Earn Millions of Euros with Dynamic Pricing Analytics
Automate personalisation and pricing processes in B2B using Qymatix B2B Pricing Analytics Software.
Artificial intelligence in B2B Sales. New Challenges – New Opportunities.
One interesting read about the use of artificial intelligence in B2B sales.
Artificial intelligence (AI) is gaining relevance in Business-to-Business (B2B) sales. Research shows that investment in the development and integration of AI and, in particular, machine learning, technology is continuing to rise.
More money pours today into AI enterprise projects than ever before. Companies should try to avoid vagueness and lack of focus on their aims and expectations, to prevent the costly failure of their AI developments – too often a cause of failure.
Implement Artificial Intelligence Today with the New Release of the Qymatix Predictive Sales Software
Karlsruhe, 24.11.2022. Qymatix Solutions GmbH has been helping manufacturers and wholesalers to increase customer lifetime value using artificial intelligence and predictive sales analytics since 2013.
Qymatix Solutions GmbH is launching a new version of its Predictive Sales Software (Software-as-a-Service). It is based on the latest research on the topics: Predictive Analytics for CRM systems, Advanced ERP Data Mining and Machine Learning for precise predictions. In summary, in the latest version of the software, the user experience is significantly optimized and the accuracy of the predictive models for B2B sales is more precise. Through which features these optimizations were made, the Qymatix development team describes as follows:
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.
Where AI already Supports Sales Today
Learn seven practical use cases where artificial intelligence is already supporting sales today.
More and more companies are using AI to streamline processes in sales and hand over unpleasant tasks to algorithms. AI is therefore assistance for sales without making employees obsolete.
The use of artificial intelligence (AI) is radically changing the way sales works. This primarily refers to sales processes under the influence of AI tools that make the work of sales employees easier, optimize operations and take away tasks that are perhaps not so readily done.
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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.
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.
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.
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 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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