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”.
In this article, you will learn how you can tell whether your company needs an ERP system with AI to predict customer behaviour.
We will also discuss the advantages of using this technology, how it works, and what is needed for it to work.
How Predictive Sales Analytics Works
Predictive sales analytics is a specialized field that aims to make sales forecasts as precisely as possible. Various statistical and mathematical methods are available for this purpose.
Machine learning (a subfield of artificial intelligence) has dramatically improved these models.
But how does it all work? The following steps are required below:
1. Thoughtful Data Collection:
For machine learning models, lots of historical data is needed to train the algorithms.
What data do you need?
For sales forecasting, you should use the data that is close to the target – past sales data. You can also add more data about competitors, customer behavior (like complaints) or external market data.
However, remember to be careful: you should be able to justify the expensive data collection by a clear cost-benefit ratio. What is the end benefit in the predictive model of a costly and time-consuming data collection of, for example, long email histories? You should find out.
In most cases, models that only use sales transaction data achieve over 90% accuracy in sales forecasts.
2. Data preparation:
“Garbage in, garbage out.” Even with the use of artificial intelligence, this principle is still applied. A very troublesome point in practice, especially when data is available in different formats.
Data must be uniformly available in a standardized form for predictive sales models. This means removing duplicates, aggregating data, and transforming it uniformly.
3 Predictive Sales Software Training
Now you’re ready to go. You can now run the prepared data into a predictive sales software. The finished software is modelled and trained with appropriate models.
Nevertheless, the next step is to test how accurately the software works with your data. It works like this:
Let’s say today is Feb. 01, 2022, and you have a data set that covers the last five years.
However, the software gets only the data up to February 2021 – four years, instead of five. This omission of certain data is called a “hold-out set”. So, the software that processed the data from 02.2017 – 02.2021, is supposed to make predictions about the remaining time (02.2021 until today). Therefore, the predictions can be compared with reality in order to see how good the prediction model is.
4. The Use in Everyday Work
Once the model is tested and validated, it needs to be executed. The software does not sell for you. You earn exactly € 0 from the best forecasts if they are not properly used.
We recommend training the end-users, i.e. the sales team or customer service, on the proper use of the algorithms.
What Do You Need to Use Predictive Sales Analytics?
Here’s how predictive sales analytics works in a nutshell. What do you need to bring to the table? Lots of historical sales data from which correlations can be derived. If you sell three products to 1,000 customers, then the combinations are not too difficult to deal with.
Predictive sales software is a tool that should be used by wholesale companies. They have the data, market experience and the need for optimal existing customer support.
Unsure if you have a case for predictive sales analytics? Why not try our free ROI calculator.
What Benefits Can You Expect From Predictive Sales Analytics?
1. More Security Through Data-Based Recommendations.
You can incorporate the recommendations of the predictive sales analytics software for your sales decisions and thus take data-based action.
Of course, humans always have the power over the software’s suggestions. A study by Yael Karlinsky Shichor (2019) shows that the most successful sales results came from a combination of AI-based software and sales reps experience.
2. Improved profitability
Sales activities are very expensive. Prioritizing customers and activities is nothing new , making success inevitable for the sales team.
Predictive sales software is a champion at prioritizing. It was created to do just that. It calculates nothing but probabilities for the future: what price is a customer most likely to accept? Which customers have a high probability to churn? Which customers are likely to be interested in what products?
A good software will compare the results to the respective sales of the customers. Thus, the sales team has prioritization based on several factors.
3. Higher Customer Satisfaction
Such sales predictions enable your sales team to respond much better to the individual wishes of customers. Suitable products should be suggested at the right times. On the other hand, you may also contact the customer because a high probability of churn has been identified. This can uncover any problem the customer may have.
Remember, dissatisfied customers often don’t contact you. They leave without notice.
4. Competitive Advantage
We’re already seeing that predictive sales analytics will be more mainstream in the U.S. We are confident that companies that use this technology will overtake those that don’t.
If you start using artificial intelligence in sales now, you will still be among the pioneers in Germany and have the chance to achieve “first mover effects”.
CALCULATE NOW THE ROI OF QYMATIX PREDICTIVE SALES SOFTWARE
How Predictive Sales Analytics Works and Why It Matters – Conclusion
Predictive sales analytics is based on models of mathematics and statistics. Good sales forecasting software uses machine learning (AI) to make more accurate forecasts.
The technology offers you many essential benefits. Your sales team can act and prioritize based on the data gathered, you can gain a competitive advantage, and you can improve customer satisfaction.
The technology will also become more established in the German B2B market. In the U.S, it’s already there. The B2C market has also been using algorithms for a long time – after all, when was the last time you had a suitable product suggested according to your preference?
When do you start with Predictive Sales Analytics in your company?
