By definition, innovation requires breakthrough thinking and the corresponding development of new solutions. But what informs that thinking? Machine learning and data science are powerful tools that can speed the innovation process and yield better results, faster.

In today’s era of sophisticated data capture, behavior tracking, connected devices, social platforms and sensor-enabled products, data represents a new “natural resource” that nearly every company already possesses in one form or another. The question is this: How can organizations leverage this valuable data resource in order to support and amplify a culture of innovation?

The key is a smart approach rooted in data science. Applied data science entails more than data collection and basic analysis; it is sophisticated and responsive, able to iterate to produce optimal results. True data science requires not only knowledge of how to build machine learning and advanced analytic models, but the skill to understand business strategy — and the ability to link it all together.

First, ask the right questions in the right way

A data science professional must be able to communicate effectively across all areas of the organization, take business questions and translate them into a workable data science solution. This requires experience in assessing the types of data available, so that a machine learning solution can be developed to answer key questions posed by the business. Those answers can then be used as input for a cycle of innovation.

These questions are likely to be simple, but finding the answers may not be: this is why data science and machine learning are so powerful. For the marketing function the key questions may be: “Which campaigns have been the most successful? Why, and how can we predict which ones will be successful in the future?” For operations it may be: “Which products are likely to have warranty issues? Why, and how can we predict these issues ahead of time and mitigate the problem?”

Answering these types of business questions with machine learning and a data science methodology leads to game-changing insights that go beyond conventional data analytics and reporting. The result is true competitive advantage based on innovative deployment of new, data-driven models.

Next, build data science into the innovation cycle

With any new product or service design and development, part of the innovation process must involve understanding how data can be leveraged as a key innovation component. Smart organizations understand how to embed machine learning and AI into the concept at the beginning of development, in conjunction with the design process, and quantify the value AI-driven learning will create.

Innovation can and should be a cycle of continuous refinement and improvement, not a one-shot effort. With machine learning and data science it can, as organizations implement data-driven solutions to enhance key benefits and capabilities.

The possibilities for rapid and effective innovation are practically unlimited. Nearly every device we use on a daily basis from a toothbrush to our favorite belt could potentially leverage data to provide insights to product teams and end users in ways we could never imagine.

For example, when designing the “smart shoe” of the future, a shoe company may think about how sensors contained in the shoe can be used to understand running and walking behavior in order to iterate the next version of the design or provide feedback to users. The data could be leveraged to predict how long the shoes will last, what shoes the wearer may enjoy next, or if product defects are becoming apparent.

It’s important to think through the practical details of embedding advanced data science solutions into product or service development. How will the data will be transmitted, stored, and then leveraged in the context of the product or service ecosystem? For example, will sensor data be transmitted to an app that then uploads it into an enterprise machine learning solution to synthesize, model and score the results to deliver insights? Or will the product be capable of doing the scoring within the device itself? These types of questions require deep understanding of the available data science methodologies and algorithms as well as the design process itself.

Finally, expand the use of data science to drive operational innovation

Data science and advanced, intelligent analytics can take data from being simply an output displayed on a dashboard or in a standardized report to an integral part of an organization’s planning and execution of new innovations and enhancements to the customer journey.

This largely untapped opportunity resides in how data is leveraged. For example, think of all of the unstructured data in the form of customer comments sitting in spreadsheets and text files throughout your organization. What if that could be used to generate actionable insight in real time? Whether you are collecting customer feedback through a traditional Net Promoter Score survey or looking to leverage operational data that is stored in a traditional business intelligence tool, advanced analytics can help deliver insights and create sustained competitive advantages.

Historically, most companies have tried to generate this insight manually, by having a few employees read through customer comments and try to capture the core thoughts behind the words. While marginally effective for small numbers of comments, this method is time­consuming, expensive and labor­intensive.

While the human brain is still the best tool to quickly understand what a customer is saying and to quantify the varying degrees of satisfaction, we humans have an inherent flaw: we are unable to be truly objective. Each of us processes what we read based on our experiences, feelings, understanding and personal context. Two people reading the same comment can have very different opinions about what it is really trying to say. This method is not very repeatable or even comparable from one day to the next.

Today, algorithms for text analytics allow companies to deliver a scientific and repeatable method of dealing with unstructured content, transforming it into structured data. This use of intelligent automation can enable comments to be analyzed and processed in real time as they are received on social media or customer surveys. The organization gains the ability to react immediately, responding to issues or starting work on potential product improvements.

The new structured data produced from the text analytics can then be used in a predictive analytics model to predict outcomes and customer behavior, with that information leveraged yet again to assess the outcome of changes. Automatically generated insight drives improvements, while making predictions that can be tested to fine-tune the experience.

The bottom line: hindsight gives way to foresight

Data science enables companies to move from relying on hindsight to looking forward. Before data science, this was extremely difficult. Organizations were always looking backward, describing the data in simple terms and generating basic understanding. More advanced interpretation and prediction was an inexact science at best, when it was undertaken at all.

Data science tools and methodologies change all that, by creating the ability to move beyond description to diagnosis, prediction and prescriptive action. The value comes not from simple efficiency, but from foresight… an approach that is truly innovative!

About the author
Kellan Williams is an advanced analytics expert and experienced data scientist with proven analytical acumen. His experience spans many sectors and his skill set has offered him the ability to have high impact roles at IBM Global Business Services, Huntington Bank, Safelite Autoglass, LBrands, and Abercrombie and Fitch.

Kellan has spent the last 12 years helping companies turn their data into actionable insights. Kellan employs advanced methodologies and cutting edge technologies to transform business problems into sustained competitive innovations. He specializes in the areas of text analytics, predictive modeling, and advanced forecasting.

Beyond the basics: how to inspire and best benefit from a culture of innovation (COI).

It should come as no surprise that innovation has become a top priority for corporations, start-ups and enterprises of all types and sizes. The degree of innovation, the playing fields where it takes place and even its very definition — all have all grown and diversified exponentially in recent years.

Where once we’d see sporadic innovations springing from major mechanical or scientific breakthroughs, today’s product, service and digital experience creators are pumping out new innovations from moment to moment. There are still solitary basement inventors, garage incubators, government think tanks, innovation labs and corporate R&D departments cranking out innovation today. But the latest — and still relatively new — innovation source is forming through the adoption of diverse cultures of innovation integrated across entire organizations.

Unless you’ve sworn off of social media, haven’t visited your LinkedIn account or opened your email recently, you’ve probably read about the culture of innovation. You may even have experienced it firsthand at work. Much has been written on the topic by the likes of McKinsey and Accenture, among others, but the common thread reveals a few key actions necessary to create a thriving culture of innovation:

• Encourage all employees to be creative and innovative
• Promote (or require) cross functional collaboration
• Reward experimentation and risk-taking
• Support (or tolerate) failure in the pursuit of learning
• Empower through a flatter corporate hierarchy

What this top-line composite list of common COI directives may lack in detail, it does provide an opportunity to augment with other attributes based on our experience with clients in the innovation space.

Increase your innovation odds through open participation

Now in our fourth decade as a leading brand and innovation firm, Tenet Partners has successfully collaborated with countless product and service clients, and their customers, to create innovative solutions. Although we are a team of experienced and talented brand and design innovators, it no longer surprises us that inspiration for great ideas can come from just about anyone, anywhere, at any time. Of course, you need a way to harness inspiration and translate that into viable, real-world solutions (more on that below), but having diverse and numerous sources of input offers clear benefit over limiting contributions to a core team or sweating it out alone.

Larger organizations have the distinct advantage of numbers, with each employee bringing a unique blend of experiences and expertise to challenges. Opening participation in innovation up to more people increases the odds of finding the right inspiration for the next breakthrough product or service. It is well worth casting the net far beyond just your internal R&D, Engineering or Consumer Insights departments.

Break down innovation silos

Many companies still have rigid divisions between very specialized functions. Sometimes those individual functions can be quite innovative, even in unexpected areas such as accounts receivable, HR, IT or custodial services. Typically, pockets of innovative thought are confined to creatively solving problems for the innovators’ own departments. By utilizing a cross-functional COI approach and cross-pollinating ideas with other departments, however, the innovative output can be far richer.

Innovative ideas developed for one purpose may be applicable to other functions, potentially delivering company-wide benefits. A filing idea in the mail room might inspire a digital application concept in software design, a cafeteria backroom workaround might lead to a unique structural packaging solution, or a quarterly boardroom meeting presentation might inspire your next great advertising campaign.

Visualize innovation concepts to make them universally understood

Numerous innovation methodologies and tools are available to companies building or flourishing in their COI approach, many incorporating variations on design thinking. Empathy, one of the cornerstones of effective design thinking, is traditionally considered a way for innovators to understand the consumers or end users of one’s goods or services and the experiences they encounter. Empathy is also invaluable internally when collaborating with cross-functional teams who do not always work, think or even communicate in the same ways. It is all too easy for inspirational seed ideas to get lost in translation without a way to share thoughts in a clear and universal way.

At Tenet we utilize our Co-Magination^® approach where design innovators, skilled in the techniques of organizing and visualizing conceptual material, are integrally involved at every step in the innovation process. When concepts are visualized on the wall, everyone can see and understand the same ideas, and be inspired by them to build iterative variations and improvements. It’s a matter of not losing the designer when design thinking.

Align innovations with your brand

Innovation usually proves most effective if it is consistent with, and supportive of, what the organization’s brand stands for. A healthy and broad-reaching employee engagement program can ensure everyone understands and lives by the same brand promise, principles and messaging. It can also lay the foundation for innovation that is consistent with the brand.

The most successful new products and services are those that build on a brand and are identifiable as “something we’d expect from them.” Innovating with your brand in mind is important; Innovation can lead to significant market successes through disruption, but disruption for disruption’s sake can be risky business and potentially dilute or contradict who you are.

Monitor and measure innovation to gauge business success

Increasing innovation across an organization has clear advantages and offers significant rewards when undertaken effectively. Determining what success means and measuring it is also incredibly important and can be equally challenging. The ability to attract and retain top talent, gain market share and increase profitability are among many factors that can be measured to gauge impact.

Knowing where you are on the path to a fully integrated innovative culture, how it is paying off, or simply where you stand versus competition, makes measurement of your COI invaluable to your business success. Tenet Partners’ ongoing Brand Power research, which we publish in our annual Top 100 Most Powerful Brands report, recently added Culture of Innovation as a metric to predict value and cash flow. As James Gregory, Tenet Partners Chairman Emeritus reports “By adding the Culture of Innovation attribute to the historical attributes, the predictability of the cash flow multiple improved from 64% to 77%.” Other organizations also see the value of measuring innovation: last year, McKinsey published a study showing a strong correlation between integrating design thinking approaches across an organization and significant, measurable revenue growth and shareholder returns.

The bottom line: enhance and go beyond the basic principles of COI

• Embrace the likelihood that your next great innovation could be surfaced or inspired by almost anyone in your organization.
• Encourage cross-functional team collaboration, even if they speak entirely different innovation languages.
• Take advantage of your organization’s size and diversity to increase your odds of successful innovation.
• Harness design-thinking talent and visualization skills to capture and communicate concepts throughout.
• Align your innovation efforts with a clear and consistent expression of your brand.
• Track how well your COI is delivering on key metrics such as talent retention, brand value and financial rewards, among others.
• Treat your COI as you would any good innovation initiative: collaborate, iterate, learn, and adjust as needed.

Patagonia is a brand that is so eco-friendly their CEO would rather teach you how to repair your fleece than sell you a new one. As a certified B Corp, their brand promise is built on environmental activism, placing business philanthropy at least on par with profits. At The North Face, on the other hand, they believe “the best way to be sustainable is to make product[s] that would last a lifetime.” As a result, their brand promise is focused on innovation and durability.

We view a brand promise as the core idea that unifies your brand. It is a distillation of your brand positioning, which is in turn built on a foundation of positioning attributes. These attributes range from foundational – meaning you must have them simply to be in your industry – to differentiating – meaning they are distinctive and set you apart from your competition. These elements, collectively your “brand platform”, are internal but drive the outward expression of your brand – including in marketing communications.

This means that while we may not be able to see the brand platforms for Patagonia and The North Face, we can infer certain priorities based on their external communications including websites, advertising and press interviews, among other things. For both brands, environmentalism / sustainability is present in their communications, but it plays a very different role for each.

For The North Face, environmentalism seems to be a foundational attribute. Sustainability is important to the brand – for example, “Protect” is one of their three core tenets of corporate responsibility, alongside “Product” and “Empower.” However, with so many other outdoor and sportswear companies focusing on their environmental credentials, The North Face feels they need something more. For them, that “something more” is making the product the hero – but sustainability still gets its play, with the idea of creating less waste implicit in their claims about durability.

For Patagonia, on the other hand, their environmental mission is their prime differentiating attribute. In the words of the founder’s nephew, “There was a strong sense from the beginning of wanting to protect the wild places.” Their “Provisions” line of sustainable food, a focus on protecting their supply chain and “Patagonia Action Works” – a network to connect individuals with grassroots environmental activists – are just some of the ways they demonstrate their dedication to corporate stewardship.

Patagonia is not alone in this trend. It is becoming increasingly common to hear brands talking about their “purpose,” beyond delivering shareholder value – at Cannes Lion 2019 the topic was discussed extensively by top brands including Unilever and P&G. The key here is not to view it as an either/or proposition –brands are seeing that authentically committing to a higher purpose can actually increase revenue from socially-minded consumers who want to reward like-minded corporations. But that word, “authentically” is pivotal – if brands are talking the talk but not walking the walk consumers will notice.

Grappling with the role of environmentalism is not a new struggle for brands, and particularly consumer brands that generate “stuff.” As far back as 1987 the U.N. published a report opining on how corporations could reconcile sustainability and development. The issue has certainly gained more attention in modern times with Millennial – and now Gen Z – influencers increasingly holding brands accountable for their operations. Environmental, Social and Governance (ESG) is becoming a byword from boardrooms to trading desks, and instances of corporations behaving badly can always be counted on to blow up social media.

What all this tells us is, in today’s culture no modern consumer brand can afford to ignore its environmental footprint – even those brands whose “purpose” lies outside sustainability. However, it is important that you approach the topic in a way that is authentic to your brand and credible to stakeholders, both internal and external. As the successes of both Patagonia and The North Face clearly demonstrate, there is no one right way to incorporate environmental sustainability into a brand platform.

About the author

Laura Scharf is an experienced strategist who has played an integral part in (re)branding and activating dozens of brands. From leading discovery—including IDIs and competitive audits—to crafting authentic, differentiating positions to defining architecture and messaging, she brings creative problem-solving and grace under pressure to all her projects. Current clients include Edwards Lifesciences, Gore, Mastercard, ACA Compliance Group, Hartford Steam Boilers and Trinity, among others.

Before joining Tenet, Laura was a strategic account director at Starfish and a brand strategist at TippingGardner. Her expertise spans B2B, B2C, and non-profit with a particular focus in professional services. She has an MBA from the Leonard N. Stern School of Business at New York University.

In a recent study published by The Society for Competitiveness, 119 consumer facing corporate brands and their revenue growth rates were studied. This study was unique in that product branding contribution to revenue growth is extensively examined but corporate brand contribution to revenue is far less analyzed.

The data examined was for the period from 2011 to 2016 and includes Tenet Partner’s CoreBrand Index® which provides BrandPower (a survey measure of Familiarity and Favorability with a corporate brand), fundamental financial data, and paid media from Kantar Media Intelligence.

This research drew on from our chairman James Gregory, Ph.D. and Jack Weichmann’s definition of the corporate brand – “that is the public’s perception of a company – the preconceived ideas and prejudices that have formed in the minds of customers” (1991, pg.2).

The CoreBrand Index (CBI) was developed to address the lack of quantitative data available on corporate brands. For over two decades it has been the basis of models that measure how the brand contributes to market cap and brand valuation.

The CBI is a telephone interview conducted among an audience of impartial observers. Respondents are business leaders who are also affluent consumers. They are Vice President (VP), Director and Manager level executives in the top 20% of U.S. businesses, based on revenue. Respondents rate their Familiarity with a list of 40 companies. Those that indicate that they know more than just the name of the company are then asked to rate Favorability on three attributes: Overall Reputation, Perception of Management, and Investment Potential. These measures are then combined to create BrandPower, a single measure that conveys the corporate brand’s size and quality among respondents. The measures are reported on a 100-point scale. Each company is rated by 400 respondents per year. Approximately 1,000 companies are tracked, many dating as far back as 1990.

Figure 1 below highlights the analytical process employed by the authors to examine how corporate brand can contribute to revenue growth.

The first step was a correlation matrix that examined BrandPower growth in different time periods compared to revenue growth. 1-, 3-, and 5-year changes were examined. The results were not as encouraging as we expected. Ultimately, it was concluded that the level of BrandPower may be impacting the relationship. Larger brands were maintaining, not growing. Smaller brands were seeking critical mass. However, mid-level brands had both mass and room to grow. This was then proven in our quintile analysis, where we saw the brands in the middle tier had the highest rates of growth for both BrandPower and revenue.

The tiers refer to the company’s level of BrandPower. Tier 1 is the strongest, followed by Tier 2 and so on. Examples of companies in Tiers 1, 2 and 3 are:

• Tier 1 – Coca-Cola & McDonald’s
• Tier 2 – Tyson Foods & Old Navy
• Tier 3 – Pet Smart & Papa John’s

Figure 2 below shows an overall regression analysis of BrandPower growth rate and sales revenue growth rate for all 119 companies.

Figure 3 below identifies the same analysis, but with only the middle tier, tier 3, companies. As can be seen by the regression conducted, the growth coefficient is nearly twice for tier three than it is for the entire group, 0.1728 vs. 0.0897. This indicates a higher rate of revenue return for BrandPower growth among the tier 3 companies than for the broader group of competitors. This result informs us that companies in the middle tier have tremendous incentive to improve their BrandPower.

Further analysis of paid media spend and BrandPower demonstrated that as paid media increased BrandPower increased. An analysis of brand valuation showed that for an incremental paid media investment of $1 million, a $2 million increase in brand valuation could be expected. This 2:1 return on investment is a significant argument in favor of investing in the corporate brand.

References

Koch, C., Puckey, B., Williams, V. (2019). Empirical Findings: The Corporate Brand, Competition Forum, American Society for Competitiveness, Vol. 17, Number 1, 2019, (1-12).

Gregory, J.R., & Wiechmann, J.G. (2001). Marketing Corporate Image: The Company as Your Number One Product. Lincolnwood, Illinois: NTC Publishing Group.