Engage Customers Through Out the Journey with Predictive Analytics

It's no longer a linear funnel, but rather a meandering trip that continues long after the sale has been completed as consumers become increasingly digitally linked.

As a result, marketers need to have a deep grasp of consumers and their intentions at every step of the customer lifecycle in order to capture and maintain their business.

In addition to increasing customer interaction with brands through digital channels, companies are now collecting larger amounts of data about their customers. This gives organizations the chance to gain actionable customer insights through predictive analytics.

Predictive analytics has been increasingly popular over the past several years as firms attempt to harness their data. Gartner expects that by 2020, predictive and prescriptive analytics will account for 40 percent of new investments in BI and analytics.

What is Predictive Analytics?

Predictive analytics, a sort of advanced analytics that combines both new and previous data in order to estimate future activity, behavior and patterns.

 

Today's marketers at every stage of the customer journey, from boosting brand awareness to educating prospects, closing the sale, improving customer service, and beyond. As a result, marketers will be able to anticipate their clients' needs and wishes at all times, allowing them to tailor engagement with each customer.

Creating the Groundwork for Personalized Engagement

Organizations must invest in a customer relationship management (CRM) platform that enables sophisticated analytics and integrations with other applications to successfully harness the power of predictive analytics throughout the whole customer experience.

CRM software, at its most basic level, aids organizations in storing and managing customer data such as contact information, purchase histories, demographics, and interaction data. Many CRM providers are experiencing significant changes in order to integrate and enable solutions that will help organizations to provide more predictive and personalized customer experiences. Salesforce and HubSpot, for example, are investing heavily in artificial intelligence (AI) to improve the intelligence of their platforms. Predictive analytics is available as a built-in function in these manufacturers' solutions as well as as an add-on to their existing platforms.

If your organization uses an outdated CRM system, you should talk to IT about changing it or licensing alternative software that may be connected with your current CRM.

Stage 1: Goal-setting

The first stage in any marketing strategy is to identify the relevant prospects to contact. Undoubtedly the most crucial step. In spite of everything else, if a marketing campaign doesn't reach the correct audience, then it will fail.

As a result, marketers should employ machine-learning-based predictive models to create a highly targeted and qualified prospect list. Machine-learning predictive models provide substantially more accurate data insight than traditional rules-based models.

Models that learn from and utilize the CRM's intelligence, such as historical information on who has purchased items or services in the past, are emerging. Defined as such, marketers must begin with a prospect list of customers who have purchased your product, reacted to an email marketing campaign or attended an online webinar (for instance). To make the list more intelligent, it must be supplemented with extra data properties. In order to sort the data intelligently, it must run through numerous machine-learning algorithms. This usually entails assigning each prospect a score so that marketers can rapidly make sense of the information.

To create a predictive model without any knowledge in data science, you need the help of a data scientist or employ a self-service, automated predictive analytics platform. If you want to save money, you should consider employing a self-serve platform that allows you to control the process yourself instead of waiting for a data scientist.

Predictive analytics are most effective when applied to the next step of the consumer journey.

Step 2: It's time to get educated!

Your future encounters with a prospect must be tailored to their individual requirements and aspirations if you want to seal the deal. Predictive analytics can help you do this in a number of ways.

First, Marketing can implement predictive analytics to display personalized webpages depending on user preferences. A machine-learning program tracks internet habits to help advertisers develop personalized online experiences.

Second, when marketers follow up by phone or email, they can personalize the interaction based on prior interactions or insights derived from external data. Machine learning can be used to sift through external consumer and business data points and apply them to existing customer records in the CRM. This method allows the marketer to learn more about the prospect outside of their professional life, such as where they went to school or whether they enjoy golf, in order to build a stronger relationship with them.

Step 3 & 4: Purchase and up-sell/cross-sell

Following the completion of a transaction with a customer, the next step is to ensure that they remain a satisfied customer. Cross-selling and upselling, when done correctly, can provide better value to customers while also increasing profitability for your company. The key is to make relevant product recommendations that match the needs and desires of the customer.

To optimize sales and customer service, predictive analytics can be used to match product offers to each customer based on demographic data, purchase history, and data from previous customer interactions—ensuring that each product recommendation is valuable and relevant.

Step 5: Satisfaction

Companies must expand faster than their churn rate, which is the percentage of customers who cancel their subscription to a service within a specific time period.

The use of predictive analytics allows marketers to predict which customers are most likely to leave, allowing them to better allocate their retention campaign budgets. A nurturing campaign offering discounts or free trials, for example, can be targeted at a first-time consumer who you think won't return.

Additionally, marketers may monitor and make course corrections in real time using predictive analytics by monitoring KPIs like sales, retention, and churn in their CRM system.

Conclusion

Increasing complexity of consumer buying journeys and need for tailored experiences make predictive analytics a must-have tool for marketers. It helps them gain a deeper understanding of their customers and allows them to sell more successfully at every point of the customer journey.