Valuating uncertainty – Paying the right price in Tech M&A Deals
Rebooting your M&A valuation capabilities
Sound familiar? In recent years, bullish investors have been aggressively snatching up stocks of e‑commerce and tech hopefuls and start-ups, often paying surprisingly optimistic prices for untested business models. In previous posts, I have already talked about how big data solutions and advanced analytics can be used to shortlist suitable transaction targets or to turbocharge the due diligence process. Here, I want to talk about new tools that are being crafted to address the special challenges of accurately pricing hyper-growth targets with uncertain future cash flows, such as e-commerce and tech players and start-ups.
Valuation methods for mature businesses in established sectors
There is in no shortage of literature on valuation methods, from discounted cash flow methods to peer company comparisons or the analysis of comparable reference transactions. Valuation experts have over the years refined an extensive toolkit with which we can reliably determine the value of businesses. The methodology for deriving company-specific multiples and determining when and how to apply them has likewise reached high levels of sophistication. But the digital economy has thrown a spanner in the works.
The problem that dealmakers are increasingly running into is that established valuation methods were designed for asset-rich businesses with reasonably predictable cash flows, and ideally with plenty of historical data. In today’s digital arena, however, established valuation methods routinely struggle to reliably price businesses that don’t fit that profile.
Valuating e-commerce and tech players and start-ups
Determining the value of targets in the tech space, especially start-ups, can be a major headache for dealmakers. First of all, start-ups lack the historical data that traditional valuations rely on. On top of that, no two unicorns are alike. Or, more generally, it is difficult to find a suitable peer group for benchmarking companies with innovative business models.
Furthermore, the balance sheets of tech companies are often heavily loaded with intangible assets, which are tricky to put a price tag on due to information asymmetries. Digital-centric businesses also often pursue a “scale fast, monetize later” ethos (you need look no further than Google). This strategy enables them to benefit from network effects that are key to their future financial success but which are difficult to factor into valuations a priori.
If a business does take off, you’re still left with the question of how long the initial hyper-growth phase will last – in other words, just how scalable is the business model? And how easily can your prospective target build up or buy in the capabilities and governance structures it needs for the organizational transformation that massive scaling at speed entails? Indeed, how likely is the company to reach the envisioned scale before other players mount an attack?
Digital companies also increasingly operate within complex and continuously evolving ecosystems. That adds layers of complexity to the question of how to reasonably price in synergies.
Given the inherent uncertainty in the e-commerce and tech start-up space, it is no wonder that dealmakers so often find themselves overshooting the mark with their bid prices.
Get better deal outcomes
There is as yet no clear consensus on how to best value tech-heavy companies and start-ups with financials subject to pronounced uncertainty in terms of historical trends, cash flows, and the value of (mostly intangible) assets. In fact, company valuations can diverge massively. Some dealmakers are successfully bridging the methodology gap with digitally empowered market surveillance and target screening.
Knowing the market well is essential and gleaning reliable market insights has never been more important. Some business intelligence providers are leveraging big data and predictive analytics to distill sector insights that reveal early signs of promising prospects or possible pitfalls. Robots are also being effectively deployed to scan masses of publicly available data and pre-screen targets, thereby streamlining the deal pipeline.
Big data tools can also prove an effective means of identifying synergies. For instance, Accenture Strategy recently helped a client identify over USD 1 billion in synergies by deploying natural language recognition software to rapidly analyze contracts, coupled with advanced analytics to consolidate category expenditure.
Screening individual companies and the information they publish is just as important. In the lead up to its contentious IPO, Lyft had claimed that it controlled 39% of the ride-sharing market. Its rival Uber contended it held a market share of 65%. The numbers didn’t add up. Soon after the IPO, Lyft’s stock price tumbled 19%, making disgruntled investors file lawsuits alleging they had been misled. Innovative dealmakers are adopting techniques such as advanced data mining or web scraping for a more thorough screening of prospective targets.
The pace of improvement in analytics is breathtaking, with artificial intelligence and machine learning increasingly empowering key steps of the valuation process. Aside from hard financials, tomorrow’s data-rich valuation models will also be able to factor in more nuanced qualitative data with a high level of consistency and predictive reliability.
Reboot your valuation capabilities
Buyers of e-commerce and tech players and start-ups need to move away from the historical data approach and begin to incorporate a more forward-looking perspective. There are plenty of tools already available for the job, and more are in the pipeline. Granted, keeping up with the latest innovations is by no means easy – and it is important to understand what works best in which context. If you need help finding your bearings in the new tech M&A landscape and rebooting your valuation capabilities, don’t hesitate to get in touch.