IN MOST ENVIRONMENTS, AND ESPECIALLY SO IN TODAY’S ENVIRONMENT, THE CHALLENGE FACING BUSINESSES IS TO FORECAST PERFORMANCE IN THE FACE OF UNCERTAINTY.
This applies both when planning for organic growth, or when acquiring new businesses (or making investments) for growth.
Companies (corporate development teams, investors, managers) sometimes make the below mistakes:
1. AIMING FOR TOO MUCH PRECISION – TRYING TO FACTOR IN ALL THE UNCERTAINTIES INTO A NEAT QUANTITATIVE FRAMEWORK.
This approach provides a false sense of security, and doesn’t actually help plan for opportunities or mitigate risks as a result of the uncertainty
OR
2. DISCARD ANALYTICAL PLANNING AND MAKE DECISIONS BASED ON GUT INSTINCT OR FEAR – BASED ON THE MARKET SENTIMENT, THE NEWS OF THE WEEK, WHAT THEY SEE COMPETITORS DOING.
This doesn’t serve them either since its no longer possible to adapt the strategy based on the intrinsic ability of the organization to execute a strategy, or to confidently pivot when market conditions change
OR
3. APPLY THE WRONG VALUATION FRAMEWORK TO THE SITUATION
Here’s a mental framework that I’ve found helpful to classify the uncertainty into four categories, so that the appropriate forecasting and valuation approach can be used.
CATEGORY 1: CLEAR ENOUGH:
This applies to industries and companies where the uncertainty doesn’t materially impact the forecast strategy for the business.
Note that uncertainty still exists (as it always does), but the business strategy does not need to fundamentally change based on how external events unfold.
This type of scenario can be relatively well understood with normal strategic analyses – by using market research, competitive analysis and the relative position of suppliers and customers.
Example: A manufacturing company in a mature market, established customer needs and a steady supply chain.
Valuation Approach: The output can be incorporated into a DCF model which factors in revenue growth, cost structure and investments
CATEGORY 2: ALTERNATIVE DISCRETE FUTURES
Based on market conditions, two or sometimes three potential outcomes are possible, and it is not easy to predict which scenario will unfold.
Example: A company launching a B2B product or service for the crypto / web3 economy. The demand for this product could be either strong or weak based on the broader adoption of web3, but is unknown at launch time.
Valuation Approach:
Assign probabilities to different scenarios that could unfold, and model the economics of each scenario.
This approach assumes that the business would pivot its strategy as the future becomes clearer, but its not clear which of the future scenarios will prevail.
For example, the web3 company might need to pivot its GTM strategy to partner vs. selling directly, or choose a product-only vs. product + service model.
Based on the possible outcomes, the valuation approaches for each scenario could be quite different since the strategy itself changes – impacting pricing, cost structure and investment needs.
CATEGORY 3: GREY-SCALE FUTURE
In this case, the future outcome changes are continuous and not discrete.
Example: A company launching an existing product (that worked in a mature market) into an emerging geography could see a range of success scenarios (from zero adoption to resounding success).
Valuation Approach:
Identify the potential future outcome and pick the one or two key underlying drivers that will predict the success of the approach.
Then build in sensitivity analysis that flexes the valuation based on the underlying drivers.
The key is to keep it simple. Using more than three drivers can over-complicate the analysis and slow down decision making.
CATEGORY 4: COMPLETE UNCERTAINTY
This can happen in situations where the underlying drivers are very hard to predict or even identify.
Example:
An established B2B and B2C telecom company that wants to develop an adjacency in multi-media and digital marketing.
The number of variables in this situation are so many that its hard to even list them all – from competition from smaller incumbents, to its ability to build or acquire the technology, to integrating the adjacency with its existing solutions.
Valuation Approach:
This is admittedly the hardest type of scenario to value a company in.
However, it’s important to resist the temptation to make decisions or investment purely on “gut instinct” and to throw data aside.
Companies should instead catalog (i) what they do know, (ii) what is possible to learn, with some effort, and (iii) what indicators to track closely that can tell them when to change their decisions.
Companies can also study how other companies in the same industry are adapting, and learn from how similar situations unfolded in other markets (or even other industries), and value the opportunity by using a peer-based or prior-transaction based approach – which reduces the risk of biased and subjective assumptions.