Global and national brands have been upended by changes brought on in omnichannel marketing as customers access search engines and social media sites that provide highly localized results.
“Brands must ensure consistent localized marketing efforts while still appealing to the unique local audience, and marketers must find ways to consolidate workflows while optimizing local channels,” Afif Khoury, founder and CEO of Soci, told VentureBeat.
To bolster this, the digital marketing software provider announced today that it has raised $120 million in its latest financing round. The funds will serve to advance use of AI and machine learning (ML), including ChatGPT natural language models along with Soci’s marketing platform for multi-location brands.
Khoury said the Soci platform aims to streamline localized marketing efforts across digital channels while adhering to brand guidelines, optimizing local search and integrating data.
The company plans on using its funding to double down on its AI investments and expand into new markets. The funding round was led by JMI Equity with participation from Vertical Venture Partners, Blossom Street Ventures and strategic investor Renew Group Private.
Presently, Soci serves more than 700 multi-location and enterprise businesses across verticals such as food and beverage, totaling more than three million locations. Its customers include Ace Hardware, Kumon and Ford.
Digital marketing catches AI wave
Central to company efforts is SOCi’s “Genius” layer of products, which have begun to roll out this year. Soci intends to differentiate itself through its advanced data science, AI and automation tools. Its platform is providing local data analysis on behalf of brands and delivering recommendations and marketing automation so that its customers can focus on other parts of their business.
“SOCi’s AI models are used both to inform and to automate,” said Khoury. “On the information front, SOCi receives inputs from dozens of marketing channels across hundreds of locations.”
The SOCi team has developed sophisticated data science models to analyze data and its correlation to outcomes such as customer engagements, foot traffic, calls, clicks and other customer lead, loyalty and revenue data.
Recently, SOCi released — as part of its Genius line — a review response management tool that integrates with OpenAI’s ChatGPT. The platform can collect reviews and analytics across various review sites and automatically respond in an intelligent and customizable manner.
“In an organization that is receiving reviews across 5,000 locations, this could take the responsibility and cost of responding out of the hands of 5,000 individuals, and dwindle it down to just five or less individuals at corporations who are reviewing the list of automated responses,” said Khoury.
To data scientists, the raw potential of AI and complex neural networks was clear from the start, or close to it. But it’s only in the past five years that device hardware has become sophisticated enough to make good on the full promise, and bring AI all the way to the edge. On-device AI is what makes AI a reality for the consumers. And now devices of every size, even with lower battery capacity, are able to handle powerful, power-efficient on-device neural networks. It’s the evolution of computing from the cloud, taking inferencing right to the source.
“We’ve spent almost a decade of research on how to make AI work best on the edge,” says Ziad Asghar, senior vice president of product management, Qualcomm Technologies, Inc. “From that, we’ve developed hardware that’s able to do more inferencing for any given amount of power, and AI software stack (Qualcomm AI Stack) and tools to bring the Connected Intelligent Edge to life.”
Leveling up AI use cases and unlocking new ones
AI use cases have made their way to devices already — AI enhanced pictures and videos, AI-based voice assistants, better sound and voice quality, real-time language translation, and more are significantly improved with connectivity and data processing, while numerous brand-new use cases are just starting to make themselves known across camera, gaming, sensors and connectivity, on all devices at the edge.
On the consumer-facing side, use cases embrace everything from smartphones, XR, compute and earbuds to connected intelligent vehicles and smart homes. On the business side, they support digital transformation in the industrial and manufacturing space, connected healthcare and a leap ahead for the AI software tools and platforms companies need to stay competitive in a rapidly changing environment.
Asghar describes the Connected Intelligent Edge itself as a network with multiple nodes, or different products, within it — and many of the new possibilities lie in these device clouds. In a smart home, for example, that might include security cameras, the cars in the garage, appliances, PCs, mobile devices and tablets, all with some amount of AI processing capability.
Those security cameras might recognize a family member in order to open up the smart lock at the front door and activate environmental controls. But the Connected Intelligent Edge also disseminates AI across the whole network, so that use cases are handled with the best accuracy with the best power consumption. If there’s not enough processing power on one product, it can be handed up the line to a more powerful device.
For instance, a security camera shifting a possible false alarm to the unit that can handle anomalies and more complex incidents. The data never leaves the device or local network, so that privacy is assured. And handling latency-sensitive use cases on the device means real-time results, and a a better consumer experience.
