Who is most likely to buy products or services offered by your company?

Knowing this, can make the entire marketing process much more efficient and therefore much more lucrative. This results in higher sales and bigger market success.

But how can we know this important information in advance? Here, smart selling using predictive lead scoring based on Artificial Intelligence (AI) comes to help!

And what makes AI-based predictive lead scoring so special and important for companies? There are certainly many different reasons and we summarize the most important ones in this blog post.

AI-based Lead Scoring is Data-Driven

AI doesn’t rely on guesswork or human judgement. It is not subjective and therefore it is not affected by human errors.

Using AI-driven lead scoring, sales teams can recognize a good lead immediately. It helps them to bundle their resources for targeting the right customers at the right time.

This way companies profit constantly from an efficient marketing strategy and higher sales.

Predictive Lead Scoring is Comprehensive

Once a model for predictive lead scoring is established, we can use machine learning to calculate the best leads quickly and easily. The model will grow even more and become more effective when it accesses more data to work with and when it sees more training examples. This data can be from various sources. This includes data from internal databases, as well as third-party tools, or even open data from the web.

AI-based predictive lead scoring can also highlight the importance of different data types to develop a complete, thoroughgoing customer profile. Those data types may be, for example, demographic data such as a name, location, or email address, as well as behavioral data like visited pages or viewed shopping items before buying.

With large amounts of data collected to analyze, lead scoring becomes more complete, detailed and exact. A machine learning model will continuously provide the sales teams with high-quality leads to pursue. Without having to sort and prioritize the leads manually, the sales team will work more efficiently and close more deals in a shorter period of time.

You can find more about AI in marketing and sales in our posts How useful is AI in Sales? and AI in Sales Forecasting – knowing the future is not just a dream anymore. Additionally, we have also shown how to speed up the sales process with predictive churn scores in this case study.

Predictive lead scoring grows with your business

AI and machine learning, used as integral part of predictive lead scoring, will get more and more advanced each day with more data available. This is made possible by re-training the machine learning algorithms on a regular basis. The algorithms will continuously learn about the customer behavior from the insights provided by the data. In each training iteration, the algorithms will adjust the model, optimize it, and provide the sales team with more and more accurate leads.

This also means that as the company grows, the machine learning model will develop too. It will evolve, expand, and at the end be highly customized for the company it is used by.

Conclusion

AI driven predictive lead scoring, based on machine learning, is the future in marketing and sales. It is the future for every company that wants to expand and sell their goods to a large audience. As a company is able to collect more data, the machine learning models will improve as well.

This fact should also emphasize that a clean data tracking is important and that predictions can only be as good as the input data permits. By now, various best practices exist which help to track data for different use cases. As part of our solutions, we are also happy to support our customers in setting up a data tracking plan and implementing a rich set of available data points.

Our team is happy to help you choose and establish an AI strategy for your optimized predictive lead scoring. For further information on predictive lead scoring and related topics, please send an email to info@goedle.io.

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