In 2013, mobile commerce contributes 17.4% of total ecommerce revenues. And by 2017, the share will go up to 25%.
According to Medio, retailers are gathering a ton of data from mobile devices, but most do not know how to put big data into full operational use or get the real value out of it. The white paper it released shows that predictive analytics and real-time tools can help personalize and improve the mobile shopping experience.
The white paper talks about the importance of personalization. It says, “A search for a product might begin on a smartphone, while order fulfillment is finalized on a PC or tablet. Being able to identify the same user across devices is necessary to create a personalized relationship; the foundation of sophisticated and intelligent user segmentation starts with being able to deduce when two users are in fact the same person.”
The paper concludes, “Smartphones and tablets are creating entire new opportunities to interact with shoppers and consumers online. Companies must create personalized relationships and experiences in real-time with their customers based on detailed and cross-channel customer understanding. Being able to apply customer centric predictive analytics is key to creating loyal relationship with your customers that will keep them coming back. Success in mobile retail will depend on the ability to adopt these new technologies and give each customer the personalized mobile shopping experience will maximize mobile retail revenue over time.”To make up for the loss of the cookie in the mobile world, advanced customer segmentation must identify unique users through the use of sophisticated matching algorithms. The report also talks about the importance of using predictive analysis tools. Advanced segmentation must include some form of predictive analytics; which can help to effectively target users who are most ‘likely to churn’ with the right content to spark re-engagement. Other uses include tailoring content for users who are ‘considering a purchase’, craft the ideal offer for ‘price sensitive shoppers’ and quickly recognizing the trends of a ‘likely high-value user’ and personalize offers that have a history of being successful.