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.
Market Basket Analysis in Excel - Example for Cross-Selling in B2B
One Useful Example of Predictive Sales Analytics Using Excel
Cross-selling is the practice of selling an additional product or service to an existing customer. Indeed, B2B companies define cross-selling in general and cross-selling analytics in particular, in many ways and with many names. One common naming used in retail or distribution is “market-basket-analytics”.
Most of us are familiar with cross-selling from our experience as online consumers. “Customers that bought X also bought Y” or “related products”. E-commerce websites make product suggestions based on a market basket analysis. The list of the possible suggestions is also known as “associating rules”. Marketing practitioners talk about “Buying Propensity”.
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.
How Predictive Sales Analytics Works and Why It Matters
AI-based programs help your sales team to sell products are services more efficiently. The programs make predictions about your customers' behaviour: who will churn? Who might pay a different price or buy an additional product?
The technology behind this is called "predictive analytics" or, in sales terms, "predictive sales analytics".
Study: Automated, Artificial Intelligence (AI)-based pricing versus Human-based pricing in B2B
To further explore the potential of automating the B2B salesperson’s pricing decisions.
You'll learn the results of a field experiment conducted by Yael Karlinsky-Shichor (School of Business at Northeastern) and Oded Netzer (Columbia University) to explore "Who makes better pricing decisions in B2B settings - humans or machines?"
We now know that algorithms and artificial intelligence are part of our daily lives. The general trust in systems like Google Maps is relatively high when looking at the number of users utilizing the platform.
Nevertheless, it often happens that we see a route suggestion that causes us to frown and think, "That seems weird to me. I don't think that's right."
What type of B2B sales will still exist in 50 years?
Will sales jobs still exist in fifty years from now? Will robots take over all sales jobs? How will artificial intelligence redefine sales management? What long-term influence does digitisation have on B2B sales?
To discuss the future of sales, let's first present how companies made Business-to-Business sales 50 years ago and how it works today. Let’s then peep inside the crystal ball to see what sales (and marketing) can look like in the future.
Data Mining in ERP for Sales - All About It!
ERP Data Mining: what we learnt from analysing 100 million of B2B sales transactions.
Data mining is the application of a varied assortment of statistical techniques to ERP datasets. Companies nowadays use data mining to predict outcomes, identify sales trends, prevent customer churn, and dynamically adjust pricing strategies.
Mining enterprise resource planning (ERP) sales data is critical in Business-To-Business, where small improvements in sales efficiency can have a significant impact on results. Mining ERP sales data helps customers to unlock a significant amount of value, discover quick-wins, and to prioritise their sales activities. Automatizing this process is possible today with the help of artificial intelligence (AI).
6 Facts from Studies on Artificial Intelligence in B2B Sales
In this post, you'll learn about six interesting findings from various studies on artificial intelligence in B2B sales.
According to a recent survey by Bundesverband Industrie Kommunikation e.V (bvik), almost all decision-makers (95%) are open to both new technologies and their organization's digital transformation, even after the crisis. Are you too?
If you've landed in this post, you may be considering how you can use artificial intelligence for your sales. You probably have a few basic questions first because just because you're open to technology doesn't mean you should rush into it, right?
How to define customer churn in B2B?
B2B Churn Rate: Definition and Calculation.
Business-to-Business (B2B) companies depend on building and developing long-term relationships with their customers to be financially successful.
However, over time, some customers will stop buying or will defect to the competition. Sales managers define this situation as “churn” or “customer attrition”. Managing and reducing customer churn is one of the most important, yet sometimes overlooked job of the sales leader.
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