The Debate: Challenges and opportunities analytics present to the innovation pipeline

Stephen Perry

Creative director, Bailey Brand Consulting

How is the use of analytics changing?

Today, data and analytics enable brand owners to be more proactive and even predictive in responding to the needs of their audience. Whether it’s culled directly from customer feedback, or aggregated from polls, surveys, social media, blogs, product reviews, sales, the Web or connected devices, data enables marketers to better understand consumer behaviors and purchasing habits—potentially in real time—across a variety of different mediums. Internally, brands can use this information to drive strategic marketing decisions; externally, the data can be leveraged in the form of proof points that help support the brand story.

Given, however, that the sources of data are so numerous, it’s more important than ever that brands recognize what data is truly helpful and which is less so. Good data and analytics enable brands to turn information into insight and allows them to create a more personalized experience for their customers. Good data also empowers brands to create products and develop content that is, above all, authentic, relevant and engaging.

How can brands collect better data for use in superior brand experience creation and design?

From demographics to geography to past purchasing habits and more, brands must know what makes their customers unique and understand how, when and where they are engaging. If you’re a brick-and-mortar retailer, consider the in-store experience from your customers’ perspective: Where are they spending their time, and how long do they spend there and what can be done to improve their experience? For Web-based businesses, data can help better understand and ultimately predict what set of circumstances trigger a purchase, a mention, a share, a recommendation or a blog post.

The key takeaway for all brands is to focus on your methodologies for collecting and analyzing data and use that information to drive innovations in products, service and brand experience.

How can marketers and designers become better at collaborating with data scientists?

Including a data scientist at the outset of an initiative, as you would a copywriter, is a great start. Marketers and designers can partner with data scientists to better understand the implications, insights and impact of the data—in effect the “so what” and the “now what.” Collaborating in the same physical space allows for shared knowledge and promotes alignment. Plus, it enables your data scientist to better understand the business objectives of a given project. Armed with this information, they can then put a strategy into place to inform better and more creative decision-making that bolsters the overall marketing efforts of the brand.

How can marketers use data as part of a brand’s storytelling device?

As always, start with a good story that uniquely addresses your audience’s wants, needs and desires. Marketers can use the data they collect to enhance that narrative based on what they know about their audience. By harnessing trusted, third-party data, brands can support their unique perspective, cause, reason for being, USP or mission—and ultimately increase their credibility among consumers (visualization techniques like infographics, charts or graphs are a great way to accomplish this). Data can also help consumers understand why your product is important and/or superior to other options available to them.

How can business leaders create more opportunities to use data to elevate the way people engage with brands?

Data enables brands to capitalize on consumer trends, including customization and personalization. Internally, any department that touches the brand needs to have access to and an understanding of this data. It’s critical that all members of a team across the entire organization use a common language and share insights to make informed decisions and work towards a common goal. The external reflection of this is a brand experience in which all touch points align to create a seamless, tailored customer experience.

 

 

Raymond Alexander Nadeau

Author, Living Brands, Collaboration + Innovation = Customer Fascination

Chief creative officer, Living Brands Living Media Communications

Former vice president of new ideas and global creative director, Coty Inc.

Creative director, Imagindustries

Former associate professor of “The Practice,” North Carolina State University.

How is the use of analytics for delivering more innovative brand experiences changing?

Argumentum ad Populum, Latin for “If many believe so, it is so” is the sometimes unavoidable fallacy that attempts to contend that what is measured and expressed via advanced, expensive analytics is the answer to everything, including the alleged insight that some use to defend what they believe and others mistake for innovation. The real question should be whether reliance on analytics, in isolation, actually always stimulates products or brands that are demonstrably superior to what exists. Analytics are tools that can lead to innovation; they are not innovation themselves. Hence, the real answer to your question is whether advanced analytics help or hurt the ability to take the risks that are the hallmark of innovation.

Analytics, no matter how sophisticated, are not always reliable or predictive without the mitigating creative leap
of marketing professionals who also consider cultural, socio-economic, political and the all-too-often real yet unspoken needs of consumers. The world has enough need; analytics should not be the cause of creating artificial new ones. In contrast, they should focus on how existing needs can be better addressed.

Take the new operating system for the Apple iPhone: I lost my ability to easily download my CDs so that I can listen to them on the go, but I can now take better underwater photos? That’s just dumb—not to be confused with innovative.

I would love to see the analytics that prompted the above change. All too often, analytics are used as the ultimate cover-your-behind safety net. So, the answer to your question is that analytics alone, do not promote meaningful innovation. It’s the interpretation of the analytics in a broader context and the very human creative leaps that stimulate innovative products that matter.

More than analytics alone, direct collaboration through any number of new interactive digital mechanisms and a resurrection of event marketing or experiential marketing as it is now called are gaining speed as enlightened brand rediscover the power of consumer touch.

Analytics are suggestive not definitive and have the capacity to hurt as much as they help. If analytics were the answer to new product innovation and related ROI and ROO (return on objectives), why would an estimated 40% to 80% of all new, fully researched, products fail? P&G, a company that played great lip service to innovation, has gained a reputation for systemization and a regimented process based heavily on analytics. Not even P&G hits the nail on the head every time. One wonders whether the measurement of the measurements might have been better allocated to more human direct interaction.

Yet, the film, Titanic, almost went direct to DVD because it did not test well. Analytics did not take into account that the film would find an audience who would pay to see the movie, again and again and again.

We’ve just come off a hotly battled Presidential election in which the sheer magnitude of failed analytics shocked even laymen. The same was true for Brexit. The surprise of both these political outcomes should cause many researchers to go back and question how and why advanced analytics failed to predict two of today’s most recent analytically overloaded, marketing catastrophes. In short, analytics are not what drive innovation. They are enzymes that only work within an eco-system of consumer understanding and appreciation. The answer to your question is deceptively simple, the answer to how analytics is escalating innovation is that they don’t—at least not by themselves.

How can brands collect better data for use in superior brand experience creation and design?

First, we must reconsider the definition of data. Popular culture, bad magazines, museum exhibits, experimental theater, political shifts and everything that touches consumers’ lives are data.

Marketing and creatives must be students of life. The modern consumer is a living performance of self—one broadcast on a digital stage. I have taken designers, marketing and business professionals on urban field trips to locations in their own cities they did not know existed. Many are surprised to see the innovation so often found in boutiques and public art among other things. Many a fashion designer has never seen one of their designs on a size 22 human body; it’s high time they did.

It is if many of these highly paid professionals, the same ones I take on field trips never leave their offices, relying instead on numbers; analytics to drive innovation in a world that they may not even know. Those charged with innovation must respect the cultural innovation that lives just below their noses and juxtapose what they see with the statistics they are provided.

How can marketers and designers become better at collaborating with data scientists?

This question should be reversed. Data scientists are not without bias and almost always draw sweeping conclusions based upon interpretation of data which may or may not have been posed to the most appropriate populations or may contain very nuanced observations that may be too subtle and thus ignored. Product development should isolate hypothesis, based upon actual cultural and consumer behavior, and use these observations to make creative leaps towards identification of what would or would not constitute innovation. Only then should the real collaboration begin—a map by which both the data scientists and innovative marketing and creative professionals test and retest technology and other product attributes that correspond with real consumers’ lives.

Today’s Fortune 500 marketing executives and agency strategists are largely recruited from Ivy League universities. Sadly, this tendency may be less appropriate for roles requiring innovation and a propensity towards creativity than liberal arts colleges or one of the many fine design schools. This is why the term “design thinking” has been coined. As recently as two years ago, when vying for a senior global creative director slot at a very reputable agency, I was told that my portfolio was too creative. That kind of thinking is not unusual particularly in consumer packaged goods. It explains why undirected data mining is another potential foil to innovation. If one were mining for diamonds and found emeralds instead, one would be foolish to discard the emeralds. Today’s market throws away an awful lot of emeralds.

Innovation requires looking beyond numbers and unrealistic expectations of the speed necessary for consumers to adopt groundbreaking products and brands. Every piece of data assessed by research should be accompanied by real anecdotal observation. For every statistical finding, we must take the liberty of creating hypothesis related to causality. Why do millennials averse to buying things still buy things? Why is it that during the last recession that it was the solid gold Rolex among the range of less expensive Rolexes that sold the best?

Finally, whatever one mines is generally buried. What we measure today was probably prompted by events and attitudes that occurred or prevailed at least a year ago. Together, data miner and marketer must discover new methods to measure “now.” Right now, the nature of the process measures yesterday. Now is the closest thing we have to tomorrow. And, in the end, fully understanding today is not truly helpful in creating the innovation that will shape tomorrow in the form of brand or product artifacts that are markers of a specific time and place. Again, there is no substitute for the creative leap.

How can marketers use data as part of a brand’s storytelling device?

What marketing and those who perform and deliver analytics often forget is that in this era of consumer empowerment and instant communication—a free, continuous focus group, via social media, is available to us. However, once again, it requires the ability to read between the lines. We could not invent a better measure of consumer need than what we can obtain for free—forging the foundation of innovation projection.

It is becoming increasingly evident that it is the consumer who should be part of the innovation process as they are the ones writing the stories of all new brands and products. Brands belong to consumers these days, not the other way around.

Consumers are humans who often turn on a dime and who are able to detect shoddy quality and outright lies. They do their own category analysis and are the most persuasive platform for peer-to-peer validation—the most influential element of storytelling.

We live in an age of collaborative branding and consumer-created brands. What that suggests is that the best way to build a narrative around a brand is to provide better brands or products, often improved through technology, that deliver a differentiated real solution to a consumer need.

Consumers will build the story if we deserve one. For instance, even though a brand like Waterford is considered an iconic Irish brand, much of its production happens elsewhere. Consumers want the brand to be Irish. Hence, the brand is Irish because the consumer wants it be Irish despite the reality. Perhaps we can attribute augmented reality to consumers. Reality is theirs not ours. Hence, our efforts to control crisis situations or manage defective products and product claims are mute and just a little bit duplicitous. The consumer won; it is we who must learn a new dance. To have more control over the fate of a brand’s story is to build it better, more in line with consumer-created expectations and genuine needs.

The mechanics of analytics enabled by sophisticated, sometimes customized algorithms and easier access to virtual consumer testing have, indeed, advanced. However, analytics, in of itself, is not always reliably predictive. It never has been. It is a set of tools within a larger toolbox, which can, when combined with other intelligence, leads to innovation. As such, innovation is a potentially dangerous friend without full appreciation of cultural influences, geo-political and economic changes, along with other aspects of living.

One could argue that analytics and most forms of data mining have resulted in more innovative, customized consumer dialogue , not necessarily, more innovative brands (particularly non-tech, non-e-commerce and non-entertainment brands). The various types of augmented, hyper and synthetic realties catering to brand experiences, add to more intimate, entertaining brand experience, they don’t write the story; however, if a narrative is too far removed or unrelated to a brand concept that delivers on satisfying a real need, new products, be they brand extensions or entirely new brands. can end up selling the artificial variations of reality versus the reality of the brand itself. Better media and analytics are not the points of all this. Brands and brand performance, differentiation and consumer satisfaction are.

Data is not always predictive. In fact, if one looks at the fully researched brands launched, only a fraction, estimated at one in eight ever truly succeeds. One need only look to our last election. Knowledge is not wisdom.

Analytics can be great assets in the innovation of brand experience and brands—not to mention justification for expenditures in the digital arena. The digital arena has advanced in its analytics, but, as of this writing, most of the techniques employed are far from foolproof and hardly a predictor of ROI. Digital metrics are akin to what I refer to as the power of “The Great and Powerful Oz.” Yet, advanced analytics has evolved to focus on the digital world versus the innovation of products. We forgot, that media for media’s sake is not the point of marketing. And, it is often the furthest thing from real innovation.

It takes a person and courage to make a creative leap. Innovation is a process, and the insight and analytics of today are not necessarily a true mirror of what is yet to come. Knowledge and insight will never be synonymous.

 

Alfred DuPuy

Executive director, North America, Interbrand

How is the use of analytics changing? 

There are probably a couple of different ways.  If I could just go back a little bit:  Traditionally, we relied on these big studies. They took a long time; they were very detailed; and they covered a ton of people. There were a lot of questions whether the interview was on the telephone, with people literally calling up other people in their houses and talking for 45 minutes; nascently online, with Internet users taking surveys; or on location with people going to a location to take a survey and answer a lot of questions.  That’s where we’ve been.

Things are changing. To deliver better products or just a better overall brand experience, in either the retail space or the business-to-business space, you can do more with analytics.

One reason is that analytics has changed the focus more clearly on the transactions that exist already. What I mean is that it’s often not a lot of extra work. Often, it’s not an extra survey for the shopper. It’s brands looking at the data that already exists and then supplementing that with quicker and different ways of chatting with shoppers.

So when you think about using data, you think about how you collect that data, which is so different today. You have much quicker surveys. You have people with computers in their pockets, with phones with more computing power than what I certainly grew up with.

That data collection and the ability to analyze that data and to analyze it quickly creates new kinds of relationships with the data themselves, right? To understand what the data is telling you is, that concept continues to change and it will continue to evolve in the sense of faster, more real-time things.

There are people out there that will argue that they have predictive capabilities, and yes, the next Holy Grail if you will.  Some really bright people are looking at algorithms using past behavior and trying to figure out what you, as a consumer, will buy going forward. And yes, as all our stock brokers tell us, past performances are not a guarantee of the future, but there are those stories. For example, the famous Target story about how the retailer is analyzing our basket and sending coupons and deals to households that the retailer knows better than they knew themselves in terms of what they’re going to buy next. Sorry, if it’s a long way to dance around the subject but there’s a lot wrapped up into it.

I do think that the future is dependent on individuals—you, me and also all sorts of consumers and shoppers—to agree to the use of data to make our lives and our experiences better. We can make it easier for brands to meet our expectations, which are always changing because we have very “liquid expectations.” What I mean by a “liquid expectation” is that when you have something that’s wonderful and seamless in one area of your life, whether it’s hailing an Uber or a certain app that helps you pay your bills, you transfer those experience expectations to when you go to a fast-food joint or when you go to a beautiful restaurant for a fine dining event.

Because of this, analytics have to continually evolve and to change in order to meet those expectations and to deliver what you’re looking for. So while a lot has happened in this area, there’s still a lot of opportunity out there for analytics firms to be able to go help and continue to meet or even exceed demands of consumers like you and me.

How can brands collect better data for use in superior brand experience creation and design? 

Some brands are able or have their brand loyalists’ permission to try things—even if it doesn’t work out. These brand fans sometimes not only forgive the brands but give them credit for trying. Sometimes, the brand learns something else in the process. What I mean with this answer is to say that the question isn’t always, How can they collect better data?  Yes, there are ways to collect better data, but sometimes it’s better to collect a bit of data and then they go and try to figure out the nuggets of what that superior brand experience creation looks like.

As an example, when Amazon tried to build a phone, it didn’t work out so well for them from a phone perspective but nobody’s really talking about it. That’s because Amazon built up equity in who they are, what they do and what kind of company they are. 

They figured out things to do with that data from that experience to create superior brand experiences elsewhere. Maybe, it was technologically with their Kindle Fire. From the process of creating that phone, Amazon also probably learned quite a bit including how to help people interact with a mobile phone. This enables Amazon to design their apps and their products and services that much better for people to feel like, “Hey, the Amazon brand experience, it’s not only superior but ever improving!”

Instead of looking simply at how to collect better data, focus first on just collecting data in either a small way or a fast way. It can be collecting data in a way that is attached already to a product or service that may be successful or may not be. 

Now, there are certain companies and brands that may not be able to get away with that.  No question about that. I know a car company will have a hard time failing fast when they bring out a new model. But I do believe that even for a car company that they have to recognize that the product cycle or the design cycle will need to match the rhythm of customer demands. We all know our attention spans are getting shorter and shorter, and our patience levels are getting reduced every single day.

So yes, the data quality matters.  I’m not suggesting that it doesn’t but it’s about getting that data quickly and doing something with that data quickly.  This is even more important. 

How can business leaders use data to elevate the way people engage with brands?

Many of us loved watching “Mad Men,” and if you think about that, it was so typical of the time. It was the Don Draper thing. Here’s the message; they will love it—just put it out there.  It will win.

That’s obviously not that way it is anymore.  The word you used earlier is exactly correct here, engagement. It’s ever more apparent now that companies do not really control their brands.  They are guiding the development now, because you and I control their brands in our pockets.  We have devices and connections to the ship that is the brand.

Successful business leaders are using the data to quickly get out their products or services. They allow their customers to engage with their brands more quickly, more robustly if you will. They are working to create those opportunities to get data quickly, and they are focusing on pushing the creation of the products and delivering those innovations.

These leaders are trying to figure out, “How am I going to get people to engage with my brands more? Because that’s how I’m going to get people to try my products more often and actually create a stronger brand going forward.” It’s really about partnership between the brand and its loyalists now.

Alfred, your answer makes me rethink the question about how can marketers use data as part of the brand’s storytelling device. Is the story also about the data collection and collaboration processes, themselves?

It absolutely is. Data feeds very much into the brand story, data feeds very much into what we say and do strategically, and can be that key case for actually helping people understand that we can deliver against what our promise is. It’s really important.

I will add that brands, companies and organizations have to measure and collect the data post experience, so that the brands can actually share those proof points. Because again, it’s about engagement.  If I, as a consumer, am hitting that accept thing at the end of that long winded legal-ease notification on my iPhone or my Samsung, I’m expecting something in return. So make sure you get me something in return. Doing this is no longer a differentiator for folks, it’s becoming table stakes.

How can marketers and designers create programs that deliver better collaboration with everyone involved, including data scientists and consumers?

When I first joined Interbrand, I’m going on my 12th year with Interbrand, I had this conversation during my second significant project on which I had a leadership role. I was in the Dayton office, and we were partnering with our New York office. When I presented some data to a senior strategist in New York, he responded, “I could have told you that before. That’s what I already thought.” I remember then telling him, “Yes, but you didn’t know. Now, you know. Now, you know this data is true and so now that you know that it’s true, you can now think about designing those experiences because the data is true. We can now go create the strategy.”

How do we create branding and marketing programs that encourage more engagement, encourage more interaction and encourage more collaboration with the customers?

I was just with a client right before this meeting, and they are in the growth business. They have to be in the growth business.

Their CEO is a long-time CEO, and this is either a Fortune 10 or 20 company. This is one of the largest companies in the world. It’s one of the oldest companies in the world. It has a wonderful legacy and heritage.

Yet, the CEO, who has been there a very long time, is increasingly under fire for not growing. So, he’s trying to figure out ways to do that. One thing that he is doing is going to his customers, and he is giving them the business case for why they should be partners with him and his team. These are the kinds of behaviors that will help bring value to that company’s customers, so that he can grow the business. And he looks outside of the category brands for how people engage with their customers as a way for him to emulate or learn lessons from.

 

R. Andrew Hurley, Ph.D.

Professor of Packaging Science, Clemson University

Founder, Package InSight, LLC

Founder and chief learning officer, The Packaging School
 

How is the use of analytics changing?

Outside of data, everything is an assumption. Data is truth. Analytics across biometric devices, such as eye-tracking, facial coding, self-report metrics and interaction logs help developers and brand owners understand the subconscious. We’re just not aware/cognitive of the impact design and marketing has on us. This data helps us with the human-centric approach—are users viewing/interacting/experiencing my design as intended? If not, let’s use the data, redesign, iterate, and test again.

How can marketers and designers become better at collaborating with data?

It’s a mutual, two-way street between marketers and data scientists. Data scientists answer questions, and marketers ask questions. It’s important that marketers understand the technology at a high level, so questions are single variable changes, and that data scientists understand the big picture of the question—what is the purpose of the analysis?

How can brands collect better data for use in superior brand experience creation and design?

By running in-context and in-store eye tracking studies, brands get an in-depth analysis of how long shoppers are looking at their product and which features command and retain attention.

Simply put, these studies can determine if the target market interacted with the design as intended. This can be tested from a tangible or digital perspective. In addition, data can help understand the overall experience—emotion can be captured. This can ultimately lead to help us understand any cognitive dissonance around the brand or design. Data itself can also be leveraged.

Package InSight worked within a category a couple years ago where the time to select a final product was within 10 minutes. (It was a home goods store). We leveraged eye-tracking data in store to iterate across designs to get that time down to less than 3 minutes. Use of data in this way helps to communicate and further the power of the brand by empowering the consumer and alleviating a pain point.

How can business leaders create more opportunities to use data to elevate the way people engage with brands?

Test everything. From Stanford’s Human-Centric model to the classic Double Diamond—all models point to prototype-iterate-test-redesign. Data is the key factor in generating a proper test and basing a redesign.

More than 90% of all new products launched in the consumer packaged goods world fail after two years. So why not test? And test again.