“Our quick, accurate and cost-effective solution enables businesses of all sizes to set themselves up with the competitive advantage of using AI to optimise their data, grow their business and boost their bottom line.”
This is according to Vian Chinner, a South African innovator, data scientist and CEO of Xineoh, a Canadian machine learning company using a consumer behaviour prediction algorithm to turn existing business data into usable insights.
Chinner explains that, currently, the amount of data generated by businesses doubles every 18 months. “The human resources that companies have in place to interpret this data, however, stays the same meaning that the potential of the data is not being realised. At the same time, the capacity of AI innovations has far surpassed that of human ability.”
According to global professional services firm, Accenture, the average business utilising AI will increase its revenue by 38% by 2022. Putting this theory into practice, popular entertainment subscription service, Netflix, has estimated that its AI algorithms save the business $1bn each year.
With this in mind, Chinner emphasises that it is as important for businesses to utilise AI in 2018 as it was to adopt electricity after the second industrial revolution.
“While more and more industries are realising what an important role AI can play in helping them achieve their goals, the options available to the majority of businesses has been limited to-date. It is estimated that there are between 5,000 and 22,000 AI engineers in the entire world. Access to this AI talent is further limited by large technology companies which have monopolised 90% of the available expertise.”
“Ruling out the big-budget option of hiring an experienced AI team in-house, alternative bespoke solutions currently available are often also too costly and time-consuming to be feasible long-term,” he adds.
In terms of more affordable or free platforms, Chinner explains that they are not as accurate as they may seem.
“Most existing algorithms have a strong bias towards recommending the most popular items. For example, if one were to predict what is in the average consumer’s monthly shopping cart, including popular items like bread and toilet paper would likely result in an overtly accurate prediction. The limited scalability of these solutions also often results in their failure when it comes to delivering on processing demand – providing thousands of results when the big data of an organisation may require processing in the millions.”
So, how can businesses realistically tap into the benefits of AI and stay competitive in their industry as the fourth industrial revolution plays out?
Proven effective in the financial, retail, media and entertainment, as well as e-commerce industries, Xineoh delivers leading results.
“Our predictions outperform others in terms of implementation turnaround, accuracy and popularity inclusion.”
“We can provide businesses with relevant customer behaviour pattern insights within just two weeks of receiving their transactional data. This is in contrast with the six to 18-month timeframe of other AI solutions on the market. Our algorithm also offers scalability for businesses working with big data and presents an exceptionally low-level of popularity bias.”
Referring back to the shopping cart example regarding popularity inclusion, Chinner explains that Xineoh’s solutions don’t just note the most popular patterns but delve in deeper, providing detailed and accurate insights on the behavioural patterns of a business’ different customer segments. For example, noting which consumers would be more likely to purchase whitening toothpaste over regular and which consumers would purchase luxury branded cereal in the R50 to R80 price range.
“By matching people with products and patterns, inventory with opportunities, and prices with spending propensity, Xineoh helps businesses improve customer satisfaction and accurately predicts their inventory needs, where and how to market their offering for the greatest target market reach, and what part of their process is most responsible for customer turnover.”
“These insights undoubtedly give a business the competitive advantage,” Chinner concludes.