2020 has not been sparing in shocks to business. But while some industries had to take a break and reconsider all operating principles, the development of technologies, including artificial intelligence, has accelerated. Just look at how AI has already transformed lending and loan management.
In 2020, businesses and marketing have become bolder in adopting AI to solve a wide range of problems. IDC predicted that this year, total global AI revenues will grow by 16.4% compared to 2020. According to analysts, they will grow to $327.5 billion. At the same time, companies are spending more on data science consulting each year.
In marketing, innovations have primarily affected the automation of routine tasks: more and more speech synthesis and analysis tools, as well as chatbots, have begun to appear. Great popularity get personalized customer offers made on the basis of machine learning. The development of these technologies will continue in 2022.
It’s one of the most prominent AI trends in marketing, and it can be used in many different ways. For example, in e-commerce, machine learning tools, by remembering preferences and lists of previous purchases, offer users products and services that are more likely to interest them. For example, fashion brand Thread has abandoned trivial “what to wear this winter” mailings and now sends weekly personal style recommendations to its customers. Thread’s advice is based on a customer’s previous choices and takes into account their favourite colours, styles, body type, and budget.
Another example is a relatively new personalization tool: emotion detection and recognition technologies. Algorithms learn to read the full range of emotions and analyze a person’s posture, gestures, and voice. This helps marketers determine consumer reactions to the products and services offered. The American startup Emotient, which develops such algorithms, experimented with one of its clients – a company selling household cleaning products. The test participant was asked to choose one of three types of detergents. The AI analyzed the person’s facial expressions when he or she heard the smell of each of the choices offered. As a result, the AI was able to determine quite accurately which one people liked the most.
Given that the race for customer attention isn’t slowing down, especially in digital marketing, personalization will continue to improve. Technology has greatly changed commerce over the last 20 years and one of the main roles was played by AI development as well as ML development.
Based on sales data, traffic to a company’s website, and social media pages, AI can calculate profits and costs for your brand fairly accurately. Predictive analytics is already considered one of the most common smart analytics tools in the world.
One of the most prominent companies using AI in this area is Amazon. The brand has developed a tool that delivers products to customers before they have even ordered them, in order to reduce the time they have to wait for delivery. The algorithm’s work is based on consumer behaviour and users’ wishlists. It is likely that by the end of 2021 platform users will be able to receive presumably desirable goods in the mail even before placing an order. Amazon will notify customers by email, and if the item is not needed, they can ask for it to be sent back.
Predictive analytics can become an assistant in the company’s call centre. For example, predict algorithm can automatically read the sex of the caller, the purpose of the call, the time of the call. Then the entire array of information is given in the form of organized data. This information allows to fine-tune marketing campaigns and to adjust the work of call-centre operators.
The era when bots were barely capable of answering even simple questions is gradually disappearing. According to Ideal, 37% of users expect an instant response in the event of an emergency, without waiting for an operator to respond. According to HubSpot research, 71% of respondents in principle prefer messages to calls.
Thus, robots allow not only to reduce a large number of employees processing requests but also to increase the speed of service. For example, Royal Dutch Airline’s BlueBot coordinates passengers in real-time when they search for tickets, cancel a reservation, or change a flight. Without AI, the company probably wouldn’t have been able to process the 1.7 million messages sent by 500,000 passengers. The demand for chatbots will continue to grow because the pandemic is still ongoing. The workload on websites and call centres not decreasing.
There are three main challenges companies face when introducing AI. First, there are inflated expectations for the technology and the disappointment that follows. It’s worth keeping in mind that AI is not a magic wand that will instantly solve all problems. Robots can perform the simplest tasks, which are usually assigned to easily replaceable performers. AI consulting can help you understand whether you really need it or not.
The second problem is the issue of “power transfer”. Many managers do not yet fully trust an unfamiliar “mechanism” and are not ready to delegate employee authority to it.
And lastly, the implementation of new technological solutions and subsequent work with them requires trained employees. Developers who understand the principles of setting up, implementing, analyzing, and optimizing machine learning algorithms also need ML consulting because this field constantly evolving. As well as managers who already know how or have been trained to use AI. Moreover, if a company wants to hire an employee to work with AI, the recruiter should have at least the minimum set of competencies needed to assess the professional aptitude of a candidate.
Processing incoming customer calls, generating the same type of analytical reports, analyzing the financial performance of current marketing campaigns, and data to build future ones – if you understand that these tasks take up most of your employees’ time every day, then implementing AI will be justified.
Forrester Research predicts that AI development will only intensify in 2021, with 35% of growth-focused companies investing in these technologies. However, active growth will also bring new challenges: for example, data security and the ethical use of AI.
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