Customer Relationship Management software gets smart
Canadian PlasticsCanadian Plastics Research & Development Software
A new machine learning solution from Montreal-based AxiPolymer Inc. is designed to boost the effectiveness of CRM software.
In this age of texting and tweeting, acronyms are everywhere, and most of them are unimportant. For example, does anyone really care what BFF, OMG or LMFAO stands for?
But for savvy plastics manufacturers, two acronyms that definitely matter are AI (Artificial Intelligence) and CRM (Customer Relationship Management software). Defined as human intelligence exhibited by machines, the general idea behind AI in an industrial setting is the ability for machines to think for us, whether they’re projecting sales forecasts or directing a factory assembly line. CRM software, meanwhile, helps businesses build strong relationships with their customers by processing and categorizing customers according to their expectations, boost conversations, make use of disconnected data to depict opportunities, create sales funnels, and carry out loyalty campaigns.
Bringing the two technologies together is the goal of a new initiative by Montreal-based AxiPolymer Inc. Founded in 2017 by polymer scientist Dr. Ata Zad, AxiPolymer provides product development services for the plastics sector, and last year the company introduced a modular, highly customized machine learning system designed to make it relatively easy for plastics processors to implement AI to manage all of its wide-ranging data in order to increase production capacity, optimize production workflows, help achieve a more accurate cash flow, improve preventative maintenance, create more accurate sales forecasts, and more.
AxiPolymer’s newest product offering builds on this, taking its AI system a step further to boost the effectiveness of CRM software. “Standard CRM software gives you a centralized database, traceability of all actions on specific project, communication details with every customer, effective management supervision and task monitoring, and easier mass marketing,” Zad said. “But CRM is not the complete answer. Its limitation is that it provides you with data, but not with any insights about that data. If you’re a processor using CRM, you still have to analyze your behaviour with every single customer, find the hidden and complex patterns in each of these business relationships, and then formulate the best way to interact with each of these clients – and most companies don’t have the resources to do all this.”
Which is where the benefits of AI come in. “Humans and software only see the input and output layers, whereas AI uses algorithms to find hidden patterns in data,” Zad said. “These algorithms are iterative, and will learn continually and seek optimized outcomes; they also iterate in milliseconds, which lets manufacturers find optimized outcomes in minutes instead of months.”
But there’s a limitation to standard AI solutions as well. “Not all AI algorithms work best for plastics manufacturing in general, or for the specifics of one particular plastics processor’s business,” Zad said. “To get the real value of an AI solution, it has to be industry-specific, company-specific, and policy-specific, which means it has to have customised deep features and customised AI algorithms.”
Each of AxiPolymer’s machine learning solutions is customized to enhance a particular company’s CRM software, so no two solutions will be the same – which makes sense, Zad said, because no two plastics processors have the same business patterns. “To optimize a customer’s CRM, we start by talking with company management to understand their short- and long-term goals,” he said. “Then we evaluate the company’s CRM database. According to those priorities and in-place CRM, we customize AI algorithms and start the initial implementation”.
By leveraging their CRM software with AxiPolymer’s AI technology, processors will be able to convert huge loads of customer data from multiple sources – phone calls, website visits, social interactions, emails, quotes and purchase orders, and more – into actionable insights that can improve relationships with those customers, Zad said. “The benefits include predictive customer service, creating more accurate sales forecasts, more effective customer categorization, and assessing your customers from the best to the less desirable so that you can prioritize and devote attention to prospective buyers.”
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