The Experience Disrupter

Excerpt from MIT Sloan article's The Experience Disrupter by Brian Halligan February 27, 2020

It’s not good enough to have a disruptive product. Your customer experience also needs to shine.

There’s been a massive wave of disruption happening in the consumer world. Taking a Lyft, play Spotify, package from Chewy, workout booked through ClassPass, using Dollar Shave Club, order from DoorDash, and check out movie on Netflix, to name a few.

The same shift is going on in the business world, such as collaborate on Slack, meeting thru Zoom, scarf down from ezCater.

We tend to think about technology disrupters like Google, Intel, iPhone, Tesla. Big technology companies with lots of patents. (In 2018, Intel was granted 2,735 patents, Apple 2,160, and Google 2,070.)1

Companies like Chewy  Dollar Shave, and ClassPass are not really technology disrupters. List of 20 companies like that have only about 50 patents total.

Instead, they are a new species of disrupter emerging in economy, called experience disrupters. These organizations all have great products, but they offer even better experiences. How they sell is why they win.

These companies have fundamentally reshaped what their customers come to expect in the experience of purchasing and using their product or service. This is a central insight of Clayton Christensen’s Theory of Jobs to Be Done, which tells us that customers don’t simply buy products or services. They hire them to do a job for them. Doing that job well for customers involves creating the right experiences for those customers, from the moment they begin to think about purchasing the product to their everyday use of that product. It’s an essential part of developing a deep relationship with customers: You solve their struggle for them.

Companies that outmaneuver the competition by excelling at the customer experience. Five things modern adaptations that allow these experience disrupters to run over the incumbents. 

They Give You Experiences You Didn’t Know You Wanted

While incumbent companies focus on product-market fit, experience disrupters work on experience-market fit. Product-market fit, when you’ve found the right mix of product for just the right target market, is considered by these companies as necessary but insufficient to get the disruption they’re really after. For experience disrupters, what matters is offering experiences that surround the product and that customers didn’t even know they wanted or could ask for.

Carvana, a killer experience disrupter, was founded in 2012 and was the eighth-largest used-car dealer in the US in 2018.2 It went public in 2017 and has a market cap of roughly $12.5 billion.

Typically, a car dealer inventory is necessary, but insufficient. To get the crazy growth it’s had, Carvana focused on the experience-market fit, to create a whole new way to buy a car, very Amazon-like experience. Choose the price range, mileage, condition, type of car, get alerted when available near you, and view a 360-degree inspection with annotated zoom-in areas to see wear and tear.

The company deals with the department of motor vehicles, taxes, registration, including delivery service and still you can return it. Carvana has taken the cringeworthy process of buying a car and automated it, institutionalized it, and made it awesome.

They Make Interactions Frictionless

The second adaptation is that experience disrupters pull the friction out of each customer interaction. The analogy of  mechanical flywheel, the less friction customer interactions have, the faster the flywheel spins. In businesses that are struggling to keep up with experience disrupters, their flywheels are full of friction. Experience disrupters are very good at reducing that tension.

Atlassian, a B2B collaboration software company, is a large company that growing very fast and very profitable, with a market cap near $36 billion. 

Like other B2B, it's marketing dept focusing less on generating new leads and more on activating current users and multiplying the number of users and teams within a customer. Instead of fighting the uphill battle for senior-level evaluation of their solution, Atlassian focuses on the ease with which an end user can invite a colleague to a collaborative project. 

The most of its transactions happen without the sales team. Salespeople negotiate the highest-sticker-price deals, or straightforward, with no commissions. The purchase price is online, and because they don’t negotiate changes in prices or terms and conditions, the contracting process is not complex — and it’s easily automated. All these decisions eliminate friction at this stage of the sale.

They Personalize the Relationship

The third adaptation is creating a personalized experience. The incumbents offer a more generic experience when they’re prospecting customers, meanwhile experience disrupters didn’t sound like tech people. The way they cater to each customer makes them less like tech companies than like ultramodern hospitality companies.

Thru Netflix’s database, the more we use their product, the better its gets at personalizing its recommendations to us. Netflix suggests new content based on viewing history, but even the finest details — such as the thumbnails that accompany each show — are tailored to an individual user’s browsing habits.

This is also happening at Stitch Fix, an online personal styling company, that went public in 2017 with market cap of $2.4 billion, offers customized clothing selection for customers and also sells the outfits. When Stitch Fix first got started, individual stylists recommended combinations of apparel solely on the basis of lengthy profiles completed by customers about their style preferences and specific measurements.

But Stitch Fix knew the value of data to deepen the accuracy of stylists’ recommendations and to give scale to the business. In addition to the initial customer profile, the company uses feedback from customers on their purchases, which items were purchased together and which were rejected and returned, and fastidious details from its merchandise about the precise measurements, textures, and aesthetics of each clothing option. This arms Stitch Fix with an opportunity to base recommendations that have progressively led to increased purchases over returns, and more additional purchases by repeat customers.

Netflix and Stitch Fix are playing the same game, use lots and lots of data to highly personalize experience. How they sell is why they win.

They Get Customers to Sell for Them

The fourth adaptation is that while the incumbents know how to sell to their customers, the experience disrupters are very good at selling through their customers. 

Emily Weiss, founder of Glossier -- a private company estimated valuation at $1.2 billion, started off as a blogger — Into the Gloss, was blowing up with beauty tips, then developing beauty products.

Weiss is next-level and a bona fide experience disrupter to not just create her own content but also encourage and enable her customers to create content. Glossier makes its products available to Top 20 YouTube beauty vlogger, sometimes even prior to public release to build buzz. Thousands of wannabes and micro influencers then imitate the most popular vloggers with their own video reviews. The result is hundreds of thousands of pieces of content out there about Weiss’s products — all created by her customers. 

Warby Parker, the eyeglasses company, mail  the glasses to prospect customers to try on, they can post photos on Instagram, and ask all their judgy friends which one they like.

They Empower Employees to Make Things Right for Customers

The fifth adaption: Experience disrupters enable customer-facing employees to fix things when they need to.

Traditionally, companies woo customers to make a purchase, but the second that purchase is made, it becomes the customer’s hassle to get service on it if there’s a problem. Experience disrupters make all these details much more customer-friendly.

Online pet store Chewy gives its customer service reps a discretionary budget to create opportunities to build goodwill with customers, and this empowerment allows for a customer experience that feels seamless. 

Chewy’s costs to acquire a future customer were very low, and the total lifetime value current customer is now very high.

Experience disrupters know how incredibly significant it feels for customers when there’s a genuine change in the power balance in post-sale interactions. 

These experience disrupters think differently, and the founders have a healthy disdain for conventional wisdom. They spend hardly any of their energy extracting value from their customers. Instead, they spend all their energy thinking, “How do I add value for my customers?”.

Here’s a summary of the five points:

• Don’t obsess completely about product-market fit. Obsess about experience-market fit. Embrace your inner Carvana.
• Remember that dollars flow where the friction is low. Mechanically remove friction. Automate like the superheroes at Atlassian.
• Personalize, personalize, personalize. Stop embracing automation without personalization — that’s what people call spam. Think like Netflix. Dust for fingerprints.
• Sell through your customers, not just to them. Let Glossier be your model.
• Rethink how customers get treated after the sale. Look at your terms and conditions. Give your customer-facing employees the tools to make things right. Delight people, the way Chewy does.

REFERENCES

1. J.J. Roberts, “IBM Tops 2018 Patent List as AI and Quantum Computing Gain Prominence,” Fortune, Jan. 7, 2019, https://fortune.com.

2. D. Muller, “Carvana Debuts as No. 8 on Used Ranking,” Automotive News, April 22, 2019, www.autonews.com.

3. L. Smiley, “Stitch Fix’s Radical Data-Driven Way to Sell Clothes — $1.2 Billion Last Year — Is Reinventing Retail,” Fast Company, Feb. 19, 2019, www.fastcompany.com.

Digital Twins.... a bit Digitization

Excerpt from Deloitte's article Digital twins Bridging the physical and digital 15 January 2020

Digital twins are multiplying as their capabilities and sophistication grow but require integrating systems and data across entire organizational ecosystems.

Digital twin would enable you to collaborate virtually, intake sensor data and simulate conditions quickly, understand what-if scenarios clearly, predict results more accurately, and output instructions to manipulate the physical world.

Today, companies are using digital twin capabilities in a variety of ways. They are becoming essential tools for optimizing entire value chains and innovating new products, capturing and analyzing massive amounts of data to build digital models, creating highly accurate diagnoses, uses a detailed virtual model in planning, maintenance, and disaster readiness projects.

Digital twins can simulate any aspect of a physical object or process, but they all capture and utilize data that represents the physical world.

Recent MarketsandMarkets research predict The digital twins market—worth US$3.8 billion in 2019—is projected to reach US$35.8 billion in value by 2025. The trend is gaining momentum thanks to rapidly evolving simulation and modeling capabilities, better interoperability and IoT sensors, and more availability of tools and computing infrastructure.  IDC projects that by 2022, 40% of IoT platform vendors will integrate simulation platforms, systems, and capabilities, with 70% of manufacturers using the technology to conduct process simulations and scenario evaluations.

At the same time, access to larger volumes of data is making it possible to create simulations that are more detailed and dynamic than ever. 

Models + data = insights and real value

It capabilities began as a tool to streamline the design process and eliminate many aspects of prototype testing. It helps engineers identify potential manufacturability, quality, and durability issues—all before the designs are finalized. Thus moving into production more efficiently and at a lower cost.

Beyond design, it transform the way companies perform predictive maintenance of products and machinery in the field. Embedded sensors feed data in real time, making it possible not only to identify malfunctions but to tailor service and maintenance plans. 

It help optimize supply chains, distribution and fulfillment operations, and even the individual performance of the workers involved in each.

Smart city initiatives are also using digital twins for applications addressing traffic congestion remediation, urban planning, and much more. 

What’s new?

Digital twin capabilities has accelerated due to a number of factors:

Simulation. The tools are growing in power and sophistication. It is now possible to design complex what-if simulations, backtrack from detected real-world conditions, and perform millions of simulation processes without overwhelming systems. Finally, machine learning functionality is enhancing the depth and usefulness of insights. 

New sources of data. Data from real-time asset monitoring technologies can be incorporated into simulations. Likewise, IoT sensors embedded in machinery or throughout supply chains can feed operational data directly into simulations, enabling continuous real-time monitoring.

Interoperability. The ability to integrate digital technology with the real world can be attributed to enhanced industry standards for communications between IoT sensors, operational technology hardware, and vendor efforts to integrate with diverse platforms.

Visualization. Advanced data visualization can filtering and distilling information in real time. The latest data visualization tools go far beyond basic dashboards and standard visualization capabilities to include interactive 3D, VR & AR-based visualizations, AI-enabled, and real-time streaming.

Instrumentation. With IoT sensors improvements in networking and security, control systems can be leveraged to have more granular, timely, and accurate information on real-world conditions to integrate with the virtual models.

Platform. Some software companies are making significant investments in cloud-based platforms, IoT, and analytics capabilities that will enable them to capitalize on the digital twins trend. Some of these investments are part of an ongoing effort to streamline the development of industry-specific digital twin use cases.

Costs versus benefits

The AI and machine learning algorithms that power digital twins require large volumes of data, and in many cases, data from the sensors on the production floor may have been corrupted, lost, or simply not collected consistently in the first place. So teams should begin collecting data now, particularly in areas with the largest number of issues and the highest outage costs. Taking steps to develop the necessary infrastructure and data management approach now can help shorten your time to benefit.

Even in cases where digital twin simulations are being created for new processes, systems, and devices, it’s not always possible to perfectly instrument the process. Organizations need to look to proxies or things that are possible to detect.

Balancing the cost/benefit analysis is critical. Most use cases, however, require only a modest number of strategically placed sensors to detect key inputs, outputs, and stages within the process.

Models beyond

Organizations making the transition from selling products to selling bundled products and services, or selling as-a-service, are pioneering new digital twin use cases. Connecting a digital twin to embedded sensors and using it for financial analysis and projections enables better refinement and optimization of projections, pricing, and upsell opportunities.

Modeling the digital future

More organizations may explore opportunities to use digital twins to optimize processes, make data-driven decision in real time, and design new products, services, and business models. 

Longer term, require integrating systems and data across entire ecosystems. Creating a digital simulation of the complete customer life cycle or of a supply chain that includes not only first-tier suppliers but their suppliers, may provide an insight-rich macro view of operations, but it would also require incorporating external entities into internal digital ecosystems. In the future, expect to see companies use blockchain to break down information silos, and then validate and feed that information into simulations. This could free up previously inaccessible data in volumes sufficient to make simulations more detailed, dynamic, and potentially valuable than ever.

Designing Artificial Intelligence (Human-Macine Interaction)

Taken from MIT Sloan Management Review's article Designing AI Systems With Human-Machine Teams March 18, 2020

The greatest potential from artificial intelligence will come from tapping into the opportunities for mutual learning between people and machines.

Artificial intelligence (AI) promises to augment human capabilities and reshape companies, yet many organizations try to implement AI without having a clear understanding of how the technology will interface with people.

Assessing the Context of AI Application

Bringing together the formal rationality of AI and the substantive rationality of humans can help companies meet their goals and optimize the chances of success. However, managers need to assess the decision-making context on two dimensions: (1) the openness of the decision-making process and (2) the level of risk. These will help figure out the teaming options for implementing their AI systems and maximizing further learning.

Openness of the decision-making process. A closed decision-making process implies that all the relevant variables have been considered and that there are predefined rules for framing decisions. An open process, in contrast, anticipates that there will be problems that aren’t well defined and that some variables may not be known in advance.
Closed and open decision-making require different approaches with regard to AI. Closed applications have well-established, structured performance indicators and work with a set of fixed variables. Open system decisions require additional information, often from multiple sources.
Assessments as to whether the process should be open or closed may vary. Consider the challenges involved with language translation that are based on preset rules of grammar and meaning,  are therefore closed. In undefined situations, the process might be assessed as open. AI systems such as natural language processing will access contextual information and learn how certain experts handle specific situations.
Level of risk. The severity of a risk depends on the specific elements. An acute risk might be tolerated if the chance of the event occurring is small. Conversely, if the chance is high, the risk may be unacceptable — even if the specific danger is small.
Knowing the risk level can help you decide whether you’ll be comfortable making decisions entirely based on algorithms or whether you’ll want additional resources like human experts on hand to help you handle unexpected situations.

What Role Should People Play?

Combinations of human awareness and AI system design can take different forms, making different configurations possible.
When the contextual factors are well defined, algorithms can “learn” by interacting with the environment through supervised machine learning. In these instances, the need for human involvement is low and act not as active decision makers but as foremen.

But when the potential consequences of bad decisions are serious, greater situational awareness is required. In such cases, humans can act as sentinels, relying on their experience to manage risky situations. Whereas algorithms may be good at identifying ill-defined processes, an experienced person might also be needed to train the AI system, taking on the role of coach.
In situations where the levels of complexity and risk are great, the need for human-machine interaction will peak to the point where it becomes a reciprocal learning relationship. In this context, the human expert is a companion in a long-term, peer-to-peer relationship.

Human-Machine Teaming Capabilities

Depending on the circumstances, humans and machines can work together in four different ways. Humans play a distinct role in each of the quadrants.

By combining humans and machines in AI systems, organizations can draw on four main teaming capabilities:
Interoperability. The interaction needs to be facilitated, systems should be able to share the right piece of information and analysis whenever it’s required. An AI system should also be able to specify the precise role that a human needs to play in the interaction.
Authority balance. In examining dealings, it’s essential to know which one has the final control and when. In low-risk situations, the ability to control for the outcome might be enough. But in high-risk situations, the process might require a more immediate response. The system could also decide to revise how authority is assigned in order to prevent actions that could endanger people or assets.
Transparency. Given the need for reinforcement loops, transparent decision-making processes are key to building trust. The human needs to know which variables, rules, and performance parameters the algorithm uses. At the same time, the machine should know which decisions the human is authorized to make in order to integrate them into the learning loops.
Mutual learning. Machines learn from various sources, including the external environment, repetitive patterns, and the expected versus actual outcomes of decisions. However, they can also develop insights from human experience and intuition. This learning takes two forms: when humans make decisions that the machine analyzes and when human experts train the machines with their intuition. Just as machines learn from humans, humans can acquire insights from algorithms. These two-way learning loops increase the overall scope and performance of the AI system.

Configurations of Teaming Capabilities

Four different ways humans and machines can work together to make decisions.

Machine-based AI systems. In settings where machine-based designs are central and no surprises are expected, machines can perform tasks independently, with humans playing only supervisory roles and making changes only when necessary. Since potential mistakes are visible and do not pose major risks, the interoperability is for audit purposes only, and transparency is not required.
Sequential machine-human AI systems. In other settings, machines are capable of performing many of their required tasks independently. But humans need to do more than monitor the outcomes — they need to be prepared to step in to deal with unplanned contingencies. This requires humans to have situational awareness and to be ready to identify events that extend beyond the capacity of the machine and intervene. To know when such interventions are required, the AI system needs to have a level of transparency.
Cyclic machine-human AI systems. In settings where the processes are open and low-risk, organizations have wide latitude for shifting decision-making authority from machine to human and vice versa. Even though a high degree of transparency may be needed, as long as the AI system is operating smoothly, the human agents’ task is to monitor the outcomes without intervening in the activity. Their role is that of a coach: to train the AI system by providing new parameters and generally improving the performance.
Human-based AI systems. Decision processes that are both open and high-risk call for human-based AI systems, with the final authority in the hands of humans. Although the AI systems may have enough stored and processed data to make educated guesses, the risk of something bad happening can’t be overlooked. Therefore, experts must maintain high situational awareness. It’s critical, moreover, that the various decision rationales be sufficiently clear and transparent to advance the learning of both humans and machines.

Successful AI implementations should draw on a variety of configurations that can be adapted to the scenario at hand, depending on the environment and human factors.

Q4 2019 spend on cloud infrastructure services by Synergy Research Group

Incremental Growth in Cloud Spending Hits a New High while Amazon and Microsoft Maintain a Clear Lead

New data from Synergy Research Group shows that Q4 spend on cloud infrastructure services increased by $2.8 billion over the previous quarter, which is by far the biggest quarterly increment the market has seen. At 37% the YoY growth rate is slowly trending down, but this is due to the massive scale of the market which forces growth rates to moderate. 

Meanwhile Amazon growth continued to closely mirror overall market growth so it maintained its 33% share of the worldwide market. Second ranked Microsoft again grew fast than the market and its market share has increased by almost three percentage points in the last four quarters, reaching 18%. 

Behind these two market leaders, Google, Alibaba and Tencent are substantially outpacing overall market growth and are gaining market share. All three saw revenues increase by 50% or more year on year. 

Four other cloud providers have substantial market share but are somewhat niche players and typically have lower growth rates – IBM, Salesforce, Oracle and Rackspace. There is than a long tail of cloud providers with a small market share.

Synergy estimates that quarterly cloud infrastructure service revenues (including IaaS, PaaS and hosted private cloud services) were well over $27 billion, with full-year 2019 revenues reaching over $96 billion. 

Public IaaS and PaaS services account for the bulk of the market and those grew by 38% in Q4. In public cloud the dominance of the top five providers is even more pronounced, as they control over three quarters of the market. Geographically, the cloud market continues to grow strongly in all regions of the world.

The 2019 market was over twice the size of the 2017 market. Given secular trends in the market we will continue to see strong growth. We will also see a continuing battle for market position between the global giants and smaller cloud providers that have a more focused geographic or service footprint.