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Interesting facts and statistics about B2B wholesale and manufacturing in Europe
Facts and statistics about B2B wholesale, distribution and manufacturing in Europe & Germany
Distribution in Europe is a significant job provider – 10 million employees. It works at the centre of the continent’s economy, covering almost all goods and services.
Most of the general public probably ignores the role of distribution on the European landscape, although it plays an essential role as the interface between importers, manufacturers, retailers and service providers.
Predictive Analytics to Understand Customer Behavior in the B2B Sector
For some B2B companies, predicting customer behavior is like guesswork. Managers sit together and try to make predictions about upcoming sales, future pricing or appropriate customer loyalty measures.
Often these forecasts are based on sales reports, sales representative’s own gut feeling and, Excel analyses created with a lot of frustration. Don't get us wrong, the gut feeling of an experienced sales team can very often be right, especially when it comes to customers they have had a lot of contact with. But what about all the other customers?
Humanoid Robots - A Blessing or a Curse?
Sophia, the first humanoid robot to get citizenship of a country - now, who’s next?
There has recently been a lot of discussions about humanoid robots and if they are a blessing or a curse in our society. Humanoid robots help humans to complete complex tasks which may otherwise be very difficult for us to do.
However, there are also speculations about these robots and if they become too far advanced that they would replace humans in the workplace, for example. Lots of scenarios in the field of science fiction showcase the possible reality that robots would lead us to the ultimate end.
Continuity versus Change - The "Rope Effect" in B2B Sales
Many talk about a change or even a transformation of business-to-business sales, but some things will remain the same in the future.
In this article, I would like to look at both sides and find an answer to the question: What remains better as it is and what was not better in the past?
I, too, got my information for this post from articles with names like "The Turbo Transformation" or "The Next Level: B2B Sales 4.0" because, of course, it is more exciting to write about what is radically changing than about what is staying the way it was decades ago. But especially in times of such substantial change, we should also think about what will probably remain as it is right now in ten or even 50 years.
The food for thought on this topic came from the successful German podcast "Gemischtes Hack". In one of the more recent episodes, the two hosts, Felix Lobrecht and Tommi Schmitt, talk about what they call the "rope effect." This paraphrase refers to objects, methods or practices, such as the rope, which has been used for thousands of years and is unlikely to find a dangerous substitute for decades to come.
In the following chapters, we will look at which sales areas are undergoing radical change and what these enduring ropes of sales will be.
B2B Dynamic Pricing - What Is It and How Can You Implement It In Your Company using Software
All prices in Business-to-Business are dynamic. Some are just more dynamic than others.
Successful companies in B2B tend to adjust their prices based on factors such as production costs, competitor pricing, supply and demand. Dynamic pricing is a pricing strategy in which businesses adjust their prices for products or services based on current market situations.
Practitioners in B2B also refer to dynamic pricing as surge pricing, demand pricing, or time-based pricing. It relies on advanced B2B pricing analytics.
Artificial Intelligence in a Construction Machinery Company
Artificial intelligence technology can be used in a wide variety of areas. In this article, you will learn how AI was used in a mechanical engineering company.
I've already written a few posts on this blog about Artificial Intelligence (AI) in sales. Simply a fascinating topic for an old sales hand like me who has worked with several different CRM systems as well as sold AI systems.
For someone in sales, the area of predictive analytics is particularly interesting - an AI is able to predict sales opportunities. That's why I report on a specific case study below.
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Predictive Sales Analytics in Excel? Yes, you could!
How to do predictive analysis in Excel: One Useful Example of Predictive Sales Analytics & Predictive Modeling in Excel
One of the critical tasks of a sales manager is to timely identify which opportunities have better chances of closing and what makes a “good” sales opportunity. Getting this job right is the essence of successful sales planning.
Key Account Managers in B2B typically serve hundreds of customers and oversee dozens of new and existing sales opportunities. They have limited time and represent one of the most valuable resources any company can have.
Four Situations Where you Should Not use Machine Learning
Machine learning (ML) is a great technology. But does it really have to be used all the time? This post is about four situations where you shouldn't use machine learning.
There is (rightfully) a lot of hype around artificial intelligence and machine learning (ML). As we said, great technologies and use cases are emerging from it. You can also find many examples that use machine learning in our blog articles. But in which situations is machine learning not necessary?
Remember, there are always exceptions to the examples to come. However, your AI project will probably be five times more difficult if these examples apply.
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Forecasting Individual Customer Lifetime Value: Why You Should Not Use External Data
There are two types of historical data to use for predictive analytics and sales forecasting: internal and external data. How to know which one to choose?
Making a forecast always requires planning under conditions of uncertainty. Successful sales executives plan and execute an accurate sales forecast using data. For an aggregated, precise sales plan, it is necessary to consider forecasting the individual customer lifetime of the client base.
There are different data sources that executives can use to forecast sales. Some of them count for internal factors while others count for external factors.
Applications of Machine Learning in the Real World
Learn about four real-world applications of machine learning here.
When you travel to a new country, have you ever had to translate a word quickly? Or how about when you are sending a reply to an email using the Gmail application? Do you notice that there are recommendations of what to reply? The examples above make use of machine learning.
From computerized translators, to email recommendations, as well as movie suggestions on Netflix, to autonomous cars. All of this is possible due to machine learning. But what is machine learning?
Machine learning is a concept that falls under the category of artificial intelligence. It is the process by which computers change the way they perform tasks by learning from new data sets. In this process, algorithms mimic the way humans learn to gradually improve their accuracy.
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