September 2021


AI fuels customer growth in the manufacturing sector

Written by: Azlina Azman
Head of Communications & Digital Engagement, Fraunhofer Project Center at the University of Twente

Artificial intelligence (AI) is a game changer in the manufacturing sector. As the main driver of what many experts are calling the fourth

industrial revolution, or Industry 4.0, AI is redefining everything from operations on the factory floor to back-office routines like sales and marketing.


AI itself is the product of one of the most pervasive technological trends of all – the exponential growth of digital data. Today’s manufacturers  routinely collect vast amounts of data from an ever-increasing range of sources, including customer touchpoints and connected devices on the factory floor. The challenge lies in making sense of this so-called Big Data in a way that is economical, scalable, and applicable in each unique manufacturing environment.


The rise of customer analytics for growth 

Recent years have seen increasingly sophisticated algorithms draw AI out of simple support roles to become critical growth drivers in many  organisations. Deep learning, active learning, and natural language processing (NLP) are just some of the exciting developments poised to change our relationship with machines. Today, AI is also changing the way businesses interact with their customers.


A recent study by the MIT Technology Review found that a third of sales and marketing teams are already using AI for customer growth. Moreover, it is expected that this figure will almost double in the next two years. After all, AI is already well established in other areas, including customer service and IT management. Indeed, the potential to drive growth by adding value to every mission-critical operation is enormous. This includes operations

which are not directly connected to customer growth as well, such as production efficiency, financial operations, and risk-management.


Here are the three main areas where AI shows great promise in accelerating customer growth:




While manufacturing plant floors have been investing heavily into automation and optimisation technologies for years, revenue has largely remained stagnant. There are two key functions which are well-positioned for process improvement – sales and pricing.


Sales teams bear the responsibility to grow their customer base while retaining existing ones. Yet despite a reduction in labour costs due to

transformational technologies on the shop floor, many sales teams are stuck with antiquated processes and solutions that lead to excessive

customer churn. This is because they often lack the insights needed to better understand their customers.


AI can help sales managers prospect more effectively, reduce customer churn, and automate many onboarding processes. Relying on human analysis alone for pinpointing, for example, when customers are at risk of defecting to a competitor, simply isn’t feasible for an organisation with thousands or tens of thousands of clients.


Machine learning can draw upon past successes and failures to determine things like which customers make the best fit for a given product or service and which ones are most prepared to purchase additional products. Empowered by AI-driven insights, sales teams can discover hidden revenue opportunities at practically any scale. They can optimise pricing, spend time rospecting for the right clients, and know who to communicate with and how, when, and why.



No one expects a marketing department to work around the clock, but that doesn’t mean their operations should be restricted to a nine-to-five routine. In fact, AI-powered systems already work around the clock behind the scenes of thousands of popular consumer and business products and services. These solutions are extremely diverse too – ranging from AI-powered discoverability engines that help customers choose the best products and services for them, to displaying ads at just the right time and place.


Applying customer analytics for growth across sales and marketing departments helps them achieve alignment and optimise efficiencies across the board. For example, labour-intensive things like direct-mail marketing and social media can be automated to free up time for teams to focus on activities which require a human touch.


AI can help continuously optimise the overall customer journey and customer experience. At the top of the sales funnel, it enables better lead targeting before moving down the funnel into a smoother and more consistent marketing process across every touchpoint. Marketers can use their automation tools to ensure a timely and well-placed execution of their campaigns across multiple buying stages. All the while, automated analytics and reporting yields a steady supply of insights which can then be applied to improve and optimise the process.



AI takes the process a step further by automatically tailoring customer experiences according to certain variables, such as past-purchase history and client personas. This can be done with enormous speed and high precision, making it possible for marketing teams to carry out their operations at a scale that simply wasn’t possible before.


Customer service

With customers now having unprecedented power to shape the reputation of any organisation they do business with, it’s safe to say that customer service is the new marketing. In a sector frequently involving huge transactions and high-commitment contracts, manufacturing should also view customer service as a critical growth driver. Thus, it stands to reason their operations should be deeply intertwined with sales and marketing.


Yet automated customer service still gets a bad reputation. Frequent complaints include chat bots which fail to provide the right answers and automated support lines which continuously shift callers between departments. However, AI is steadily closing the gap between automated and human-powered customer support.


Innovative manufacturers are already looking into how they can use AI to improve customer service. Key areas of interest include predictive maintenance, and the ability to identify and resolve known issues with automated troubleshooting steps. After all, most customers would rather help themselves, provided they can find reliable solutions to their problems quickly, than pick up the phone or send an email.


AI can run self-diagnoses for predictive maintenance, which should substantially reduce the number of support tickets opened. This, in turn, will increase customer satisfaction – a direct driver of growth. Similarly, using AI to analyse vast amounts of customer feedback across the multitude of platforms ranging from support forums and knowledgebases to third-party review sites, can yield insights into more pervasive issues. Support teams can then escalate these recurring issues to resolve them in less time and identify which issues need a human touch.



Final words


Manufacturers face increasing pressure from regulatory bodies, environmental initiatives, and economic challenges associated with sudden, and often unpredictable, shifts in demand. Thus, the cost of not investing in AI for optimising mission-critical operations, is substantial, and it’s only likely to get higher. The good news is that most plants already have huge amounts of potentially valuable data at their disposal. The next step is to implement a way to transform that data into insights that can drive growth.