By Greg Silverman, May 22, 2020
Data scientists have done a good job poring over data to find absolute truths in an effort to explain consumer behavior. But looking only at historic data overlooks the complexities of today’s market and is limited in its ability to predict future market trends. That’s why businesses are turning to predictive analytics.
Predictive data analytics empower teams to use their data on hand - even if it’s limited — along with other business intelligence tools to make hypotheses about the future. With this newfound knowledge, businesses shift their investment decisions to meet the fluid needs of customers and unexpected changes in the market.
The definition of predictive analytics encompasses many different statistical elements. It is best characterized by this idea: Rather than answering questions like “What happened when we changed our marketing mix?” Predictive data analytics, as defined by Gartner, aims to determine what is likely to happen if we change our marketing mix.
Using a number of mathematical and scientific techniques such as agent-based modeling, behavioral economics, network science, and machine learning, analytics teams utilize a mix of information and tools to anticipate the future. Teams compare historical data with actual results from the analysis to identify a course of action that capitalizes on changing consumer behavior, or find a potential issue in the forecast and take measures to fix it.
With the rise of permeable competitive boundaries, rapidly changing consumer needs, and broader scale government interventions, it’s crucial for businesses to begin looking ahead. Predictive analytics goes beyond observable influences and sees more than the past. It sees how things have interacted and are likely to change the future.
Ultimately, the best algorithms depend on the type of system being run, the industry, and the business’s goals.
Many analytics tools are only equipped to handle complicated problems, but not necessarily the complex questions that today’s business teams need to solve. Complicated problems require a very linear path to a solution and are technical in nature. Statistics, spreadsheets, and large data stores are utilized to come to a solution that is typically predictable because it relies on past data.
On the other hand, complex problems and ideas are fluid with many moving variables. There’s no one set system to reach desired results, but rather a combination of assumptions, data, and machine learning that help create an effective strategy. Predictive algorithms, when paired with all of these techniques and predictive statistics, allows businesses to answer complex questions about the future.
Predictive analysis, and specifically predictive models, are used for everything from human behavior forecasting to trying to adapt to unexpected events. By gaining insight into the future behavior of consumers and the market, business decision-makers make strategic choices regarding product and marketing strategies, among other things.
Predictive analytics allows for flexible modeling and fluid use of data that matches the current market. By running scenarios aided by machine learning, businesses benefit from faster decision making for a number of complex business operations.
When analytics teams wield comprehensive predictive analytics solutions, they tackle complex ideas in an efficient and effective way. Beyond providing more accurate and faster results to internal teams, predictive analytics are changing the way businesses make strategic decisions by:
1. Creating a digital twin of the market
When historical data, predictive statistics, and machine learning come together with predictive analytics, businesses create a simulation model that closely mirrors the state of the current market. Transactional data provides insight into past trends and why they occurred while machine learning and artificial intelligence help businesses reach educated assumptions about where the market is heading. Additionally, these tools effectively assess consumer behavior from available first-party data to create a realistic mathematical representation of a marketplace.
2. Providing a probabilistic approach
Predictive analysis market simulation allows internal teams to run through hypothetical scenarios to put in place a real plan of business action. While holding known consumer behavior rules in place, predictive analytics takes on a probability-based approach that accounts for emergent variables and Monte-Carlo distributions into the outputs of the model. This reporting approach allows users to account for unexpected events.
These models correlate what businesses invest in a brand to meet how consumer preferences change. Ultimately, these models prescribe interventions that affect sales. The combination of simulation algorithms and emergent phenomenon is ideal for businesses juggling multiple products that compete with different brands. They identify actions that business decision-makers take to make consumers choose their brand over an alternative.
Teams across the company gain insight into what strategic decisions lead to desired outcomes while accounting for the chances for an unexpected phenomenon in the market.
3. Supporting intuition
Predictive analytics embrace internal assumptions and are heavily aided by what-if analysis and other self-service analytics tools. Marketing simulation platforms encourage business users to ask complex what-if questions like “What if consumer attitudes change?” to mitigate the risk of their business decisions.
Models that are accessible to teams across a company aim to support judgment and assumptions by providing qualifications for it. Predictive analytics analysis is most beneficial when applied to complex problems and a flexible approach to modeling. With the Concentric software platform, advanced analytics teams break down data silos and deploy an enterprise predictive analytics capability to make better decisions faster.
Contact us today to begin enhancing your business’s strategic decision-making process.