Marketers have access to a wealth of data in this modern age – from customer data stored in CRMs and CDPs, to the data stored in the variety of platforms used for campaign activation. However, some 92% of marketers still claim that delivering actionable data insights is difficult – and more than a third (36%) say it’s extremely difficult.
It’s easy to report and assess on what has already occurred, but using data from the past to predict the future is evidently much trickier. As humans, we’re not always the best at looking ahead with clarity and objectivity– machines are much better placed to crunch all that data and draw conclusions. Enter predictive analytics – the use of data, statistical algorithms, and machine learning techniques to forecast future outcomes.
What is predictive analytics in marketing?
In the context of marketing, predictive analytics helps organizations make informed decisions about customer behavior, preferences, and tendencies. This insight can be used to optimize marketing strategies, target specific audiences, and increase the effectiveness of marketing campaigns.
More specifically, predictive analytics can aid marketers in tasks like churn prediction and cross-/up-selling
, by delivering valuable insights that can be applied at exactly the right time. Simply put, predictive analytics takes much of the conjecture out of marketing and when applied correctly, ultimately results in enhanced marketing performance and sets a brand on a clear course for growth. This is because data-driven decisions can help marketers implement a strategy that really hits the mark – meeting prospects where they are in the customer journey, with messaging that is personalized and effective.
With predictive analysis models in place, marketers can :
- Increase customer retention: This is highly valuable, considering 26% of consumers say they stopped using or buying from a business in the past year. With this information, companies can develop targeted retention strategies to prevent customer churn and increase customer loyalty. This may include targeted offers, personalized communications, or loyalty programs. So, not only do existing customers feel accounted for, their perception is of a business that is keeping a keen eye on their needs.
- Increase targeting: You can make best use of your budget and free up marketers to concentrate on the fun, creative aspect. By better understanding your audience and what they want, improved audience engagement and increased ROI should follow. That’s what Mastercard saw with the help of IBM’s predictive analytics platform and achieving:
- 81 creative variations
- 144% lift in CTR from the start of the campaign
- +54 campaign CTR above their benchmark
- Optimize future marketing campaigns: If a campaign is not performing as well as expected, predictive analytics can be used to identify why and make adjustments to improve performance. This continued, iterative improvement is what can really give a brand its competitive edge.
- Predict customer lifetime value: Based on their purchase history, demographics, and interactions with the company, companies can develop retention strategies to focus on the customers who are most valuable to the business, leveraging some of the other benefits of predictive analytics – in particular, personalized communications to encourage high-value customers to remain loyal to the company.
However, all of this relies on having access to good data and methodically extracting those actionable insights that many marketers haven’t yet gotten to grips with.
The predictive analytics process is iterative and requires collaboration between data scientists, business analysts, and subject matter experts (marketers, in this context). The goal is to produce a model that supports effective decision-making.
The predictive analytics process typically involves the following steps:
1. Start with a question: Any successful process starts with working towards finding a solution to a question or problem you are looking to solve. This will determine what data is collected and where it needs to be collected from.
2. Data collection: The next step in the predictive analytics process is to gather data from various sources such as customer databases, transaction records, and market research studies. The data must be relevant, complete, and accurate to produce meaningful insights.
3. Data cleaning and preparation: This involves removing any irrelevant, missing, or duplicate data, as well as transforming the data into a format that is suitable for analysis.
4. Exploratory data analysis and model selection: Now it’s time to perform an exploratory data analysis, which involves analyzing the data to gain insights into patterns, trends, and relationships. This can be done using statistical methods or data visualization tools. After exploring the data, the next step is to select the appropriate predictive analytics model. This involves choosing the most suitable algorithm based on the data and the problem being solved.
5. Model deployment and monitoring: The final step is to deploy the model into production. This means making the model available for real-time predictions and integrating it into business processes. The predictive analytics process is not complete once the model is deployed. The model must be continuously monitored and updated to ensure it remains accurate and relevant.
Predictive analytics measurement models
Three of the best options for marketers when it comes to model selection are:
• Cluster model: A type of predictive analytics model that groups similar data points into clusters or segments based on their characteristics. In marketing, cluster analysis can be used to segment customers based on their behavior, demographics, and other attributes. For example, a company might use cluster analysis to segment its customers into groups based on their purchase history, product preferences, and demographic characteristics.
• Predictive model: A model that focuses on the use of historical data to make predictions about future outcomes. In marketing, predictive models can be used to make projections about customer behavior, such as which customers are likely to make a purchase or which customers are at risk of churning. Predictive models can even be used to predict the success of marketing campaigns and optimize pricing strategies.
• Recommended filtering: A model that helps ensure the quality and accuracy of the results, there are several recommended filtering techniques for predictive analytics including feature selection (selecting the most relevant variables or features from the data for analysis); outlier detection (identifying and removing data points that are significantly different from the majority of data points) and data imputation (filling in missing values in the data). By filtering the data and selecting the most relevant variables, organizations can improve the accuracy and quality of their predictive models and make more informed decisions.
The benefits of predictive analytics are clear, and with the growing number of options available to customers, the value it can bring to marketing effectiveness and business growth has never been greater. Hotwire’s IQ suite of data and analytics solutions can fuel your marketing programs with the power of predictive analytics, from leveraging intent data to build cluster and propensity models to building predictive paid media programs.