Tag Archives: cost structure

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.

How Machine Learning Changes the Game

Insurance executives can be excused for having ignored the potential of machine learning until today. Truth be told, the idea almost seems like something out of a 1980s sci-fi movie: Computers learn from mankind’s mistakes and adapt to become smarter, more efficient and more predictable than their human creators.

But this is no Isaac Asimov yarn; machine learning is a reality. And many organizations around the world are already taking full advantage of their machines to create new business models, reduce risk, dramatically improve efficiency and drive new competitive advantages. The big question is why insurers have been so slow to start collaborating with the machines.

Smart machines

Essentially, machine learning refers to a set of algorithms that use historical data to predict outcomes. Most of us use machine learning processes every day. Spam filters, for example, use historical data to decide whether emails should be delivered or quarantined. Banks use machine learning algorithms to monitor for fraud or irregular activity on credit cards. Netflix uses machine learning to serve recommendations to users based on their viewing history and recommendations.

In fact, organizations and academics have been working away at defining, designing and improving machine learning models and approaches for decades. The concept was originally floated back in the 1950s, but – with no access to digitized historical data and few commercial applications immediately evident – much of the development of machine learning was largely left to academics and technology geeks. For decades, few business leaders gave the idea much thought.

Machine learning brings with it a whole new vocabulary. Terms such as “feature engineering,” “dimensionality reduction,” “supervised and unsupervised learning,” to name a few. As with all new movements, an organization must be able to bridge the two worlds of data science and business to generate value.

Driven by data

Much has changed. Today, machine learning has become a hot topic in many business sectors, fueled, in large part, by the increasing availability of data and low-cost, scalable, cloud computing. For the past decade or so, businesses and organizations have been feverishly digitizing their data and records – building mountains of historical data on customers, transactions, products and channels. And now they are setting their minds toward putting it to good use.

The emergence of big data has also done much to propel machine learning up the business agenda. Indeed, the availability of masses of unstructured data – everything from weather readings through to social media posts – has not only provided new data for organizations to comb through, it has also allowed businesses to start asking different questions from different data sets to achieve differentiated insights.

The continuing drive for operational efficiency and improved cost management has also catalyzed renewed interest in machine learning. Organizations of all stripes are looking for opportunities to be more productive, more innovative and more efficient than their competitors. Many now wonder whether machine learning can do for information-intensive industries what automation did for manual-intensive ones.

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A new playing field

For the insurance sector, we see machine learning as a game-changer. The reality is that most insurance organizations today are focused on three main objectives: improving compliance, improving cost structures and improving competitiveness. It is not difficult to envision how machine learning will form (at least part of) the answer to all three.

Improving compliance: Today’s machine learning algorithms, techniques and technologies can be used on much more than just hard data like facts and figures. They can also be used to  analyze information in pictures, videos and voice conversations. Insurers could, for example, use machine learning algorithms to better monitor and understand interactions between customers and sales agents to improve their controls over the mis-selling of products.

Improving cost structures: With a significant portion of an insurer’s cost structure devoted to human resources, any shift toward automation should deliver significant cost savings. Our experience working with insurers suggests that – by using machines instead of humans – insurers could cut their claims processing time down from a number of months to a matter of minutes. What is more, machine learning is often more accurate than humans, meaning that insurers could also cut down the number of denials that result in appeals they may ultimately need to pay out.

Improving competitiveness: While reduced cost structures and improved efficiency can certainly lead to competitive advantage, there are many other ways that machine learning can give insurers the competitive edge. Many insurance customers, for example, may be willing to pay a premium for a product that guarantees frictionless claim payout without the hassle of having to make a call to the claims team. Others may find that they can enhance customer loyalty by simplifying re-enrollment processes and client on-boarding processes to just a handful of questions.

Overcoming cultural differences

It is surprising, therefore, that insurers are only now recognizing the value of machine learning. Insurance organizations are founded on data, and most have already digitized existing records. Insurance is also a resource-intensive business; legions of claims processors, adjustors and assessors are required to pore over the thousands – sometimes millions – of claims submitted in the course of a year. One would therefore expect the insurance sector to be leading the charge toward machine learning. But it is not.

One of the biggest reasons insurers have been slow to adopt machine learning clearly comes down to culture. Generally speaking, the insurance sector is not widely viewed as being “early adopters” of technologies and approaches, preferring instead to wait until technologies have become mature through adoption in other sectors. However, with everyone from governments through to bankers now using machine learning algorithms, this challenge is quickly falling away.

The risk-averse culture of most insurers also dampens the organization’s willingness to experiment and – if necessary – fail in its quest to uncover new approaches. The challenge is that machine learning is all about experimentation and learning from failure; sometimes organizations need to test dozens of algorithms before they find the most suitable one for their purposes. Until “controlled failure” is no longer seen as a career-limiting move, insurance organizations will shy away from testing new approaches.

Insurance organizations also suffer from a cultural challenge common in information-intensive sectors: data hoarding. Indeed, until recently, common wisdom within the business world suggested that those who held the information also held the power. Today, many organizations are starting to realize that it is actually those who share the information who have the most power. As a result, many organizations are now keenly focused on moving toward a “data-driven” culture that rewards information sharing and collaboration and discourages hoarding.

Starting small and growing up

The first thing insurers should realize is that this is not an arms race. The winners will probably not be the organizations with the most data, nor will they likely be the ones that spent the most money on technology. Rather, they will be the ones that took a measured and scientific approach to building their machine learning capabilities and capacities and – over time – found new ways to incorporate machine learning into ever-more aspects of their business.

Insurers may want to embrace the idea of starting small. Our experience and research suggest that – given the cultural and risk challenges facing the insurance sector – insurers will want to start by developing a “proof of concept” model that can safely be tested and adapted in a risk-free environment. Not only will this allow the organization time to improve and test its algorithms, it will also help the designers to better understand exactly what data is required to generate the desired outcome.

More importantly, perhaps, starting with pilots and “proof of concepts” will also provide management and staff with the time they need to get comfortable with the idea of sharing their work with machines. It will take executive-level support and sponsorship as well as keen focus on key change management requirements.

Take the next steps

Recognizing that machines excel at routine tasks and that algorithms learn over time, insurers will want to focus their early “proof of concept” efforts on those processes or assessments that are widely understood and add low value. The more decisions the machine makes and the more data it analyzes, the more prepared it will be to take on more complex tasks and decisions.

Only once the proof of concept has been thoroughly tested and potential applications are understood should business leaders start to think about developing the business case for industrialization (which, to succeed in the long term, must include appropriate frameworks for the governance, monitoring and management of the system).

While this may – on the surface – seem like just another IT implementation plan, the reality is that it machine learning should be championed not by IT but rather by the business itself. It is the business that must decide how and where machines will deliver the most value, and it is the business that owns the data and processes that machines will take over. Ultimately, the business must also be the one that champions machine learning.

All hail, machines!          

At KPMG, we have worked with a number of insurers to develop their “proof of concept” machine learning strategies over the past year, and we can say with absolute certainty that the Battle of Machines in the insurance sector has already started. The only other certainty is that those that remain on the sidelines will likely suffer the most as their competitors find new ways to harness machines to drive increasing levels of efficiency and value.

The bottom line is that the machines have arrived. Insurance executives should be welcoming them with open arms.