Tag Archives: competition

How to Avoid Commoditization

How can a company liberate itself from the death spiral of product commoditization?

Competing on price is generally a losing proposition—and an exhausting way to run a business. But when a market matures and customers start focusing on price, what’s a business to do?

The answer, as counterintuitive as it may seem, is to deliver a better customer experience.

It’s a proposition some executives reject outright. After all, a better customer experience costs more to deliver, right? How on earth could that be a beneficial strategy for a company that’s facing commoditization pressures?

Go From Commodity to Necessity

There are two ways that a great customer experience can improve price competitiveness, and the first involves simply removing yourself from the price comparison arena.

Consider those companies that have flourished selling products or services that were previously thought to be commodities: Starbucks and coffee, Nike and sneakers, Apple and laptops. They all broke free from the commodity quicksand by creating an experience their target market was willing to pay more for.

They achieved that, in part, by grounding their customer experience in a purpose-driven brand that resonated with their target market.

Nike, for example, didn’t purport to just sell sneakers; it aimed to bring “inspiration and innovation to every athlete in the world.” Starbucks didn’t focus on selling coffee; it sought to create a comfortable “third place” (between work and home) where people could relax and decompress. Apple’s fixation was never on the technology but rather on the design of a simple, effortless user experience.

But these companies also walk the talk by engineering customer experiences that credibly reinforce their brand promise (for example, the carefully curated sights, sounds and aromas in a Starbucks coffee shop or the seamless integration across Apple devices).

The result is that these companies create something of considerable value to their customers. Something that ceases to be a commodity and instead becomes a necessity. Something that people are simply willing to pay more for.

That makes their offerings more price competitive—but not because they’re matching lower-priced competitors. Rather, despite the higher price point, people view these firms as delivering good value, in light of the rational and emotional satisfaction they derive from the companies’ products.

The lesson: Hook customers with both the mind and the heart, and price commoditization quickly can become a thing of the past.

Gain Greater Pricing Latitude

Creating a highly appealing brand experience certainly can help remove a company from the morass of price-based competition. But the reality is that price does matter. While people may pay more for a great customer experience, there are limits to how much more.

And so, even for those companies that succeed in differentiating their customer experience, it remains important to create a competitive cost structure that affords some flexibility in pricing without crimping margins.

At first blush, these might seem like contradictory goals: a better customer experience and a more competitive cost structure. But the surprising truth is that these two business objectives are actually quite compatible.

A great customer experience can actually cost less to deliver, thanks to a fundamental principle that many businesses fail to appreciate: Broken or even just unfulfilling customer experiences inevitably create more work and expense for an organization.

That’s because subpar customer interactions often trigger additional customer contacts that are simply unnecessary. Some examples:

  • An individual receives an explanation of benefits (EOB) from his health insurer for a recent medical procedure. The EOB is difficult to read, let alone interpret. What does the insured do? He calls the insurance company for clarification.
  • A cable TV subscriber purchases an add-on service, but the sales representative fails to fully explain the associated charges. When the subscriber’s next cable bill arrives, she’s unpleasantly surprised and believes an error has been made. She calls the cable company to complain.
  • A mutual fund investor requests a change to his account. The service representative helping him fails to set expectations for a return call. Two days later, having not heard from anyone, what does the investor do? He calls the mutual fund company to follow up on the request.
  • A student researching a computer laptop purchase on the manufacturer’s website can’t understand the difference between two closely related models. To be sure that he orders the right one for his needs, what does he do? He calls the manufacturer.
  • An insurance policyholder receives a contractual amendment to her policy that fails to clearly explain, in plain English, the rationale for the change and its impact on her coverage. What does the insured do? She calls her insurance agent for assistance.

In all of these examples, less-than-ideal customer experiences generate additional calls to centralized service centers or field sales representatives. But the tragedy is that a better experience upstream would eliminate the need for many of these customer contacts.

Every incoming call, email, tweet or letter drives real expense—in service, training and other support resources. Plus, because many of these contacts come from frustrated customers, they often involve escalated case handling and complex problem resolution, which, by embroiling senior staff, managers and executives in the mess, drive the associated expense up considerably.

Studies suggest that at most companies, as many as a third of all customer contacts are unnecessary—generated only because the customer had a failed or unfulfilling prior interaction (with a sales rep, a call center, an account statement, etc.).

In organizations with large customer bases, this easily can translate into hundreds of thousands of expense-inducing (but totally avoidable) transactions.

By inflating a company’s operating expenses, these unnecessary customer contacts make it more difficult to price aggressively without compromising margins.

If, however, you deliver a customer experience that preempts such contacts, you help control (if not reduce) operating expenses, thereby providing greater latitude to achieve competitive pricing.

Putting the Strategy to Work

If your product category is devolving into a commodity (a prospect that doesn’t require much imagination on the part of insurance executives), break from the pack and increase your pricing leverage with these two tactics:

  • Pinpoint what’s really valuable to your customers.

Starbucks tapped into consumers’ desire for a “third place” between home and work—a place for conversation and a sense of community. By shaping the customer experience accordingly (and recognizing that the business was much more than just a purveyor of coffee), Starbucks set itself apart in a crowded, commoditized market.

Insurers should similarly think carefully about what really matters to their clientele and then engineer a product and service experience that capitalizes on those insights. Commercial policyholders, for example, care a lot more about growing their business than insuring it. Help them on both counts, and they’ll be a lot less likely to treat you as a commodity supplier.

  • Figure out why customers contact you.

Apple has long had a skill for understanding how new technologies can frustrate rather than delight customers. The company used that insight to create elegantly designed devices that are intuitive and effortless to use. (Or, to invoke the oft-repeated mantra of Apple co-founder Steve Jobs, “It just works.”)

Make your customer experience just as effortless by drilling into the top 10 reasons customers contact you in the first place. Whether your company handles a thousand customer interactions a year or millions, don’t assume they’re all “sensible” interactions. You’ll likely find some subset that are triggered by customer confusion, ambiguity or annoyance—and could be preempted with upstream experience improvements, such as simpler coverage options, plain language policy documents or proactive claim status notifications.

By eliminating just a portion of these unnecessary, avoidable interactions, you’ll not only make customers happier, you’ll make your whole operation more efficient. That, in turn, means a more competitive cost structure that can support more competitive pricing.

Whether it’s coffee, sneakers, laptops or insurance, every product category eventually matures, and the ugly march toward commoditization begins. In these situations, the smartest companies recognize that the key is not to compete on price but on value.

They focus on continuously refining their brand experience—revealing and addressing unmet customer needs, identifying and preempting unnecessary customer contacts.

As a result, they enjoy reduced price sensitivity among their customers, coupled with a more competitive cost structure. And that’s the perfect recipe for success in a crowded, commoditized market.

This article first appeared on carriermanagement.com.

What Comes After Predictive Analytics

Historically, “analytics” has referred to the use of statistical or data mining techniques to analyze data and make inferences. In this context, analytics typically explain what happened (descriptive analytics) and why (diagnostic analytics). If an insurer saw its customers moving to its competition, it would analyze the characteristics of the customers staying or leaving, the prices it and its competitors offer and customer satisfaction. The analysis would help determine what was happening, who was leaving and why. In contrast, predictive analytics focuses on what will happen in the future.

“Predictive analytics” has a fairly broad definition in the press but has a specific meaning in academic circles. Classical predictive analytics focuses on building predictive models where a subset of the available data is used to build a model using statistical techniques (usually some form of regression analysis — linear, logistic regression etc.) that is then tested for its accuracy with the “holdout” sample. Once a model with sufficient accuracy is developed, it can be used to predict future outcomes. More recent predictive analytics techniques use additional machine learning techniques (e.g., neural network analysis or Bayesian probabilistic techniques).

Insurers have used predictive analytics for almost two decades, but, despite its usefulness, it has two main drawbacks:

  • Focus on decision versus action: Predictive analytics can tell you what is likely to happen but cannot make recommendations and act on your behalf. For example, a predictive model on the spread of flu can determine the prevalence and spread of flu but cannot tell you how to avoid it. Similarly, a predictive model of insurance sales can determine weekly sales numbers but is incapable of suggesting how to increase them.
  • Reliance on single future versus multiple alternative futures: While we can learn from the past, we know that it may not be a good predictor of the future. Predictive models make linear predictions based on past data. They also make certain assumptions that may not be viable when extrapolating into the future. For example, regression requires the designation of a dependent variable (e.g., insurance sales), which is then described in terms of other independent variables (e.g., brand loyalty, price etc.). While this method can help predict future insurance sales, the accuracy of the numbers tends to decrease further into the future, where broad macro-economic and behavioral considerations will play a greater role in sales.

Prescriptive Analytics

In response, there are a number of firms, authors and articles that propose “prescriptive analytics” as the next stage of the analytics continuum’s evolution. Prescriptive analytics automates the recommendation and action process and generally is based on machine learning techniques that evaluate the impact of future decisions and adjust model parameters based on the difference between predicted and actual outcomes. For example, insurers could use prescriptive analytics for automatically underwriting insurance, where the system improves its conversion ratio by adjusting price and coverage on a continual basis based on predicted take-up and actual deviations from it.

However, while prescriptive analytics does address the first of predictive analytics’ drawbacks by making and acting on its recommendations, it usually fails to address the second shortcoming. Prescriptive analytics relies on a single view of the future based on historical data and does not allow for “what if” modeling of multiple future scenarios. The critical assumption is that the variables used to explain the dependent variable are independent of each other, which in most cases is not true. While the analysis can be modified to account for this collinearity, the techniques still fail to use all of the available data from domain experts. In particular, prescriptive analytics does not take into account the rich structure and influences among all the variables being modeled.

Complexity Science

In addition to prescriptive analytics, we believe that complexity science is a natural extension of predictive analytics. Complexity science is an inter-disciplinary approach to understanding complex systems, including how they form, evolve and cease to exist. Typically, a system that consists of a few well-known parts that consistently interact with each other in a way we can easily understand is a “simple” system. For example, a thermostat that can read (or sense) the temperature and reach a given target temperature is a simple system. At the other end of the spectrum, a system with a very large collection of entities that interact randomly with each other is a “random” system. We often use statistical techniques to understand the behavior of the latter. For example, we can gain an understanding of the properties of a liquid (like its boiling point) by looking at the average properties of the elements and compounds that compose it. The fundamental assumption about such systems is that its parts are independent.

In between simple and random systems are “complex” systems that consist of several things that interact with each other in meaningful ways that change their future path. For example, a collection of consumers watching advertisements, talking to others and using products can influence other consumers, companies and the economy as a whole. Complexity science rejects the notion of “independence” and actively models the interactions of entities that make up the system.

Complexity science identifies seven core traits of entities and how they relate to each other: 1) information processing, 2) non-linear relationships, 3) emergence, 4) evolution, 5) self-organization, 6) robustness and 7) if they are on the edge of chaos. Unlike a random system, the entities in a complex system process information and make decisions. These information processing units influence each other, which results in positive or negative feedback leading to non-linear relationships. As a result, properties emerge from the interaction of the entities that did not originally characterize the individual entities. For example, when a new product comes on the market, consumers may purchase it not just because of its intrinsic value but also because of its real or perceived influence on others. Moreover, the interactions between entities in a complex system are not static; they evolve over time. They are capable of self-organizing and lack a central controlling entity. These conditions lead to more adaptive behavior. Such systems are often at the edge of chaos but are not quite chaotic or entirely random.

Two parallel developments have led to complexity science’s increased use in practical applications in recent years. The first is the availability of large amounts of data (or big data) that allows us to capture the properties of interest within each entity and the interactions between them. Processing the data allows us to model each entity and its interactions with others individually, as opposed to treating them as an aggregate. For example, a social network is a complex system of interacting individuals. We can use complexity science to understand how ideas flow through the social network, how they become amplified and how they fade away.

The second development accelerating complexity science’s use is the inadequacy of classical or statistical models to adequately capture complexity in the global economy. Since the financial crisis of 2007/8, a number of industry bodies, academics and regulators have called for alternative ways of looking at the world’s complex social and financial systems. For example, the Society of Actuaries has published a number of studies using complexity science and a specific type of complexity science called agent-based modeling to better understand policyholder behavior. In addition, health insurers are building sophisticated models of human physiology and chemical reactions to test adverse drug interactions. As another example, manufacturers are modeling global supply chains as complex interacting entities to increase their robustness and resiliency.

Agent-based modeling is a branch of complexity science where the behavior of a system is analyzed using a collection of interacting, decision-making entities called agents (or software agents). The individual behavior of each agent is modeled based on available data and domain knowledge. The interaction of these agents among themselves and the external environment can lead to market behavior that is more than just the aggregate of all the individual behaviors. This often leads to emergent properties. Such models can be used to evaluate multiple scenarios into the future to understand what will happen or what should happen as a result of a certain action. For example, a large annuity provider has used individual policyholder data to create an agent-based model in which each one of its customers is modeled as an individual software agent. Based on specific policyholder data, external socio-demographic and behavioral data, as well as historical macro-economic data, the annuity provider can evaluate multiple scenarios on how each annuity policyholder will lapse, withdraw or annuitize their policy under different economic conditions.

In conclusion, as companies look to capitalize on big data opportunities, we will see more of them adopt prescriptive analytics and complexity science to predict not just what is likely to happen based on past events but also how they can change the future course of events given certain economic, political and competitive constraints.