Deep learning technology is becoming increasingly integral in the designing of market strategies. Brands can improve customer engagement, acquisition, and retention by integrating deep learning into their strategies.
Deep learning is more widely used than most people realize — it’s a technology that allows smart speakers to understand commands, aids Google’s search algorithm in ranking web page results, and determines where online advertisements appear on a person’s desktop or mobile app.
These examples demonstrate how deep learning technology benefits marketing departments, and the use cases are critical to their marketing strategies. To take full advantage of deep learning, marketers must become acquainted with the technology and its other applications.
How deep learning and marketing intersect
As marketers are well aware, producing highly customized, meaningful content is critical to both maintaining and attracting loyal customers. As per a 2018 Accenture survey report, more than 90% of customers are more likely to engage with brands that share relevant deals and tips, therefore, marketers must use every tool at their disposal to ensure that their most valuable customers have the best experience possible.
With deep learning technology, brands can meet and even surpass expectations by offering:
Deep learning can dissect unstructured consumer data, such as who they are, what they want, and when they make transactions, instead of wasting precious time making employees analyze customer data. These results allow marketing teams to segment their audience down to the individual level rather than targeting a broad audience, such as OTT platforms that make recommendations based on what a consumer has already watched and enjoyed.
It’s not uncommon for customers who spend time online, to see a range of advertisements. Companies can leverage deep learning advertising services to determine where to put online advertisements in order to get the best response. Since the prevailing business model relies on real-time bidding, the entire pay-per-click ecosystem is based on deep learning. Companies can auction off an ad to a consumer or a competitor with a higher bid in a matter of seconds by using data about site visitors to determine the value of a given impression. This ensures that consumers react to and act on the advertising they see on their devices.
It’s clear that voice technology is becoming more common among customers, with estimates projecting that the number of smart speaker buyers in the United States will exceed 23 million by 2021. In fact, according to Voicebot’s November 2020 Smartphone Voice Assistant Consumer Adoption report, nearly 30% of users said they would prefer voice interaction with their mobile apps. This opens up a new channel for marketers to engage with customers and exchange knowledge. Brands can use deep learning to build a personalized branded voice that reflects a brand’s personality to maintain brand continuity throughout devices. Consumers will have a higher degree of confidence in them as a result, and they will have a greater customer experience.
Natural language understanding
Deep learning can be used by conversational interactive voice response systems and smart assistants to understand natural human speech that helps users to make requests. Instead of calling a traditional call center, a customer could use their smart speaker to request a product return. The voice assistant can then launch the company’s voice app and walk the customer through the return procedure. Customers don’t have to recall specific phrases or terms to start a task because natural language understanding software will know that requests like “Can I send my purchase back?” or “I would like to make a return” mean the same thing.
Automated customer service
Customer experience is crucial for brands to understand what customers want and expect. However, maintaining a large team of customer service professionals can be incredibly costly. Marketing teams can use deep learning to address customer service problems at any time using AI-powered chatbots, smart assistants, and conversational integrated voice response systems. This frees up customer service representatives to handle more business-critical problems or respond to inquiries that deep learning can’t handle.