There is no such thing as a bad idea. But in business, some ideas are clearly better than others. When your ideas are based on solid data and research, which helps anticipate your customers’ needs, they are worth their weight in gold. Predictive marketing analytics enables business owners and marketers to focus on their best ideas and concentrate on growth.
What is Predictive Marketing Analytics?
Predictive marketing analytics is a branch of marketing analytics that involves using data, statistical algorithms, and machine learning techniques to predict future outcomes and trends in marketing campaigns and customer behavior. The goal is to leverage data-driven insights to make better informed decisions and optimize marketing strategies for improved performance and ROI (Return on Investment).
If predictive marketing analytics sounds complicated, don’t worry. You’ve probably already been doing it for quite some time.
If you’ve ever looked back at seasonal data (for example, relating to the holidays) to plan your next season’s campaign strategy, you’ve already engaged in predictive marketing analytics. However, as with all other marketing strategies, the more sophisticated your approach to predictive marketing analytics, the greater your opportunity to optimize its impact.
The 4 Stages of Predictive Marketing Analytics
Predictive marketing analytics seeks to answer the following questions:
- What Happened (Descriptive Analytics): Descriptive Analytics refers to the analysis of historical data to understand and summarize past events, trends, and patterns. It is the foundational stage of the analytics process and provides valuable insights into what has happened in the past.
- Why “X” Happened (Diagnostic Analytics): Diagnostic Analytics is the stage of data analysis that focuses on understanding the reasons behind past events and outcomes. Diagnostic Analytics helps marketers, and analysts uncover correlations and causal relationships between different variables to gain deeper insights into marketing performance and customer behavior.
- When “X” Will Happen (Predictive Analytics): Predictive Analytics is a branch of advanced analytics used to forecast future outcomes or trends based on historical data and statistical algorithms. It involves the use of various statistical and machine-learning techniques to identify patterns, relationships, and correlations within data that can be used to make predictions about future events or behaviors.
- How Can “X” Happen (Prescriptive Analytics): Prescriptive Analytics is the most advanced stage of data analysis. It goes beyond Descriptive Analytics and Predictive Analytics to provide actionable recommendations and optimized decision-making strategies based on the predicted outcomes.
Types of Data Interrogated in Predictive Marketing Analytics
There’s a good reason why marketing data is often referred to as “big data.” The availability of information to make marketing decisions is enormous. Listing every data source and usage in this short blog post would be impossible. However, the following will give you a general overview:
- First-Party Data: This is data collected directly from interactions your customers have across the various marketing channels you own. First-party data should be readily available to you across your existing MarTech stack, including any email marketing, marketing automation, CRM, and analytics platforms.
- Real-Time Data: This is used when immediate access to the latest information is crucial for making timely decisions or taking quick actions. For example, suppose real-time data shows that a campaign is not delivering an expected volume of engagement per the marketing team’s objectives. In that case, that campaign can be paused and steps taken to optimize it before too much money is wasted.
- Historical Data: This represents a record of events, transactions, measurements, or observations that occurred in the past and has been preserved for analysis. Historical data is instrumental when setting marketing objectives.
- Contextual Data: This refers to information that provides the necessary background or circumstances surrounding a particular event. This data might relate to a specific marketing event’s timing and location or other influential factors such as the weather, economy, or competitive landscape.
Predictive Analytics and Measurement Models
Just as there are multiple data sources, there are also many different predictive analytics measurement models available to marketers. The deployment of each model will broadly reflect the sophistication of the marketing organization deploying predictive analytics as a strategy.
Common analytics measurement models include:
- Cluster Analysis: A technique used to group similar data points based on their similarities in terms of features or attributes. By creating clusters of data points with similar characteristics, predictive models can be tailored to specific segments or subgroups, which may lead to more accurate predictions and better insights.
- Propensity Analysis: This type of predictive modeling aims to determine the likelihood or probability of a particular event or behavior occurring for an individual or entity. It is often used in marketing to predict the likelihood of a specific action, such as making a purchase, clicking on an ad, subscribing to a service, or churn.
- Recommendation Filtering: AKA Recommendation Systems. This model aims to predict the items or content that a user is likely to be interested in, with the goal of enhancing user experience, increasing engagement, and driving sales or conversions.
- Forecast Analysis: This measurement model is a type of predictive modeling used to predict future values or trends based on historical data. Forecasting is commonly applied in time-series analysis, where data is collected over regular intervals of time, such as daily, monthly, or yearly.
- Time-Series Analysis: In time-series analysis, data points are recorded chronologically, and each observation is associated with a specific timestamp or period. Time-series predictive analytics is particularly valuable for understanding patterns, trends, and seasonal variations in data and predicting future values based on historical trends.
Industries Using Predictive Analytics for Marketing
There really is no limit to the type of organization that can benefit from the insight provided by predictive marketing analytics.
Whether you work in finance, healthcare, higher education, hospitality, or retail, there will always be data that you can use to predict future engagement and set marketing objectives.
Regardless of the vertical your business works in, if you’re not digging deeper into your available data, you are leaving cash on the table. Worse still, you’ll be throwing money down the drain.
Benefits of Predictive Analytics in Marketing
Knowledge is power. Predictive analytics takes a lot of the guesswork out of your marketing strategy. This approach will help you plan and execute more efficient marketing campaigns and reduce waste.
You can use predictive marketing analytics to:
- Create messaging that connects with your audience
- Reduce time spent on targeting the wrong people
- Enhance lead prioritization practices with lead scoring
- Improve customer acquisition
- Improve customer retention rates and predict churn rates
- Safeguard against the loss of cookies
- Optimize campaign success
- Increase team efficiency
- Influence future product development
The Process of Implementing Predictive Marketing Analytics
While predictive marketing analytics is an incredibly sophisticated marketing strategy, it is available to businesses and marketing organizations of all shapes and sizes.
Like all great marketing strategies, the implementation of predictive marketing analytics starts with a simple objective.
During the project definition stage, you’ll want to create a list of priorities and then figure out what is desirable and what is possible before getting internal buy-in. Don’t worry if you can’t tick everything off your list in rapid succession; successful marketing is often a process of making those marginal gains.
The next steps on your journey include:
- Data Collection
- Data Processing
It’s then just a case of rinse and repeat, being careful to learn from previous iterations and building back better. Throughout the process, you’ll learn where there are gaps in your MarTech stack and marketing team’s knowledge that need addressing as you move forward.