Keys To Developing A Covid-Era Business Model

Data, online marketplaces and user platforms are increasingly crucial to competing in the remote, distributed intelligence Covid-era economy. JL

Lanham Napier and colleagues report in MIT Sloan Management Review:

The pandemic has exacerbated the lack of resilience in business models which struggle to compete against digitally centric companies that can leverage data and machine learning to create insights, intelligence, and capabilities across the organization. To remain relevant, companies must build business models that ensure three components are working together: AI that 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 - powered by AI - and a platform for users.
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 partners (such as Apple’s developer network) have an added advantage. These new, three-pronged models go far beyond SaaS and include the following: 

A community of active B2B and B2C users that creates a network effect due to their interactions. A marketplace that delivers offers from sellers and suppliers to meet customers’ needs. 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. 

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. 

AI, when used with care and compassion, enables companies that are data-, machine-, and network-centric to begin understanding the feelings of their customers and suppliers. 

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 network-centric 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 returns instead of the decreasing value of investment that many companies see as they expand. 

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: 

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. 

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. 

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.