Predictive analytics is the use of big data, statistics, and modelling techniques to make predictions about future consumer purchases. It looks at live as well as historical data and identifies patterns in this data to determine the likelihood of that same pattern happening again in the future. Specifically it refers to this data application being on a solus consumer basis. Predictive analytics is a proactive, rather than a reactive, approach.
It can help to forecast purchasing imminent product purchases, identify the most suitable marketing channels, for example, using predictive personalisation solutions – PPS, predicting the behaviour of individual customers actions and reactions, to a perpetually greater degree of accuracy and offer the right products at exactly the right time.
Big Data, Predictive Analytics and customer experience (CX)
Brands can now collect a vast amount of data and use it for valuable insights to significantly improve the overall customer experience. From click and conversion rates to purchase and payments history to social media comments and conversations, first-party data is still widely available and accessible to use for product and service improvements.
And the same goes for customer service, and data may be collected across multiple touchpoints for key contact centre metrics like average handling time, first contact resolution, customer churn, contact channel, or social media comments to provide knowledge of how satisfied they are with the brand.
With the rise of Big Data, Machine Learning (ML), and Predictive Analytics, insights are becoming the status quo for improving processes and customer experience. It applies to every industry, especially ecommerce and retail, where businesses rely on purchases by repeat customers. Investing in a great customer experience is part of a good growth strategy and leads to an increase in sales.
Companies are now consolidating data across all platforms to get a bigger picture on how to offer exceptional customer experience. For example, Big Data allows businesses to use predictive analytics to target customers through personalised content. PPS takes this further and actively utilises the data through its autonomous methodology to deliver the personalised product selections directly to each consumer exactly when they have the highest potential buying propensity. Indicative, as it is, of the potential right time for their greatest needs and desires being fulfilled.
Brands can then use this information to enrich and improve their customer experience. Below, we’ll bring out ways to leverage big data to improve and help take a proactive, rather than a reactive, approach.
But first, let’s explain how to define essential customer experience that can improve and determine how Big Data ties into the complete end-to-end customer experience.
Customer experience metrics and predictive analytics
How and why do use big data and predictive analytics now, to improve customer experience? Predictive analysis can improve two main customer success metrics by taking a proactive approach: customer retention and customer satisfaction.
Customer retention is the ability of a business to keep its customers over some time. High customer retention rates, e.g., when customers keep buying from the same brand over a more extended period, mean that customers tend to return and continue to buy from them.
Customer satisfaction refers to an overall customer experience and how happy customers are with it. All behavioural patterns of a customer journey are cared for, where customers are happy and satisfied with the overall customer experience, including pre-purchase, purchase, and post-purchase phases.
It is where big data and predictive analytics have the power to make or break the customer experience. Keep reading to find ways to leverage analytics to improve your customer experience.
Before diving in, let’s briefly look at how e-commerce customer service has gained more importance over the past couple of years and why it is significant for online businesses.
Importance of customer experience in ecommerce
Personalisation in customer experience is now more critical than ever. Businesses increasingly pay attention to it to maximise AOV/CLV returns, minimise RoR, maximise ROI, stand out from the competition and leverage all competitive advantage. Personalising can generate a 20% increase in customer purchases, roughly equivalent to a 10% boost in sales and conversion rates, found a survey by McKinsey.
For example, Amazon and Zappos both aim to offer top customer service and customer experience to beat the competition. Customers turn to these online retailers as they know they can get guaranteed support and secure and reliable service.
Even though no company has gone bust solely due to bad customer experience, it can play a considerable part and has become an essential parameter for businesses. It can be a make or break point in gaining new business and retaining loyal customers.
Improving customer service using predictive analytics
Predictive customer analytics aims to analyse large datasets to detect patterns and trends to improve customer satisfaction – commonly reflected in higher ROI through more purchases and greater loyalty.
Predictive analytics allows customer experience to transform into a proactive rather than reactive approach, making it much more personalised to increase customer loyalty, sales, and customer satisfaction.
Big Data and predictive analytics aren’t magic; however, they can elevate the customer experience and take it to the next level. For your ecommerce marketing strategy, for example, a predictive analysis could help forecast the best sales channels to market on and when to promote.
Big Data and predictive analytics help build and implement tools that can offer exceptional customer experience. An estimated 80% of consumers are more likely to commit to a purchase if the retailer provides a personalised experience, as found by Epsilon research.
It means consumers expect personalisation to an extent when interacting with their favorite brands. Below we list five ways predictive analytics can help to improve and enhance customer experience:
Predict what your customers want to keep them returning
It is a known fact that retailers and other customer-facing businesses rely heavily on acquiring new customers and retaining existing ones – the later being 6x more efficient. Each interaction to attract or retain them is essential to get to know them better, whether in person or online.
Therefore, feeding all data and comments across all social media channels about customer interactions, conversations, and reviews can help predict customer behaviour and use it ultimately in customer service interactions. For example, as this data is already in the system about the customers, support agents can use them to personalise their calls or interactions without spending time asking.
Accurate customer satisfaction rates
Compared to methods, predictive analytics uses multiple variables to predict and calculate customer satisfaction rates. A neural network model considers a broader array of indicators to have an accurate insight into how happy customers are and retain them.
Predict customer churn to retain them
Predictive analytics allows identifying those customers who are most likely or are at risk of leaving. This modelling tool identifies high-churn customers, which makes it easier to proactively rather than reactively retain them, either through sending personalised product selections. The customer churn model can be used regularly to identify customers at risk of leaving.
Predict customer needs to personalise content
Having customers who keep buying from you is what all companies aim for. After all, 80% of sales usually come from 20% of the loyal customers. Businesses focus on acquiring new customers but should also focus on retaining existing ones.
What if we could predict every nuance of each consumer’s likes, needs and desires provided by their site visits, to then offer them the most suitable selection of products? By knowing what your customers need and what excites them, brands and retailers can recommend the exact products or services to their needs at the right time, thus increasing sales. PPS delivers this via their inbox to ensure this data is personal to you and not your competitors, directing them back to your site to capture the benefits of that knowledge as further product purchases.
Predictive analytics can identify buying patterns of a specific customer behaviour to promote relevant products and trigger purchases. For each customer the decision model predictive analysis simulates the customers’ journey of a specific product, which helps identify which product or channel marketers should reach out to them.
Conclusion
Predictive customer analytics can help ecommerce brands and retailers retain loyal customers and personalise their offers and marketing campaigns, thus increasing sales. Proactiveness and personalisation are the keywords here: predictive analytics helps businesses become proactive rather than reactive when it comes to customer experience and engagement.
Predicting customers’ needs and shopping behaviour, and identifying certain patterns, can determine customer groups at risk of leaving, help win them back, and ultimately personalise to upsell to create more value.
Enjoy a free trial of predictive personalisation software on your site.
Article classification:
Modest knowledge of ecommerce marketing preferable.
