Sumo Logic is a cloud-based Continuous Intelligence platform, based on a subscription revenue model, serving primarily customers from IT departments, development, and security, both from small organizations and enterprise, leveraging on self-serving digital channels for new customers’ acquisition, coupled with inside and field sales representative to expand the adoption of the platform.
Sumo Logic evolution
Founded in 2010 as a cloud-native log analytics platform, Sumo Logic evolved over the last decade to comprise more use cases that span from security to compliance. As of 2020, Sumo Logic turned into a Continous Intelligence platform.
By using the VTDF Tech Business Model Framework we’ll look at four components of Sumo Logic business model:
- Value Model
- Technological Model.
- Distribution Model.
- Financial Model.
Today Sumo Logic is a Continuous Intelligence platform that processes all sorts of data (from unstructured to structured and semi-structured), able to cover several use cases and serve different types of profiles within the same organization.
Sumo Logic vision is to democratize machine data, making insights from this rich source available to all.
It does that through an operational model that follows the logic of a Continuous Intelligence Platform covering a wide range of use cases for customers.
Some of the use cases, that help Sumo Logic to build its set of value propositions are:
- Monitoring and troubleshooting of applications and cloud and on-premise infrastructure.
- Managing audit and compliance requirements.
- Detecting and resolving modern security threats.
- And extracting critical key performance indicators from various types of machine data to gain visibility into customer behavior, engagement, and actions.
Mission: continuous intelligence, and customer-centric software
Sumo Logic’s mission is to create the leading machine data analytics platform that delivers continuous intelligence for modern applications.
Sumo Logic defines it as Continuous Intelligence, or a business model that combines software applications that directly execute business strategy, delivering differentiated customer experiences in an ‘always-on’ constant fashion and that expresses itself through customer-centric software solutions.
The whole point of a Continuous Intelligence Platform is to enable organizations to collect, ingest, and analyze applications leveraging on AI/ML and make decisions in real-time.
The three pillars of a customer-centric software according to Sumo Logic
The whole point of continous intelligence is to help companies, in theory, to move towards continuous innovation.
And Sumo Logic identifies three key pillars for its Continuous Intelligence model:
- Agile technology infrastructure.
- Continuous development and delivery models.
- Continuous Intelligence to achieving real-time insights.
Sumo Logic opportunity developed on top of old assumptions, part of the previous era of intelligence software. Some gaps in the markets who helped Sumo Logic build its solutions were and are:
- Manual processes: with previous software solution with lengthy set-up time, and hard to scale.
- On-premise solutions: who were the norm in the past, but that as Sumo Logic highlights cannot scale to handle the volume, velocity, and variety of data ingestion required to deliver continuous intelligence.
- Point solutions: where partial data sets with incomplete information, which create inconsistent results, and thus are not able to provide the intelligence needed.
- Outdated licensing models: following the on-premise solution, licensing models who not only hard to set up, were and are also complex and expansive to run, do not provide the sort of service that companies moving toward digital transformation might need.
Based on those gaps, how is Sumo Logic fulfilling them? The primary value that Sumo Logic provides is that of Continuous Intelligence Platform, which can be summarized in four key value propositions:
- Operational Intelligence (with root cause analyses helping companies identifying operational problems, to enhance the customer experience).
- Security Intelligence (with detection of real-time threats and incidents).
- Business Intelligence (by analyzing customer behavior, engagement, and actions).
- Global Intelligence (through, perhaps, a benchmarking service that leverages machine learning to uncover global KPIs and key risk indicators at a global level within the organization).
Sumo Logic customer composition
Sumo Logic primarily serves three types customers profiles within SMBs and enterprise organizations:
- IT operations.
- Development and operations.
- And security.
Sumo Logic customer composition ranges from small and medium-sized enterprises to businesses in the Fortune 500. As of April 2020, Sumo Logic counted over 2,100 customers globally and 125K users. The customer base is made primarily of three tiers of ARR (annual recurring revenues):
- Below $100K ARR: over 1700 customers as of April 2020.
- Over $100,000 of ARR: 329 as of April 30, 2020
- Over $1 million of ARR: 27 as of April 30, 2020
As the platform started to expand more aggressively in the last three years, it pushed toward sales activities to improve the number of customers over $100K and a million ARR per year.
Some interesting use cases comprise companies like JFrog and Qualtrics, clients of Sumo Logic for 4 and 5 years, respectively.
The Sumo Logic technological model is based on five pillars/principles, which gives the rise to its operational model:
- Modern application architectures.
- Multi-cloud adoption.
- Continuous security.
- Continuous collaboration.
- Data-driven intelligence.
Sumo Logic is made of different technological building blocks. Its Continous Intelligence Platform has several parts and we might argue the most important intangible asset for the company is the set of machine learning proprietary technologies built into its services comprise:
- LogReduce and LogCompare: which is an ML model to quickly identify and narrow down problems from vast logs within the database, so that similar logs are clustered for a more effective detection analysis.
- TimeCompare: an ML model comparing outcomes from any query in a specific time range to detect a change in behavior of applications, infrastructure, users, or business trends.
- Outlier: an ML model monitoring deviations in a time series to identify potential issues, security problems, or anything that might have a business impact.
- Predict a set of machine learning models and mathematical techniques helping users predict outcomes and prevent production, security, or business issues.
In the future, every software company will turn into an AI/ML company and Sumo Logic gives a good view of this trend which is consolidating.
R&D consists primarily of investments in research, design, maintenance, and other minor enhancement to the platform. Some of the key R&D expenses are also skewed toward the development of AI/ML models built into the software platform.
While in the first years of operations many algorithms were built (the main patent is the Log Data Analysis) to make the platform take a leap forward, in the last years substantial investments in R&D have been made to support and enable the platform to sustain its operations as it scaled.
Going forward we can expect this trend to continue as the engineering force will be used to sustain the additional scale the platform has to achieve to surf a market that is growing and developing around continuous intelligence.
Distribution Model and Go-to-market strategy
Within the current distribution model of Sumo Logic, we have two core sub-strategies:
- Go-to-market strategy to expand its customer base.
- And the distribution strategy to continuously grow the business.
Sumo Logic follows a “land-and-expand go-to-market model” (to bring in new larger accounts with wider adoption, quickly) moves around five pillars:
- Self-serving digital channels (primarily website, digital branding campaigns, email marketing, and social media marketing), to activate new trials, on the basic plans the platform offers.
- Inside sales team, which will help customers throughout the process of successfully adopting the software platform and in case, switch to higher-tiers plans.
- The field sales team will engage with customers and executives within customers’ organizations to offer more custom solutions or wider adoption of the platform within the organization.
- Partner channel, which comprises technological partnerships (deep integration with the Sumo Logic platform, like perhaps the third-party hosting of Amazon AWS, Microsoft Azure, or Google Cloud within the cloud-native platform); system integrations and resellers; and managed service providers.
- Community: Sumo Logic offers a multi-level certification program made of approximately 12,000 Sumo Logic “certified users” who also act as advocates for the brand, thus in theory, helping the adoption of the platform by new customers.
Most of the sales efforts in the last years have been made toward expanding the customer base of customers with an ARR of over $100K per year, and over $1 million per year, while also working on customer retention.
Marketing activities are focused instead on brand reputation and awareness, generating demand for the platform. Those marketing activities comprise email and event marketing, digital advertising, social media, and other PR activities. Sumo Logic also hosts its annual flagship customer conference called “Illuminate” that targets three main customer profiles:
- IT operations.
- Development and operations.
- And security.
For the sake of analyzing the financial model we’ll look at the revenue generation, cost structure, profitability and cash generation.
Sumo Logic follows a SaaS model where, through a subscription, customers get access to its platform. The subscription plan is organized in multi-tiered packages based on various factors (that as highlighted by Sumo Logic, comprise the volume of data to be ingested, duration of data retention, and breadth of access to platform features and functionalities).
Sumo Logic mixed its subscription revenue model with the cloud-native platform to overcome the outdated software license model, where enterprise clients might face unforeseen charges based on the volume of data ingested.
Instead, Sumo Logic incentives customers to expand their adoption of the platform, as they can analyze large volumes of data without incurring into overage fees, or fall into unpredictable pricing structures.
Additional revenues are generated if customers want to benefit from premium support, beyond the basic support provided within their subscription package:
Cost Structure (how Sumo Logic supports its revenues)
In order to deliver its solution and make sure the sales are successful in the first place, Sumo Logic costs are associated with third-party hosting fees related to its cloud platform, amortized expenses for internal-use software (and acquired developed technology). Third-party cloud hosting fees costs are tied to the greater adoption of the platform.
Therefore the more the customers’ base will grow and the existing customers will expand their adoption of the platform, the more data hosting expenses will be incurred by Sumo Logic.
In addition, in order to enable a successful adoption or expansion of the platform functionalities, a customer success team supports the customer, and that is included in the cost of the subscription.
As the platform scales it needs to make sure to keep its cost of sales under control to prevent to eat up the gross margins. Perhaps, as of 2020, while the cost of sales expenses increased from over $22 million in 2019 to over $29 million in 2020, the gross margin still improved as the revenues grew faster:
While the standard support is comprised of the customer’s subscription package, the premium support service (which includes 24/7 access to a technical account manager) is instead charged separately.
As of April 2020, Sumo Logic is cash negative, thus it burns more cash than it brings in. This is, in part, normal for a company trying to scale a new vertical, with massive resources spent on sales and marketing activities.
In addition, as the revenue model is based on a subscription service, the high acquisition costs buried into the sales and marketing expenses will be paid over the years. If the enterprise customers are retained with multi-year contracts this will pay off for the acquisition costs and also improve the profitability of the company (the over $107 million costs for marketing and sales made up almost 70% of the total revenues by April 2020, although $4.5 million were paid by Sumo Logic as settlement expenses).
With $114 million in cash and cash equivalents, Sumo Logic might have sufficient resources for at least 12 months of operations. Going forward, striking a balance on its financial model is critical.
Putting it all together
- Value Model: Sumo Logic value model moves around Continous Intelligence, and a new licensing model for software, served through a subscription service which enables unlimited usage for the platform. The platform primarily runs on the cloud, thus preventing costs and complexity associated with on-premise software solutions and price volatility that comes with it. Over the years, the company expanded its use cases to become a cloud-based platform serving several players with the organization (primarily serving IT operations, Development & Operations, and security).
- Technological Model: the technological model is driven by proprietary AI/ML models helping identify patterns and narrow down root causes, with the logic of real-time discovery, always-on, and available across the organization. This model moves around the assumption that any type of data (from structured to unstructured) can be sourced and ingested to create continuous insights for the organization.
- Distribution Model: the distribution model moves around a land-and-expand go-to-market strategy and a distribution model that is built on five main pillars: self-serving digital channels (to drive demand and trials on the platform), inside sales team (to drive acquisition of new customers and upgrade to higher-tiers), field sales team (to drive expansion on enterprise customers), parter channel (to expand use cases, brand awareness, and lower platform costs), and community (to reduce subscription churns, and drive new sales through community members working as informal advocates for the brand).
- Financial Model: Sumo Logic financial model is based on a subscription-based revenue model, supported by third-party data hosting costs to deliver the cloud-based platform and support services included in the subscription service. As a result of scaling up the platform and expanding the customer base, the company is not profitable, and it generates negative cash flows.
- Striking a balance: Going forward for a solid tech business model, Sumo Logic will need to find a balance between its operational side (keep scaling by reducing the cost of sales and sales and marketing costs) and financial side (enable its subscription contracts to pay off for the high customers’ acquisition costs). Also, as the company will scale, it might benefit from higher demand and network effects, which might help it become more valuable for customers while lowering the acquisition costs toward new accounts.
Key Highlights of Sumo Logic’s Business Model Evolution:
- Founding and Evolution: Sumo Logic was founded in 2010 as a cloud-native log analytics platform. Over the years, it expanded its use cases to cover a wide range of areas, transitioning into a Continuous Intelligence platform by 2020.
- Value Model: Sumo Logic’s Continuous Intelligence platform processes various types of data (unstructured, structured, and semi-structured) to cover use cases in IT operations, development, and security. Its goal is to democratize machine data and provide real-time insights.
- Vision and Mission: The company’s vision is to democratize machine data and make insights available to all. Its mission is to create a leading machine data analytics platform that delivers continuous intelligence for modern applications.
- Customer-Centric Software: Sumo Logic emphasizes three pillars of customer-centric software: agile technology infrastructure, continuous development, and continuous intelligence. It aims to enable continuous innovation and rapid decision-making.
- Value Propositions: Sumo Logic addresses gaps in the market, including manual processes, on-premise solutions, point solutions, and outdated licensing models. Its key value propositions include operational intelligence, security intelligence, business intelligence, and global intelligence.
- Customer Composition: Sumo Logic serves customers in IT operations, development, and security across SMBs and enterprises. Its customer base includes a range of organizations, from small enterprises to Fortune 500 companies.
- Technological Model: The Sumo Logic platform incorporates proprietary machine learning technologies, such as LogReduce, TimeCompare, Outlier, and Predict, to provide real-time insights and problem-solving capabilities.
- Distribution Model: The company employs a “land-and-expand” go-to-market strategy. It uses self-serving digital channels, inside sales teams, field sales teams, partner channels, and a community of certified users to expand its customer base.
- Financial Model: Sumo Logic operates on a subscription-based revenue model, offering different tiers of packages based on data usage and features. The company is investing heavily in sales, marketing, and R&D to scale and acquire market share, resulting in negative cash flow as of 2020.
- Striking a Balance: As Sumo Logic continues to grow, it will need to find a balance between scaling its operations, controlling costs, and achieving profitability. It will also aim to leverage network effects and customer expansion to improve its financial position.
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