The Art of Artificial Intelligence in B2B Sales
AI in B2B sales: How exactly can B2B companies use Artificial Intelligence to support their sales?
The applications of artificial intelligence (AI) are very diverse. It is not without reason that big players such as Apple, Facebook, Google or Samsung pour billions in the development of new AI technologies.
The management consultancy KPMG estimates that global AI investments will increase from twelve billion US dollars (2018) to 232 billion by 2025.
Level Up! How AI Upgrades B2B Sales Processes.
Perhaps you know the difference between effectiveness and efficiency. But do you take it into account in your daily work in sales? In this article, I show you how to upgrade your sales processes from effective to efficient to avoid "game over" in the next few fiscal years
Sales people often reach their goal - the sale of one or more products - even without technical support. However, high costs are usually incurred along the way. For example, through superfluous contact attempts, customers who leave the company, or unrealized price potential.
This fact provides a terrific example of the distinction between effectiveness and efficiency.
Five Mistakes in the Implementation of New Technologies (AI) in Sales
Introducing new technologies for B2B sales is not always easy. In this article, you will learn which mistakes you should avoid.
Joachim Meyn has many years of experience in B2B sales and has managed many projects in the area of CRM implementation. The following five mistakes when implementing new technologies are based on his experience.
This article is closely linked to my article on the acceptance of new technologies in sales.
This article applies not only to introducing new technologies in sales but also to introducing significant innovations or restructurings in all departments. In many companies, such processes run pretty smoothly, while in others, problems are on all fronts, and resistance is forming everywhere.
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.
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.
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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Market Basket Analysis in Excel - Example for Cross-Selling in B2B
One Useful Example of Predictive Sales Analytics Using Excel
Cross-selling is the practice of selling an additional product or service to an existing customer. Indeed, B2B companies define cross-selling in general and cross-selling analytics in particular, in many ways and with many names. One common naming used in retail or distribution is “market-basket-analytics”.
Most of us are familiar with cross-selling from our experience as online consumers. “Customers that bought X also bought Y” or “related products”. E-commerce websites make product suggestions based on a market basket analysis. The list of the possible suggestions is also known as “associating rules”. Marketing practitioners talk about “Buying Propensity”.
