The market remains hot for selling your small to medium-sized business (SMB). As many businesses seek to grow through acquisition, SMBs become attractive targets. They may be part of a national strategy to “roll up” local SMBs into a national brand. Or they may represent a key piece of a supply chain or essential market presence. For these reasons, this is an ideal time to consider selling your SMB.
Challenges Faced in Selling an SMBs
But these opportunities come with challenges. There is a closing window for these smaller acquisitions as economic headwinds increase(1). This means buyers are looking for deals that can close quickly, create value for their shareholders, and where the risk of integration is relatively low.
The central component to both completing an M&A transaction, and a successful post-sale integration is data. Financial data is the basis of every M&A valuation, and a good confidential information memorandum or presentation (a “CIM” or a “CIP”) will demonstrate past profitability and future opportunities. Buyers need to know how they can scale the SMB’s profitability, team, operations, market presence, and value into the future. As a result, the buyer must be able to see the impact of the SMB’s unique value propositions inside the financial data. Producing data to tell the SMBs story consistently can be a challenge.
Almost all company’s seeking to sell have financial data, but operations data is often harder to come by in an SMB. Financial transactions must be recorded, and those records can be used to tell the story of a businesses to suitors. But operations data in an SMB is often informal and embedded in the experience and culture of the leadership and workforce. This means, to tell the best story, a sometimes costly and time-consuming process is required to extract that knowledge.
AI and Machine Learning More Theory Than Reality for SMBs
Businesses that scale are relying more and more on AI and Machine Learning. These scalable data systems require large, standardized datasets to identify patterns. This need for data often places them outside the reach of SMBs.
AI, or Artificial Intelligence is creating a machine to simulate what human intelligence can do(2). This uses human intelligence as the baseline and works to automate and scale what humans are capable of. Machine Learning takes AI to the next level. Instead of simply replicating what human intelligence can do, machine learning creates algorithms that allow machines to figure out their own approach to solving a problem(3). Instead of copying what humans do after they have learned a task, machine learning copies how humans learn, applying rules of trial and error and discovering the best way the machine can accomplish the same task.
Both AI and machine learning require huge amounts of data and trial and error to be successful. The more data they have, the more opportunities to learn from trial and error. With today’s massive processing power, machines can analyze data in hours that would take humans a lifetime. That ability to process massive amounts of data is fundamental to both AI and machine learning.
But, if there is little or no existing operations data in a business, no data exists to feed into these AI and machine learning systems. As a result, AI and machine learning exist in the largest market players and exert more of a top-down rather than bottom-up effect.
SMBs Priced Out of AI
Large SaaS providers will tout their ability to leverage AI and Machine Learning. But for that to work your business must fit into their existing data model. For SMBs, this requires an expensive data-creation, or data-conversion process, the costs of which are often prohibitive and difficult to predict or control. As a result, many small business owners decide it’s not worth it.
Those businesses that do take the plunge into one of these larger systems often discover that it is easier, and less expensive to change your business to fit the software than to modify the software to your business. As a result, SMB owners face a choice between giving up what makes them distinctive and being able to scale.
Definition Before Data
The solution is to focus on defining who you are and what makes you distinct, and then collect the data to support that. In an M&A process it is preferable to offer a clear structure with minimal data, rather than a convoluted structure with data that does not demonstrate your distinctive value.
Since AI begins by replicating what humans are doing, the first step is to document your processes and define the roles of who does them. Companies that have clearly documented processes and roles often sell more quickly and for higher prices than those without(4).
Successful M&As often result because the seller can demonstrate to the buyer a specific history of profitability that the buyer can expand and scale. Telling a compelling story and backing that up with clear financial and operations data is key. The buyer is looking for the critical component you will contribute to their growing enterprise. If you clearly present what they are looking for, they will determine the value they are willing to place on the transaction based more on what they can do with the SMB than its historical financial results alone.
Working to define and document processes and roles will help you identify the key places to capture data going forward. Depending on where you are in the M&A cycle, you can decide whether to invest in that data capture yourself or recognize that will be the responsibility of the buyer. While your company may be more valuable with that data in place, having well defined processes and roles is essential to a smooth, and profitable M&A transaction.