The only constant is change: how to navigate in cloudy waters
Long-term planning felt easier before 2020. Prior to the global pandemic, the world was more predictable, so having a long-term vision felt like a safe bet. Clients would run analyses to predict the development of certain categories, and the success of innovations, based on the assumption that the world was going to stay pretty much the same.
Regression analysis were a useful way to understand the development of a category against macro factors such as weather, GDP growth, disposable income, as well as industry variables such as competition, or population dynamics.
This has now changed, and a lot of statistical models that were based on a sound understanding of historical trends aren’t as reliable. Predicting the evolution of a category and trying to control certain variables (for example media spend and capital expenditures) cannot be based 80% on historical trends and 20% on forward looking insights – the equation needs to be rebalanced. Current tools drive brands to prioritize short-term decisions, as they’re navigating uncertain waters, without a lighthouse to bring them home.
However, companies still need to know where they’re going, to have a point of view, and a plan for the next 15 or 20 years. So, here are some tips that you can use to guide your compass when the path isn’t clear.
How to keep making decisions when everything changes
Things aren’t smooth sailing anymore, the tides have turned and taken the security of calm waters with them. We’re left navigating muddy, rough seas. Everything is volatile, and everything keeps changing.
But decisions still need to be made. While a good assessment of the past is still necessary, we also need to have a stronger structural understanding of the market and the causal relationships that can drive it to help us make better choices.
A lot of the methodologies that we bring to our clients are more relevant today than ever before. We still deliver heavy statistics, but we focus on getting that firm, systemic understanding of the market before looking towards the future.
System Dynamics Modeling
Setting our course with this much needed knowledge is our System Dynamics Modeling technique. This model isn’t just following the latest trends – its designed to capture the complex cause and effect relationships within your system, even accounting for feedback loops. The technique is part of the wider System Thinking field, and helps us understand non-linearity, vicious (or virtuous) cycles, and accelerations.
Let’s look at the development of a beverage category. We would consider historic trends to predict the level of consumption based on info such as how much the population will grow, disposable income, household dynamics, GDP, and rural vs urban penetration. When thinking about growth however it’s important to consider the curveballs in the non-linearities. For instance, you need to understand the sources of growth (what brands or categories will we be “stealing” from), the forces of consumer trends and word of mouth that can lead to sudden acceleration of adoption, and the “ceiling” we cannot go beyond; there’s a limit to how much people can consume in terms of beverages, and there’s a limit to how much of their disposable income they’re willing to allocate to a certain category.
A new pressing phenomenon that most people can relate to is climate change – it’s extremely hard to predict climate change based on historical data. As the globe continues to heat up, some new elements of the planet are triggered and adding additional (yet unknown) dynamics to the equation. This is why a structural understanding is needed to predict how future variables may impact and influence the world, the market, consumer values, and attitudes.
In practice: juicing up brand opportunities
A beverage brand wanted to launch a low-calorie juice product and wanted to know how big the opportunity was and how much the brand could have grown. We investigated how many people consumed the juice, and whether they consumed regular juice or diet juice. We ran a primary piece of research that investigated the drivers and barriers for this new product as well as the main brand. Once we had all the pieces of the puzzle, we then relied on causal relationships and structural understanding to size up this opportunity.
We could have gone the route of statistics based on historical data of similar launches; however, we didn’t want the sizing and strategy of their new product to be limited by what their competitors did in the past.
We looked at different demographics – we weren’t expecting older generations to pick up on this new product launch, as it was more targeted to teens. We did the math to answer questions such as: how much of the current consumers would drink more of the master brand through getting to know this new product? How many people could the brand win over with the right drivers? Would this growth come at the expense of other products in the main brand portfolio? To answer our questions, we ran these numbers against the historical data as a backup validation.
Ultimately, we cannot rely on black-box statistical trends anymore. We need to open up that box, find clarity in the cloudy waters, and build causality and structural understanding through models that are meant to provide us with an outlook into the future.