How Decision Intelligence Reduces Cart Abandonment and Increases Revenue

Nearly 70 percent of online shopping carts today are abandoned, leaving a great deal of sales revenue on the table for retailers and e-commerce providers. To reduce cart abandonment, brands are relying on data that will inform them of shopper preferences and shape their marketing, sales and customer service experiences. Unfortunately, the sheer volume of commerce traveling through digital channels and the data it generates makes traditional analytics — like business intelligence dashboards — antiquated. Companies shouldn't rely on legacy approaches and data structures that don't take advantage of innovations such as automation, artificial intelligence, and machine learning.

Taking a modern analytics approach can help companies generate faster insights into consumer behavior, enhance decision making, and ultimately reduce cart abandonment and increase revenue. Retailers that leverage their data as an asset, coupled with decision intelligence, can generate insights and make faster data-driven decisions.

What is Decision Intelligence?

Every modern organization is prioritizing developing a data stack comprised of superior data ingestion, storage, and analytics tools. AI and ML applications are the foundation of the analytics layer, but these solutions are further supercharged by decision intelligence with deeper insights, greater usability, and more flexibility. Decision intelligence goes beyond current analytics capabilities by enabling users to quickly understand what is happening, why it's happening, and how to improve outcomes. The technology inherently enhances business decisions, helping data crunchers quickly identify anomalies, reduce costs, and find new growth opportunities.

Retailers interested in these types of tools should look for platforms with three key characteristics:

• Ease of use that's powered by natural language and modern user experience
• Flexibility to create custom business logic, models and code
• Intelligence driven by AI/ML-powered insights and automation

How Can Decision Intelligence Reduce Cart Abandonment?

To make the most of a decision intelligence investment, retailers need to feed the solution with sources from customer, clickstream and sales data. Customer data includes anything from contact information and demographics to conversion rates. Behavioral and opinion data help segment customers into even more personalized categories like loyalty program members, weekend shoppers, and direct marketing campaign targets. Transactional data is fueled by interactions and engagement; this information is aggregated any time a customer purchases or makes a service request.

Once this data has been compiled, retailers can power their decision intelligence tools to uncover anomaly drivers related to lost revenues from cart abandonment and customer churn. These insights can then be used to shape decisions related to marketing, sales and customer service activities.

For example, upselling and cross-selling are two of the most tried-and-true tactics for increasing revenue. Through purchasing patterns, decision intelligence can help retailers identify the best opportunities for upselling and cross-selling, predict when customer segments will buy certain offers, and recommend upgrades to products or services that customers are most likely to accept.

Another benefit of advanced analytics that will boost revenue is increasing lifetime value (LTV). Calculating LTV is closely related to profitability, so improving average LTV is critical to improving cash flow. Decision intelligence can help extend the LTV by identifying high-performing customer segments and targeting them with customized marketing campaigns and personalized offerings.

It's no secret that customers prefer a personal touch when it comes to engagement. The true challenge is finding opportunities, driven by data analytics, to engage at the right times with the right offers. Decision intelligence enables anyone to analyze all the data across sources more quickly and get a more complete picture. Utilizing AI and statistical techniques, coupled with decision intelligence, delivers insights into the hands of business people and decision makers faster. Ultimately, organizations can increase their agility and better react to changing preferences and behaviors of shoppers.

Brands and retailers that manually comb through data are missing growth opportunities, leaving money on the table, and seeing carts abandoned. Bolstering analytics efforts with automation, AI and machine learning will maximize the success of data initiatives.

Decision intelligence puts advanced data analysis in the hands of even the least technical of users, helping them uncover new revenue opportunities, identify sales trends that indicate why performance has changed and what can be improved in the future, and expedite critical decision-making processes that enhance the business’ bottom line.

Ajay Khanna is the CEO and founder of Tellius, the first decision intelligence platform to handle ad hoc query and compute-intensive ML/AI workloads.