Earlier this year, Target Corporation lost nearly 25% of its $100B market capitalization following a disappointing earnings report. A few weeks later, the stock fell again as the company announced that it would be reducing prices due to rapidly increasing inventories. Walmart, a retailer four times larger than Target, lost 20% of its value over the same period claiming changing consumer behaviors and continued supply chain challenges were responsible. Shockwaves spread across the retail landscape as markets scrambled to process the impact of underlying trends. This may have you wondering: “In a world spinning with constant news of inflation, spiking energy costs and supply-side woes, why would deflationary trends like ramping inventories be hitting some of the world’s largest businesses?”  

If your business is consumer products, many challenges of the pandemic era have become abundantly clear: hiccups at the top of the supply chain due to lockdowns, shortages in shipping containers and port infrastructure, a massive transition from consumption of services to goods and housing and breaking news every week are impacting all these factors as they shift and churn. While understanding how all these dynamic inputs impact your bottom line might seem like an impossible machine, they all boil down to one core concept: forecasting consumer demand.  

Whatever the incarnation, be it sales operations, inventory, or revenue management, it is someone’s job to predict future demand as input to a variety of investment and staffing decisions. It can be done terribly, as a trendline of historic quarterly sales with a seasonal adjustment applied—an approach completely unable to respond to a shifting macro environment. It can also be done incredibly well, with dynamic tools in the hands of multiple stakeholders sitting on real-time data that responds to the slightest change in consumer preference, sentiment or spending power.  

Below, we outline three key elements to building successful demand forecasts that will keep the pulse of consumer engagement no matter how unpredictable the world can be.  

1. Look to First Party Digital Data for an Accurate View of Individual Customer Behaviors Over Time 

The first thing to note: Organizations need to use web and/or app engagement data as the foundation, ideally first-party data blended with media exposure and eCRM for a more holistic view across the customer lifecycle. These data types are most critical and valuable due to their real-time nature and reflection of active shopping behavior. After all, if a consumer is no longer interested in buying a product from you, they won’t be visiting your website to read about it. This is the signal you want. It is also important to have data tracked via a robust web analytics platform (such as Google Analytics or Adobe Analytics) collected via a logged-in state or a first-party cookie. This will enable consistent visibility into the same consumer’s behavior over multiple visits, especially if your products have a longer consideration cycle.  

Additionally, having a good tagging strategy and metadata is critical. Organizations will want data scientists to mine the data to understand exactly what users were engaging with at each stop across digital properties. One of the big mistakes we’ve seen among companies who use digital data for demand prediction today is that they look at all the behaviors on aggregate, which can mask a dip or rise in demand behind outlier behaviors. Organizations’ goal should be to predict the demand for each individual and then aggregate demand at the other end. Otherwise, they risk forecasting inordinate demand for a single consumer or household.  

Also, strive to migrate complex engagement data (“log-level” data) into a flexible big-data environment. This should be done so that data science models and business applications can be easily built on top of it. A good example of this would be the big-data warehousing products within any of the ‘big three’ cloud providers (AWS, GCP and Azure).  

While it might sound like a lot, most mid-to-large-sized organizations already have most of the key elements in place and will simply require a few small pieces to complete the puzzle. Building the infrastructure can take as little as three weeks or as long as three months, depending upon the current maturity and toolkit. But the value is there: Many companies who accurately predicted the Covid-19 demand shock and subsequent demand spike did so by getting real-time signals from individual consumers based on changing digital engagement with their brands and products.  

2. Empower Data Scientists and Engineers to Design and Automate New Demand Models—but Don’t Sleep on Strategy.  

Another lesson every business has learned over the past decade is that all the data in the world is worth nothing if you don’t know how to use it. A small, dedicated task force of data scientists, engineers and at least one strategist is ideal for building this capability.  

The strategist role is critical for developing any sort of data science application, akin to a product manager but with more specialized skills to serve as a subject-matter expert on digital data and sources. This person acts as a steward of the business to ensure data scientists and engineers have the appropriate context in designing their analysis and setting up the infrastructure to support it. “Demand” as a concept isn’t one-size-fits-all. Multiple ideas and approaches need to be evaluated and prioritized over the course of the project. With that in mind, the strategist also acts as a liaison to the stakeholder teams when decisions need to be made regarding proxy measures, model outputs and historical techniques for comparison.  

Data scientists ensure the data is organized to interpret cause and effect, that the model is as accurate as possible, and that the output is responsive to new information entering the ecosystem. If they’re working in a cloud environment, they will have access to data processing tools and ML-as-a-service. The data science team will likely lean on those tools and their native integrations with the data platforms to develop scalable and up-to-date demand models.  

Data engineer(s) should ideally have expertise in ML Ops and some exposure to digital analytics and demand-side platforms, as source data can be somewhat ugly and difficult to work with. Key tasks for this team will be the processing of source data, staging of data for analysis (and eventually reporting) and automating the model outputs. The latter is of critical importance, since getting updated forecasts frequently is the key to understanding shifting trends and reacting before it’s too late.  

Working together, this team can generate not just improved demand forecasts to inform downstream applications such as inventory, but also outputs powering higher-funnel tactics such as dynamic creative optimization and offer management. Keeping the team online as new capabilities launch and managing a roadmap of prioritized future applications is a great way to get continual value out of your digital infrastructure. 

3. You’ll Need User-Friendly Tools if You Want to Drive Adoption of New Techniques  

The only thing better than having all the smartest tools in the industry: actually using them. Getting the outputs of the organization’s data assets into the hands of decision-makers is just as important as developing those assets themselves. Business intelligence (“BI” or Data Visualization) tools and specialists are the keys to disseminating new data that suits each stakeholder’s needs. BI specialists should work with stakeholders to understand their requirements and with the engineers to produce user-friendly outputs from the models upon which they can design visualizations.  

Reframing team roles and advancing technology and tools allows businesses to democratize critical data and serve business units based on their purpose and key decision points. Regional merchandisers may want to see shifts in demand with the ability to drill down on specific geographies. Others may want broader, national demand (or individual product propensity) visualized alongside incentive, inventory or media spend. Targeting a few stakeholders early who are interested in trying new techniques can be important; building internal advocacy and developing case studies early on can speed up the process of getting new tools to market.  

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Companies seeking to accelerate growth by improving their value proposition may be stopping short by solely focusing on identifying the total addressable market (TAM). While identifying attractive market spaces is the right first step, a market-led growth approach alone is insufficient. It can lead to false assumptions that make a business vulnerable. Successful value propositions require a deep exploration of target customer needs, not just market size, to inform how to unlock growth.

The Limitations of a Market-Led Growth Approach: Starbucks’ Expansion

To see the limitations of using only a market-led growth approach, recall in 2008 when Starbucks announced the closure of 600 US stores. At the time, Starbucks was confronted with the challenge of continuing to drive same-store sales while rapidly expanding its footprint. The brand created new offers around wi-fi services, music and an expanding menu of food and drinks while opening stores at a rapid pace.

However, Starbucks’ customers were starting to seek out other coffee chains. Companies like McDonald’s and Dunkin Donuts were improving their value propositions around coffee to regain forfeited shares. In the process, Starbucks diverted investments in delivering a great coffee experience in favor of investing in near market adjacencies with little success.

“Successful value propositions require a deep exploration of target customer needs, not just market size, to inform how to unlock growth.”

This led Starbucks to deliberately rethink its value proposition and develop a focused set of customer-led growth moves to turn around the business. And where did Howard Schultz start? With the customer. Starbucks launched a series of new moves including the introduction of a Pike Place blend, delivering whole bean coffee to stores and purchasing new machines designed for better coffee brewing. Starbucks later continued with one of the most engaging loyalty programs in retail to continue to drive same-store sales. They have continued to evolve their value proposition in countries against customer needs, with frappuccino beverages ranging from red bean green tea in China to dulce de leche in Argentina. Starbucks has even created uniqueness in its more than 30,000 stores around the world.

Why Value Propositions Should Be Aligned with Customer Needs: An Example From Cigna

Sharpening one’s value proposition against target customer needs and supporting it with market-shaping moves can become an essential motivator on “why buy from us”. But it’s not just retailers who seek out developing strong customer-led, value propositions. We see categories like health insurance and financial services driving massive transformations in their market-facing propositions and investing greatly to understand customer needs.

Take the health insurance challenge of continued cost pressure, rising cost burdens, and trying to create engagement and value beyond the policy. To combat this, global health services company Cigna has made significant investments to truly understand its customers, recently completing a three-year study of 200,000 consumers on health incentives. The company has been on a journey to strengthen its value proposition with a focus on integrated capabilities and connected, personalized solutions that advance whole-person health.

Cigna started this proposition development through a thoughtful augmentation of value-added services for employers and employees built around customer needs. Cigna services support a range of issues employers care about for employees such as life assistance, financial wellness, health advocacy, wellness and travel. The company is taking these services to the next step piloting Cigna Health Today, an Amazon Alexa voice skill aimed at proactive health engagement.

In 2018, Cigna distributed more than $255 million in cost savings back to customers for completing 2.5 million health goals. Further, in December 2018 Cigna announced a $67 billion acquisition of Express Scripts Holding as a move to strengthen the company’s position as a one-stop-shop for health needs. Core to Cigna’s value proposition is finding a more complete way to engage and support customers beyond just insurance.

A Customer-Informed Value Proposition to Drive Uncommon Growth: JP Morgan Chase

In financial services, a lot of focus has been placed to thwart off emerging FinTech and BigTech (e.g., Alibaba, Apple, Amazon) and winning with the next generation of affluent millennial consumers. JP Morgan Chase shocked audiences during a time of industry-wide increased operational cost pressure by offering 100K point consumer signup bonuses on its new millennial-focused Sapphire Reserve card. The card hit its annual acquisition goal in two weeks and Chase even ran out of the signature metal cards.

JP Morgan estimated quarterly losses at the end of 2016 at over $300 million. This did not come absent of a strategy than invested heavily on deeply understanding millennial consumer financial services and spending needs down to the “plunk” factor of dropping the metal card. JP Morgan Chase was seeking to build one of the strongest value propositions behind Sapphire and extend the offer well beyond credit cards.

JP Morgan Chase, CEO Jamie Dimon shared that despite acquisition costs expensed over 12 months, the benefits would come over several years. Fast-forward just over 2 years since the launch and the results are impressive. The average Sapphire Reserve cardholder income is ~$180K and the average sales volume of $39K, a true “top of wallet” card. What’s more impressive is that 96% of cardholders actively use their cards and annual renewal rates are >90%. The company does 2X the industry average in merchant processing volume over $1.2 trillion and has raised its credit card Net Promoter Score with customers 18 points since 2012. JP Morgan Chase hasn’t stopped there, the company continues to find meaningful ways to deliver customer value (new “moves”). Evolving its Premier Platinum checking accounts to launch Sapphire Banking, with new perks like no ATM, foreign exchange, or wire transfer fees. Sapphire Banking also includes free online stock and ETF trades along with access to special experiences. Sapphire is giving valued customers an ever-widening list of perks and services to keep people locked into the ecosystem.

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When Charles Dickens said, “It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness,” he may have been comparing a typical customer journey map to an ideal state experience.

The Benefits of Experience-Led Transformation

Look around: the “big four” Amazon, Apple, Facebook and Google all achieved initial critical mass by providing singularly satisfying, consistently outstanding experiences.

The world’s top innovative companies of 2019, such as Meituan Dianping (handles virtually ANY type of booking and delivery in China), Grab (squeezed Uber out of Singapore and broadened into food, travel booking, financial services and now even health insurance service delivery), Stitch Fix (the radically data-driven personal styling at scale fashion business), or Sweetgreen (fast casual, farm to table restaurant chain empire) – are all built on the backs of consistently satisfying experiences, at scale.

These companies move far beyond optimizing customer experience (CX), they are reaching the new “X frontier” – creating a harmonious environment for employee experience (EX) and partner experience (PX) in their respective value chains.

Because of these disruptors, expectations for general experience delivery has been raised everywhere. Sixty-seven percent of customers say standards for good experiences are higher than they’ve ever been; 76 percent expect companies to understand what they need and meet their expectations; and 64 percent find customer experience to be more important than price when it comes to making a purchase. Clearly experience drives customer value creation and loyalty.

We know it pays off for businesses, big time. Experience-driven businesses report between 1.6x – 1.9x higher YoY financial value growth because of improved retention, repeat purchase rates, average order size, and as a result, higher customer lifetime value (CLV).

In a nutshell: the rules have been reset and expectations raised. Experience is critical to success and risk of inaction intensifies– so WHY are so many organizations STILL nowhere close to transforming their approach to experience?

Common Challenges of Transforming Experiences

Two of the biggest challenges we hear from executives are:

1. CX is a systematic and daunting task that involves every single muscle of the organizational mind, body and soul. Where do I start?
2. The payoff sounds nice in theory, but how do I know if we are making progress? How do I measure this?

Where to Start with Experience-Led Transformation

There is such a thing as CX maturity. Knowing where your organization is on the maturity curve will help you determine where to start and prioritize battles to fight now vs. later. There are six criteria that typically defines an organization’s CX maturity level:

• Vision & Strategy: to what degree does the organization (from CEO to frontline) share a CX-centric vision and govern CX with a formal, top-down, enterprise-wide process?
• People & Culture: to what degree does the focus on CX drive the formal and informal cultural rituals and processes; do CX dedicated teams, cross-functional integration, and on-demand access to formal training exist, and is consistently adopted?
• Design & Deliver Experiences: to what degree does the organization translate the CX vision to detailed blueprints and use it to manage the day-to-day operations and understand the degree to which the delivery matches the designers’ intention and deliver on the specific customer needs?
• Segments & Data: to what degree does the organization have clear alignment on the customer segment(s), their priorities, and integrate multiple sources of data to track the holistic customer experience across online and offline channels, and have a clear data integration, live dashboard reporting, socialization & governance process in place?
• Analytics, Measurements & Continuous Improvements: to what degree does the organization utilize integrated data and advanced analytics to guide continuous decision making, investment prioritization, improvement process and pinpoint ROI?
• Agile Management: to what degree is the CX management process dynamic, real-time, based on maximizing value capture and is tied to key individual incentives?

The Power of Organizational Alignment

Most organizations are at an “ad-hoc” state, characterized by having siloed functionality and lack of commitment to holistic CX training, no coherent governance process or CX aspirations, limited conceptual journey mapping that treats all customers alike, and reactionary insights meant to describe the past rather than prescribe future actions.

If your organization is at the “ad-hoc” state, aligning leadership on the vision of transforming CX and justifying the investment by creating a case for change is crucial – without leadership alignment and focus businesses prolong the vicious cycle and incur wasteful costs.

“These companies move far beyond optimizing customer experience (CX), they are reaching the new “X frontier” – creating a harmonious environment for employee experience (EX) and partner experience (PX) in their respective value chains.”

Once organizations achieve alignment, the next step prioritizes the most critical use cases (i.e. prioritizing CX investment, reducing churn) and identifies the things standing in the way from delivering against these use cases. Approaching CX in this way ensures that the transformation will have the greatest impact and more importantly allows for the creation of a targeted initiative pipeline to close the gap.

One of our financial service clients wanted to prioritize CX investments to maximize value, and quickly realized the organization achieved greater maturity for data integration and analytics.

They were drinking from the proverbial data fire hose and had the data scientists in place to analyze these disparate types of data; however, they lacked connection to the customer or the business to provide a data-driven way of prioritizing opportunities.

3-Pronged Approach to Transform Customer Experiences

To overcome these challenges, we engineered a three-pronged approach to help transform CX:

1. Lead with the Customer: fine tuning the segmentation to lend more insights about customers, identifying needs and motivations of highest value customers
2. Connect to Business Impact: making sure all analytics and data process is empowered and managed so that the results can tie to business impact
3. Drive Data-Backed Decisions: pointed the insights gained through analytics toward specific business leaders and their KPIs to drive better/more informed decisions

These three areas were then translated into specific work streams with assigned owners from various functions of the organization to can start to create the first wave of quick wins for the CX transformation journey.

How to Track Your Transformation Progress

Once the transformation gets initiated, how do you know if progress is being made?

Big leaps in technology, data, process and governance will register on the maturity assessment itself in a year or two. For less significant improvements a finer odometer needs to be used more frequently – to demonstrate quick wins and proper momentum, testing and learning, or quick course correcting.

A best-in-class B2B CX measuring system includes four components, each with a distinct role to play:

• Annual Customer Survey: provides a comprehensive view of customers on ongoing topics of interest – a mechanism to track progress over time. Offers an opportunity to test a greater variety of questions and stimuli, including new hypotheses from BU leaders and marketing teams. Typically, CX metrics included in the annual customer survey would include customer satisfaction, NPS, specific touchpoint assessments based on recent experience, and competitive benchmarking.
• Transactional Survey: provides a more nimble and frequent view of the most critical leading and lagging indicators identified by internal leadership and the annual surveys – all to help drive short- to mid-term decision making. This might be in the form of a website pop up survey right after you finish a transaction and have that experience fresh to mind.
• In-Depth Customer Research: provides an opportunity to conduct in-depth research (typically qualitative) on specific subjects based on acute or broader strategic questions. Helps generate new ideas and opportunities to track/test in the annual survey efforts. For instance, the sales are down this month in a specific region and leadership is wondering if there are CX mistakes they need to learn from – given the narrow concentration and timely manner of this issue, in depth qual is best chosen over a survey
• Account Conversations: plays a vital role in identifying account-specific issues and insights through the relationship managers.

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As customer experience (CX) becomes a central battleground for financial services companies, a number of new questions have been hounding experience leaders, product owners, marketers and operations heads:

• How do I measure what truly matters across the experience?
• How might I align a mix of functions, business units and regions behind a unified view of what matters?
• How might I motivate these groups to coordinate in delivering a superior experience where it matters most?

We have worked with clients across a broad spectrum of measurement sophistication. On the one end, some have spent millions on sophisticated measurement software, only to then struggle with translating their firehose of data into actionable insights. And on the other end, some still rely on a mix of CSV files and manually-generated reports across disparate systems – and struggle with finding meaning across the disjointed, hard-to-compare data.

“In our work, we have sought to make measurement more actionable by defining a unified CX measurement framework.”

We have found that there are five key tenets that can help companies measure CX in ways that provide clarity, improve decision-making, and ultimately drive business impact.

1. Start With What Matters Most To Customers

Leaders at large organizations will know all too well that it’s tempting to only measure interactions and transactions that sit within their domain. Yet, this common mindset produces an incomplete view of what truly matters to customers across their entire journey.

For a large financial institution in North America, we discerned what was meaningful to measure by starting with a customer-led view of what truly mattered to them across their end-to-end experience journey. We used qualitative techniques such as in-depth interviews and ethnographies to reveal pivotal moments across the experience. We then used quantitative research to sharpen our understanding of customer behavior at key moments and clarify how these influenced specific business outcomes.

2. Define a Unified Framework Across Levels and Functions

Most large organizations have multiple CX measurement frameworks, techniques, KPIs, and reporting mechanisms. While each of these might serve the purpose of distinct management levels and functions, they also create multiple and different versions of ‘what truly matters.’ This makes it particularly difficult for cross-functional teams to translate insights into action.

In our work, we have sought to make measurement more actionable by defining a unified CX measurement framework. Such a framework can typically span different management levels and functions while also identifying relationships across key measures that allow a more cohesive view.

With such a framework in place, senior executives, managers and front-line operators can all form a shared narrative about the firm’s CX performance, issues and opportunities. Executives can use high-level KPIs to measure the overall company CX priorities. Managers can use more detailed KPIs to define actionable milestones in service of the overall priority and allocate investments. Front-line operators can leverage a highly detailed subset of metrics to mobilize plans, establish service-level targets and track progress.

3. Build a Better Model with Leading and Lagging Indicators

The process of developing a unified CX measurement framework requires a sharp eye toward identifying the right measures that accurately describe customer impact and eventually business impact. Getting this part right often falls on ensuring we consider a broad range of data (ideally, data related to operational measures, customer sentiment/perception, customer behavioral response, and business outcome) as well as robust econometric models and analytics that connect CX measurement explicitly to financial value.

For example, in developing a model that derived relationships across different CX metrics for a large U.S. financial services firm, our data and analytics team made sure to:

• Account for time dynamics where observations in one time-period are linked to observations in different time-periods
• Capture interaction and endogeneity by allowing variables that are jointly determined to ensure estimates account for simultaneity and interaction of variables
• Measure non-linear relationships and account for diminishing returns to ensure true influence is isolated
• Control and capture influence of macro-economic changes and shocks that influence the business (e.g., shifts in interest rates or regulatory changes
• Account for uncertainty based on probability — identifying expected outcome, what’s possible, and likelihood through Monte Carlo simulations

Our analytics team was also able to parse out what leading indicators managers should frequently look at (such as engagement, digital activity) and how these eventually predicted lagging indicators (such as customer acquisition, retention, advocacy) and ultimately financial performance. Most importantly perhaps, the model was translated into a what-if simulator that allowed our client to assess the likely financial impact from a variety of potential CX improvements.

4. Look Within, and Beyond, Your Industry for Comparisons

Competitive benchmarks are useful when trying to understand the areas of the experience to invest in. However, we believe it’s a mistake these days to compare your CX to just your competitors alone. Your customers are certainly going well beyond and comparing it with leaders across multiple categories – and this is especially true in sectors where satisfaction is systemically low.

For example, for a large global insurance provider, our research revealed that their CX scores within a key market in Asia were higher than most of their competitors – especially in parts of the journey that mattered most. However, a closer look also revealed that industry-wide scores in this market were significantly lower than other comparable markets, reflecting a more systemic, sector-wide level of customer dissatisfaction.

Despite temptations of proclaiming that they were providing a “leading experience,” managers at this insurer quickly agreed that they had no appetite for being “the best of the worst.” Instead, they recognized this as a clear opportunity to leap-frog their competitors and newer disruptors by doubling down on their relative strength in CX.

5. Invest Disproportionately in Defining and Developing a Measurement Governance Model

The most sophisticated of measurement strategies can end up failing if they are not accompanied by a governance model to deploy, maintain and ultimately act upon insights that evolve or transform the CX.

In our experience, a successful governance model typically solves for three key questions:

• What people across what organization/functions will deploy, maintain and act on experience measurement reporting and insights?
• What management processes will be required to drive systematic deployment, maintenance and actionable CX improvements?
• What data, technology and interactive tools will be required to acquire, store and provision KPIs at the various levels of fidelity required across the organization

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Among the major challenges marketers face in the year ahead will be creating and retaining a loyal customer group. It is a challenge because of the difficulty of brand building in an era with fast-changing media, much under audience control, and because e-commerce and digital communication make it difficult to integrate messages and deliver on-brand customer experiences.

How to Build a Loyal Customer Base

How do you create, manage and leverage a loyal customer base in this environment? To empirically address that question we leveraged the database associated with Prophet’s Relentless Relevance 2015 study in which 400 brands from 29 categories were assessed on more than 20 measures of brand relevance. The goal was to determine what drives two loyalty levels: the satisfied and the committed.

The satisfied are those who buy regularly, often out of habit, because they are satisfied with the brand’s performance over a long time period. They perceive the brand to be familiar, dependable with consistently good experiences and easy to buy. The brand has become a comfortable habit and there is no reason to change. For some low-involvement products, the satisfied are the core loyalty group.

“They perceive the brand to be familiar, dependable with consistently good experiences and easy to buy.”

The committed have a more intense, involved relationship with the brand. They are more likely to have an emotional attachment, to receive self-expressive benefits and to have a user experience that goes beyond merely functional benefits. They are also more likely to be brand supporters, even telling others about the brand and its user experience. For some high-involvement products, a brand should aspire to have a committed group.

How do these groups differ with respect to what drives their formation and nurtures them over time? From the Prophet study, indicators of the two loyalty types and five potential drivers of loyalty were identified. The extent to which each of these five indicators impacted (explained variation in) the two levels of loyalty were explored statistically.

The “satisfied” group was represented by the phrase, “one of my favorite brands,” which reflects satisfaction and a lack of motivation to change to another brand. The “committed” group was represented by the phrase, “I can’t imagine living without,” which suggests there is a functional or emotional attachment that is so intense that the absence of the brand would be upsetting.

5 Drivers of Brand Loyalty

There are five variables that have been uncovered to be potential drivers of brand loyalty; several have multiple indicators that are combined. These variables are:

• Dependable: described as “always deliver to expectations,” “I can depend on,” “I trust” and “consistent experiences”
• Better: described as “better than others,” and “only brand that does what it does”
• Social media: described as “has interesting and engaging content online”
• Light emotional connection (LEC): described as “makes me happy”
• Heavy emotional connection (HEC): described as “connects with me emotionally,” “makes me feel inspired” and “has a purpose I believe in”

Satisfied Brand Users

Consider the satisfied model. The “dependable” variable has more than three and a half times the explanatory impact as does the “better” variable. This confirms the hypothesis that satisfied loyalists are driven by habit, familiarity, comfort and satisfaction, and being better is not as important as delivering the brand promise. They are instead going to stick to the brand as long as it delivers. Controlling for “dependable” and “better” (perceived superiority), the “light emotional connection” variable has a meaningful role, about equal to that of the “better” variable, while the “heavy emotional connection” variable has zero impact. Finally, the “social media” variable, as expected, was not a driver, essentially zero.

The satisfied group included brands that do not engender much passion or emotional connection. Of the top 50 brands of the Relentless Relevance 2015 database, 15 were classic brand names that largely delivered functional benefits and were extremely high on the dependability measure. Leading the way with positions in the top 25 were Betty Crocker, Band-Aid, Clorox, KitchenAid and Folgers. All were extremely high on the “dependable” and trust dimensions as well. That reinforces the hypothesis that delivering to expectations may not be glamorous, but it can drive a brand’s ability to create and keep a loyal segment, which can be the basis of a healthy long-term business. There’s also likely some emotional benefit linked to the nostalgia of growing up with these brands. They become part of the fabric of people’s lives.

Committed Brand Users

Next, consider the committed model. The “better” variable has a large impact, about equal to that of the “dependable” variable. The “heavy emotional connection” variable has an explanatory power equal to the “better” variable and more than twice that of the “light emotional connection” variable.  Finally, the “social media” variable is now more of a player, albeit smaller than the other variables.

The committed customer group is necessary if you want to be a leader in more involving categories. This is the group that can deliver social buzz and net promoter scores. And it can defend you when you have an unfavorable incident. But to create and nurture this group, it is clear that brands must get beyond “dependable” to “better” and get beyond happy to deliver a meaningful emotional feeling that connects and inspires. The top brands on the committed scale such as Apple, Microsoft, Netflix and Chick-fil-A, also score high on the emotional connection, inspiring and having a purpose. Apple, in fact, is in the top two on each of these dimensions. They are clearly more than functional, high-use brands that deliver satisfaction. Social media also became relevant. The committed will likely include people that are influencers on social media, and they have an impact far beyond their number.

The strength of the “dependable” dimension to explain the committed status of a brand is noteworthy. Many of the top brands on the committed scale such as Apple, Netflix and Microsoft ranked extremely high on dependability and ease of use. Amazon, added to the study in 2016, was also extremely high in the committed and dependability measures. The ability of these high-tech, innovative brands to deliver an astonishing level of performance on the dependability dimensions is a crucial and largely unrecognized element of their brand strength. In general, they deliver on their brand promise without frustration or disappointment.

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