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.
Predictive Sales Forecasting: Answers to 5 Questions of Salespeople
Why should we use predictive sales forecasts in sales? This article is aimed at anyone thinking of using AI for more efficient sales planning and sales management.
Every Saturday morning, Mr. Meier visits the magazine store around the corner to buy the weekend edition of his favourite newspaper. This has been going on for half a year now. The saleswoman knows Mr. Meier by now, and because he stops by every Saturday, she always addresses him with the same question as soon as he enters her store: "Good afternoon, Mr. Meier! The weekend edition, as usual?"
For Mr. Meier, buying his newspaper on the weekend has become a ritual. It's a pattern that repeats itself every week. The saleswoman has recognized this pattern and addresses Mr. Meier about it, almost automatically.
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.
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.
Better Customer Retention in B2B Wholesale Through Algorithms
Learn three ways to increase customer loyalty with algorithms and Predictive Sales Analytics in B2B wholesale.
Should computer programs be able to increase customer loyalty in wholesale? Yes. And no. We'll discuss what is exactly meant by this in this post.
According to a survey by Roland Berger, customer loyalty was already a top priority for wholesale companies in 2016. However, 1 in 5 wholesale companies also believed that their efforts in the area of digitalization were not yet sufficient to survive the digital competition.
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.
Why is internal data considered more reliable and easier to collect than external data?
Simply explained: Why internal data is better for predictive analytics in B2B.
Companies use sales forecast to make business decisions. They also employ them to predict future developments better than their competitors. However, reliable predictions are rare, and sales teams try to play a safe card by applying external forecasts. Companies are nevertheless better off using their in-house data - with predictive analytics.
"There are three types of lies: lies, damn lies, and statistics." This quote from Benjamin Disraeli, a British statesman and 19th-century novelist, fits the situation in companies very well.
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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.
What is a Predictive Score Model in B2B Sales? How Can You Create Yours?
A predictive score model is a formula to calculate a probability.
There is a 70% chance that you will read this entire article. How do I know this? Because I used a predictive score model. The score is the probability of you reading to the end of the article (or one minus the likelihood that you will not – the exact opposite).
My example in the paragraph above is a well-known application of predictive analytics in marketing. The most common examples in business-to-business (B2B) sales are lead scoring, churn (or customer attrition), cross-selling, and pricing.