What Might Adobe’s Virtual Analyst and Other Machine Learning Tools Do for Retail?
In September, Adobe announced the launch of new tools within Adobe Analytics that tap machine and adaptive learning technologies to better understand the preferences and tastes of users.
The platform collects data on behalf of subscribers and applies Adobe’s AI and machine learning framework, Adobe Sensei, to continually analyze trends based on similarities, and micro and macro events. The platform eventually will make recommendations on next steps, for instance actions that could be taken as a result of insights garnered through email remarketing or targeting campaigns — a boon to busy retailers looking to capitalize on consumer trends and insights rapidly, even in-store.
“We took our time to build the new virtual analyst, spending years rigorously validating the technology with real customer data and training the AI model to make sure the outputs solved real problems,” John Bates, director of product management for Adobe Analytics, states in a release that accompanied the September announcement. “A major global brand in the early testing program told us the insights were comparable to adding over 100 data scientists to their team. We are happy with the feedback thus far and look forward to rolling this technology out more broadly.”
Adobe says the system prioritizes data based on business and user context, and identifies nuances in historical data to better understand that context. What else can retailers expect to gain?
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“The virtual analyst will always search and analyze a company’s data, prioritizing changes that it finds interesting,” the release from Adobe states. “Deep learning models allow it to assess every possible data point across all customer interactions, from how long consumers spend on a website, to movements between app and web. These ‘unknown-unknowns’ contain insights that brands not only didn’t know, but also had no idea to look for. There simply is not enough time or resources to uncover these secret lottery ticket insights buried deep in data. A retailer, for instance, could begin to see critical nuances in how different demographics buy clothes online, or fix broken experiences that bleed money.”
Virtual analyst will continue to learn from users’ preferences and consumption patterns, eventually providing more relevant insights, the company states. As the system gains understanding from “like” or “not like” recommendations, it will share more informed personalized content or offerings.
What else can retailers expect to gain?
“Retail is well-placed to benefit from the intersection of artificial intelligence, machine learning and Big Data,” writes Prannoiy Chandran in the Towards Data Science blog. “There is a need to manage and track a large number of items across various categories, track consumers’ shopping habits and above all, maintain a compelling brand that keeps consumers coming back.”
Supply chain planning and insights, optimized route and delivery planning, and price optimization/promotions each are poised to improve through machine learning insights, Chandran writes.
Ultimately, retailers that use machine learning technology and tools to improve efficiencies and productivity while driving down costs stand to realize the most gains.
At the end of the day, any tool that helps retailers analyze and make sense of the ever-expanding pools of data available to them — through loyalty card programs, in-store Wi-Fi, beacons and related IoT technologies — will likely also differentiate retailers and help remain relevant for the long haul.
Machine learning capabilities will continue to evolve to benefit marketing, commerce and supply chain use cases. Retailers that look to integrate the technology today stand to gain a great deal.