Pathway to Perfection: Predictive Analysis for Personalized Customer Journeys

Smartphones and computer technologies evolved consistently, revolutionizing consumer behavior, purchasing patterns, and expectations. It leads to a 360-degree digital transformation, generating large data volumes that need analysis for actionable business insights. These insights are the key to developing a personalized customer experience that also improves the customer journey for your business. This data analysis is done using predictive analytics.

Wisdom of predictive analysis

Predictive analysis analyzes historical data to make predictions about the future events while identifying potential issues and their mitigation strategies. It also analyzes consumer behavior, browsing patterns, products/services searched, and purchase patterns to provide insights for improving customer experience.

• Clustering models – It groups customers based on different variables, segmenting them into brand-based, behavioral, and product-based clusters.
• Collaborative filtering models – This is a recommended model based on evaluating customers’ needs by analyzing the products/services available.
• Propensity models – This model collectively defines the actions customers should take in the future.
• Forecasting models – It is used for both front and back-end CX development, forecasting customer requirements and analyzing their purchasing history.
• Churn models – This model identifies high churn-risk customers helping brands to focus on them.
• Optimization models – Undertaking multiple forms, these models use contact policies and business constraints to understand the customer trade-off to maintain while optimizing various elements of CX.

Hence, you can dive deep into the pain points, gaps, and positive customer reviews to increase your service and product usability, which delights your customers.

Let’s briefly understand the predictive analytical models used for CX improvement:

You can address the following three critical factors by leveraging the predictive analytical models:

• Offering hyper-personalized shopping experience to customers
• Building more innovative supply chains and merchandising
• Developing intelligent retail operations

There is more to Predictive analytics than just analyzing existing data

Predictive analysis can go beyond the general accountability of analyzing existing data. It helps you identify the data needed to foresee new users and categorize each user based on predefined segments using custom algorithms. The inclusion of Artificial Intelligence and Machine Learning fills in the gap in expected customer behavior, supporting the personalization of customer services.

Predictive analytics solutions can be used further to analyze the product engagement rates on media platforms through discussions and reactions to the engagement activities. Analyzing product engagement rates helps acquire new customers and look into the feedback generated by existing customers. Making it easier for you to increase customer loyalty and ROI value as you begin to learn about the usability obtained by existing customers and retain them.

Now, you might wonder how to apply this analytical model. It’s pretty simple! You need to abide by the following stages to ensure its smooth application.

• Exploration
• Visualization
• Testing
• Prediction
• Predictive analytics at scale

Pick the right one for your business

Even through the stages of applying, the motto to implement predictive analytics will be different for all businesses. Thus, selecting the correct analytical type to boost the CX outcome for your business should align with one or more of the following –

• To predict customer needs
• To achieve real-time product feedback
• To identify risk factors for a low customer retention rate
• To optimize the existing pricing model
• To maintain balance in staff/employee availability
• To gain a real-time understanding of the marketing trends

However, the right analytics should fulfill the business objectives and optimize resource utilization.

Multiple areas to apply

With accuracy in data collection and helping you in decision-making, it won’t be wrong to say predictive analysis can be applied in multiple circumstances.

• It can be used to lower the delayed payment rate
• Understand customer satisfaction drivers through customer interaction data and transactions
• Develop predictive scores by identifying journey features
• Set up cross-functional teams to provide customer services

Predictive analysis sets the benchmark for collecting user data and propelling the future of customer experience by compelling you to implement data-driven decision-making to outpace the competition.

Leveraging predictive analytics is beneficial and essential to understanding the pain points that create a gap between customer service and customer experience. Your business can maintain accuracy in its customer services and improve brand value by actively identifying the patterns, factors, and trends in the data.

If you want to implement predictive analytics to enhance your customer experience and improve business performance, we are happy to help. With rich domain knowledge and industry experience, our expert CX solutions leverage Predictive Analytics at its optimum. Conneqt with us today!