10 Ways Predictive Analytics Can Help You Achieve Your Marketing Goals
Today, the competition among businesses is fierce – especially online.
People have more options than they ever have before, right at their fingertips. They want to find convenient solutions quickly. They may even expect businesses to offer custom-fit solutions to their needs and personalized customer experiences unmatched by the competition.
If you fail, customers may take their business elsewhere.
According to SuperOffice, 90% of businesses compete mainly on customer experience (CX). Taking CX seriously is an excellent way to stand out from the noise that’s saturating your industry to help you win loyal customers.
To create the best possible buyer’s journey for your target audience, you must first know them well and understand their behaviors and actions at different touch points.
Only then can you create truly personalized, seamless experiences to guide your leads through the sales funnel successfully. If you can master this skill, you’ll continually improve your digital marketing efforts and drive more brand loyalty online.
One effective way to build unique experiences for your customers is by using predictive analytics. Using critical consumer data can help you predict future behavior among customers. Incorporating key findings that give you a deeper understanding of consumer trends into your marketing strategies can push you ahead of competitors in your industry.
The predictive analytics industry is growing at a rate of 23.2% year over year, meaning businesses are catching onto this trend fast. If you don’t start incorporating these strategies into your marketing, you could quickly fall behind your competitors.
Once you know your current and potential customers well, win them over time and again by not only reaching them where they are but leading them where they want to go next.
Today, 90% of businesses compete primarily on customer experience.
Predictive analytics can help you connect with customers on a deeper level so you can stand out from the crowd and get ahead of the competition.
Predictive analytics involves using data to predict future user behavior, events, and results.
You can form accurate predictions by studying historical and current data and statistics.
Reduce risks by removing a lot of the guesswork from your processes and strategies. More accurate predictions can lead to fewer mistakes, faster growth, and higher ROI in your business.
What is Predictive Marketing Analytics?
Predictive marketing analytics uses data to make predictions about user behavior and future events and results. To form predictions about your customers and marketing results, predictive analytics mines data and uses a combination of statistics, predictive modeling, artificial intelligence (AI), and machine learning. You can form accurate predictions or determine how likely it is that something may occur in the future by studying current and historic patterns in data.
The three main types of business analytics are:
Descriptive analytics: To predict future events, you can start by looking at descriptive analytics – historical data and performance – to determine what has already occurred.
Predictive analytics: Next, look at predictive analytics to determine what is likely to happen in the future. This involves looking at past data and using algorithms to predict future events.
Prescriptive analytics: Finally, you can decide what to do next based on what you’ve already done or what’s already occurred. Determine the best course of action by considering what is most likely to happen.
How Does the Predictive Analytics Process Work?
Using predictive analytics effectively involves a multi-step process. The following outline will give you an overview of what goes into this process (which may require an engineer or data analyst to complete).
Start by asking the right questions: Determine what questions you want to answer or what outcome you’re hoping to achieve. Having clear questions in mind will help you chart the right path to get the answers you’re looking for. An example could be, “What marketing qualified leads (MQLs) are most likely to make a purchase this month?”
Collect the right data: Develop a plan for collecting and organizing the data that will give you answers to your questions. You may need to pull from historical data, demographic information, and firmographic characteristics.
Analyze the data you’ve collected: Analyze your data for helpful information to help you form conclusions about your questions (i.e., descriptive analytics). You can go deeper by asking more specific questions here, digging into data to find the answers.
Use statistics to form hypotheses: After finalizing your list of questions and creating hypotheses, use statistics to build and test the conclusions you’ve developed. Test each hypothesis and trust the data you uncover.
Create a predictive model: After testing and then either validating or eliminating each hypothesis based on your statistical data, you can create a predictive model. Again, you’ll use statistics to predict future customer events, outcomes, or behaviors. You may need an engineer or data analyst to help you complete this stage.
Deploy your new model: Use your data for actionable insights and to direct future marketing and sales strategies and campaigns.
Monitor your model over time: Monitor and track new tactics and campaigns you deploy, and report on their performance over time. Adjust and create new models as needed. Keep in mind that external variables (like seasonal fluctuations) can throw off your data, so you may need to adjust or replace your model occasionally for it to remain accurate.
There are three main classes of predictive models. Again, this gives you a high-level, basic overview.
Cluster modeling: This predictive model can help you segment your customers into different groups based on multiple variables. Cluster modeling allows you to target specific personas or demographics based on behavior data, past product purchases, or brand engagement.
Propensity modeling: This model can help you determine how likely different consumers are to perform an action or disengage with your brand. Valuable data may include a customer’s propensity to buy, convert, churn, engage, or unsubscribe and predictive lifetime value.
Collaborative (or recommended) filtering: Using past customer behavior, you can develop a model to identify new sales opportunities. Use this model for recommending relevant ads, products, and services to your audience. This is useful for upselling and cross-selling to current customers.
10 Practical Ways to Use Predictive Analytics in Marketing
Here are ten specific ways to use predictive analytics to enhance your marketing efforts to help grow your business moving forward.
1. Targeting and Segmenting Your Audience
Using behavioral and demographic information, you can segment your leads and customers to create new campaigns tailored to where your audience is in the buyer’s journey. Creating specific, targeted campaigns can help you effectively move prospects down the sales funnel and further engage current customers.
There are three primary ways to use predictive analytics to target and segment your audience:
Affinity analysis: This method involves segmenting customers based on attributes they share.
Response modeling: By looking at how customers responded to certain stimuli, you can predict how likely it will be for future customers to react similarly.
Churn analysis: Also called attrition rate, churn analysis will show you what percentage of customers you lost during a specific period. You can also determine how much potential revenue or opportunity you lost because of losing those customers.
2. Distributing Targeted Content
Learning which types of content resonate best with your audience (or different audience segments) and which channels they’re using most can inform future content marketing decisions. By customizing your content creation and distribution strategies, you can deliver more personalized experiences for leads to increase your probability of moving them down the sales funnel and converting them to customers.
3. Predicting Customer Behavior
By combining data from past campaigns with demographic information you’ve collected about your customers, you can build a model to help predict future customer behavior. Rate customers based on how likely they are to make a purchase or take a particular action, so you know when and how to approach them through marketing.
4. Predictive Lead Scoring
Without the proper process in place, you could spend considerable time and resources chasing down people who aren’t even interested in what you offer. Lead scoring can help you avoid this by qualifying and prioritizing leads based on their interest, urgency, and authority to purchase.
Lead scoring involves assigning values (points) to individuals based on where they are in their buyer’s journey (or sales funnel). The higher the score you give to a lead, the more qualified that means they are. The data you use to generate a lead score might include information they formally submit to you, actions they’ve taken, and how they’ve engaged with your brand across different channels.
Developing scores for different types of leads can help your marketing and sales teams prioritize the right ones – focusing on those most likely to become future customers. By predicting future buying habits, your team can meet leads where they are and effectively lead them to the next step of their journey.
You can send high-scoring leads straight to your sales team. Low-scoring leads may not be worth pursuing at all. Those with medium scores may need a push in the right direction (e.g., engaging with a strategic marketing campaign that leads them down the funnel).
5. Predicting Customer Lifetime Value
Using the same methods covered under “Targeting and Segmenting Your Audience,” you can also predict your Customer Lifetime Value (CLV). Using historical data, you can identify which customers are the most profitable, which marketing activities generate the highest ROI, and which segments of your audience are the most loyal.
Knowing your CLV will tell you how valuable a customer is to your business throughout their relationship with you. This can help you also estimate how valuable they will be in the future. You can predict the expected lifespan of your relationship and how much revenue it will bring in. You’ll then understand how much it costs to acquire new customers and can plan your marketing budget and expected ROI accordingly.
6. Acquiring New Customers
After segmenting your audience, you can create identification models using customer data. Your goal here is to identify prospects that resemble your current customers so you can target them effectively and nurture them into leads and customers.
7. Determining Better Product or Service Fit
Using a combination of customer behavior data, lead information, and historical purchase data, you can better understand what your current customers want from you. You can then use this information to predict what else they could want or need in the future. Develop new product and service ideas that dig deeper and better meet the wants and needs of your customer base.
8. Upselling and Cross-Selling to Current Customers
You can also use data you’ve collected on your customers’ purchasing behaviors to cross-sell or upsell to them to increase profits. By identifying patterns in behavior, you can market to current customers more effectively.
For example, let’s say you run a marketing firm that sells content marketing software and a complementary social media tool. You’ve found that 40% of your customers who start with a subscription to your content marketing program add on the social media tool after six to 12 months. You decide to create a specific marketing campaign that targets current content marketing customers around the six-month mark to increase your upsell rate to 60%.
9. Reducing Your Customer Churn Rate
Churn rate is the rate at which customers stop doing business with you. It’s commonly expressed as a percentage of subscribers. For example, if you run a marketing firm and sign on clients through yearly contracts, it could be defined as the percentage of regular clients you lose within a specific timeframe.
The goal is to have a higher growth rate than churn rate. Using predictive analytics, you can spot red flags that arise before you lose a customer. If there’s a trend, you can identify where and when your business is going wrong. Recognizing potential issues can help you proactively address problems for your customers before you lose them.
10. Optimizing Future Marketing Campaigns
The more information you have, the better you can plan and implement your marketing campaigns . More precise targeting and messaging can help you build more robust and authentic campaigns that connect with leads and customers. This should ultimately lead to more successful outcomes.
Predictive analytics not only reduces risks by taking a lot of the guesswork out of your process but can also lead to faster growth and higher ROI within your organization. Incorporating these tactics may not guarantee success, but they can increase your likelihood of succeeding by informing your future practices and decisions.
Create the Right Content for Current and Future Customers
Using predictive analytics will only get you so far. You also need high-quality content marketing to engage consumers at every stage of the buyer’s journey. Giving your leads the right content in the right place at the right time is essential to successfully execute your digital marketing strategy.
Marketing Insider Group is a content marketing agency designed to help businesses build successful, ROI-producing platforms online. We can help you attract quality leads and drive new traffic to your website consistently. Our customized packages are built to meet your unique needs, empowering you to reach, engage, and win new customers for your business.