For a long time, I have been engaged in search engine optimization of websites, which in turn helps websites to rise in search engine results. Such actions should contribute to the growth of business in general by increasing the organic users on the site, but unfortunately in practice, this is not always the case and business may not grow, or even work at a loss.
I know cases when unscrupulous SEO companies simply deceive their clients, for example, they promise a guarantee of getting into the top keywords, promise to increase traffic by N percent, I have also met cases of guarantees of receiving leads, so-called lead generation, but they all ended up failing and clients lost their money. In the future, I will write an article about what you should pay attention to when working with an SEO company or inbound link building service, so as not to get into trouble, but in this article I want to touch something else, about this below.
During my work, I have repeatedly encountered clients complaining about the lack of profit. Often this is due to the fact that the emphasis is placed on the wrong choice of keywords, in other words, keywords simply can not convert sales. For example, a client believes that certain keywords are important for him, but in fact, they do not convert into profit, or initially, the semantic core was created with errors.
Artificial Intelligence Examples, Use Cases based on AI and ML Technologies
Today, I would like to share the outcomes of my cooperation with a pharmaceutical company located in the United States. The business is focused on extending their research according to the needs of patients, as well as improving their products depending on the feedback from their clients. They contacted me to help them work on ways of communicating with their clients and potential customers.
Real Example of AI in Pharmaceutical Project
After a long discussion, filling out a briefing, deep penetration into this niche, discussing the goals and capabilities of the client, we divided the whole process of cooperation into several stages: analysis, development and implementation. Since the task was not trivial, I wanted to give the same solution, something crazy and at the same time effective.
Our research has shown that clients who want to report side effects of a particular drug or defects of the product, mostly communicate through the phone – approximately 70% of all contact attempts. This type of communication is especially problematic for the company as each case has to be treated individually and requires a lot of time from the support team.
Customers on the other hand, were not always satisfied with the information they were getting from the support team as the problems are unique. That resulted in low customer satisfaction which in the long term can strongly affect the sales.
Artificial Intelligence Technology Solutions
Exactly at that moment I came up with the crazy idea (so it seemed to me at that moment), to use modern technologies for lead generation, namely artificial intelligence solutions. Solutions based on artificial intelligence and machine learning allow companies to better understand their customers, offer more relevant products, as well as get accurate and reliable information from their company data.
Nowadays, when everything is virtual, the implementation of artificial intelligence and machine learning technologies into the pharmaceutical industry is a big step forward in business development. Using artificial intelligence and machine learning algorithms, pharmaceutical companies successfully join the global market and create an incredible boom in a short period of time.
Addepto was chosen as a partner and AI Consulting Company. It was associated with many aspects, first of all, the financial component, as for the implementation of my crazy ideas has limited budget, and after a brief meeting with Edvin (founder of Addepto), it was clear that these guys will make all my dreams come true for not all the money in the world. For that, a special thanks to them and my recommendation!
Real-Time AI Voice Analytics
And so, the first thing that needed to be done was to improve customer service, increasing employee efficiency and customer satisfaction. To solve the previously mentioned problems with phone service, it was necessary to have something universal and flexible, such as CRM that would cover our requirements and solve our problems, as well as be open-source and scalable at any time. Unfortunately, we couldn’t find such a product in the market, the main problem was that we couldn’t make changes to fit our requirements. If you know such products, I would be glad to meet them in the comments.
The CRM we created could record conversations, had an autoresponder function outside of business hours, also had an analytics system, all the data was displayed on a compact and graphical dashboard so that the company owner (my client) always had important information about the number of calls, the percentage of clients served, and so on.
But there was also one feature that allowed for customer service, and real-time voice analytics at the same time. Our CRM with ML could measure pauses in the conversation in real-time, how many times an agent interrupted a customer, the tone of voice of both customer and agent, and whether the voice was dynamic and interesting or repetitive and boring.
It then provides real-time feedback to the agent so he has an idea of how the customer is feeling during the conversation. In addition, the AI provides recommendations on how to communicate with customers, which leads to stunning business results. The system provides step-by-step instructions to agents in real-time, thereby increasing employee efficiency and customer satisfaction.
Then we went even further, in less than a month we collected so much data that we were able to cluster customers who called, into different groups. That helped us improve customer satisfaction as well as make the job easier for the customer support team.
Predictive Dialer programs select a phone number and dial it for a call center agent, reducing the time it takes to manually dial phone numbers every day. Predictive Dialer predicts how long it will take an agent to answer a call and its availability using algorithms and mathematical formulas.
Virtual AI Assistant
AI Chatbot is a powerful technology tool that is built to communicate with consumers and solve their problems using artificial intelligence solutions.
AI-powered chatbots deliver faster and more personalized customer service. AI virtual agents can understand and access customer data. Therefore, by understanding the needs of consumers, chatbots help consumers to make quick and easy Internet transactions.
Customer Segmentation for Better Personalization
With the help of unsupervised learning algorithms, companies could divide customers into different groups that can be targeted. The algorithms take into account all available functions and create a wide variety of clusters.
The customer segmentation model allows companies to effectively allocate marketing resources and maximize opportunities for cross-selling and additional sales. Moreover, customer segmentation affects the improvement of customer service, increased loyalty and customer retention.
So, summing up, we continue to work on the project, in general we have been working on it for 12 months, after 10 months the cost of implementing these developments have been fully repaid. For ethical reasons I can not talk about the cost of the project and its profit.
In addition, I have collected a small selection of examples, which in my opinion are worthy of attention. And I would like to implement in my projects something similar
Real-life examples of AI and ML: 1-800-Flowers
1-800-Flowers, in partnership with the IBM Watson AI system, has created an AI customer service bot that receives orders through its website and mobile application. This chatbot accepts customer orders more intuitively than the traditional online ordering form by using Natural Language Understanding (NLU) and Natural Language Generation (NLG).
To handle phone calls to the support service, the brand Sensory Fitness developed Sasha, an AI voice assistant. The AI voice assistant uses natural language understanding (NLU) and natural language generation (NLG) technology for dynamic conversations. In addition, the voice assistant Sasha has built-in text – to-speech technology (TTS), which talks to callers aloud. This solution allows the company to save $ 30,000 a year.
The North Face
The North Face, a major e-commerce retailer, is a great example of a company adopting AI to better understand its consumer’s requirements. Using the IBM Watson AI solution, they help online buyers in finding their perfect jacket. Through AI voice input, the company asks customers questions such as “Where and when will you wear the jacket?” While other IBM software analyzes hundreds of items to identify perfect matches based on real-time feedback and own research, including the weather in the area.
Statistics – AI in Pharmaceutical Industry
- According to recent research conducted by USM, about 50% of global healthcare companies plan to implement AI strategies and broadly adopt the technology by 2025.
- OpenText surveyed 125 pharmaceutical executives to determine how familiar each respondent is with AI technologies within their industry. The survey results revealed that an interest in AI increased to 85% in 2020.
- Artificial intelligence and machine learning have a huge impact on the pharmaceutical industry.
- AI-powered and machine learning solutions allow companies to better understand their customers, offer more relevant products and get reliable insights from their company data.
- Nowadays, customers have a simpler, faster, more personalized and more convenient way to communicate with companies than ever before.
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