Social networks constantly gather unlimited amounts of data. It is practically unthinkable for humans to sort this data, and examine it, or even utilise it. However, we do have artificial intelligence applications that are embedded in these social media frameworks. As a consequence, this technology takes different forms.
Chatbots are an artificial intelligence software that can maintain a conversation or a discussion with a user using natural language on different platforms such as email applications, websites or mobile applications (Dagnon, 2018; Frankenfield, 2018). Chatbots are able to respond quickly, in very superior and completely assuring expressions in the interaction between computer systems and humans. However, on the technical side, chatbots are only a fundamental evolution of a question-and-answer system based on natural language processing (Frankenfield, 2018).
Applications that use chatbot technology humanise dialogues between machines and people, thereby improving the customer experience. Moreover, they contribute to companies’ great opportunities to develop the customer integration process while optimising customer services.
In order to produce good results, the chatbot has to accomplice two responsibilities. We have to keep in mind that human intervention is required to develop, monitoring and optimising the chatbot’s technology framework. (Dagnon, 2018).
Chatbots have several functions. They can guide potential customers to brands and products in instant messaging applications, think of it as a chatbox when you visit an e-commerce website. The chatbot’s function will help you in navigating the website whilst creating a better user experience for the user with the brand itself. Businesses can use chatbots to have automated interactive conversations with website’s visitors and help them answer their queries 24/7. Chatbots are also great for accomplishing this task; they also track and analyse customer-shopping history. With this overview of Internet users’ behaviour, the brands can at any time, modify and retarget the digital campaigns in favour of the recommendations made by the collected data, thus increasing the rate of conversion. Most customer inquiries and complaints are resolved quickly with chatbots. They can answer FAQs, track shoppers through the numerous processes and render fast and efficient customer service. The use of chatbots can handle simple inquiries, so businesses release sales and customer relationship teams to focus on demanding responsibilities.
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Regrettably, Chat bots won’t succeed humans. Their role is restricted to automating core tasks and enabling marketing teams to focus on more creative work. Chatbots also need updating and regular maintenance. A chatbot technology’s success relies on the collaboration of several stakeholders. These include IT developers, customer service, marketing department, and sales team, to create a tool that allows solving customers’ critical problems. Thus, chatbots are not unique solutions, but instead landing pages have a singular and flexible purpose (Dagnon, 2018).
Social selling is the art of using social networks to find, interact, understand, develop and take advantage of sales offers. It is the most modern method of developing strong relationships with potential customers to keep the brand in mind (Newberry, 2017).
It is merely the use of social tools to engage in customer relationship techniques. We have to describe what social selling is not. It is not about fooling people with ads, tweets, or unsolicited content. This is spam, and it is definitely not social selling.
Social selling is about acquiring contacts and developing relationships and listening to customers so that companies can present a solution to the current problem while meeting an urgent need to make life easier to the client (Newberry, 2017). Social selling also holds its success using artificial intelligence technology and tools such as chatbots already mentioned before. Social selling makes it possible to have a more straightforward and more fluid sales process. The use of these technologies makes it possible to optimise the digital strategies deployed on social networks and increase the profiles collected, which is the companies’ main objective (Newberry, 2017).
Predictive analytics refers to the use of statistics and machine learning to analyse behaviour and derive predictions. All the same, humans are very predictable because we all have routines like waking up in the morning, brushing your teeth, taking a shower, getting dressed and having breakfast. This aspect of prediction allows the marketing specialists to know what will happen in the future and adapt the marketing campaigns according to the thing (Stelzner, 2018).
Considering we are predictable and have a general perception, machines have made these predictions more specific.
The predictive analysis focuses mainly on the detection of events. For marketing services, forecasts are a time series of events. For example, a marketing specialist can know when to engage a customer service to handle his target’s requests (Stelzner, 2018).
It should be noted that the predictive analyses are more than 70 years old. Most people are surprised to hear that this discipline dates back so long as they think learning and automation are new technologies. However, theories and mathematical formulas have existed for several years (Stelzner, 2018).
The most notable change is the power of data processing and computers’ ability today to leverage data. They can process a lot of data in less time. In theory, predictive analysis is achievable on paper, but it will take a significant amount of time.
AI Generated Content
Targeted advertising has become fundamental. Artificial intelligence is now helping marketers read further data and promote digital campaigns. All this is meant to understand Internet users’ intentions and suggest content better suited to their expectations (Kreimer, 2018).
Artificial intelligence allows marketers to produce content automatically for simple stories, such as ports reports and stock analytics.
Google has also launched new machine learning algorithms, where they create generated content targeted to specific user’s demands.
AI generated content is created through rules. Users provide datasets, and the AI technology will develop narrative around this data. Artificial intelligence can help companies save time and energy and motivate employees to focus on more demanding tasks.
Although the AI’s content seems to be growing, the challenge is great; computers cannot react on their own. They urgently need human help. Because artificial intelligence is not aware of human emotions, a machine will not know what we interpret as funny, even if we introduce these aspects into its rules (Kreimer, 2018).