The first step in migrating to predictive analytics is to become comfortable leveraging
historical data as we start to amass our big data. This means capturing the actions of our customers as we move them through our sales funnels. True predictive analytics happens by collecting data sets about customers and the industry in a centralized manner. As we collect our data, predictive modelling will find patterns in the data that enables machine learning.
A common term used today is
data lake. It refers to a database that houses all our data, that can be mined with predictive analysis tools to quickly find the answers we need. A study by analysts Aberdeen found that companies that use a data lake for storage and analytics, outperform those who do not by 9%.
Top 3 predictive analysis measurement models to start with:
Cluster Models: Audience breakdown based on past brand engagement, past purchase and demographic data.
Propensity Models: Evaluates a consumer’s likelihood to do something.
Recommendations Filtering: Evaluates past purchase history to understand where there might be additional sales opportunities.
PMA is excited to bring its marketers the importance of leveraging data. We hope you had the opportunity to listen to PMA’s Town Hall on predictive analysis to hear how the fruit and vegetable industry is using this powerful tool. Look forward to PMA’s Insight Solution in February 2021 that will give you access to point of sale data and shopper sentiment research that you can use to begin your data lake on the platform.