Customer Data & Analytics Blog

Machine learning can help companies avoid the commodity trap

Janet Wagner | 3 minute read

Zylotech_Machine Learning Can Help Companies Avoid the Commodity Trap_062719_headerMarketing teams at any company can fall into the commodity trap- where the competition for products and services are based solely on pricing. And many companies specialize in selling commodity products, making it all the more difficult for their marketing teams to avoid falling into the commodity trap. Today, however, companies selling commodity products have access to a wealth of data- data that often contains information about customers, manufacturing processes, operations, logistics, and worker expertise.

This blog post highlights several examples of how companies are leveraging multiple sources of data and applying machine learning (ML) and customer analytics in ways that allow them to avoid the commodity trap. 

Educating customers about using products wisely

When it comes to commodity products, your first thought might be utilities- utilities provide products such as electricity, gas, and oil which are treated as commodities. However, many utilities have discovered a wealth of consumer insights contained in the massive volumes of data generated from multiple sources such as smart homes, distributed energy resources (DERs), sensors, and grids. Utilities could leverage machine learning and customer analytics to educate customers about their energy use, and market smart energy products to the right customers. For example, many consumers want to reduce their carbon footprint and look to utilities to help them achieve that goal. Utilities could help customers achieve that goal and avoid the commodity trap by offering customers personalized analytics and demand response programs.

Personalized smart energy programs are already offered by some utilities. Five major utility companies in Japan are working with the Japan Ministry of Environment to reduce the country’s overall carbon footprint. The utilities provide personalized, easy-to-understand energy usage reports to residential customers, which number about 60,000 residents per utility territory using the platform. The utilities are using ML-powered data analytics to educate customers about energy efficiency and how their energy use compares with other residential customers. ML-powered analytics allows the utilities to provide customers value beyond a competitive price by marketing the right information at the right time to raise awareness about energy efficiency. 

Sharing company expertise with customers

Some commodity products require expertise to implement and use. And much of this knowledge is stored on siloed company systems, external hard drives, and employee mobile devices. Zylotech_Machine Learning Can Help Companies Avoid the Commodity Trap_062719_subOrganizations could use machine learning and customer analytics to extract valuable information from these data sources and then share that information with customers- in particular, information containing the knowledge and experience needed to use specific products effectively. A business selling commodity products that require specialized expertise to use could avoid the commodity trap by marketing the overall value of working with the company, which includes sharing with customers the expertise needed to use the products. 

A Harvard Business Review article explains how a company that specializes in providing commercial explosives to companies in the oil and gas, mining, and quarrying industries is leveraging ML-powered customer analytics to avoid the commodity trap. The commercial explosives company digitized all their internal company data and combined it with data from its customers. The digitized internal data includes a vast array of company knowledge such as techniques used for each blast, the outcome of each blast, and the conditions of the equipment at each site. The company is using machine learning to build predictive models based on this combination of data. The models are integrated with an application that provides personalized blasting recommendations to customers on demand. The company markets its commodity products not only based on price but also the value of their expertise, much of which is shared with customers automatically through their online app.

Avoid the commodity trap with ML-powered customer analytics

Machine learning allows companies to leverage customer data in new ways and gain insights that enable marketing innovation. As markets become more globalized, businesses will need to provide value beyond competitive pricing. ML-powered customer analytics and innovative marketing strategies are crucial to avoiding the commodity trap.

If you liked this post, check out our recent blog post: Why machine learning matters to B2B companies.

Topics: machine learning