Bots, natural language interfaces to boost m-commerce sales to $2.1t by 2021

Bots, natural language interfaces to boost m-commerce to $2.1t by 2021
Credit: Georgejmclittle/

Mobile commerce services will see a serious boost in sales once merchants integrate technologies such as bots and natural language interfaces into their services. According to new findings from Juniper Research, purchases of goods via mobile will total $2.1 trillion by 2021 – a 100% increase from this year’s projection for spend on physical and digital goods – as such technologies are adopted.

And they will be adopted, says Juniper, if only because online shoppers don’t respond well to standard keyword search and menu-driven systems, in part because such systems aren’t that good at divining customer intent. Turns out that’s important (from the press release):

The new study, Mobile & Online Remote Payments for Digital & Physical Goods: Opportunities & Forecasts 2016-2021 […] suggested that where merchants deployed conversational interfaces, such as bots and natural language search, they would be able to far better understand the consumer’s intent. The North Face, for example, has developed an intelligent digital assistant to help consumers choose the appropriate product. Meanwhile, Facebook, Google and storefronts such as Etsy are investing heavily in similar solutions.

“Product search and discovery is a key stage in the shopper journey” noted research author Steffen Sorrell. “Offering a conversational consumer interface, then marrying intent with contextual product data will drive merchant differentiation.”

Juniper says a lot of this being driven by mobile – specifically, increasing use of smartphones in digital retail, which the report says is the underlying cause for stakeholder development in bots, natural language processing and “disruption at the payment gateway.”

The latter item reflects the finding that there is substantial activity to improve the experience at the point of payment:

Disruptive players are simplifying the consumer experience by challenging tried-and-tested but frustrating checkouts. For example, Klarna eschews card number entry and usernames and passwords. Instead, shoppers can enter simple-to-remember information, such as their email address and postcode.

Meanwhile, the research examined how machine learning is used to minimize the chance of payment rejection. Adyen, for example, are using the technology avoid payment rejection due to bad formatting or misrouted connections to the acquiring bank.

You can download the whitepaper, OS-Pay to Shake Up Remote Retailhere.


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