Shaking Up Sales, Loyalty With the Human Touch

It’s easy to rely on the latest breakthroughs in marketing technology to drive sales, meet quotas and secure our jobs. But in the end, we’re all human. And that human touch goes farther than the latest integration to your marketing stack. A great example of how the human touch builds brands and always will is Shake Shack.

Shake Shack logoWith all of the emphasis on customer journeys and engagement, a whole new genre of marketing technology has cropped up. We have Web content management, programmatic, social listening and customer experience platforms , just to name a very few of the many tools available. In fact, in February of this year, Gartner reported that 89 percent of marketers expect customer experience software to be key to how they set their brand apart and build value among customers. With the feedback mechanisms inherent in customer experience management (CEM) platforms, marketers can identify attitudes, issues, needs and reply quickly with relevant messaging and encouragement to take the next step in a brand’s journey toward lifetime value.

All of the computerized processes for tracking and deploying messaging are all we marketers need to secure repeat sales, referrals and lifetime value from our customers. Or not.

It’s easy to rely on the latest breakthroughs in marketing technology to drive sales, meet quotas and secure our jobs. But in the end, we’re all human. And that human touch goes farther than the latest integration to your marketing stack.

A great example of how the human touch builds brands and always will is Shake Shack.

What started as a fundraiser to rebuild Madison Square Park in New York City, has blossomed into a highly successful business and example of what can happen when you build a brand around human values not just shareholder value.  In 2001, Shake Shack started as a hot dog cart selling hot dogs, chips and lemonade to help support the Madison Square Park Conservancy’s first art installation. Its quick success resulted in a permanent kiosk just three years later and a larger menu, which now stands out among the best in all markets it serves across the globe for burgers, shakes and custards, all made from natural ingredients.

But if you ask Edwin Bragg, VP of marketing communications for Shake Shack, the foundation of the brand’s success goes back to its roots and the initial goal of bringing people together to enjoy good food, events and times just being with others.

As Edwin puts it:

“Shake Shack was built around the goal of bringing people together for a good community cause and to simply bring people together, too, while serving high-quality food that represented the American culture of gather events, like BBQs in the park, fun, and relaxation with friends and others in your community.”

In just a few years, the Shake Shack community has grown to 81 Shake Shacks in the U.S. and 132 worldwide, including locations in Dubai, London and Istanbul.

More than the latest marketing technology for capturing customer data, segmenting into key personas and deploying relevant communications about offers and products,  Shake Shack has succeeded by staying true to its goal of bringing people together. Beyond serving great food in great locations where people want to gather and relax, Shake Shack has built a running community among its customer base, the Shake Shack Track and Field.

Started by Shake Shack’s general manager in Philadelphia, Allen Ng, the first run went so well the brand decided to repeat it and now it has 10 chapters across the country. Each chapter meets up once a month for a group run, which ends at the local Shake Shack location where all runners get a drink on the house. And like the Shake Shack menu, this program has expanded to include bike rides and yoga sessions. In short order, the program gained more than 4,000 followers on the Shake Shack Track and Field Facebook page. More importantly, it gained support, enthusiasm, loyalty among consumers and a brand community.

This story inspires me because, even with all of the technology we have available to monitor manage and maximize our customers’ value to our brand, nothing takes the place of the human touch. Building experiences that go far beyond your brand matter. Not just in building running clubs that bring people together and cement loyalty and repeat sales. But in bringing people together to work toward common causes, like raising funds to restore art, vibrancy and community at a local park in a busy city.

As you build your marketing strategies and technology plans, its critical that you build in customer experiences that are not managed or executed by a computer system, and that don’t just work to make masses of customers feel noticed among the multitude of others. Find ways to create human interaction and bulid communities in person, in real-time and around real values. Brands who do this will be the brands that succeed far after the latest innovation in technology has been replaced and long forgotten.

Stop Blaming Marketing Problems on Software

When faced with a large amount of unrefined, unstructured and uncategorized data, we must indeed fix the data first. Let’s not even think about blaming the data storage platforms like Hadoop, MongoDB or Teradata here. That would be like blaming rice storage facilities for not being able to refine rice for human consumption.

DataI often hear statements like “Our client has a Tableau problem.” Or, it is something about Hadoop or data platforms, as in “We have an issue with Hadoop.” What did it do, use offensive language? I wonder what the real issue is.

In any case, such general statements don’t help much. I guess a medical doctor feels the same way when she hears that her patient has a headache. What does that even mean, headache? What kind of headache? Prolonging or sporadic? Throbbing or sharp pain? Overall, or one-sided? Or, do you just want to avoid conversations with your spouse?

Symptoms are not always related to root causes. Why would marketers think they have a problem with Tableau? Isn’t that a reporting and display tool? Unless one doesn’t like the way a bubble chart comes out, nothing really is a Tableau problem.

More often than not, reporting issues stem back to the data. What could be the major issues with the report? Inaccuracy, inconsistency or just plain suckiness? If the data on the report don’t make any sense, we must dig deeper. And let’s not forget that reporting tools are not even designed to handle heavy-duty data manipulations. But if the report doesn’t make any sense or is hard to understand — well, then — let’s blame the designer of such a report, not the toolset.

For the record, I do not represent analytical toolset companies like SAS, SPSS or Tableau. Maybe they should share some blame, because they must have sold the toolsets as an almighty data mining tool that just does it all. But I am addressing the issue this way; as, at least for now, forming proper questions, defining problem statements, data modeling (for analytics), report design and, most importantly, deriving insights out of the report solidly remain as human functions.

Let’s break it down further. When faced with a large amount of unrefined, unstructured and uncategorized data, we must indeed fix the data first. Let’s not even think about blaming the data storage platforms like Hadoop, MongoDB or Teradata here. That would be like blaming rice storage facilities for not being able to refine rice for human consumption. In other words, we should not put too much of a burden on the data collection and storage systems when it comes to data refinement.

Data refinement should be dealt with as a separate entry altogether; between data collection (such as Hadoop) and data delivery (such as Tableau), each requiring different skillsets and expertise. Such data refinement work includes:

  • Data Hygiene and Edit: As no data source is immaculate. In fact, many analysts waste their valuable time on fixing dirty data (and following the steps listed below).
  • Data Categorization and Tagging: As uncategorized freeform data must be put into buckets and properly tagged for advanced analytics (refer to “Free Form Data Are Not Exactly Free”).
  • Data Consolidation: As disparate data sources must be “merged” (to create a “360-degree view of the customer” around a person, for example), or “concatenated” (to increase coverage by adding similar types of data).
  • Data Summarization and Variable Creation: To transform data to describe different levels (transaction, emails, customers, companies, etc.), as in converting transaction or event-level data into “descriptors of individual customers” (refer to “Beyond RFM Data”).
  • Treat Missing Values: As no data will ever be fully complete, we need to fill in the gaps either with statistical models or business rules (refer to “Missing Data Can Be Meaningful”).

If the salesperson who sold you the reporting toolset promised that the product would do all of these things, well, just ignore him. Even in the age of AI, these steps must be performed by separate machines (or teams) trained for specific tasks. Simply, machines are not that smart yet; AI trained for “recognition” won’t be able to “predict” and fill in the blanks for you. That also means that these are not to be done by human analysts all by themselves.

Nonetheless, the steps listed here must be completed before the reporting or any other analytical work even begins. We can even say that the reporting step is the simplest one of all. But only if the reports are designed properly first. And that is the catch.

No amount of pretty charts can be meaningful if there is no story behind it. That would be like watching a movie filled with so-called state-of-the-art special effects with no character development or viable storyline. That may work as a trailer, but that’s about it. Now, if you are an analyst having to present findings to a client or your boss, you don’t want to be the one who loses steam five minutes after the meeting begins. A 40-page PowerPoint deck? So what? What does all of that mean? What are we supposed to do about it?

Can Software Really Predict Our Emotions?

Technology experts and sentiment analysis software developers are claiming that we can now infer people’s feelings by analyzing big data. It’s based on what we say in social media. As direct marketers, we know our copy and content are most successful when we tap into the emotions and lift the feelings of our customers and prospects that motivate them to

Technology experts and sentiment analysis software developers are claiming that we can now infer people’s feelings by analyzing big data. It’s based on what we say in social media. As direct marketers, we know our copy and content are most successful when we tap into the emotions and lift the feelings of our customers and prospects that motivate them to take action.

While I’m skeptical how sentiment analysis can be used without provoking consumer backlash, maybe we should reflect on this claim that software can predict people’s feelings.

In my last blog, I shared this thought-provoking quote from contemporary literature author Maya Angelou:

“I’ve learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel.”

Let’s take a deeper dive to see if this claim of inferring feelings from social media posts is not only possible, but if it’s smart. Or shameful.

A recent Wall Street Journal article on the topic of big data (“Marketers Want to Know What You Really Mean Online: Sentiment Analysis Aims to Decipher the Nuances of Social-Media Posts“) cites several examples of how it works. The article goes into more detail, but in summary, the process works like this:

  1. Software now can break down tweets and status updates to extract the literal meaning of what’s being said. This step is called natural-language processing.
  2. The software determines the emotion behind the statement. Was it written in earnest, or was it snarky? Was the emotion strong? That is: enthusiastic, angry or sad?

This technology has been used by pharmaceutical companies, hair product companies, food companies, political organizations, and even for the State of the Union address.

What the article doesn’t tell us is if the technology actually worked to increase engagement and ultimately sales.

The resulting analyses of sentiment analysis can be far from 100 percent accurate, but could be one of many resources used in your messaging strategy. Context, cultural and colloquial nuances, and length of message can lead software algorithms astray. The shorter the message, the more difficult it becomes for algorithms to correctly interpret intent. As we all know, people often misinterpret sentiment when reading each other’s messages (consider how many times you’ve read an email that was intended to be cute or poke fun, but backfired).

The CEO of a sentiment analysis software company is quoted in the WSJ article as saying that, “right when a person is first diagnosed with cancer, they are the most optimistic. So he advises pharmaceutical clients to target ads based on the emotion the person is experiencing in the moment.”

Is this smart, opportunistic, creepy or offensive? My mother is currently dealing with cancer and this feels to me like an example of cold-hearted marketers tapping into raw emotions and feelings of a vulnerable person’s emotional state-of-mind. I’m more personally involved, obviously, but using big data on someone just diagnosed with cancer feels shameful (and notice I’ve used the word feel or feelings three times in this paragraph).

On a different and more appropriately used level, sentiment analysis can be effective when monitoring social media for complaints. It enables marketers to more quickly address a complaint and correct a problem for the customer. This feels like a powerful and appropriate use of sentiment analysis.

If we take to heart Maya Angelou’s quote that people will always remember how you made them feel, taken across an emotional line in the sand, marketers would be well served to remember that the good feeling of the moment could quickly turn into a negative your customers and prospects will never forget.

Marketing Automation Is Not Marketing Strategy

Too often these days, I hear B-to-B marketers mouth claims like, “We got this new [fill in the brand] automation tool, so now we can reduce headcount.” Or, “Once this automation system is installed, it will take our marketing to the next level.” This worries me. Marketers sometimes see automation as a silver bullet. But it’s only a tool

Too often these days, I hear B-to-B marketers mouth claims like, “We got this new [fill in the brand] automation tool, so now we can reduce headcount.” Or, “Once this automation system is installed, it will take our marketing to the next level.” This worries me. Marketers sometimes see automation as a silver bullet. But it’s only a tool. Marketing automation doesn’t identify your best target audiences. It can’t develop value propositions. No way will it make the tough decisions among competing investment options. I’m reminded of Mike Moran’s great book title, Do It Wrong, Quickly. In other words, marketing automation doesn’t work without strategy.

Remember ten years ago, when CRM came along? Déjà vu all over again, to echo Yogi Berra. Marketers thought that the new CRM software would solve their customer service and customer retention problems. Expectations dashed. Not only was it a nightmare to get up and running, the software served only to automate the processes—good or bad—that companies already had in place.

Even the marketing automation software vendors themselves recognize the importance of strategy, for their own success, as well as that of their clients. Think about it: If their clients can’t get the value from the software, their revenues are going to be impacted.

So education campaigns are underway. Marketo, for example, sponsored a compelling study by Sirius Decisions that explains the importance of a strong process in driving results when using marketing automation software. Their data shows that companies using automation combined with a reasonable lead management process—inquiry generation, qualification, nurturing and hand off to sales—produced four times the sales volume of companies with automation but with weaker processes.

Eloqua, too, makes a strong case for strategy in its guide, “6 Pitfalls to Avoid in Your Marketing Automation Journey,” which contains the important reminder to avoid putting “too much focus on technology, and not enough focus on buyers.”

So, what should we be doing with automation, to ensure its success? Three things come to mind.

  1. Be realistic about what it can and can’t do. Automation is not a silver bullet that you can set and forget. So make sure real humans are thinking through the essential tasks of identifying your key audiences, understanding their needs, scoping out their buying processes and developing contact strategies to move them along, in your direction.
  2. Clean up your database. By now it’s clear that the database is the single most important success factor in B-to-B marketing communications. So don’t be automating messages that can’t or won’t be delivered to the right targets.
  3. Train up your team. Too many marketing groups are leaving the campaign automation system to a set of junior staffers who interface with the tools, deploy campaigns and report results. I am not saying the marketing VPs should be executing campaigns, but to get the right mix of strategy and tools, we need better integration. Senior marketers should be deeply aware of the capabilities of the software. And junior staffers need training in strategic marketing thinking.

Are there other success factors in B-to-B marketing automation you can share?

A version of this article appeared in Biznology, the digital marketing blog.