How AI leads to developments in CRM

While Artificial Intelligence is still at an early stage of development, it does present opportunities to improve CRM. We’re already starting to see certain AI tools making their appearance in the market and some businesses are starting to experiment with them or even actually use them.

The truth is that up until now, the only way to describe artificial intelligence is as “promising” and it will take some time before this translates into tangible results. Certainly, AI -as we know it from the movies- is still a ways off, but its already existing form aims at solving specific problems in specific situations. In that sense, artificial intelligence can prove to be extremely useful.

More specifically, when it comes to CRM, the objective of AI is to support sales and marketing, which are both run by human personnel. It can very well play a consulting part and point out opportunities and issues, but it is up to the people to make the final decision and take the necessary action.

For example, the so-called lead scoring is a fertile field for AI in CRM. Artificial intelligence can analyze the wealth of information concerning prospects and rank such prospects according to the probability of them making a purchase. This list will be then presented to the sales personnel -with the most promising prospects appearing first- so that they can decide how to proceed.

This kind of predictive lead scoring is based on collecting information on prospects from as many sources as possible and analyzing this info, in order to evaluate whether there is a possibility of purchase. In general, the more information being fed into the system and the more advanced the algorithms, the more precise the results will be.

Of course, such systems require adequate training in order to perform as required. Training can either be conducted by AI specialists or by sales and marketing personnel that have an adequate understanding of the subject matter.

There are also other aspects of AI in CRM that will follow, with the most interesting one being speech recognition. Nevertheless, this also requires finding solutions to important issues.

For example, a truly effective AI application needs to understand the customer’s tone of voice, and while there is progress being made in this field, generic speech recognition applications are not yet ready to be put into action.