Artificial intelligence is part of our daily lives. Without realizing it, we are in constant iteration with different forms of artificial intelligence, which, in general, make our lives more comfortable. We can interact with them in different ways: on the internet, equipment in our house, on our phone, in the car, etc.
- Google search is a sophisticated AI system that ranks web pages. It not only processes each page’s content but also learns from the iterations that users have with these pages.
- Cars today are full of AI systems to maximize our security. Functionalities such as smart braking systems, parking sensors, and rain detectors are all part of various car manufacturers’ average range.
- Chatbots can be found on any website with customer services, always willing to answer any question. In some cases, it is hard to tell that we’re not talking to a real person.
- Amazon uses AI to offer services tailored to our needs. By synthesizing millions of previous customer experiences, it can smartly provide us with the right product.
- Through a complicated AI algorithm, Facebook can suggest whom we should add as a friend. It can be surprising how it manages to find an old friend we haven’t had contact with in many years.
- Netflix uses AI not merely for movie recommendations, and auto-generation, and personalization of thumbnails, but also content quality control.
- Microsoft, Apple, Google, and many other players use facial recognition technology to identify users and improve security.
AI is no longer a theme of science fiction films; it is part of our life; it is part of our business. Leaving this topic aside is losing the wave, letting yourself be overtaken by competitors, causing you to lose your business.
A survey by McKinsey reveals that artificial intelligence techniques can develop about 45% of individuals’ activities in companies.
“By 2025, an estimated 95% of customer interactions will be supported by AI technology.”
Everything is centered around having the information, being able to process that information, and making better decisions. AI provides the ability to handle a large amount of data and get the right answers. In many cases, the information is in front of us; we simply don’t know how to organize, contextualize, and act on it.
Learning from the experience
To make predictions or decisions, without being explicitly programmed for each particular case, AI systems mainly use one of the following machine learning algorithms: supervised, unsupervised, or reinforcement.
- Supervised learning algorithms use historical data and their respective results to map the input data (i.e., all relevant information known a priori) with the results obtained. This historical data is known as training data. It is used to improve the model, which will allow for increasingly better predictions.
- Unsupervised learning algorithms analyze the data looking for patterns; before the analysis, there are no results. The idea is to identify clusters of data that should be treated in the same way. By analyzing each of these clusters, we can find previously unexpected results.
- Finally, the reinforcement learning algorithms. This type of algorithm is more advanced than previous algorithms. It uses only the current data and, based on this, returns a set of answers. Each answer is a possible path and changes the existing data in a specific way. The new data then feeds into the model, starting the whole process again and getting a new set of answers.
The Data is King
Getting the best possible results from a predictive model requires you to know how to best leverage the available data. The first step is to collect data related to the noticed situation that must be predicted.
According to a report by Cognilytica, data preparation and engineering tasks represent over 80% of the time consumed in most AI and Machine Learning projects. Selecting the correct information directly influences the optimization of the Machine Learning model to use and the quality of the prediction results.
“The value is not in software, the value is in data, and this is really important for every single company, that they understand what data they’ve got.”
Senior Advisor, Mckinsey and Co.
The key is to have the information needed to feed the model. Historical data on sales, upgrades, downgrades, returns, cancellations, loyalty, etc. is applicable to feed any machine learning model that one intends to adopt. All of this information can be used to generate leads and up-selling and cross-selling opportunities. With the help of artificial intelligence techniques, tasks that would take days will be carried out in seconds, leaving more time for salespeople to interact with the market. This, in turn, improves the quality of the offer and strengthens the relationship with the customer.
If you have reached this point, you understand the relevance of AI to optimize the sales sector’s performance, which leads us to the point: how can AI help increase sales?
It’s easier to sell what the customer wants to buy.
This statement may seem obvious; however, the critical question is how to know what the customer wants to buy. Making a mistake in the offer presented to the potential customer can irreversibly damage the sales process. It is essential to make the correct qualification of the lead. Supervised models using Logistic Regression can help in this theme. Based on the sales history, models can be trained and be used to qualify new offers correctly. For existing customers, data from previous acquisitions is relevant to allow for up-selling and cross-selling activities.
Keep recurring customers
Supervised models can also help to decrease churn. Based on customer behavior, dissatisfied customers can be identified, and countermeasures that increase their satisfaction can be proposed, maximizing revenue.
AI can help to identify and prevent possible fraud actions. The fraud can be perpetuated by malicious sales representatives or customers who have somehow identified the company’s vulnerabilities. There may be some fraudulent actions well known to the company where the correct measures have already been taken to prevent them. Still, there is always the possibility of new forms of fraud for which the company is not prepared. In this sense, the unsupervised models are useful. Identifying a new cluster or outsiders can trigger a more in-depth analysis, discovering a possible fraud action that could significantly impact the company’s finances.
Always in touch
Maintaining high-quality customer support is the key to maintaining customer satisfaction and return on sales. Customer iteration data can be used to analyze sentiment in customers’ current requests, prioritize them, and automatically redirect them to the correct department, saving time, and improving the service quality.
What relevant data you have?
It’s essential to collect and select the best information relevant to the problem before creating and training Machine Learning models. In the end, this brings us to the ultimate question: what kind of information do you have access to train such models? Please leave a comment on how machine learning can improve your business.
Originally published at https://www.linkedin.com.