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With the rise of Big Data and Artificial Intelligence, marketers have more powerful analytics tools at their disposal than ever before. Data-backed customer insights can be used to enhance marketing efforts at every stage of the funnel, and one of the most effective tactics is using predictive analytics.
In this article, we’ll discuss what predictive analytics is, why businesses need it, how to measure it, and best practices for implementing it for better marketing performance, higher ROI and, ultimately, faster success.
- What Is Predictive Marketing Analytics?
- Part 1: 8 Use Cases for Predictive Marketing Analytics
- Part 2: Predictive Marketing Analytics Measurement
- Part 3: Implementing Predictive Marketing Analytics for Optimized Business Decisions
What Is Predictive Marketing Analytics?
Before we explain what predictive analytics is, here are some facts about just how big Big Data is:
- Over 2.5 quintillion bytes of data are created every single day
- By 2020, it’s estimated that 1.7 MB of data will be created every second for every person on earth.
Think of it this way: using available data for planning, designing and deploying a marketing campaign is like having a superhero cape that almost guarantees better results.
In other words, predictive analytics analyzes patterns based on historical and transactional data that can be processed further for identifying future risks and opportunities.
The steps in the predictive analytics process are:
- Defining outcomes: Determine which business questions you want the data to answer, like “How many of my products is a repeat customer likely to buy in the next 12 months?”
- Data collection: Have a plan for which data you need, how you plan to collect it, and the best ways to organize it.
- Data analysis: Inspect data for useful information and form conclusions about your customers.
- Statistics: Test the conclusions.
- Modeling: Create predictions about your customer’s future behavior.
- Deployment: Utilize the data to inform marketing strategies and implement tactics.
- Model monitoring: Track and report on the effectiveness of predictive data-driven campaigns.
Here are some quick definitions of the different types of business analytics:
- Descriptive analytics is the first stage of business analytics in which you look at historical data and performance.
- Predictive analytics is the second stage of business analytics in which past data is used with algorithms to predict a future outcome.
- Prescriptive analytics is the third stage of business analytics in which you determine the best course of action.
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8 Use Cases for Predictive Marketing Analytics
Once the whole process has been completed, predictive analytics can be applied to various functions. Here are eight of the most popular use cases for optimized predictive analytics in marketing:
1) Detailed Lead Scoring
Lead scoring means ranking leads based on where they are in the funnel. It allows marketing and sales divisions to collaborate in a more meaningful way, since every lead is different. With prescriptive analytics, every lead will be scored based on its readiness to purchase. This helps to inform the next step in marketing or selling to a prospective lead based on predictions about their future buying habits.
2) Lead Segmentation for Campaign Nurturing
Lead nurturing, which belongs to the early stage of the buying process, requires planning and strategizing. Using demographic and behavioral data, predictive analytics can help businesses group leads by segment and create lead nurturing campaigns that are tailored specifically to move the process further down the sales funnel.
3) Targeted Content Distribution
Which types of content work better for certain leads can be answered with predictive analytics. Once you know not only which type of content resonates with a specific audience, but also what channel to best reach them on, you can customize content creation and distribution. When leads receive higher-quality communication from an organization, this increases the probability of sales conversion.
4) Lifetime Value PredictionCustomer Lifetime Value is the true measure of marketing ROI. Click To Tweet
Customer Lifetime Value (CLV) is how much a customer is worth to you throughout the entire span of your relationship with them. With predictive analytics, you can take the historical data of each customer and use it to forecast the future lifespan of your relationship with them as well as how much revenue that relationship is likely to bring in. These estimates can help you to set budgets for customer acquisition, giving you a more accurate and expected ROI.
5) Churn Rate Prediction
Churn rate is the rate of attrition, which is the percentage of subscribers or users who stop their subscriptions within a certain period. To grow, a business must have a higher growth rate than churn rate. With predictive analytics, you can identify the warning signs that alert you to the loss of a customer and allow you to provide the necessary follow-up or nurturing before it’s too late.
6) Upselling and Cross-Selling Readiness
Using the available data about customer buying behavior, businesses can upsell, cross-sell or combine both to increase profit. For example, if you know that 30% of customers who buy product A from you come back to buy product B within six months, you can then market product B to customers shortly after they buy product A to speed up that process and capture those who might not have otherwise considered purchasing product B.
7) Understanding Product Fit
Equipped with historical purchase, behavior and leads data, businesses can better understand exactly what customers’ needs and wants are. This may translate to developing future products to further meet those needs or improving upon existing products that aren’t meeting their sales targets.
8) Optimization of Marketing Campaigns
With predictive analytics, businesses can better plan, develop, strategize and implement future marketing campaigns. The more you know up front, the more successful your targeting and messaging will be.
By applying predictive analytics in organizations, risks can be significantly reduced because decisions will be made based on data, not merely unproven assumptions that rely on instincts and some educated guesses. Many successful e-commerce ventures adopt predictive analytics in their marketing efforts and, of course, it should be no surprise that Amazon is the king of using data to target and remarket to customers with great success.
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Predictive Marketing Analytics Measurement
When we’re talking about measurement, we’re talking about two things: metrics and analytics. These terms are often used interchangeably, but they’re actually two very different things.
- metrics are individual data points related to one specific measurement
- analytics is combining metrics to achieve a more holistic view of the data and draw conclusions
Google Analytics obviously fall under “analytics.” It examines website and campaign metrics like sessions, page views, bounce rate, traffic sources, exit pages, goals, interactions per visit, social overview and acquisition overview. Each metric can be further categorized based on dimensions, which include device types, regions, languages and browsers.
In marketing, there are six metrics that are typically used in calculating performance and ROI:
1) Customer Acquisition Cost (CAC)
CAC is the average amount of money spent to acquire a new customer. It is calculated based on the total sales and marketing cost divided by the number of new customers within a certain time period. You can create two types of CACs: the 100% online CAC and the combination of online and offline CAC.
2) Marketing Percentage of CAC
What’s the percentage of the CAC that pertains to marketing cost? To come up with the ratio, the total marketing cost is divided by the sales and marketing costs.
3) Ratio of CLV and CAC (CLV:CAC)
You’ll get the ratio by dividing the Customer Lifetime Value (CLV) – or the Lifetime Customer Value (LCV) – by the Customer Acquisition Cost (CAC).
4) Time to Earn Back CAC
It’s important to know how long it will take you to earn back the money you spent to acquire each customer so that you can set future marketing budgets and realistic revenue goals. Figure out the total time (weeks, months, quarters or years) needed to earn back the CAC.
5) Marketing Originated Customer Percentage
This metric measures how much of your new business comes from your marketing leads. After dividing the total number of leads in a month with the total number of new customers, you’ll get the Marketing Originated Customer Percentage.
6) Marketing Influenced Customer Percentage
This metric measures the role that your overall marketing efforts had in your acquisition of new customers. To find this figure, the new customer total is divided by the total number of customers who actually engaged with your marketing activities.
These six marketing metrics provide a foundation for predictive marketing analytics, helping you with modeling and scoring categories. In other words, by understanding these metrics, the analytics can be properly designed to provide the required data sets.
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Implementing Predictive Marketing Analytics for Optimized Business Decisions
Using metrics based on marketing goals allows you to translate them into a marketing model that truly works in the implementation phase. Before executing a campaign, make sure to identify the existing marketing analytics and their figures. These past results would serve as the “before” picture, which you can compare with the “after” picture by using predictive analytics performance.
The cycle of predictive marketing analytics starts with data access, data exploration, modeling and implementation of marketing campaign. The modeling phase is the next stage in the process, after understanding the metrics and the available data. Metrics and data wouldn’t mean much if there was no useable model.
For instance, there are three primary classes of predictive models:
- Cluster modeling is a way of segmenting customers into groups based on several variables at once. With it, you can target demographics and personas using behavioral clustering, product-based clustering and brand-based clustering.
- Propensity modeling is used to predict customer behaviors based on predictive lifetime value, likelihood of engagement, propensity to unsubscribe, propensity to convert, propensity to buy and propensity to churn.
- Collaborative filtering is primarily used for recommending products, services and advertisements based on past variables, including buying behaviors. This filtering is common for upselling, cross-selling and next-selling.
In predictive analytics, regression analysis also plays a major role. A business analyst can recognize the correlations between the customer and their purchases by using “regression coefficients.” With this, they can create a score that can be used to predict the possibility of future purchases.
There are three basic scoring categories that marketers use:
- Predictive Scoring – in which prospects, leads and accounts are prioritized based on their likelihood of purchasing action.
- Identification Models – in which prospects are identified and acquired based on similarities with existing customers’ variables.
- Automated Segmentation – in which leads are segmented for custom and personalized contents.
There are tons of tools available to help you with predictive marketing analytics, at a variety of price points, including:
Find one that fits within your budget and – this is important – find one that easily integrates with the rest of the tools you’re capturing data with across platforms. This will make it easier to transform your metrics into a powerful tool for better analysis, predictions, segmenting and targeting.
Predictive analytics is the key to successful marketing campaigns. It integrates the correlation between metrics and better business results with advanced strategies to bring more impact across the customer life cycle.
Predictive analytics does, however, require strong understanding of “before” marketing analytics metrics to serve as the foundation for modeling frameworks and scoring categories. After analyzing the historical and behavioral data sets and their models, you’ll be able to use them in comparison to the “before” data.
Overall, predictive analytics allows you to make marketing campaign and other business decisions in a more informed manner. But as with other parts of life, predictive analytics doesn’t guarantee success. It merely increases the likelihood of success.