How to avoid the Causality Trap of Black-Box Machine Learning
Sales Acceleration with Predictive Analytics Software: How to avoid The Causality Trap of Black-Box Machine Learning.
Successful B2B managers use AI-based predictive analytics software to accelerate sales. However, they often want to find for themselves what the characteristics of their main profitable customers and leads are.
You know this situation well. You want to predict success. You would like to create a set of rules that your sales team can follow to avoid customers from churning or to find hidden sales opportunities. Why cannot your AI sales software do that?
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Strength in price setting is your profit driver number one
Everybody knows, B2B pricing analytics represents one of the most critical levers to boost earnings. Still, this reality is a sometimes-overlooked fact in Business-to-Business (B2B).
However, getting B2B pricing right can represent both financial success and substantial competitive advantage. Pricing is not only a driving factor in for-profits, but it also impacts how customers and markets perceive brands.
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How to improve your B2B Customer Journey with Predictive Analytics
About the b2b Customer Journey Management and how Predictive Analytics can help.
Predictive analytics, customer-centric selling, and optimization of the customer journey (CJ) have long been part of everyday life in the B2C sector. In the B2B industry, things look somewhat different. First projects are starting, the theory has already been heard and understood - but there is still a lot of uncertainty regarding the concrete implementation.
That is no wonder. There are some fundamental differences between the B2C and B2B sector. For example, business customers often have higher expectations of the business relationship. It is more important for salespeople to build a personal relationship with their customers and to know their customers.
If Your Data Is Bad, Your Sales AI Tools Are Useless
Artificial intelligence (AI) is steadily advancing in B2B sales. AI is changing the way customers are buying and therefore how salespeople should work.
The gathering of data, its quality, the source systems, all play a central role in the implementation of AI in Sales. Poor data quality may be hard to measure, but it takes an essential part in the application and execution of artificial intelligence systems and predictive analytics.
How B2B Data Analytics is Changing Sales
How Big Data and Advanced Analytics Are Revolutionizing Sales in B2B, and What Managers Should Know About It.
B2B customer buying experience is radically changing. This trend has led to dramatic changes in the sales profession over the past decade.
“Nothing is so painful to the human mind as a great and sudden change”, wrote Mary Wollstonecraft Shelley in Frankenstein. Sales management in B2B is experiencing an abrupt and rapid transformation.
The Case for AI in B2B Sales
Why artificial intelligence in B2B sales is unavoidable and what you can do about it.
Adapt or die – a fact of life for B2B sales nowadays. Key Account Managers know this too well, for they have very different tasks and jobs, as compared with just ten years ago.
E-commerce is by no means just a new sales channel. Order-takers roles will disappear in the next decade. This shift in skills will strongly influence how salespeople are selected and how they perform. Also, artificial intelligence impacts the role of B2B sales.
Artificial Intelligence in Sales: B2B Algorithmic Management
Modern data-driven management in B2B sales is where Big Data meets Artificial Intelligence. Using AI for sales efficiency.
Although algorithmic management boasts a fancy, new name, managing a workforce using data is not necessarily a new postulate. Just remember that “The Principles of Scientific Management” were published by Frederick Taylor in 1911 and soon became a culprit of the data-driven management.
Algorithmic management is Taylorism in times of big data and artificial intelligence. It uses machine learning to manage and control workforces. Millions of people employ algorithmic management when ordering food, buying online or taking a cab. Millions of workers respond to algorithms. For some, the future of management, for others a depressing picture.
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Industrial distribution: Identify potential for cross-selling with predictive analytics
How wholesalers and distributors recognize cross-selling potentials with predictive analytics.
Many manufacturers now sell their products directly to end consumers via web marketplaces. Unfortunately, wholesale trade and industrial distribution are coming under increasing pressure as a result.
Calculating cross-selling potentials using modern data mining can be a suitable strategy for keeping pace with the competition.
A glance at the analyses of the Federal Statistical Office shows that almost 90 % of Germans make online purchases at least once a year.
Four Dangerous Signs That You Will Miss Your Sales Targets This Year
With a growth of 10.8 %, the year 2010 looked optimistic for the German media market, compared with 2008 and 2009. The improvement included online marketing, free magazines and trade journals, amongst others. Everyone was betting on an active recovery for the key market players.
However, on January 18, 2011, the second biggest European print service providers, Schlott Gruppe AG from Germany, opened insolvency proceedings.
How to use Big Data to stop customer churn in B2B | Predicting Customer Churn
Sales leaders in business-to-business (B2B) organisations are under constant pressure to spot new business opportunities.
It is, however, a too often neglected fact, that some of their current customers will churn and recurring revenues will not return.
Besides attracting new customers, succesful B2B firms also direct their efforts into retaining existing ones.