Guide to Predictive Analytics
01/20/2023
Predictive analytics is becoming a growing component across many marketing functions, but not all brands have embraced this strategy yet. Do you want to stay on top of trends and get ahead of your competitors? Then read our comprehensive blog post to get educated on all things predictive analytics—what it is, how it works, its benefits and challenges, the different types of predictive models, use cases, examples, and how to start incorporating it into your marketing strategy. Enjoy!
What is Predictive Analytics?
In a general sense, predictive analytics is the use of data, algorithms, modeling techniques and machine learning to make predictions about future outcomes and performance.
What is Predictive Marketing Analytics?
As it pertains to marketing, predictive analytics is a type of analysis which uses current and historical data to predict marketing outcomes and potential scenarios. The “big picture” goal of predictive analytics in marketing is typically to increase the effectiveness of a brands’ marketing strategies and improve the cost effectiveness of marketing investments.
How Predictive Analytics Works
Predictive analytics uses historical data to predict future events. Breaking this down further, the historical data is used to build a mathematical model that captures important relationships in the data and how they drive outcomes. These predictive models are then used on current data to predict what will happen next or to suggest the best course of action to take in order to achieve the desired outcome.
The Importance of Predictive Analytics in Marketing
It is crucial for brands to incorporate predictive analytics into their marketing strategy. Because predictive analytics helps marketers understand consumer behaviors and trends, foresee future shifts, and suggest how to adapt marketing campaigns accordingly, this allows a brand to stay on top of current trends and get AHEAD of competitors—all while better optimizing market spend. Seems like a no-brainer, doesn’t it?
Benefits of Predictive Analytics
The main benefits of predictive analytics in marketing are as follows:
- Improve marketing campaign performance: simply put, predictive analytics can tell you what’s working and what isn’t within your marketing campaigns, so that you can improve your marketing campaigns and serve the right ads to the right people at the right time.
- Improve decision-making: knowing what is working and what is not will help decision-makers make better, more informed decisions about what direction to take the brand’s marketing strategy, which marketing channels to invest in, which to reduce and how to optimize the overall marketing budget.
- New revenue opportunities: predictive models can be built to measure the contributions of marketing campaigns across both digital and offline conversion points. Today, many marketers rely on clicks to measure the value of marketing and this approach completely misses traditional media, offline conversions and short-changes channels that don’t have high rates of click activity such as video and display. As a result, marketers miss new revenue growth opportunities because their measurement approach ignores a large part of the potential value driven by a campaign. Models the better measure beyond clicks provide the brand a better path forward- especially as cookies disappear from the marketing ecosystem.
- Cost reduction: by using advanced predictive analytics to understand and predict consumer behaviors, less money will be spent on wasted efforts, targeting and personalization will improve which will yield much more cost effective results.
- Prepare for the loss of cookies: with the loss of cookies, most third-party data is going to disappear. As a result, marketers will need to rely heavily on cookie-less methods—such as predictive analytics—in order to measure the performance of their campaigns without the use of PII. Check out OptiMine’s marketing measurement analytics solution, which is 100% future-proof and cookie-less.
Challenges of Predictive Analytics
Although most brands would reap the benefits of predictive marketing analytics, there are some challenges to consider:
- Skill sets and experience: predictive analytics solutions are oftentimes designed by people with a deep understanding of statistical modeling and data, which can limit a brand’s efforts when they lack skilled personnel to drive such initiatives. Predictive analytics require highly experienced data scientists, statisticians and technical personnel to achieve reliable results.
- Adoption: predictive analytics solutions typically live as standalone tools, which can mean that stakeholders and key decision makers may be left out of the planning process. When solutions aren’t well integrated with key teams and decisioning, or they lack leadership sponsorship and accountability, the solutions can lack impact for the business. Or worse, they create political issues among teams who aren’t aligned.
- Not actionable: predictive analytics may not include important data, business assumptions or realistic inputs, leaving them less desirable and actionable. Sometimes when key stakeholders are left out of the process of the predictive analytics buildout, they lack trust and confidence in the analytic guidance and are less willing to adopt it. Or, key data or business context isn’t accounted for in the analytics, and these gaps create issues for the brand and lower performance.
Predictive Analytics Models
There are various types of models that are used in predictive analytics but there are two main types of statistical approaches: parametric and non-parametric statistics. Parametric statistics are based on assumptions about the distribution of population from which the data was used. Nonparametric statistics are not based on assumptions- the data can be collected from a sample that does not follow a specific distribution. From here, there are several different types of predictive models used to address different types of questions. They include:
Classification Model
This type of predictive analytics model categorizes information based on historical data. Classification models are a type of supervised machine learning, which read some input and generate an output that classifies the input into some category. For example, a model might read an email and decide whether to classify it as spam.
Clustering Model
This type of predictive analytics model takes data and sorts it into different groups based on common attributes. For marketers, a clustering model can quickly separate customers into similar groups based on common characteristics and from there, allow the marketer to devise strategies for each group to drive better outcomes.
Decision Tree
A decision tree model relates different decisions and possible outcomes. These outcomes can be the results of events, the costs of efforts, or opportunities and utilities. In this tree-like model, each branch represents a choice between alternatives. The leaves on each branch are a decision choice. The decision tree model has flexibility and adaptability allowing the users to evaluate new possible scenarios and potential outcomes.
Forecast Model
As one of the most widely used predictive analytics models, the forecast model predicts by estimating the values of new data based on learnings from historical data. These models are popular because they are very versatile. In marketing, they’re used to forecast sales or other business outcomes.
Outliers Model
The outliers model can identify anomalous data (data that deviates from the norm) either in isolation or in conjunction with other numbers and categories. This type of predictive model is mainly used to identify fraudulent transactions in the finance/retail industries.
Time Series Model
The time series model examines data over time to determine a correlation. This predictive model works by using different data points (taken from historical data over time) to develop a prediction of an outcome or a trend within a specific time period. Time series models can forecast for multiple regions or aspects simultaneously and can also take into account extraneous factors that could affect the prediction, such as seasonality, the economy, weather impacts, and many other elements.
OptiMine has built an automated, high-scale time series modeling solution to help marketers measure advertising performance and impacts.
Uses for Predictive Analytics
There are many different ways that marketers can incorporate predictive analytics into their marketing strategy. The main use cases for predictive analytics in marketing are as follows:
Measure marketing performance
By using predictive analytics, marketers are able to measure their marketing performance. Knowing what is working and what isn’t within your marketing strategy is key to brand growth and success.
Identify core audiences or customer segments
Insight into consumer behavior enables marketers to identify core audience segments that are more likely to convert. This allows marketers to focus more attention on specific customers and not waste ad spend on consumers who are less likely to respond to their marketing efforts.
Understand consumer behavior
The insights from predictive analytics can give marketers an understanding of consumer interests and behavior based on past interactions. This then helps marketers better target their messaging and ultimately improve customer experience and brand loyalty in the long run.
Optimize resources and spend
With predictive analytics, marketers can use models and algorithms to optimize their efforts. Optimization guides the marketer to better allocations of investments, more optimal outreach and communications, or to determine the right pricing and promotions to offer to specific sets of customers.
Predictive Analytics Examples
See below for a few examples of predictive analytics in action:
Measure marketing performance.
As cookies die off and marketers have less and less data to identify consumers and the ads they’ve seen, predictive analytics can be used in the form of econometric time series models to measure marketing performance. This technique, frequently called “marketing mix modeling” or “MMM” for short, allows marketers to move past identity data to measure campaign impacts.
Send marketing campaigns to customers who are most likely to buy.
If your brand only has a small budget for a marketing campaign and you have millions of customers, offering discounts in un-targeted promotions is a profit-draining exercise. So, predictive analytics can help forecast the customers who have the highest probability of buying your product, and then you can send the coupon to only those people in order to optimize revenue and margin.
Decrease churn rate.
The churn rate is the percentage of subscribers/customers/users who stop their purchases/subscriptions within a certain period of time. In order for a brand to grow, the churn rate must be lower than the growth rate. With predictive analytics, you can identify the warning signs that alert you to the potential loss of a customer so you are able to provide the necessary nurturing/follow-up before it is too late (decreasing your brand’s churn rate).
Discover audience segments that make the most sense for your brand.
Predictive models can help a brand identify the types of customers in its portfolio by grouping them based on a multitude of characteristics such as behaviors, demographics, interests and more. These models can identify a brand’s most important customers, allowing them to focus investments in attracting and retaining these high value customers to help improve the business.
How to Start Using Predictive Analytics in Your Marketing Strategy
Want to start using predictive analytics in your marketing strategy, but not sure where to begin?
1. Define the question you want to answer
Before you get to the data, you need to define the question(s) you want to answer with your predictive modeling. This will also guide to the correct approach or predictive modeling method that will work best.
2. Collect the data
Once you know what question(s) you want to answer, then you need to begin collecting the data that is necessary to answer it.
3. Analyze the data
Once you’ve collected the necessary data, you need to do an analysis. In this step, you’ll be able to see which direction you should go with your marketing campaigns, strategy, etc.
4. Construct and test your hypotheses
It is time to crunch some numbers, test your hypotheses, and see what results you can extract.
5. Deploy predictive model
Once you’ve completed all of the previous steps, it is time to deploy your predictive analytics model—may it lead you to marketing campaign success and more.
On the hunt for the right predictive analytics to measure marketing performance for your brand? Look no further: OptiMine is your fastest path to ROI and overall marketing success—and, we do all the hard work for you. Contact us today!