Xineoh uses AI, including deep learning, simulation, and optimization algorithms, to predict consumer behavior.
With this information, Xineoh's platform uses your transaction data to forecast demand, recommend relevant content, optimize prices, and recommend products.
We spoke with Xineoh Chief Product Officer Herman Scheepers to learn more.
In a single sentence or statement, describe your company.
Xineoh is an AI platform that rapidly turns your raw transaction data into increased profits and customer satisfaction, by helping you get the right content and product to the right audience at the right time and the right price.
How does your company use artificial intelligence in its products?
We use state of the art deep learning, simulation, and optimization algorithms to predict consumer behavior. We discover the hidden underlying patterns and trends that drive interest and sales, and use this to do integrated operational demand forecasting, relevant content, media, and product recommendations, pricing optimization, inventory optimization, market segmentation, and churn mitigation.
What are the primary marketing use cases for your AI-powered solutions?
Retail operations, including sales and marketing.
What makes your AI-powered solution smarter than traditional approaches and products?
A unique feature of our solution is a baseline model that can be trained and then used for multiple use cases, including ones that you did not perhaps initially think of, at low incremental cost. In terms of one key aspect, product recommendation and content tailoring, it has shown superior accuracy.
Are there any minimum requirements for marketers to get value out of your AI-powered technology? (e.g. data, list size, etc.)
It depends on the use case, but in machine learning and AI content is king. For meaningful results, a year or two of historical data is needed.
Who are your ideal customers in terms of company size and industries?
Our technology is suitable for any organization with valuable consumer and operational data to mine, except perhaps very small organizations.
What do you see as the limitations of artificial intelligence as it exists today?
We think there are several, including the availability of top tier data scientists. There are also issues with AI not really being semantically aware, a lack of appreciation of where the real business value lies, and organizational maturity to adopt. We believe that we address some of these issues with our solution platform approach, giving customers a low risk, rapid deployment route to AI success.
What do you see as the future potential of AI in marketing?
We see AI as a means to shrink the world from the view of the consumer. It will allow organizations to really only market what's relevant to certain groups based on latent common characteristics that are not possible to pick up using traditional methods. In addition, using cutting edge natural language processing and generation, relevant and engaging content can be created on the fly. We see AI marketing becoming a must-have informational aid from the perspective of the consumer, and communication tool for the marketer.
Any other thoughts on AI in marketing, or advice for marketers who are just starting with AI?
Get to know the technology and all the aspects that you need to understand to be able to apply it as business as usual. There are many niche solutions that internal data scientists or general platforms can solve. If you want to engage with your customers and target markets at scale, rather go with specialist platform providers with models and algorithms. This will dramatically lower, even eliminate, project risk.
Paul Roetzer
Paul Roetzer is founder and CEO of Marketing AI Institute. He is the author of Marketing Artificial Intelligence (Matt Holt Books, 2022) The Marketing Performance Blueprint (Wiley, 2014) and The Marketing Agency Blueprint (Wiley, 2012); and creator of the Marketing AI Conference (MAICON).