Determining the fair market value of any organization big or small is a difficult endeavor. In a competitive bid scenario, purchaser bids may vary by large magnitudes. Why would one organization pay significantly more while others are unwilling to pay any more than the book value of the assets? Organizations are willing to pay for ‘goodwill’ if they see opportunity. What does opportunity mean? Opportunity may mean the organization is a strategic purchase; it may have greater value as a suite of products rather than a standalone product. You often see this scenario play out in the tech space where, for example, Google acquires a small startup for their technology. Another opportunity a potential purchaser may see is untapped business potential through missed market opportunity, investment, or execution. Savvy investors are looking at all angles to unlock hidden organizational value. However, the traditional sales process doesn’t fully allow analysts to understand all angles of the organization. Creating an analytics platform before the sales process will allow analysts to find hidden gems resulting in maximum sales price.
Over the past few years, I’ve been fortunate enough to work on the sale side of 2 large organizations. Through working with these organizations, I’ve noted some huge benefits to creating an analytics platform before the sales process.
By creating a single platform to analyze data, one view of the organization is created with data that always reconciles. This means that less time is spent on explaining differences between multiple sets of data. More time will be spent on explaining strategic direction and growth opportunities, in turn, raising the sales price.
The traditional sales process is a static data room in which business reports are loaded but do not change dynamically. A static data room has many cons, all of which include more work and time. With multiple interested parties, each potential purchaser is looking for different variations of the same data to build valuation models. The static data room concept forces the analysts to ask for the same data with multiple variations. For example, if an analyst is attempting to understand customer retention (raises the value of the company) and only have revenue figures posted in the data room, they will ask for revenue by customer. The selling organization will then need to pull a sales report and ensure that it reconciles before posting to the data room. An analytics platform eliminates this by giving the purchasing organization the ability to drill down and pivot on dynamic data creating their models on the analytics model rather than many data sets.
From a time perspective, having live and dynamic data has many advantages. The sales process usually runs the course of several months, rather than updating every report in a static data room, the model will be automatically updated once the month closes. This benefits all parties. The analysts looking to purchase the organization can update their models automatically rather than manually updating all information in the data room. From banks to private equity firms, model creation is duplicated with different assumptions and largely the same inputs. There is a high level of risk associated with manual data entry creating the possibility for errors.
Creating a limited access analytics model allows more people to fully understand the organization. With more people analyzing potential opportunities in the organization through efficiencies or revenue potential, there will likely be a premium associated with these opportunities as there will be an additional upside to the model. Additional upside creates more future potential cashflow creating a higher present value and return on investment.
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