The rise of predictive analytics has opened doors for marketers to understand consumers better and take the next step in automating how they communicate with their customers/prospects. Predictive tools are no longer just focused on analyzing data but are now able to predict customer behavior. It means that these tools can actually ‘predict’ what actions are likely to happen in the future, based on your current data. With this insight, marketers can now use targeted email marketing to increase response rates and conversions.
How exactly is predictive marketing transforming email marketing? What should we expect in future trends? And what components of predictive marketing are critical to success?
Let’s take a deeper look at how predictive analytics helps you craft the perfect email campaign for each customer.
What is Predictive Marketing and How Does It Affect Email Marketing?
The term ‘predictive marketing’ refers to using customer insights to create personalized content that drives the right customers down the purchase funnel at the right time. The goal of predictive marketers is to maximize the value per customer.
Predictive marketing works by analyzing customer behavior, interests, and preferences to provide them with customized offers throughout their buying journey. It enables marketers to curate the perfect email for every subscriber and send them at the most appropriate time, significantly boosting conversions. This is different from behavioral email marketing that sends emails based on the past behavior of customers. Predictive marketers use rich data sets to understand each customer’s unique needs and preferences to build a personalized experience.
Targeting the right customers at their known stages in the purchase journey produces better results than traditional mass-email marketing campaigns. For instance, using AI to create granular content for specific demographics can help marketers skip broad messaging and, instead, go straight to personalized emails.
This empowers marketers to anticipate and understand key customer behaviors, such as:
Their evolving purchase intent
- Possible date and time of future purchases
- Most sought after product categories
- Risk of them churning
- Customer lifetime value
What Do the Numbers Say?
As reported by MarTechAdvisor, a survey of 579 marketing decision-makers has revealed key insights about the impact of predictive marketing in emails.
According to the results:
- 82% of marketers consider email marketing to be a necessity for keeping up with their competitors.
- 78% of the marketers agree that soon, all marketing will become predictive.
- 81% of the marketers are willing to increase their foothold in predictive analytics while driving key marketing decisions.
How Predictive Marketing enhances Emails
Predictive marketing can significantly improve the overall email marketing strategy while cutting costs. Following are some notable ways in which this is done:
1. Recognizing the Interests of Customers
Predictive analytics can simplify the steps of sales nurturing after customers have subscribed to the email list. This makes it easier to execute email marketing:
- Sending welcome emails to new subscribers
- Leveraging A/B testing to understand the best performing emails
- Using the right emails to make customers interested in the brand and its products
Since predictive marketing can help to segment customers into relevant small groups, it becomes easier to pinpoint the real customers.
2. Personalizing Subject Lines Based on Customer Preferences and Behavior
By using deep learning algorithms in predictive marketing, you can segment your subscriber lists into different buyer personas to create tailored engagement strategies for each group. Since every group is now delivered a specific subject line that they are most likely to engage with, it can significantly improve open rates.
The following numbers dictate how important personalization is in email marketing:
3. Using Dynamic Content Based On Customer Behavior and Preferences
Using real-time data and AI allows marketers to understand their subscribers and serve them specific content that speaks to their needs and interests – not just what they’ve done in the past. This results in better open rates, higher click-through rates, and more conversions.
By identifying how customers are most likely to respond to emails, as well as what type of content they’re most interested in, you can provide highly targeted emails that will produce better engagement.
Using real-time feedback and AI makes email marketing more dynamic and personal – resulting in a positive impact on your business’s bottom line.
4. Using recipient data to ensure higher deliverability rates for their emails
By identifying key factors that impact email deliverability, such as IP address blacklists, marketers can ensure greater success when it comes to email reach.
For instance, marketers can find out which ISPs are more likely to block their emails by using predictive algorithms to sift through the “Spam Complaints” report of any ISP in their list. They can then work towards improving delivery rates for their emails by taking steps to avoid being marked as spam, such as creating a clear unsubscribe option.
5. Personalizing email content to real-time intent
With predictive analytics, emails can be customized for every subscriber’s current stage in the buying process. This helps to provide hyper-relevant email offers instead of sending generic messages that may lack context.
Emails can also be customized for specific customer behaviors or click-through rates. For example, marketers can create two different versions of the same email. One version is sent to customers who have opened an email within the last ten days, and another version is sent to those who haven’t opened in 30 days.
6. Identifying risk indicators against churn
Predictive marketing leverages AI to identify red flags in subscriber behavior that demonstrates the threat of churn. This allows you to act quickly and save your customer relationships before they leave forever!
For example, when an email subscriber hasn’t engaged in a while, marketers can send an email to ask them why they haven’t opened any emails lately. If the subscriber’s response is something like ‘I’m getting too many emails’ or ‘You send me too much promotional stuff’, you know that the account has at least some problems.
However, if the subscriber responds with ‘I’m travelling a lot’ or ‘My work inbox is getting clogged’, then you know that you can take corrective measures to win back their business.
This type of dynamic segmentation allows marketers to be proactive and deliver personalized content – which keeps your customers happy and reduces churn rates.
7. Building better omnichannel experiences
Predictive analytics allows marketers to look at all interactions that a user has with any touchpoint and understand what is relevant when it’s relevant to them. This helps you deliver a personalized experience throughout the entire lifetime of the customer.
In addition, because your customer data is always up-to-date, you can make smarter decisions – such as knowing when it’s the best time to cross-sell or upsell with the right emails.
So, before making any big decisions around marketing campaigns, you can leverage predictions to ensure that you’re making data-driven decisions and setting yourself up for success.
Key Use Cases of Predictive Marketing in Email
Let’s look at some key examples of how predictive marketing fits into the email marketing universe and what do the end results look like:
- Predicting the interest of a prospective customer in a specific email promotional offer.
- Selecting and sharing relevant products to upsell or cross-sell to customers based on their likelihood to buy.
- Detecting the signs of dissatisfaction among customers and preventing the likelihood of churn with relevant engagement emails.
- Recommending the best combination of products and services to customers to increase their cart values.
- Predicting the likelihood of a new customer to convert into a loyal customer and using the right incentives to do so.
- Calculating the potential lifetime value of all customers and identifying the ones with the highest value.
Thanks to predictive models in email marketing, marketers can now sift through copious amounts of customer data to take key customer engagement decisions. Such data-heavy approaches have given marketers today the opportunity to pinpoint customers’ needs at ‘quantumesque’ levels, making the future of email marketing as bright as today!