Explore what marketing AI is capable of. Get inspired with 5+ use cases.
Marketing professionals are fortunate to be able to exploit AI’s full potential: all types of artificial intelligence (AI) are available for them. Here, we’ll take a closer look at how AI in general and each AI type in particular help marketers reach their customers, engage them, convert and turn loyal.
Marketing AI in action
Artificial intelligence enables a computer system to analyze tons of big data, reveal dependencies in historical records and apply the gained knowledge to new inputs. This ability rests upon machine and deep learning algorithms that open up predictive capabilities.
In a word, AI is apt in learning and powerful in making predictions. However, these AI capabilities only become available when the system is rightly designed and trained on a sufficient amount of high-quality data. Besides, AI should learn to continuously minimize the deviation of its predictions from the real values. None of these conditions happen by themselves but require the involvement of professional data scientists.
To see how AI is working in marketing, let’s explore how Netflix personalizes their artwork. Their personalization algorithm scans each user’s viewing history to find the aspects that might have determined the user’s choice in the past (for example, a user could prefer the movies of a certain genre or with a particular actor starring). Relying on the findings, AI maps a new movie title it’s going to recommend to the most relevant piece of artwork, which it chooses out of several available options based on the user’s preferences. This is how the algorithm supplies each Netflix user with personalized artwork, thus inducing them to watch a recommended title.
Fortunately, Netflix’s case is not the only smart way of using AI in marketing. Read on to get inspired with 5 more use cases.
5 examples of how to use artificial intelligence in marketing
Demand forecasting powered by analytic AI
Companies can use deep learning algorithms – the enablers of the most advanced AI – to forecast customer demand. To be trained, such algorithms require at least a 2-year history of daily sales data containing all the required details. For example, if demand forecasts are meant to be on the SKU level, the sales data should also be per SKU. In the historical data, AI identifies complex non-linear dependencies that influence customer demand and further applies them to make predictions about the demand level.
Having weekly forecasts for the upcoming 52 weeks, marketers will be able to determine demanded and trending products, allocate the advertising budget and plan promotional activities accordingly.
Product recommendations powered by functional AI
To recommend a product, AI needs to analyze all the purchasing histories to identify the preferences of individual customers and group the customers with similar preferences together. With the analysis findings in mind, AI scans the products that an online store visitor is either viewing or putting into the cart, predicts and recommends the products that the customer may also like.
Concierge robots powered by interactive AI
Trained on dialogues related to hotel services and tourist attractions, robots like Robby Pepper, Italy’s first robot concierge, provide hotel guests with 24/7 multi-language support. Thanks to natural language processing technologies, such robots can interact with hotel visitors, answering their typical questions.
Though the main task of such a robot is to provide great customer service, marketers can benefit from it as well. First, there’s no need to win (or win back) those customers who are already satisfied with the company’s service. Secondly, the robot contributes to increased brand awareness. Being a sign of innovation, it’s frequently communicated on the news, as well as spread by word of mouth.
Influencer marketing powered by text AI
AI helps companies to automatically classify texts written by thousands of influencers and sort out the ones where the company name is mentioned (for instance, Salesforce AI allows classifying social media posts). Besides, AI can define the message tone and assign a relevant task to the marketing team. For instance, if an influencer is the brand’s advocate – the company would probably like to thank the influencer, and if the brand is criticized – the company can mitigate a budding conflict.
Product search by images powered by visual AI
Online customers frequently experience difficulties with finding the product they’ll love with the help of a search bar or available filters. With these search options, a customer should often guess what product terminology a certain brand uses. Say, if a footwear brand’s filtering is based on conflicting categories, a customer may tick ‘Flats’ not knowing that there’s also the ‘Sandals’ category further on the list. Thus, they’ll see only the shoes with closed toes and miss the whole array of open-toe flats that the second category hides.
To improve customer experience with their website (as well as boost sales), marketers can consider visual search. It works as follows: a customer chooses an image of the shoes they like, while AI identifies the product’s features (like color, heel height, and decorations), scans the whole product portfolio for the products containing similar features and shows such products to the customer.
Let’s recap: How marketing can benefit from AI
Marketers may use AI to create an impeccable customer experience and, correspondingly, boost customer loyalty. AI provides them with valuable tips of what a customer might like or how a customer might behave.
Besides, AI can help marketers make their company’s website or a brick-and-mortar store convenient for their visitors by enabling smart search options, delivering personalized content or recognizing customers and their emotions.
Finally, AI saves hours of manual analysis. For instance, social media monitoring would require days or even weeks if done manually by the marketing team but AI can do it in minutes.
About the author: Irene Makaranka is a Data Analytics Researcher at ScienceSoft, an IT consulting and software development company headquartered in McKinney, Texas. With a focus on business intelligence, big data and data science, Irene explores trends, technologies, key challenges and solutions in the world of data analytics.