"The Only Thing That Is Constant Is Change"
Heraclitus of Ephesus
- c530 BCE to 475 BCE
What is Simulation?
At its simplest, simulation recreates a system or a process in software, allowing you to test different scenarios and forecast outcomes in this simulated environment.
Market Simulation vs. Other Analytics Methods
Traditional analytics methodologies like statistics, regression, and machine learning reveal correlations, or patterns, in data. Machine learning, today’s go-to method for prescriptive analytics, starts with pushing a lot of data through an algorithm, which learns the patterns and delivers a model you can use to make predictions about future data. Machine learning is ideal when you need to answer questions like “Is this person likely to default on a new loan?” or “Which subscribers are likely to churn?” In general, the more data, the better the accuracy of the predictions.
With machine learning, the insights developed allow specialized analytics professionals, such as data scientists and statisticians, to interpret the predictions and develop theories on how their market works in relation to the questions they ask, and ultimately to make decisions that affect business outcomes.
But market simulation is different. It begins with domain expertise to develop a model of the market in which you operate. Data is used to calibrate or tune, the model so that you can develop the most accurate representation of the market you are trying to analyze. The market simulation can then be used to answer many strategic go-to-market questions. The key piece of the puzzle is the inclusion of causal factors of human behavior, including influence, perception, and network effects like word-of-mouth. With market simulation, it’s the level of domain expertise that improves the model, not just the amount of data.
Market simulation works well when you have complex questions, limited data, and high uncertainty or volatility. Questions like “what happens to revenue if I change my marketing mix?” or “how do I launch a new product in the most effective way?” or “what if a competitor enters the market with a new product that undercuts our pricing?” or “how do I improve my customer experience?” and many other market-centric questions are commonly addressed through market simulation.
"The fundamental difference between
the approaches is that pattern recognition
relies on correlation, while simulation relies
on human knowledge of causation."
From "3 Advantages to Using Simulation in Predictive Analytics"
by Tim Lindeman
The Science Behind Market Simulation
How To Simulate a Market
Define Your Market
The first step in populating a market simulation is to define the players in the market, including the competition. For every decision a person makes, they choose between two or more alternatives. These 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.
Create Simulated Consumers
Next, you need to recreate how your simulated consumers make decisions by accounting for their current preferences or perceptions in the market. You may even account for how different segments of the population have different preferences. Many companies use market research, segmentation studies, or other business intelligence to weight the factors people may care about, such as price or quality, convenience or speed.
Incorporate Factors That Influence Decisions
Next, you incorporate all the other factors that influence individuals’ choices. Oftentimes, these factors include the marketing activity of a particular company, competitive actions, and in some cases, word-of-mouth.
Train the Simulation
The goal of creating a market simulation is to mirror the real-world as accurately as possible. 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 even more reliable in predicting outcomes.
With a calibrated simulation model, you can begin experimenting 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 strategic “levers” to pull 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, such as safety features or other amenities. Companies are also able to leverage market 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.
Concentric uses your data and easily-sourced market research to simulate your market, allowing you to ask what-if questions about Pricing, Attribution, Competitive Positioning, and more.
Use Cases for Market Simulation
Market simulation offers the flexibility to answer a wide range of your most important what-if business questions:
Market Simulation in Action
Want to know how marketing simulation is used in the real world? Here are a few deeper dives into some of the use cases for marketing simulation.
Marketing Mix Optimization
“Half the money I spend on advertising is wasted; the trouble is I don’t know which half.” John Wanamaker coined this famous phrase at the turn of the last century, but it still rings true. Companies have a difficult time determining which marketing programs produce results because of how marketing programs affect and influence buyers. With market simulation software, companies can build their own marketing mix and attribution models in-house, as well as forecast the impact of their optimized media plan – instead of waiting months for a report.
Understanding how customers perceive a business, and how this perception affects revenue, is difficult or impossible to achieve with traditional analytics solution. Word-of-mouth, recommendations, social influence, and other very human factors make it difficult to capture and analyze data with machine learning. Market simulation is built to include these factors, offering an ideal platform for asking questions around how perception will increase visitors, drive sales, and improve perception.
A new product launch is a complicated undertaking, with multiple marketing touchpoints that could include press, online and offline advertising, in-store displays, word-of-mouth, and much more. Using market simulation, companies test out different launch strategies to see how the market will react, helping them predict the success of each launch strategy before executing in the real world, reducing risk and providing confidence in the final launch plan.
How do companies know what price is best for reaching their revenue goals? Many use a cost-plus model where they take the cost to produce and sell the item and add some percentage for profit. But this could lead to pricing miscues where they are leaving profit on the table or they price themselves out of the market. With market simulation, you incorporate the dynamics of the market – competition, consumer perception, and bigger industry trends like the closing of brick-and-mortar stores – to create pricing strategies that maximize revenue and perception.
How to Take a Market-Centric Approach to Predictive Analytics
Have you been hired to build a transformational analytics capability?
Is building a data-driven culture one of your organization’s top priorities?
In under 30 minutes, you will learn why this new analytics approach is disrupting the predictive analytics space. We call it market simulation – designed to answer your what-if business questions, faster.
- Why market simulation is emerging as what’s needed in business analytics
- How market simulation is different than other predictive analytics approaches
- How to populate a market simulation using the Concentric Market software platform
- What business challenges market simulation is used to solve