Article | December 28, 2021
Successful manufacturing marketing strategies are all you need to grow your business and make it visible in every way to your target customer group. Many manufacturers are now becoming vigilant towards B2B marketing and have started forming an individual marketing budget in their annual budgets.
“We should quantify marketing to inform what we do – not to decide what we do.”
– Rory Sutherland, Vice-Chairman, Ogily
As per Statista, nearly half of B2B organizations said they’re planning to boost their content budget in the next year.
As a result, B2B marketing for manufacturers must be redesigned and smartly strategized in order to be more effective and fruitful.
This article will focus on the significant challenges manufacturers face in B2B marketing and how manufacturers use the three most sales-driven manufacturing marketing strategies.
4 Biggest Marketing Challenges in B2B & Manufacturing
Develop Tailored Experiences
You have a few seconds to capture the customer's interest. When done correctly, personalization may help. With persistent multi-channel marketing, you may strengthen your brand in target areas. Additionally, an account-based marketing approach enables you to focus on important clients while generating customized content for them. Integrate agile methods to test novel ideas across your business without demanding extensive approval. Further, crowdsourced content, B2B communities, and advocate marketing should be prioritized.
Convert Leads into Sales
With the right strategy, you may generate more high-quality leads. Relate marketing expenditures to sales and demonstrate the impact of marketing on the bottom line. Align marketing and sales by focusing on the customer's purchasing journey. Increase the quality of your leads, transparency, and collaboration with your partners.
Measure Marketing Performance
Marketers will be asked to demonstrate ROI and forecast future actions. Proactively calculate the MROI (Marketing Return on Investment) on marketing and sales investments. Determine how to get the most out of your marketing budget by doing more with less. Focus on making data-driven judgments rather than relying on guesswork.
Maximize the Marketing Tech Investment
As a manufacturer, you have access to a number of tools and resources. You will need to collaborate with your technical team to integrate it. Collaborate with your IT team to effectively adapt, innovate, and apply technology. By integrating current technologies, you can automate and improve marketing campaigns more efficiently.
“Marketing professionals have to act as conveners and connect the dots so that there is alignment between stakeholders like sales and operation teams and executive leadership on what products and services will drive growth in any given quarter."
– Maliha Aqeel, Director of Global Communication, Fix Network World in conversation with Media7
3 Best B2B Marketing Strategies for Manufacturers That Drive Sales
Consider Purchasing an E-commerce Platform
Consumer behavior is driving manufacturing transformation, particularly the shift to digital channels. Manufacturers who still handle consumers solely by phone, fax, or email risk losing their loyalty as their worlds and tastes grow increasingly digital.
Manufacturers have clearly acknowledged the digital transition in 2021. This year's Manufacturing & E-Commerce Benchmark Report says 98% of manufacturers have, or plan to have, an e-commerce strategy. Moreover, 42% of manufacturers who engaged in e-commerce and digital said it strengthened client connections.
How does e-commerce benefit manufacturers?
Distributes a customized catalog to your customers
Ascertains those spare components are visible
It allows customers to customize items online
Sells your whole range online
Increases your consumer base
Focus on the User Experience and Interface (UX/UI)
The term "User Experience" refers to all elements of an end user's engagement with a business, its goods, and services. The purpose of user experience is to establish a connection between company objectives and user demands. An engaging user interface or user experience keeps users engaged and consumers pleased. Additionally, it enhances the rate of return on investment (ROI). That is why it is necessary to maintain great UI/UX quality.
How does UX/UI benefit manufacturers?
Increases the number of conversions
Support is less expensive
It helps with SEO
Brand loyalty is increased
Embrace an Omni-channel Strategy
Millennials represent 73% of those making buying decisions for companies. Part of this means offering a seamless, consistent shopping experience across a variety of channels. With the right CRM solution, you'll eliminate a lot of the legwork associated with targeting specific buyers. Manufacturers can leverage omni-channel to increase availability, promote sales and traffic, and connect digital touchpoints.
How does Omni-channel benefit manufacturers?
Supports marketers in developing trust
Enhances the user experience with the brand
It clarifies a complex subject
Developing a successful manufacturing marketing plan is all that is required to set your organization apart from the competition. Consider thinking outside of the box and developing innovative manufacturing marketing strategies that will surprise your targeted customers and keep you on their minds at all times. B2B marketing for manufacturers has long been a priority, since manufacturers frequently overlook this aspect of their business when they should. Utilize the above-mentioned sales-driven manufacturing marketing methods to assist your organization in growing and reaching the maximum range of target prospects.
What is the goal of business-to-business marketing?
B2B marketing's goal is to familiarize other businesses with your brand name and the value of your product or service in order to convert them into clients.
How can manufacturers energize their market presence?
Manufacturers may boost their market presence by advertising on various social media platforms, opting for native language ads, and partnering with influencers to promote their products or services.
Article | December 21, 2021
Consumer demand has shifted dramatically in recent years, and manufacturers are trying to adapt to this shift. To maintain high product quality, minimize costs, and optimize supply chains, manufacturing analyticshas become essential for manufacturers.
Manufacturing analyticsis the process of gathering and analyzing data from various systems, equipment, and IoT devices in real-time to get essential insights.
As technology takes over and enhances many of the processes we used to handle with manual labor, we are freed up to use our minds creatively, which leads to bigger and better leaps in innovation and productivity.
– Matt Mong
Manufacturing analyticscan assist in maintaining production quality, boost performance with high-profit returns, decrease costs, and optimize supply networks.
This article will outline manufacturing analyticsand present a list of possible application cases. It will also highlight the benefits of manufacturing analyticsfor any shop floor or factory.
Manufacturing analytics: An Overview
With manufacturing analytics, we can streamline and speed up the entire process. Data interchange and automation helps in speeding up the production process. Manufacturing analyticsuses predictive manufacturing, big data, Industrial IoT, network virtualization, and machine learningto produce better scalable production solutions.
Manufacturing analyticscollects and analyses data from many sources via sensors embedded in machinery to identify areas for improvement. Data is collected and presented in an easy-to-understand structure to illustrate where difficulties emerge throughout the process.
In short, manufacturing analyticscollects and analyses large volumes of data to reveal insights that might improve performance. Users can also obtain automated business reports to reply in real-time.
Why Manufacturing analytics is Vital for Leading Businesses
There are numerous benefits of manufacturing analyticsthat drive any company’s production and overall manufacturing business growth. The benefits of manufacturing analyticsfall into three distinct categories as below.
It reduces the overall cost: Analytics may save a significant amount of money if used more efficiently. Labor costs are also reduced due to automation and semi-autonomous machinery. Similarly, preventive and prescriptive maintenance programs may save money while enhancing productivity.
It boosts profits for businesses: Manufacturers can respond swiftly to changes in demand using real-time insights in production, inventory management, and demand and supply forecasting. For example, assume the data indicates that they are approaching their maximum capacity. In such instances, they can increase over time, increase capacity, modify procedures, or tweak other production areas to adapt and maintain delivery times.
Other unforeseen benefits: There are several advantages to the increased capabilities enabled by manufacturing analytics. These benefits include lower energy use, safer environmental practices, fewer compliance failures, and more customer satisfaction.
Five Real-world Applications of Manufacturing Analytics
A machine's analytics uses aggregate data from real-time detectors to anticipate when it needs to be replaced or functioning irregularly. This process helps predict machine failure or equipment defects.
Analytics can assist in determining a plant's capacity and how many products are produced by the unit in every production cycle, which is helpful in capacity planning. In addition, analytics may help determine the ideal number of units to create over time by considering capacity, sales predictions, and parallel schedules.
Predictive analytics solutions can automate maintenance requests and readings that shortens the procedure and reduce maintenance expenses.
Product development is an expensive process in manufacturing. As a result, businesses must invest in R&D to develop new product lines, improve existing models, and generate new value-added services.
Earlier, this approach was in place by repeated modeling to get the finest outcome. This approach can now be modeled to a large extent, with the help of data science and technologically superior analytics. Real-world circumstances can be replicated electronically using "digital twins" and other modeling approaches to anticipate performance and decrease R&D expenses.
Many factors that might help in the plan significant capital expenditures or brief breakdowns can be explained using historical data and a few high-impact variable strategies. For example, consider the seasonality of products like ice cream. As a result, historical market data and a few high-impact factors can help explain numerous variables and plan major capital expenditures or short-term shutdowns.
In addition to demand forecasting, predictive analytics incorporates advanced statistical techniques. With predictive analytics, a wide range of parameters, including customer buying behavior, raw material availability, and trade war implications, may be taken into consideration.
Warranty support may be a load for many manufacturers. Warranties are frequently based on a "one-size-fits-all" approach that is broader. This approach introduces uncertainty and unanticipated complications into the equation.
Products may be modified or updated to decrease failure and hence expense by using data science and obtaining information from active warranties in the field. It can also lead to better-informed iterations for new product lines to minimize field complaints.
Managing Supply Chain Risks
Data may be recorded from commodities in transit and sent straight from vendor equipment to the software platform, helping to enable end-to-end visibility in the supply chain.
Manufacturing analyticsallows organizations to manage their supply chains like a "control tower," directing resources to speed up or slow down. They may also order backup supplies and activate secondary suppliers when demand changes.
Businesses should adapt to changing times. Using analytics in manufacturinghas altered the business industry and spared it from possible hazards while boosting production lines. Industry 4.0's route has been carved. Manufacturing analyticsis the key to true Industry 4.0, and without it, the data produced by clever IoT devices is meaningless. The future is data-driven, and success will go to those who are ready to adopt it. The faster adoption, the sooner firms go ahead of the competition.
How can data analytics help manufacturers?
Data analytics tools can help manufacturers analyze machine conditions and efficiency in real-time. It enables manufacturers to do predictive maintenance, something they were previously unable to accomplish.
Why is data so crucial in manufacturing?
Data helps enhance manufacturing quality control. Manufacturers can better understand their company's performance and make changes by collecting data. Data-driven manufacturing helps management to track production and labor time, improve maintenance and quality, and reduce business and safety concerns.
What is Predictive Manufacturing?
Predictive manufacturing uses descriptive analytics and data visualization to offer a real-time perspective of asset health and dependability performance. In addition, it helps factories spot quality issues and takes remedial action quicker by eliminating the waste and the cost associated with it.
Article | December 7, 2021
Machine learning in manufacturing is becoming more widespread, with businesses like GE, Siemens, Intel, Bosch, NVIDIA, and Microsoft all investing heavily in machine learning-based ways to enhance manufacturing.
Machine learning is predicted to expand from $1 billion in 2016 to USD 9 billion by 2022at a compound annual growth rate (CAGR) of 44% throughout the forecast period, according to Markets & Markets.
The technology is being utilized to cut labor costs, achieve better transition times, and increase manufacturing speed.
“I advocate business leaders get to know more about what AI can do and then leverage AI in proofs of concept.”
– Michael Walton, Director and Industry Executive, Microsoft speaking with Media 7
Machine learning can help enhance manufacturing processes at the industrial level. This can be achieved by assessing current manufacturing models and identifying flaws and pain factors. Businesses can rapidly address any difficulties to keep the manufacturing pipeline running smoothly.
Let us explore how machine learning is transforming manufacturing operations.
How Machine Learning Is Transforming Manufacturing Operations
“The greatest benefit of machine learning may ultimately be not what the machines learn but what we learn by teaching them.”
- Pedro Domingos
Machine learning in manufacturing is revolutionizing manufacturing operations and making them more advanced and result-oriented, so let's have a look at how this is unfolding.
Allows for Predictive Maintenance
Machine learning provides predictive maintenance by forecasting equipment breakdowns and eliminating wasteful downtime. Manufacturers spend far too much time correcting problems instead of planning upkeep. In addition to enhancing asset dependability and product quality, machine learning systems can forecast equipment breakdown with 92% accuracy. Machine learning and predictive analytics increased overall equipment efficiency from 65% to 85%.
Increases Product Inspection and Quality Control
Machine learning is also utilized for product inspection. Automated inspection and supervision using ML-based computer vision algorithms can discriminate between excellent and bad products. These algorithms simply need excellent samples to train; therefore a fault library is not required. However, an algorithm that compares samples to the most common errors can be built. Machine learning reduces visual quality control costs in manufacturing. Forbe's says AI-powered quality testing can boost detection rates by up to 80%.
Logistics-related Tasks Are Automated
To run a production line, industrial companies need considerable logistics skills. The use of machine learning-based solutions can improve logistics efficiency and save expenses. Manual, time-consuming operations like logistics and production-related documentation cost the average US business $171,340 annually. It saves thousands of manual working hours every year to automate these everyday procedures. Using Deep Mind AI, Google was able to lower its data center cooling bill by 40%.
Creates More Business Opportunities
Machine learning is frequently used in the production process. Substantial data analysis is required to create new items or improve existing products. Collection and analysis of huge amounts of product data can help find hidden defects and new business opportunities. This can help improve existing product designs and provide new revenue streams for the company. With machine learning, companies can reduce product development risks by making smarter decisions with better insights.
Protects Company’s Digital Assets
On-premise and cloud-based machine learning systems require networks, data, and technological platforms to function. Machine learning can help secure these systems and data by restricting access to vital digital platforms and information. Humans’ access sensitive data, choose applications, and connect to it using machine learning. This can help secure digital assets by immediately recognizing irregularities and taking appropriate action.
Harley Davidson's Sales Climbed by 40% Using Albert – The ML & AI-Powered Robot
Today, traditional marketing is harder to break through. It's easy to see why Albert (an AI-powered robot) would be a good fit for Harley Davidson NYC. Thanks to machine learning and artificial intelligence, robots are producing news stories, working in hotels, controlling traffic, and even running McDonald's.
Albert works well with social media and email marketing. It analyzed which customers are more likely to convert and modifies the personal creative copies on its own for the next process.
Harley-Davidson is the only company to employ Albert in its business. The company evaluated customer data to find prior consumers who made purchases and spent more time browsing the website than normal. Albert used this data to categorize customers and scale up test campaigns.
Using Albert, Harley-Davidson's sales climbed by 40% and leads increased 2,930%, with half coming from high-converting ‘lookalikes' detected by AI and machine learning.
The groundbreaking benefits of machine learning are the pillars of machine learning applications in manufacturing. Machine learning in manufacturing helps enhance productivity without compromising quality. According to Forbes, Amazon has automated warehouse logistics picking and packing using a machine learning system. With Kiva's help, Amazon's typical ‘click to ship' time dropped from 60-75 minutes to 15 minutes. So, industry leaders are seeing fantastic outcomes, and machine learning in manufacturing is the future.
How is machine learning used in manufacturing?
Machine learning is used in manufacturing to improve product quality and uncover new efficiencies. It unquestionably aids in the identification and removal of bottlenecks in the manufacturing process.
Which two forms of machine learning are there?
Machine learning is divided into two forms: supervised and unsupervised. In supervised machine learning, a machine learning algorithm is trained using data that has been labeled. Unsupervised ML has the advantage of working with unlabeled data.
What is a machine learning model?
A machine learning model is a file that can recognize patterns. In order to learn from a set of data, you must first train a model using an algorithm.
"name": "How is machine learning used in manufacturing?",
"text": "Machine learning is used in manufacturing to improve product quality and uncover new efficiencies. It unquestionably aids in the identification and removal of bottlenecks in the manufacturing process."
"name": "Which two forms of machine learning are there?",
"text": "Machine learning is divided into two forms: supervised and unsupervised. In supervised machine learning, a machine learning algorithm is trained using data that has been labeled. Unsupervised ML has the advantage of working with unlabeled data."
"name": "What is a machine learning model?",
"text": "A machine learning model is a file that can recognize patterns. In order to learn from a set of data, you must first train a model using an algorithm."
Article | March 31, 2021
Everyday the supply chain is jeopardized. A freighter stuck in the Suez Canal has severe ripple effects in raw material goods making their way around the world. Trade tariffs and unpredictable consequences from COVID have encouraged many US manufacturers to reshore bringing jobs stateside. This strategy will shift the supply chain challenge to a staffing challenge.
As the manufacturing industry is poised for rapid growth over the next 24 months, hiring the best workers once again becomes the top challenge. As the workforce is vaccinated and reshoring the supply chain becomes a clarion call for industry, finding the right people with the right skills forces plant managers, operations managers, and HR managers to find new and innovative recruiting strategies. FactoryFix is an online platform that matches vetted manufacturing workers with companies seeking specific skill sets. This platform sets a new standard in how small to mid-sized manufacturers hire talent across the U.S.