Will today’s consumers act in the same ways that yesterday’s consumers did? Will they buy the same products, respond to the same promotions, or accept the same price points?
Savvy marketers know that is not a reliable assumption, but those marketers also know that data on past consumer behavior can be a powerful tool for predicting future consumer behavior. When used to fuel predictive data modeling, consumer data provides marketers with invaluable insights into future consumer trends and patterns.
Gathering Data
The first step in predictive data modeling is obtaining the necessary data. The digitalization of marketing has dramatically increased the amount of in-house data available to marketers. However, external data can also play a role in predictive modeling, especially with campaigns that seek to connect with new markets.
Data scraping, sometimes known as web scraping, is one tool that is used to obtain data from external sources. Scraper bots automatically scrape, or extract, data from websites. Scraper bots can target user data, product data, or data on competitors.
The latest tools that have been developed for web scraping dramatically increase the speeds at which data can be collected. They can precisely target certain data, identify it on targeted sites, and collect it. Once the data is obtained, it can be deposited in spreadsheets in a way that is ready for data analysis.
Discovering Past Trends
Once data is collected, the predictive modeling approach analyzes it to uncover trends in consumer behavior. This can reveal not only the specific actions that consumers have taken but also the types of consumers that have taken those actions and the steps that have led up to the actions. For example, an analysis can reveal the type of consumers — including characteristics like age, gender, location, or other demographics — that have responded best in the past to marketing efforts utilizing certain channels.
Predicting Future Outcomes
By identifying past patterns of behavior, predictive modeling gives marketers the information that they need to begin exploring what is likely to happen in the future. Past patterns in customer acquisition can be used to develop predictions on the market segments that marketers’ future campaigns should target. Past data can also be helpful in uncovering the best up-sell or cross-sell opportunities for consumers, which can help to improve the customer experience.
Once predictions are developed, predictive models are used to test them. The models leverage machine learning to explore whether the data as a whole supports the marketers’ speculations on future consumer behavior. When the model supports the predictions, marketers can move forward with greater confidence in their campaigns; when the model refutes the prediction, marketers can adjust and retest.
While predictive data modeling is far from foolproof, it can greatly assist marketers in identifying the latest consumer trends. Experts have pronounced that the future of marketing will be data-driven. Those who do not take steps to understand and employ the tools that can extract insights from data will find themselves at a competitive disadvantage.
The author, Neil Emeigh is CEO of Rayobyte
