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.
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.
Read more
Sales Management in times of Artificial Intelligence – Five tips to redefine B2B Sales
Artificial intelligence in sales: our top five tips!
Sales processes, salespeople and sales management should adapt to the brave new world of artificial intelligence. Artificial intelligence is taking over the administrative tasks that consume much of the managers’ time. It is doing it faster, better, and at a lower cost. It has pros and contras. AI will redefine management.
In B2B Sales, artificial intelligence (AI) refers to Enterprise Resource Planning (ERP) and Customer Relationships Management (CRM) systems equipped with machine learning and automatized data mining features.
B2B E-commerce Analytics: Why Predictive Analytics is now critical
Predictive Analytics in B2B e-commerce has become decisive.
The sale of products and services via B2B platforms is on the rise. However, precisely because of the intense competition for comparable offers there, companies should automatically analyze their customer data to enable a personalized customer experience and identify churn risks.
For private consumers, buying online is ubiquitous. The Business-to-Consumer (B2C) sector is dominated by e-commerce platforms such as
Predictive Analytics: Three incredible future examples you should know about
Three Possible future scenarios in unexpected application areas of Predictive Analytics.
Let's explain the function of Predictive Analytics (PA) in simple terms: A software uses algorithms to analyze existing data and calculate probabilities of future events.
Due to big data and advances in technology, predictive analytics is becoming increasingly accurate and can predict events with an astonishing hit rate.
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.
What is the fuzz about Predictive Analytics? One example of B2B Sales will make you start today.
B2B sales went through several transformations in the past decade. Mobile is ubiquitous, CRM Systems are universal and “customer journey” achieved mainstream status. However, Predictive analytics is cutting a “before and after” in B2B sales.
The ability to make predictions radically changes sales in business-to-business (B2B). It brings enormous advantages to pricing, reduces churn and improves sales planning.
Read more
How can a classic salesforce get started with predictive analytics?
In the future, it will be all about reading customer data correctly and drawing the right conclusions for customer strategy. That means a paradigm shift for the classic, contract-trimmed salesforce.
Is the classic field salesforce as known to companies and customers - slowly but surely - dying out? The digital transformation, currently the most critical driver of change, suggests this, and it has significant implications for B2B business and for internal processes.