May 16, 2020

How to grow manufacturing sales effectiveness with lead velocity

Connected Manufacturing
Nina Church-Adams, Senior VP, ...
4 min
manufacturing sales
Many manufacturing marketers have traditionally focused on the number of leads as a key metric. It’s easy to work under the theory that the more leads...

Many manufacturing marketers have traditionally focused on the number of leads as a key metric. It’s easy to work under the theory that the more leads that flow into the top of the sales funnel, the more that will convert to a completed sale. Yet, more isn’t always better. A laser focus on lead velocity, that is, improving the conversion rate for leads as they move through the sales pipeline, translates into greater revenue returns while increasing sales confidence.

When Marketing focuses on volume, Sales ends up going through high numbers in the hopes of finding the hot leads included in the mix. While there is a finite number of customers in the market for a product at any given time, there is a point of diminishing returns. When Marketing sends Sales too many, they risk losing the Sales department’s trust and with it, the value of any leads they pass on.

Velocity, the rate at which leads convert as they travel through the sales funnel, is the answer. Here are four clear reasons why:

Increase in Lead Generation ROI

Whether a direct lead, or a lead through the channel, manufacturing marketers today have to maximise their use of time and resources to prioritise prospective customer interest. Organisations that nurture leads experience an increase in lead generation ROI. After all, the velocity with which leads are converted to sales-qualified leads increases efficiency while ensuring Sales spends its time having high quality conversations. For example, Bisco Industries, a manufacturer of electronic components and fasteners, implemented lead nurturing and benefited from a 1,400% lead generation ROI, winning the 2017 Nucleus Research ROI Award.

Segmentation, tailored content and lead nurturing

While over half of manufacturing marketers don’t currently segment prospects at all, tailoring content to individual buyers increases lead conversion significantly. Velocity-centric marketing encourages segmentation, nurturing each lead based on things like product interest, position, industry, and more. According to Wakefield Research, 77% of manufacturing organisations conduct more than 10 promotions a year with 46% running more than 21.

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Case in point: Last year Bisco Industries began delivering near real-time content to prospects, tailored to how those prospects liked to engage with the company. In doing so, they nurtured prospects through the buying journey from initial engagement to quote request, to placing an order and setting up a Bisco credit account. These customers were then further nurtured, adding new product lines to their account. With this approach, tailoring content to different prospects at different times in the buying process, Bisco increased its conversion rate by 1,285%. While Bisco only grew new opportunities by 4%, its enhanced conversions resulted in an average annual benefit of $301,388.

Increased sales efficiency increases sales impact

Even small, incremental increases in conversion rates can result in significant sales impact. When marketers focus on lead volume, a huge amount of effort is wasted with clearly unqualified leads - effort that could be used on leads that have a higher likelihood of conversion and ROI. In addition, a bonus side effect to an increase in velocity is that Sales can reinvest previously wasted effort in upsell and cross-sell activities, making them even more productive.

Sales Marketing Alignment

In order to affect positive change in conversion across the sales funnel, it’s important for the Sales and Marketing teams of a business to work together, and to make sure they have common definitions for the sales and marketing process. For example, both departments need to ensure that they agree on what counts as a lead, an MQL, SQL, Opportunity, etc. With these definitions in hand, they should then measure the lead conversion rate from each stage to the next. Key questions to ask are:

  • What is the current velocity of prospects in the pipeline?
  • Which prospects move faster today and why?
  • Are there any bottlenecks in the process?

The more effective the funnel, the more efficient it is at converting at all levels, which can have an important impact on marketing and sales productivity - and most importantly, on sales outcomes. Segmentation and personalised nurturing campaigns will speed up the rate of conversion velocity and increase the quality of leads that are sent to the sales organisation. This will in turn grow Sales’ productivity, help them win more customers and make more sales, and increase Sales’ confidence in the marketing process and the marketing team.

Nina Church-Adams is Senior VP, Marketing at Act-On where she manages the company’s global marketing function, bringing more than 14 years of marketing leadership experience building teams, products, and brands at industry leading companies such as Nike and American Express.

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May 11, 2021

5 Minutes With PwC on AI and Big Data in Manufacturing

SmartManufacturing
ArtificialIntelligence
bigdata
Technology
Georgia Wilson
6 min
PwC | Smart Manufacturing | Artificial Intelligence (AI) | Big Data | Analytics | Technology | Digital Factory | Connected Factory | Digital Transfromation
Manufacturing Global speaks to Kaveh Vessali, PwC Middle East Partner (Digital, Data & AI) on the application of AI and Big Data in Manufacturing

Please could you define what artificial intelligence is, and what Big Data is?

AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications. 

Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data. 

How does AI and Big Data work together?

Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important 

How can this be applied to a manufacturing setting?

Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.  

Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?

One view is, again, the economic benefits of AI, which come in manufacturing as a result of: 

1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies. 

2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services. 

Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030. 

How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?

It’s really about the investment you make now, in order to futureproof your business. 

We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success. 

What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?  

AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including: 

1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process. 

2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation. 

Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest. 

What are the challenges when it comes to adopting AI and Big Data in manufacturing?

PwC research has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically. 

Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation. 

Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:  

  • 40% → Technologies not mature  
  • 40% → Workforce lacks skills to implement and manage AI  
  • 36% → Uncertain of return on investment  
  • 33% → Data is not mature yet 
  • 32% → lack of transparency and trust  
  • 24% → Work councils and labour unions  
  • 22% → Regulatory hurdles in home & important markets  

One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured. 

Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this. 

What are the current trends in AI and Big Data in manufacturing?  

  • We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing 
  • Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence 
  • There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation

What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months? 

Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys. 

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