The Missing Page in Your Analytics Playbook: Market Simulation
By John Pasinski, September 2, 2019
Since the early 2000s, companies around the globe have been grappling with how to leverage data to drive more intelligent business decisions. In recent years, investment in the space has grown rapidly. Yet, according to a recent study conducted by NewVantage Partners, 53% of executives state they are not yet treating data as a business asset. Although investment continues to increase, businesses aren’t seeing a significant return on investment.
53% of executives state they are not yet treating data as a business asset.
Today, analytics is predominantly focused on tracking performance or uncovering insights in big data. But what happens when the insight is identified? Will the actions taken off of that insight really make a difference for the business? What if you could test the ideas spurred by the insight in a simulated environment to understand the impact of your actions? Advances in machine learning and cloud-computing now make this possible at the speed business demands.
Simulation is the missing step in successfully leveraging analytics as a business asset. It’s not a new concept. It’s something the average person has come into contact with whether they realize it or not, like when checking the weather forecast, which is a simulation of the atmosphere. While NASA has simulated space systems since the 1960s, in recent years, simulation has become scalable, so businesses are now able to leverage the technology to test ideas in a simulated environment and predict the outcomes.
Historically, building a simulation required a team of PhDs to manually build a model using agent-based modeling. This process was cumbersome and did not yield answers at the required pace to become actionable. Recent technology advancements in machine learning, cloud-computing and software development now make simulation possible at the speed business demands for a wide variety of use cases. Financial organizations use simulation to spot indicators of the next financial crisis. IT leaders use simulation to predict data breaches. Manufacturing companies use the technology to support production planning. Meanwhile, cities and government organizations leverage simulation to create better transportation routes.
In business, leaders — from the CFO and CMO to head of sales—are beginning to use market simulation to better understand which insights and strategies will deliver growth and investment efficiencies. For example, broadcasting company E.W. Scripps leverages simulation to forecast media spend ROI for its advertisers.
Put simply, market simulation is able to recreate the dynamics and rules of how a population of people in a given market behave, influence each other, and make decisions. With so many variables and rules at play, there is a level of complexity involved that is best achieved through the support of machine learning-powered simulation models. Market simulation is being used by a number of industries, but anyone can leverage the technology to simulate a given market in order to validate which data-driven insights will lead to better business strategies, with fewer unintended consequences. Here’s how it works:
Step One: Define Your Market
The first step in building a market simulation is to define the market competition. For every decision a person makes, they choose between a series of alternatives. These alternatives may include competing products, services, brands, or the choice to do nothing at all.
For example, a new TV streaming service would identify companies like Netflix, Amazon Prime Video and Hulu as direct competitors along with cable, satellite and premium channels. However, it’s important to also consider that a consumer may opt out of watching any programming.
Step Two: Create Simulated Consumers
Once the market is defined, then you need to identify the attributes that influence the decisions of individuals in the market and what their current preferences or perceptions are for the alternatives in the market. The simulated population may be divided into segments of consumers that have different behaviors, attitudes, and preferences. This recreates a population in a simulated environment. In order to do this, many companies use market research, segmentation studies, or other business intelligence to weight whether people care more about price or quality, convenience or speed.
Step Three: Incorporate Factors That Influence Decisions
Next, you incorporate all the other factors that influence individuals’ choices. Oftentimes, this includes the marketing activity of a particular company, competitive actions, and in some cases word-of-mouth.
Step Four: Train the Simulation
The goal of creating a simulation is that it mirrors the real-world. In order to do this, the simulation must be calibrated by adjusting settings until there is enough confidence that the simulation can reliably predict future outcomes.
This is an on-going process since there will always be new data and learnings that need to be added to represent an evolving market. People aren’t static, so a simulation can’t be either. Over time, this allows the simulation to become smarter and even more reliable.
Step Five: Test Scenarios
With a calibrated simulation model, a company is able to begin testing insights to see how various product updates, pricing strategies, marketing campaigns, and other factors can influence consumer decision making. Ultimately, the goal is to identify which “levers” to pull or strategies to implement in order to increase the likelihood that consumers select your product over other offerings. For example, a car manufacturer may seek to understand the impact of adding TV monitors to its line of minivans. Through market simulation, that manufacturer can clearly see the impact on sales and may decide to invest in TV monitors or pull a different lever altogether.
Companies are also able to leverage simulation to better understand the impact of scenarios they don’t have control over, such as a new competitor entering the market or the rise of gas prices.
Simulation is meant to build trust in data-driven insights, empowering businesses to test new strategies before making an investment. It’s the final step in realizing data as a powerful business asset that delivers ROI.