Marketing professionals are constantly on the lookout for new strategies to locate and entice their intended customers. However, in today’s fast-paced and ever-changing digital marketing scene, reaching your target audience and delivering results is becoming increasingly difficult. And Ai marketing helps achieve that.
According to a survey conducted by data analytics firm Teradata, 80% of enterprise-level firms are currently adopting AI in their operations (32% of those in marketing). However, over 90% of respondents expect considerable obstacles to complete acceptance and incorporation. (source: marketinginsidergroup.com)
What is AI in Marketing?
The simulation of human intelligence processes by machines, particularly computers, is known as artificial intelligence. Smart systems, natural language processing, speech recognition, and computer vision are examples of AI application domains.
AI technologies, in general, work by consuming huge volumes of labelled training data, analysing the data for patterns, then using these patterns to forecast future outcomes.
Many people think of AI marketing as science fiction rather than reality, but it’s not a far-fetched concept; it’s already here. As per Salesforce, only 29% of marketing executives employed AI in 2018, but that percentage is expected to rise to 84% by 2020. According to IDC, global spending on artificial intelligence hardware, software, and services will top $340 billion by the end of 2021.
In the age of artificial intelligence, how will marketing change?
The next big thing in marketing is artificial intelligence (AI). It will sway businesses from marketing automation and towards individualised interactions. However, there’s a huge contradiction here: artificial intelligence is required to humanise the marketing business.
Customers have always been connected thanks to easy, digital, and real-time technology. They have become needier and more self-centred as a result of the abundance of on-demand applications and services and the impact of social networking sites on how people interact, engage, and converse.
They expect things right now. It makes sense that they become more irritable. They want an interaction that is tailored to them. They also desire new and unique experiences.
This isn’t a new concept. Over the previous ten years, digital marketers have witnessed an extraordinary shift. Personalization, cross-channel and multichannel integration, fully responsive design, and dynamic interaction have all been discussed by professionals for a long time.
However, despite all of this time and effort, there has been little marketing originality to further engage customers in the manner they anticipate and appreciate. The key now is that artificial intelligence and data are easily accessible to modify the scene.
Marketing requires a new roadmap now more than ever, and AI (and algorithms) will be the trigger for this long-overdue transition. The transition from automation to personalisation, and ultimately anticipation and forecasting, will be realised.
Will AI marketing replace traditional marketing?
Over the last century or more, people have been bombarded with a wide range of marketing and advertising messages. For the longest period (and still today), these conventional media approaches have taken the shape of Newspaper advertisements, Leaflets, Magazine advertisements and billboards.
Of course, these media operated admirably since they were the primary means by which consumers learnt about new goods/services. They would see an advertisement, hear about it on the radio, or read about it in the newspaper.
Artificial intelligence (AI) and machine learning take the uncertainty out of marketing. It aids marketers in better understanding contemporary clients in areas that were not previously possible. AI marketing blends intelligent technology with human creativity to learn, comprehend, and communicate with customers.
All on an individual level through highly personalized and timely communications that force them to stay involved. AI also aids advertisers in crafting highly tailored messages, with relevant material delivered in the channel, on the gadget, and at the moment they desire.
What are some of the examples of AI Marketing?
Starbucks Serves Personalized Recommendations Using Predictive Analytics
According to Aberdeen Research, organisations that use predictive analytics to detect client needs can boost organic revenue by 21% year over year, compared to an average of 12% without predictive analytics.
Starbucks is an example of a company that collects and analyses client data through its loyalty card and smartphone app, basically AI marketing. In 2016, they revealed intentions for personalisation.
Ever since they’ve developed a wonderful app. It keeps track of purchases, including where they were made and when they were made. Starbucks employs predictive analytics to process this information and send personalised marketing messages to consumers.
When a user visits a nearby retailer, these include suggestions and promotional deals aimed at increasing the customer’s average order value.
Alibaba launching a FashionAI store.
Alibaba, the world’s largest retailer, has launched a real “FashionAI” store in Hong Kong to use AI to improve the clothing market experience. Alibaba installed intelligent clothing tags that sense when an item is handled.
As well as smart mirrors that show apparel data and propose complementary items. Alibaba also intends to link the physical store to a virtual wardrobe app, which would allow customers to see the clothing they put on in the shop.
Alibaba’s technological innovations are a response to changing customer demand. As per a survey conducted by the National Retail Federation, 80% of buyers believe that retail technologies and innovations have improved their online shopping experience, while 66% feel the same about traditional shopping.
Amazon launching Amazon Personalize
Amazon was one of the first companies to use machine learning to provide personalised product suggestions. Nonetheless, extending these capabilities to organisations that use Amazon Web Services has proven difficult for the business.
Amazon Personalize delivers Amazon’s machine learning and AI to AWS clients to be used in their apps, which became generally available in June 2019.
Since its initial launch, Amazon they have improved Amazon Personalize’s capability to the point where it can now give up to 50% improved personalized suggestions along with a variety of rapidly-changing item genres, such as ebooks, videos, music, and media articles.
Sephora’s Chatbots
Chatbots are growing faster than any other brand communication channel, according to Drift’s current State of Conversational Marketing research, with usage jumping by 92% between 2019 and 2020.
Sephora was an early supporter of artificial intelligence. In 2017, they started giving beauty tips via a chatbot on Kik.
Opening with a questionnaire about their product preferences, Sephora’s chatbot assisted customers in narrowing down their options. In the cosmetics sector, where the selections might be overwhelming and impossible to buy without trying in reality, item preferences are incredibly beneficial.
Sephora acquired useful data from their chatbot, and the experiment resulted in enough interaction that the company has now created additional chatbots on Messenger.
What are the challenges faced in AI marketing?
IT Infrastructure is inadequate
A robust IT infrastructure is considered an essential AI-driven marketing strategy. Artificial intelligence is capable of handling huge amounts of data and information. This necessitates the use of high-performance hardware.
The cost of setting up and running these computer systems might be rather high. They will probably require frequent maintenance and updates to stay running properly. This can be a major stumbling barrier, especially for smaller businesses with limited IT expenses.
Fortunately, there is an alternative way to resolve the issue. While major corporations may choose to design and maintain their own AI marketing tools, smaller businesses can benefit from cloud-based alternatives.
In exchange for a monthly or yearly charge, cloud software companies supply all of the IT equipment and people required to run AI software. For organisations with limited IT capacity to construct in-house systems, cloud services are the logical alternative.
AI Software isn’t trusted
AI is a fairly new and complicated technology. This implies that the overall population (and even non-AI-trained technical staff) may be wary of it.
Popular culture does not help in this sense, with multiple films adopting a “rise of the machines” narrative to indicate that we should be scared of the advancements in technology.
Of course, reality differs greatly from science fiction, but organisations must exercise caution when employing AI software to ensure that some apps appear accurate and human-like.
Honesty and clarity can go a far toward boosting consumer confidence in AI. The “black box” mystery of AI software is addressed by revealing how AI algorithms use consumer data to make judgments (and when and where the customer contributed this data). It helps to build customers’ trust.
Regulations and Privacy
AI is still a young and developing field. Over the next few years, the laws regulating it are likely to alter and strengthen.
Businesses that use data from EU-based clients to drive their AI algorithms are already affected by the gathering and usage of data. The GDPR regulations, which went into effect in 2018, require businesses to be more cautious about how they gather and use information from consumers.
For logistical purposes, certain firms may be prohibited from storing data offshore, which means they may be unable to use cloud-based AI marketing companies.
Final Verdict
While these obstacles may hinder the introduction of AI solutions in some organisations or limit the types of information that can be analyzed or used, there seem to be lots of other options.
All companies must focus on ensuring that AI software is used ethically and in a way that benefits their customers rather than just their profit line.
Marketing departments will be under more pressure to show executive stakeholders the value and ROI of marketing. Organizations will use AI technologies to help them achieve these goals, allocate appropriate cash to successful initiatives, and provide quantitative measures that show how effective campaigns are.
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