Standardized, accessible data is essential to get maximum benefits from the technology
By Rosalind Stefanac
At Snowflake’s World Tour in Toronto in October, attendees ranging from developers to C-suite executives, saw demos showcasing AI-driven software tools on the Montana-based company’s cloud platform being used to standardize customer data across various sectors of a business and offer insights to improve service within minutes.
“We were also able to give the loyalty data (acquired through partner Scene+) to our businesses in almost real time,” he said.
On the retail side, Gowtham said AI will be immensely useful for both the back and front ends of the grocer’s business in terms of better forecasting to ensure product availability as well as enabling better pricing and customer insights.
“AI-powered recommendation engines can dynamically come up with pricing recommendations depending on market conditions and what the competitor is doing from region to region,” he said. “The possibilities really are endless.”
Shannon Katschilo, country manager, Canada, at Snowflake, said it’s the “democratization” of information at all levels of an organization now available through generative AI that will fuel innovation and bring value to the customer.
“Before the data was only accessible to the few highly skilled workers … and now it can be centralized,” she said.
Besides Sobeys and Scene+, Katschilo pointed to Canadian retailer Psycho Bunny Inc. (a men’s clothing retailer) that is using the Snowflake AI data cloud to enhance customer experience.
“Centralizing all their customer data from various sources into a single platform is really going to help their teams make more data-driven decisions to support their customers,” she said. “At the end of the day, it’s about making sure their customers have a seamless experience.”
Katschilo also cited the example of a call centre where an agent has instant access to a customer’s information to either purchase a product, check a warranty or solve an issue.
“It’s about increasing the time to value,” she said. “This reduces call times, which has immense business value and gives customers instantaneous answers.”
As for chatbots, generative AI is driving their evolution from generic information providers to customer service assets fully embedded in an organization’s workflow data to answer specific customer questions.
“The customers will never be booted out to an agent or redirected to a website,” Andrew Hall, vice-president of Data Science and Data Management at Moneris, said. “The easy part of this has already been realized, but it’s the backend integration of legacy applications that will be more difficult.”
He said most customers have done a first-use case with AI, so now it’s about “getting the economies of scale right” and ensuring their next uses cost a little less while providing a great customer experience.
Fortunately, third-party AI data clouds such as Snowflake, Alphabet Inc.’s Google Cloud BigQuery and Amazon.com Inc.’s AWS are making the integration and creation of standardized data foundations more accessible to businesses.
“It means we have to do a lot less of the data engineering work and can move more quickly to deploy models once the data is available and ready,” Hall said. “By pulling all our data into this repository and connecting applications, we can have data sets specific to a customer’s footprint and we can really know what services they’re using.”
Since Moneris customers are often small-business owners, he expects this to be a game-changer.
“Our customers are hungry for data like this … and the more insights and analytics and solutions we can provide for them, it’s going to make their lives so much easier,” he said.
Having centralized repositories of data for AI tools also bodes well for those companies that have already invested in data to improve customer service with little payout to date.
“We’re truly at a stage where the complexity has been abstracted away and anyone will be able to access analytics to improve services,” Hall said.
Of course, these changes also present challenges in terms of privacy and governance that companies and their technology partners have to keep top of mind.
“We’ve taken painstaking rigour in our analytical platforms to ensure we’re not storing sensitive information … and that it is only meaningful to us on the inside,” Hall said.