How To Achieve Acceptance of New Technologies in Sales?
Joachim Meyn worked in sales for many years and reports here on his experiences and learnings about the introduction and acceptance of new technologies in this area.
From the title, one could now deduce that sales has a problem with the acceptance of new technologies. But, new technologies and innovations are not accepted differently in sales than in other areas of the company.
In general, however, it must be considered here that Germany - like Austria - generally belongs to the so-called "late adopters.” In other words, new ideas and/or technologies/innovation are generally adopted more slowly and later than, for example, in the Anglo-Saxon countries. One can also say "when the whole thing has matured".
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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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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.
Artificial intelligence in Retail - What can Wholesale Learn from Retail?
How is artificial intelligence (AI) being used in retail and what can wholesalers take away from it?
In this post, I will describe a real-world example of a case in the realm of artificial intelligence in B2C retail. You will learn about the opportunities and challenges that were encountered when using AI systems.
Of course, it is particularly interesting to see which factors can be transferred to B2B wholesale.
Will Artificial Intelligence be Picasso of Tomorrow?
Can Artificial Intelligence be creative?
The cover picture shows the faces of four people. One person does not exist - artificial intelligence (AI) created it. Can you guess who? Number 1, 2, 3 or 4? Read the article to the end and we will tell you.
Today we stray a bit from our usual focus on modern data analysis with predictive analytics and B2B sales. We dedicate ourselves to the question of whether artificial intelligence can be creative.
How to use Predictive Analytics in your CRM for Truly Data-Driven Decisions?
Successfully implementing Predictive Analytics for data-driven decisions in your CRM requires more than just software.
Every successful sales team wants to become data-driven. Companies that successfully execute predictive analytics in their CRM are easy to identify.
They take care of their salespeople and successfully utilise their current data and processes. They employ the standard software and adapt them to their situation. Finally, they introduce Predictive Sales to CRM step by step and learn from their experience.
Why a CRM System with Predictive Sales Analytics and AI?
CRM and Sales Analytics: In this article, you will learn what you can expect from a CRM system with artificial intelligence.
Using CRM systems have long been a common practice in large companies today, even if not always successfully implemented. Small and medium-sized enterprises (SMEs) often still have some catching up to do.
Meanwhile, the next expansion stage has long been underway. Predictive sales analytics/artificial intelligence (AI) is being added to the CRM systems.
Correlation does not equal causality - KPIs in Sales
Watch your step! Sales managers and managing directors in B2B confuse correlation and causality.
Data-based decisions in sales are not always ad-hoc better than intuition. The reason for this is the frequent confusion between the terms causality and correlation.
How nice it would be if managing directors or sales executives regularly knew why something happened. Why individual customers churn; why one product does not sell well or sells more than others; why in the end a promising sales lead does not become a customer, regardless of how good our salespeople are.
AI Data Visualization for Advanced Sales Analytics
Understand your sales data and achieve a much higher business success through an intelligent visualization from Qymatix AI.
Sales Analytics Visualization
“A Picture is Worth a Thousand Words.” That is how Qymatix works. With only one sales data visualisation you can directly access all critical information available at any time: where are the Low-Hanging Fruits, Cross-Selling Quick Wins, Customers at Risk of Churning and Price Setting Potential. Sales managers are now able to implement and track the most appropriate sales KPI and to measure the overall status of existing customers, segmented turnover and profitability and new customer acquisition.
