SOLUS — AI that enables hyper-personalized customer engagement at scale
In This Week in Innovation, Jeff Roster invites Sandeep Mittal, Co-founder of Solus.ai, and Co-founder of Iterate.ai, Brian Sathianathan to discuss how Solus and AI can power hyper-personalized customer engagement. AI is one of Iterate’s #5forcesofinnovation and is a subject that has been a pivotal focus for a few years. Since its establishment, Solus has garnered a lot of traction and is now going global with both B2C and B2B use cases.
Our low-code middleware platform, Interplay, has over 475 pre-built nodes for AI, IoT, and other big-data 3rd party applications. As part of our drive for innovation, we are constantly seeking the best players in each space and integrating with them wherever possible.
Solus.ai was launched in 2019 by Sandeep Mittal who had a vision of enabling hyper-personalized customer engagement. SOLUS is a system of intelligence (SoI) that helps brands nudge customer behavior by providing the decisions needed to do so — product recommendations, offer selections, reasons to talk, timing, content, and more — at the individual level. They deliver a payload of micro-decisions making customer engagement personalized through a continuously learning system that relies on machine learning and data to generate the most up-to-date recommendations for customers in the retail industry.
Solus’ platform is used by retailers that reside mostly in Europe and Asia, but it originally established its presence in the United States and Guatemala. They currently assist three of the top five retailers in the U.S that collectively cover over 100 million customers. Some of these retailers include notable brands the likes of Bennetton, Puma, Tommy Hilfiger, and Marks and Spencers. Outside of the retail industry, Solus.ai is engaged with industries that focus on securities, mutual funds, CPG, hospitality, hotels, and travel to name a few.
Problems that retailers face and how Solus can help
The main problem that Solus has recognized among retailers is scalability. Retailers and brands have to rely on tech companies like Solus.ai to look at holistic data. Many retailers lack access to enough data — which is largely due to their lack of investment. Solus has noticed that many retailers do not purchase reports that cover purchase history, browsing history of customers, cart items, and so on.
Sandeep states that “the best predictor of future behavior is past data,” alluding to the notion that although many retailers lack access to data, the best data consist of behavioral indicators by “past” customers. There is also an offline problem and a scarcity of customer interaction and engagement data that is hard to collect. The way this data is tracked is through kiosks, mobile numbers, inventory, feedback, and any sort of collectible data out there. Additionally, Solus faces tracking limitations from the recommendation learning system API. The best way to resolve this is by seeking out “data-rich” customers in order to better understand “data past” customers.
Solution — SOLUS powers the decisions
Solus has a grading system that uses a GANs methodology to learn from past behavior and adapt according to the best possible outcome. Given a training set, this technique learns to generate new data with the same statistics as the training set. There are learning systems and expert systems that work off of each other via A/B testing to predict the best customer engagement solutions for retailers.
Solus serves to provide four key services to retail brands:
- Recommendations — Hybrid recommender that learns the best approach for each customer. This is essentially the recommender of recommendations, a system built to offer customers the most accurate product and offer recommendations.
- Reasons to Talk — A learning system that gives clients and customers “reasons to talk”. It tracks built-in best practices including product recommendations, lifecycle, offers, milestones, behaviors, retail events, product adoptions, onboarding, and feedback scores for customers.
- Content — Assists with content creation like SMS copy, push-notification copy, email subject line.
- Contact Optimization — Learns contact optimization, channel, and timing at a customer level.
The combination of these tools serves two functions purposes: Proactive and Reactive. Proactive allows customers to reach out to customers via push notifications over email, SMS, and general notifications to retain customers. The goal of Reactive is to personalize customers’ experience by tracking their activity on retail store apps, websites, in-store actions, and calls.
Use Cases that Solus implements to optimize their customer’s experience
The Nudge
A use case that uses signals to provide retailers with micro-decisions such as personalization fields (150+ to choose from), online and offline datasets, reasons to talk generated responses and recommendations, words and phrases, product recommendations, and timing and positioning of messages.
The Recommender
A recommendation system that delivers insights based on customers’ offline and online purchases, nonpurchase signals, target market, and customers with similar interaction behavioral patterns.
Experience Personalization
Personalized messages that are sent to customers with a “hook” that keeps them engaged. Customers will receive SMS messages like “Hi Sandeep, over 5400 people like you have been using our amazing summer offers! Don’t miss out!” Solus determines Reason to Talk, Recommender provides customers the best offers available, and Solus determines the day and time that messages are sent out to customers through Solus’ machine learning system.
Guided Selling
This is a tool that “guides” users to seek out products they need depending on their past purchasing behavior and customers with similar requests. Through a simple query, Solus determines the products, units (amount of product ordered), and delivers recommendations to customers about products that may benefit them using past data trends.
Why SOLUS stands apart from other customer engagement service providers:
Retailers’ experience with Solus’ proprietary platform is at the heart of what they do and work towards. They work at the Segment of One level, ensuring that decisions around optimal engagement are taken for individual customers. Solus is an AI system that uses learning systems that uses a reinforcement system that ensures that retailers don’t have to rely on set rules. Solus covers offline and online data that covers a wide customer demographic — inactive, new, not on digital platforms, and the like.
Solus measures nudge, conversion rate, lift, revenue, ROI, NoC (net of conversion), and analytics. Over the span of 2 years, Solus has delivered 5–7% topline impact in terms of revenue, 75X ROI, 1.5 to 2X on Lift and NoC, an 80% improvement in open rates and 50 to 300% improvement in CTR, and have a system that is 75 to 85% on a recommendation accuracy score. These are the many KPIs that should attract retailers and brands to Solus’ customer engagement platform.
Retailers can take advantage of Solus’ software by learning how to interpret data models relevant to their consumer’s behaviors, keeping track of measurement metrics, and having a point of contact to bridge the knowledge gap of the services that Solus provides to retailers and brands, leading to more informed business decisions.
Impacts of AI in retail and SOLUS’ role:
Throughout the course of time, it has been evident that the demand for technical-minded people has been increasing at alarming rates. In retail, more jobs are being rapidly created due to the new spaces (like customer engagement software) being discovered. Some of these positions include data tagging, data cleanup, creating metadata, and overall data capture through photos and videos. To nobody’s surprise, these jobs require AI experts that understand how to conduct these tasks with precision and up-to-date methods.
Over time, AI has grown from a $170 billion dollar industry into a $500 billion industry — and it’s not slowing down anytime soon! Computer software companies with niche services like Solus, that focus on consumer engagement with retailers and brands have tapped into a space that is heavily reliant on customer feedback, activity, and interactions. Now more than ever before, marketing organizations are being constantly pushed, driven, and challenged to understand customers’ online and offline product engagement in the retail sector.
What should the 93% of retail businesses that have not incorporated AI into their business operations do?
Retailers should be constantly experimenting with the machine learning systems they have in place — as technology is constantly evolving and changing, and adapting is required to stay afloat in today’s technologically-driven world. Retailers should focus on their ML models rather than what their competitors are doing. Assuming that their competitors have the answers, better systems, and clientele will distract retailers from focusing on their main priority, their consumers. Retailers should focus on building, designing, and testing systems that rapidly evolve and experiment via real-world retail scenarios, as it will keep them on their toes and learn how to change processes if needed. In addition, identify “rules” that do work and adjust the ones that don’t by iterating and running tests — but don’t discard them either.
What are you waiting for? Contact us here to learn more about Iterate.ai and how it can (and will) transform your company’s customer engagement goals.
