Disclaimer: This blog post was written by an external contributor about applications of predictive modeling in marketing. The approach laid out in this document is not reflective of how Recast approaches predictive modeling. The actual model specification can be found in the technical model specification.
When it comes to marketing, nobody knows with 100% certainty what will work. Nobody has a crystal ball. Marketers are making assumptions, looking for positive signals. Based on those signals, you either lean in or make adjustments.
Traditional marketing and planning looks like this: run experiments based on a hypothesis, cut out what doesn’t work, optimize what is working and wait for the results to come in.
However, the average marketing campaign lasts 3 months, and 3 months is a long time to spend (and not to mention a lot of money) on a campaign before realizing that you should pull the plug.
Marketers need to move beyond traditional marketing and planning cycles, running experiments on the idea of “it could work”. Instead, marketers need to lean into data-driven marketing models that offer accurate, real-time data that is distributed broadly and quickly and based on consumer behavioral patterns. Otherwise they won’t prioritize the right tactics to support their strategy.
Marketers can use predictive modeling to identify potential opportunities with higher accuracy and reliability than traditional planning methods. In this article, we’ll walk you through what predictive modeling is, why you should use it and how to get started.
What is predictive modeling
Predictive modeling is a statistical technique that analyzes historical data to predict future outcomes. Predictive modeling builds a mathematical model that identifies relationships and trends in the data and predicts the outcomes based on these insights.
Predictive models are making their headway in marketing as approximately 84% of marketing leaders have integrated predictive modeling in their marketing strategies.
Predictive models are trained to recognize and deduce behavioral patterns, evaluate risk and predict future behavior. Historical data is used to train an algorithm. An algorithm might learn, for instance, a credit card holder’s purchasing pattern which includes multiple, repeated transactions for less than $20 at Starbucks every weekday morning across three weeks.
If the credit card is used for a transaction out of this behavior pattern, (known as outlier modeling) like a random $5,000 purchase on a weekday morning, it could indicate fraud. The credit card company will then flag this transaction as it falls outside of the behavioral pattern and it would be up to the credit card holder to prove they made this transaction. Predictive models can be applied to a variety of scenarios: fraud is an obvious one, but also to forecast consumer spending habits and optimize advertising campaigns.
Predictive modeling in marketing
Predictive modeling is used in marketing to identify patterns and trends specifically in customer data, such as demographics, purchasing habits and preferences. Predictive modeling in marketing allows marketers to predict customer behavior, which can be used to optimize digital marketing campaigns and improve ROI.
For example, predictive models might make a prediction that visitors who make more than two visits to an e-commerce site within two weeks are 20% more likely to convert to a buyer.
Marketers can then use these indicators to direct their acquisition efforts and ad spend towards this segment— browsers who are at a high risk of converting. Predictive modeling helps marketers understand their current customer base, segment customers, into different groups and identify potential issues.
Another use of predictive modeling is flagging potential customer churn, based on analyzing quantitative and qualitative data that uncover patterns and correlations.
Types of predictive models used in marketing
There are four main types of predictive models used in marketing. Each type of model has a specific use and uses a particular type and set of data. The types of predictive models used in marketing are typically parametric models.
This means that the predictive models use a fixed set of parameters, which are estimated from data and are translated into likely outcomes based. Parametric models are useful for assuming how likely certain behavior is, like repeat purchases.
Here are the four main types of predictive models used in marketing:
1. Propensity modeling
Propensity modeling in marketing includes analyzing customer behavior and data to identify people interested in your product or service, who are likely to take action and become customers. One example of propensity modeling in marketing is predictive lead scoring.
Predictive lead scoring uses user demographics, buying history and other relevant data to create a predictive model that can be used to predict the probability that a lead will convert to a paying customer. A score is assigned to the lead based on how likely they are to convert.
Marketers can use this data to create relevant marketing messages or prioritize the sales team’s efforts when a prospect has a score in your lead scoring model that indicates a high chance of a conversion. The goal of propensity modeling is to optimize ad spend and marketing resources on the individuals with the highest probability of conversion, to improve a company’s conversion rates and overall sales.
2. Cluster modeling
Cluster modeling in marketing involves segmenting customers into different groups based on variables such as demographics, purchase history and lifestyle choices. The idea of cluster modeling is to identify different customer segments to target with tailored communication.
For instance, an e-commerce app can use cluster modeling to segment customers that are parents and advertise discounts on children’s items and clothing. Likewise, customers can also be segmented according to their high purchase frequency and then the marketing team can launch a campaign offering discounts to those customers.
The main goal of cluster modeling is to create better campaigns, offer a better customer experience and improve marketing efficiency. If you’re launching tailored campaigns based on previous buying history or lifestyle choices, the campaigns are likely to be more effective.
3. Collaborative filtering
Collaborative filtering is a predictive model used to recommend items or services based on past behaviors. Collaborative filtering makes automated suggestions by analyzing user behavior data, customer demographic data, and the user or item-based similarity metrics to identify people who have similar tastes and behavior.
A very common example of collaborative filtering is Netflix’s “you may be interested in this” feature. Netflix takes users’ viewing history, movie/episode preferences and demographic data to make automated suggestions of movies/episodes viewers may also like.
The aim of collaborative filtering is to make recommendations that are tailored to the individual customer. These predictions and recommendations also act as upselling opportunities. For instance, if a customer uses a product or service in a particular way, collaborative filtering can be used to make a recommendation and also upsell the customer to a better plan.
4. Time series modeling
Time series modeling in marketing examines data over a specific period to identify and predict future trends. Time series modeling consists of analyzing data such as sales data, web traffic and abandoned cart data to identify patterns and trends that can predict future customer behavior or sales.
Time series modeling is used to better understand customers and the business. For instance, time series modeling can identify potential slow or busy periods based on trends from previous sales data. Time series modeling can also determine what marketing channels each contributed to sales, as in Marketing Mix Modeling.
The main aim of time series modeling is to optimize the timing, resourcing, and duration of campaigns, and deepen understanding of the business and customers.
Why is predictive modeling important in marketing
Predictive modeling is important in marketing because it eliminates guesswork out of marketing and optimizes marketing spend. Predictive modeling provides marketers with detailed information about customers which allows marketers to make informed decisions about marketing strategy and tactics.
Predictive modeling is also a great way to get stakeholder buy-in especially if a marketer wants to test out new ideas. Marketers can use predictive modeling to demonstrate why they should launch certain types of campaigns.
When marketers better understand buyer behavior, they’re able to drive growth and sales. With the insights from predictive modeling, marketers can create more effective campaigns and tailor marketing efforts based on customer preferences— resulting in higher ROI.
Additionally, predictive modeling not only helps marketers to identify opportunities that can lead to increased engagement and an increase in sales but also identify risks. If marketers are aware of risks, marketers can implement risk mitigation techniques such as pivoting marketing strategies in order to remain competitive.
Challenges of predictive modeling in marketing
There are two main challenges of predictive modeling in marketing:
Data quality
The quality of data plays a major role in predictive modeling for marketing. Predictive modeling is only as good as the data behind it. Poor quality data will lead to wrong predictions and misrepresentation of facts.
In this context, poor data quality refers to the accuracy and availability of data. For instance, if the sales data for a specific period is available but incomplete and a marketer wants to use time series modeling to predict future sales— the results won’t be accurate.
Organizations need to make sure they have a wide-angle approach to data collection and quality. This includes data from the CRM, Google Analytics, store analytics and the payment processor etc.
Tool stack
Predictive modeling tools can be extensive, hard to use and costly. Asides from the tools that will do the predictive modeling, companies also need to invest in data visualization tools to present the data.
Companies that are looking to cut costs or have tighter budgets may not be able to invest in predictive modeling tools in-house or from external vendors. Predictive modeling requires significant time investment before you can act on the insights, so without executive buy-in you likely won’t have the support to move forward. If a company is looking for a fast solution to an immediate problem, predictive modeling likely isn’t suitable.
Additionally, some predictive modeling tools have a steep learning curve and companies may need to invest significant time and resources in their marketer’s learning and development.
How to apply predictive modeling in marketing
Predictive modeling can be applied to various functions in marketing. Here are a few use cases of predictive modeling in marketing:
Marketing mix modeling (MMM)
MMM is a technique used in marketing analytics that identifies the best combination of marketing strategies to maximize the return on investment (ROI) in performance marketing. MMM uses statistical analysis to look at sales or ad performance over some time to determine what caused that performance.
It’s a predictive model that helps marketers make future decisions based on historical data. For example, a marketer may run a mix modeling analysis to determine the performance of email and social ad campaigns. Based on the analysis results, the marketer will know where to optimize their campaigns or where to reallocate ad spend.
Tools like Recast help marketers identify ad performance and quantify ROI per channel. Recast measures media-driven revenue, revenue driven by promotion and model results are updated weekly— ensuring that marketers work with the most-up-to-date results.
Churn prediction
Churn prediction is a predictive modeling technique used to predict the likelihood that a customer will churn within a certain time frame based on customer behavior. Customer behavior includes, how many times the product is used, overall engagement with the product and the number of complaints made to a company’s customer support team.
For instance, predictive models can predict whether a customer will cancel their subscription based on the number of complaints made to a company’s customer support team. Marketers can use the insight from churn prediction models to improve customer retention.
Marketers can focus on the customers who are at the highest risk of leading and launch targeted re-engagement campaigns that improve customer retention and reduce customer churn.
Lifetime value prediction
Lifetime Value (LTV) prediction modelling is a technique used to predict the net profit value associated with a customer throughout their relationship with a company. For example, an e-commerce store can use LTV prediction modeling to determine the total value of a customer based on sales from repeat purchases.
Customer historical data is used to forecast how much revenue they can potentially bring in, along with a predicted lifespan of the relationship with the business. As well as historical data, the costs associated with customer acquisition are also included in prediction models.
This technique enables marketers to identify the value of individual customers. With this information, marketers can target and optimize marketing strategies towards highly valuable customers— for a higher ROI.
Get started with predictive modeling
Marketers should consider predictive modeling as part of their marketing strategies as predictive modeling can help marketers understand their customers and business. Marketers can use the insights from predictive modeling to determine which strategies are effective and tailor campaigns to customers’ needs.
However, integrating predictive modeling into a marketing strategy has a learning curve and marketers need to be prepared to invest in their learning and development. With a combination of learning, patience and willingness, marketers will see an improvement in their marketing efforts.
