Modern Business Models Will Drive the Post-Pandemic World
Modern Business
Models Will Drive the
Post-Pandemic World
www.mgireservationsandbookings.co.uk
To remain relevant and resilient, companies and leaders must strive to
build business models using three key components for growth.
In the face of a global health and
economic crisis, many traditional
companies have suffered tremendous
losses, and some have shuttered their
doors. Those that heavily rely on physical capital (for example, stores, goods) and human
capital (for example, services) were already
vulnerable in economic downturns. The pandemic has exacerbated the lack of resilience in
these business models, which have struggled
to compete against digitally centric companies
that can leverage data and machine learning to
create valuable insights, intelligence, and capabilities across the organization.
For instance, compare companies
whose products are like air (customers rely
on them all day long for business, personal
or financial use) with those that are like
haircuts (customers use them sporadically;
they are nice to have but are not critical to
their needs). Those in the former category
that are being used constantly with little
effort have proved to be resilient even in
times of crisis. We typically know these as
software-as-a-service (SaaS) products, such
as Salesforce for business or Amazon Prime
for consumers. In addition, those companies that combine SaaS with multisided
platforms (like marketplaces) to fulfill their
customers’ needs through a network of
Intrinsic Security @ The Heart of Transformation
Cyber-security is taking center stage. Security breaches are becoming increasingly prevalent and making front-page news, while attack vectors are proliferating and the sophistication of attackers is reaching new levels. With widespread adoption of cloud, exponential growth of Edge, and the rapidly expanding remote workforce, security challenges continue to intensify.
Security must evolve; a more all-encompassing approach is needed. Intrinsic Security is a new strategic approach where simplicity is essential, security by design rather than bolted on, and security is proactive rather than reactive. Intrinsic Security includes considering how to secure the endpoint, the data center, clouds and everything in between; it allows enterprises to handle threats in a more effective, cohesive and simpler way.
Why you and your organization need to think about limiting the “blast radius” of any possible security breach.
what organizations must do to ensure they remain secure:
Defining security in the cloud era
How the move to widespread homeworking has accelerated the awareness of security vulnerabilities
Why an all-encompassing approach that simplifies and enhances security posture is essential
How intrinsic security can empower teams with deep context and insights that accelerate how they identify risk and respond to threats
Today’s world of always-on applications and APIs have availability and reliability
requirements that would have been required of only a handful of mission critical
services around the globe only a few decades ago. Likewise, the potential for rapid,
viral growth of a service means that every application has to be built to scale nearly
instantly in response to user demand. These constraints and requirements mean that
almost every application that is built—whether it is a consumer mobile app or a back‐
end payments application—needs to be a distributed system.
But building distributed systems is challenging. Often, they are one-off bespoke solu‐
tions. In this way, distributed system development bears a striking resemblance to the
world of software development prior to the development of modern object-oriented
programming languages. Fortunately, as with the development of object-oriented lan‐
guages, there have been technological advances that have dramatically reduced the
challenges of building distributed systems. In this case, it is the rising popularity of
containers and container orchestrators. As with the concept of objects within object-oriented programming, these containerized building blocks are the basis for the
development of reusable components and patterns that dramatically simplify and
make accessible the practices of building reliable distributed systems. In the following
introduction, we give a brief history of the developments that have led to where we are today.
In the early 2000s, the rise of the internet and large-scale datacenters consisting of
thousands of relatively low-cost commodity computers networked together gave rise
to the widespread development of distributed systems. Unlike client-server architec‐
tures, distributed system applications are made up of multiple different applications
running on different machines, or many replicas running across different machines,
all communicating together to implement a system like web-search or a retail sales
platform.
Because of their distributed nature, when structured properly, distributed systems are
inherently more reliable. And when architected correctly, they can lead to much more
scalable organizational models for the teams of software engineers that built these
systems. Unfortunately, these advantages come at a cost. These distributed systems
can be significantly more complicated to design, build, and debug correctly. The engineering skills needed to build a reliable distributed system are significantly higher
than those needed to build single-machine applications like mobile or web frontends.
Regardless, the need for reliable distributed systems only continues to grow. Thus
there is a corresponding need for the tools, patterns, and practices for building them. Fortunately, technology has also increased the ease with which you can build dis
tributed systems. Containers, container images, and container orchestrators have all
become popular in recent years because they are the foundation and building blocks
for reliable distributed systems. Using containers and container orchestration as a
foundation, we can establish a collection of patterns and reusable components. These
patterns and components are a toolkit that we can use to build our systems more reli
ably and efficiently.
partners (such as Apple’s developer network) have an
added advantage. These new, three-pronged models
go far beyond SaaS and include the following:
1. A community of active B2B and B2C users that
creates a network effect due to their interactions.
2. A marketplace that delivers offers from sellers
and suppliers to meet customers’ needs.
3. A secured data lake powered by AI that enables
customized offers and insights.
We call this new winning combination a modern
business model (MBM). In fact, MBMs occupy four
of the top 10 spots of the S&P 500’s most valuable
companies: Apple, Amazon, Alphabet (Google), and
Microsoft. And they are not alone — Shopify, Spotify,
and others have adopted this new AI-powered,
subscription-based model with marketplaces.
Based on our own machine learning analysis of
the Russell 3000 Index (see “Comparing the Resilience
of Modern vs. Legacy Business Models”), we found
that SaaS, marketplace, and modern business models have proved to be more resilient than their legacy
business model counterparts in times of disruption.
While adopting a full MBM is not possible for
many legacy companies (which rely on physical and
human capital), SaaS companies are well positioned
to add AI-powered data lakes and marketplaces of
sellers and partners. (Our team has also created an
assessment model for SaaS companies to determine
whether they have MBM potential.) And if your company is a marketplace, it may also be primed to achieve
the modern status; the question you need to ask yourself is, can you create a subscription service that is
critical (like air) to your buyers’ and sellers’ offers? By
adding these key components to your growth strategy,
you can begin to move from laggard to leader.
Product-Led Growth Is the
Future of SaaS Growth
To put a fine point on the power of the “like air” SaaS
solution, product-led growth is a growth model that
focuses on the product itself to drive customer acquisition, retention, and expansion.
With a modern business model, companies must
provide a valuable B2B or B2C software solution that
becomes critical for users as they perform their daily
functions. To do that, MBMs use AI to generate and
present data, to both business and consumer customers, that’s used in combination with SaaS tools to create
greater value. Machine learning enables valuable insights that drive action for a business’s ecosystem of
product users. For example, one of our portfolio
companies, Fiix, is a cloud-based maintenance management system and emerging marketplace. AI tracks
and analyzes parts and inventories and alerts users if a
critical part is projected to run low, allowing the customer — or even the machine
— to solve the issue by ordering the required part.
Imagine a marketplace
that matches salons and clients. Before this marketplace
scales to critical mass, it’s very
easy to disrupt. However, if
we add a SaaS solution that
allows owners to keep track of
operational data like appointments, payments, inventory,
and client profiles, and power it with AI, the value increases. In this example, AI can facilitate personalized
offers from sellers to buyers. Based on their observed
wants, needs, and purchase behaviors, clients are sent
reminders to schedule their next appointment, and
buyers receive alerts for low inventory or the need to
place orders. A good example of this is Mindbody, an
online marketplace and AI-driven software solution
for boutique gyms, salons, spas, and their clients. The
private software maker, which was acquired in 2019 for
$1.9 billion, has a successful MBM that is integral to a
user’s daily workflow.
Network Growth Is Critical to
AI and Data Generation
In today’s digitally centric world, increased access to
people and their data have made offer personalization possible, and even expected by users. Business
and consumer customers want to feel especially
important, regardless of how fast your company is
growing. However, many organizations focus on
themselves — their internal processes, people, and
products — and spend little time or effort on customer engagement and loyalty other than social
media likes. Profitable growth begins with creating
more promoters and fewer detractors. MBMs use AI
and machine learning to increase customer loyalty
by recognizing and serving the needs of customers
with an almost human-level degree of understanding and personalization — or empathy at scale.
Given that empathy is the ability to understand and
share the feelings of another person, our belief is that
AI, when used with care and compassion, enables
companies that are data-, machine-, and networkcentric to begin understanding the feelings of their
customers and suppliers. It even enables the sharing
of those feelings among their network participants
so that their partners can
meet their needs with offers of goods and services.
An example of this is
DigniFi, which uses machine learning and data
to match consumers who
need car repair financing
with lenders that want to
reach those consumers
with myriad offers.
In the marketplace
environment, this means better matching to users’
needs and creating a tailored experience, by surfacing
the information and features that are most relevant
to them. The value scales with each additional participant, which drives community development and
growth. As more matches take place, the data and
insights expand in exponential fashion, leading to
improved user experience, more features, and more
value. This growth attracts more users, which continues the flywheel cycle of more data to improve the
community experience. As the network grows, it becomes harder for community members to leave for
a competitor, especially if this is where everyone is.
Eventually, the marketplace grows to an impassable
data lake — competitors in the industry will struggle
to cross it. In this way, AI is an essential component
for MBM businesses to foster customer empathy and
create supplier value on an unprecedented scale.
What gives MBMs absolute advantage over
traditional and SaaS business models is that MBMs
actually become stronger as they get bigger. AI and
machine learning allow MBMs to see greater reAI, when used with focuses on the product itself to drive customer acquisition, retention, and expansion.
With a modern business model, companies must
provide a valuable B2B or B2C software solution that
becomes critical for users as they perform their daily
functions. To do that, MBMs use AI to generate and
present data, to both business and consumer customers, that’s used in combination with SaaS tools to create
greater value. Machine learning enables valuable insights that drive action for a business’s ecosystem of
product users. For example, one of our portfolio
companies, Fiix, is a cloud-based maintenance management system and emerging marketplace. AI tracks
and analyzes parts and inventories and alerts users if a
critical part is projected to run low, allowing the customer — or even the machine
— to solve the issue by ordering the required part.
Imagine a marketplace
that matches salons and clients. Before this marketplace
scales to critical mass, it’s very
easy to disrupt. However, if
we add a SaaS solution that
allows owners to keep track of
operational data like appointments, payments, inventory,
and client profiles, and power it with AI, the value increases. In this example, AI can facilitate personalized
offers from sellers to buyers. Based on their observed
wants, needs, and purchase behaviors, clients are sent
reminders to schedule their next appointment, and
buyers receive alerts for low inventory or the need to
place orders. A good example of this is Mindbody, an
online marketplace and AI-driven software solution
for boutique gyms, salons, spas, and their clients. The
private software maker, which was acquired in 2019 for
$1.9 billion, has a successful MBM that is integral to a
user’s daily workflow.
Network Growth Is Critical to
AI and Data Generation
In today’s digitally centric world, increased access to
people and their data have made offer personalization possible, and even expected by users. Business
and consumer customers want to feel especially
important, regardless of how fast your company is
growing. However, many organizations focus on
themselves — their internal processes, people, and
products — and spend little time or effort on customer engagement and loyalty other than social
media likes. Profitable growth begins with creating
more promoters and fewer detractors. MBMs use AI
and machine learning to increase customer loyalty
by recognizing and serving the needs of customers
with an almost human-level degree of understanding and personalization — or empathy at scale.
Given that empathy is the ability to understand and
share the feelings of another person, our belief is that
AI, when used with care and compassion, enables
companies that are data-, machine-, and networkcentric to begin understanding the feelings of their
customers and suppliers. It even enables the sharing
of those feelings among their network participants
so that their partners can
meet their needs with offers of goods and services.
An example of this is
DigniFi, which uses machine learning and data
to match consumers who
need car repair financing
with lenders that want to
reach those consumers
with myriad offers.
In the marketplace
environment, this means better matching to users’
needs and creating a tailored experience, by surfacing
the information and features that are most relevant
to them. The value scales with each additional participant, which drives community development and
growth. As more matches take place, the data and
insights expand in exponential fashion, leading to
improved user experience, more features, and more
value. This growth attracts more users, which continues the flywheel cycle of more data to improve the
community experience. As the network grows, it becomes harder for community members to leave for
a competitor, especially if this is where everyone is.
Eventually, the marketplace grows to an impassable
data lake — competitors in the industry will struggle
to cross it. In this way, AI is an essential component
for MBM businesses to foster customer empathy and
create supplier value on an unprecedented scale.
What gives MBMs absolute advantage over
traditional and SaaS business models is that MBMs
actually become stronger as they get bigger. AI and
machine learning allow MBMs to see greater reAI, when used with
c
turns instead of the decreasing value of investment
that many companies see as they expand.
Failing to Prepare Is Preparing to Fail
You’ve probably heard the classic advice to start with
the end in mind. Another way of saying it: Failing to
prepare your business for success in today’s modern
environment is preparing to fail. There are three factors of success in a pre- and post-COVID-19 world:
1. A data- and AI-centric strategy that drives insights from every interaction and helps match
customers’ wants and needs with suppliers’
products and services at scale.
2. A SaaS product that is as critical as air, providing
a reason for both customers and suppliers to interact with your company all the time.
3. A marketplace that goes far beyond your own
offers, in which your sellers and partners meet
each and every need of your customers.
Companies that neglect these three critical ingredients will mistakenly think that somehow the
products and services they market, make, and sell
will suffice in a world in which customers can get whatever they want, wherever they want it, from
whomever they want it. To remain relevant and resilient, companies and leaders must strive to build
business models in a way that ensures that these
three components are working together: AI that
enables and powers a centralized data lake of enterprise data, a marketplace of sellers and partners that
make individualized offers based on the intelligence
of the data collected and powered by AI, and a SaaS
platform that is essential for users.
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