loyal customers. This can create an environment where hit-and-run customers generate revenue while reducing profitability. By the time the problem is recognized, it may be too late to save the company.
  2. Walk in your customers’ shoes to find the pain points. The easier and more enjoyable you make the shopping experience, the less people care about the price. Test every marketing channel to see how easy it is to understand and navigate the buying process. When you have finished, watch someone who doesn’t normally shop your business test it. Fix everything that needs it.
  3. Integrate channels for efficiency and effectiveness. Consistent messaging and the ability to cross channels with ease provide quality branding and keep people engaged. Find ways to make the channels work together where they leverage strengths in one to offset weaknesses in others.
  4. Optimize communication to insure exposure and accessibility. Email deliverability, copy effectiveness, website usability and social media engagement can be optimized to maximize the return. Paying attention to the details makes the difference between a good communication and a great one.
  5. Educate visitors on products and processes. People that understand the products your company offers and how to use them tend to buy more. Create content that teaches the best ways to use products and services. Your prospects will convert and customers will keep coming back.
  6. Simplify Everything. Making the buying decision and purchasing process simple endears people to your company. Life is complicated. Shopping with your company shouldn’t be.
  7. Target to provide the right offer at the right time. Part of the simplification process is making it easy for people to buy what they need with minimal effort. Targeting people with the right message based on their behavior improves the shopping experience.

Make Your Brand Blossom

This week I have flowers on my mind. It is planting season here in the Colorado Rocky Mountains … a bit later than most areas of the country. My husband and I live at 8,100 feet near Pikes Peak and the log home that our five acres is built on is frequented by deer, rabbits, foxes, coyotes and wild turkeys. In addition, all sorts of birds from owls to bluebirds to magpies and hummingbirds flit about. For many, where we live is too remote. For us, it is our sanctuary.

This week I have flowers on my mind. It is planting season here in the Colorado Rocky Mountains … a bit later than most areas of the country. My husband and I live at 8,100 feet near Pikes Peak and the log home that our five acres is built on is frequented by deer, rabbits, foxes, coyotes and wild turkeys. In addition, all sorts of birds from owls to bluebirds to magpies and hummingbirds flit about. For many, where we live is too remote. For us, it is our sanctuary.

While we love the splash of color that annuals and hanging baskets add to our flower beds, over the years we have reluctantly succumbed to making more and more of our landscaping bloom without us. Older and wiser than when we first moved here, we have learned to give into the wildlife who view our flowers as food, the early summer hailstorms that can decimate all our hard work in minutes and our often-on-the-road travel schedules that do not allow much time for all the things that plants crave on a near daily basis: weeding, deadheading, transplanting, watering, fertilizing and tending.

So it was with great delight that I learned about Proven Winners Shrubs in Country Living magazine as I flew home from my last business trip. This company captured my attention with a colorful full-page ad that casually highlighted one word over a gorgeous Hydrangea plant: OVERACHIEVER. This particular plant is called the Invincibelle® Spirit, and is positioned as one that requires minimal care, supplies abundant blooms, and is easy to grow. This is our kind of shrub. As a matter of fact, the entire line of Proven Winners feels like it was created just for us.

Since being enchanted with that ad, I researched this brand further and learned that Proven Winners Shrubs’s tagline, “A better garden starts with a better plant” informs of all its offerings and helped focus the company’s 2013 consumer campaign. In this clever and effective promotion, Proven Winners’ top 10 shrubs are personified with titles such as: Prodigy, Humdinger, Workhorse, Charmer and even Survivalist (one we are particularly attracted to, given our above mentioned conditions!).

Here’s how Proven Winners describes its unique point of differentiation:

Proven Winners partners with the top plant breeders around the world to ensure our varieties are vigorous, healthy, vibrant, and unique. Once a Proven Winners plant makes it to your house, you’ll fall in love. Proven Winners plants are:

  • Easy to grow and care for
  • Covered with blooms
  • Bright and colorful
  • All-season bloomers
  • Disease free
  • Trialed and tested

Meanwhile, these full page ads and product adjectives tell Proven Winners’ story succinctly and engagingly and direct customers to their site for more information and a free gardening guide. This spot-on, brand enhancing campaign makes its brand blossom.

Using this example as a creative springboard for your brand, how can Proven Winners inspire you and your team to “storysell” your products in a new, unusual and humdinger way?

Here are a few inspirational seeds to prompt internal conversations amongst your brand builders and product developers:

  • Can you easily identify your company’s top 10 “proven winners” and what your customers love about them?
  • What playful titles might you assign them?
  • What specific problems do these products solve?
  • How do these products erase or alleviate these pain points in your customers’ lives?
  • In what areas of your competitive landscape do they help your brand overachieve?

Take some time this season to cultivate new ways to make your brand blossom.