Mobile shopping is hitting its stride this holiday season.
In fact, both PayPal and eBay have realized triple digit gains in mobile payment volumes so far, according to an article in Mobile Commerce Press.
Still, for retailers and other companies to attract and convert mobile shoppers, they need to use data analysis and data discovery tools and techniques to understand what customers are looking for as well as the devices they’re using (to better optimize the experiences for the specific types of devices).
They also need to use data analysis and data discovery to understand how customers are using their mobile devices for holiday shopping, and product/price comparisons.
One of the biggest gripes that mobile consumers have is that mobile web sites are difficult to navigate. People often complain that mobile sites are too cluttered and that they take too long to load.
One of the biggest shortcomings of mobile web sites is that they aren’t optimized to work with mobile devices such as iPhones, Androids, and tablets. A recent holiday shopping study by LivePerson finds that half of mobile users stress the importance for a company to offer a mobile app or to optimize its mobile website.
Data discovery and data analysis can help retailers and other companies identify the kind of functionality sought by the types of mobile customers and high-value prospects they’re targeting.
Consumers share these sentiments in a number of ways, including surveys and other forms of customer feedback as well as comments they post in social media about their mobile experiences and interactions they have with contact center agents.
As part of this, retailers can also use data discovery and data analysis to identify how customers are using their mobile devices along with the types of devices they’re using for different purposes.
Some brick-and-mortar retailers are looking to strengthen their in-store sales – and avert “showrooming” where consumers conduct product/price comparisons in a store but then purchase online – by offering mobile functionality that simplifies the shopping experience.
A Forbes article by McKinsey & Co. consultants Nora Aufreiter and Brian Gregg, points to the way Walmart is placing QR codes next to 3D toy graphics to allow a holiday shopper to scan an item, make a purchase directly and receive free shipping.
Blended with location-based information about consumers as they walk near or into a store, retailers can also use data analysis and business intelligence to create and provide relevant offers to shoppers on the fly.
According to a recent study by PriceGrabber, 82% of consumers plan to use mobile apps to save money on holiday shopping this year. Retailers can use data analysis and data discovery to identify the types of products customers have already purchased and to help assess the types of offers that are most likely to resonate with certain customers or customer groups to generate more targeted offers and increase conversion rates.
Retailers can help make the holiday shopping experience easier for mobile customers by providing them with useful information.
For instance, a retailer can use data discovery to determine when a mobile customer is in a store, viewing that retailer’s mobile website (and others) to conduct product and price comparisons.
Once a retailer is able to detect this behavior and identify the product or products being researched, it can generate an automated response to offer the customer a link to product reviews made by other customers on its site.
Next Steps:
- Subscribe to our blog to stay up to date on the latest insights and trends in data analysis and data discovery.
- Please join us on Tuesday, December 18th at 1 p.m. EST for our complimentary webcast, “TIBCO Spotfire Delivers Game-Changer – Brings The Power Of Discovery To Big Data,” presented by Lou Jordano, Director of Product Marketing, TIBCO Spotfire. In this webcast we will demonstrate the generally available version of Spotfire 5, the next generation of data discovery and business analytics, offering breakthrough capabilities to speed analysis of big data.
