The power to accurately predict events and occurrences has helped people gain an edge in business, politics, and in life in general. It’s traditionally been very hard to do – more art than science – but in recent years, that has changed. The rise of big data and machine learning has led to methodologies that can be applied to all kinds of scenarios. Often called “predictive intelligence” or “predictive analytics,” these methodologies have been used to more accurately predict the weather, financial markets, elections, even the building of a competitive baseball team.
A key goal in ecommerce is improving the customer shopping experience by making it more engaging and relevant to consumers. To achieve this, there needs to be an understanding of user intent. Companies are leveraging data and predictive intelligence to develop a better understanding of their customers, and to provide a tailored, scalable user experience.
Predictive intelligence in ecommerce takes many forms, such as a product recommendation engine which analyzes a consumer’s purchase history and online behavior. With this data, the engine determines the user’s intent-to-purchase to provide a tailored experience. This approach works. Case in point: Icebreaker found that its shoppers clicked on Commerce Cloud Product Recommendations 40% more often, leading to 28% more revenue from recommended products and an 11% overall increase in average order value.
Predictive intelligence is based on observation of customer behavior and, with every action taken, the building of a profile of preferences. That information is then used to anticipate customer needs and predict which content to deliver – in real time, across any channel.
This concept can also be applied to page loads.
Fast-loading websites are crucial to creating an enjoyable user experience. By combining Salesforce Commerce Cloud’s ecommerce solutions with Instart Logic’s services, customers have reduced their load times in half.
Instart Logic’s Multi-page Predictive Prefetching solution predicts what pages a user will navigate to next, and optimizes delivery of those pages, preloading the content they are most likely to request. This is one of the ways retailers can provide a great customer experience – by removing performance issues that can often lead to shopping cart abandonment.
It works by leveraging a unique client-side architecture, using machine learning algorithms to collect and analyze information from real user interactions to predict the pages a user will visit next. With this knowledge, a retailer can speed up their page load time, thereby improving the user experience and conversions.
Transparency Market Research forecasts that the overall market for predictive analytics will reach $6.5 billion by 2019, up from $2 billion in 2012. According to Salesforce benchmark survey, companies using predictive intelligence saw increases of 10% in online revenue, 35% in email click-through rates, and 25% on email conversion.
The incredible amount of available data has created the perfect opportunity to meet customer demand. What’s more, customers have come to expect this type of relevance and speed from online retailers. In today’s world of ecommerce, predictive intelligence has evolved from a nice-to-have feature into a must-have tool. That’s why it’s being weaved into the fabric of commerce.