Understanding the wants and needs of consumers — ideally, before they even know it — remains a constant necessity for advertisers. Artificial intelligence (AI) is now simplifying this task significantly, particularly with the advent of deep learning. As technology and AI progress, so must advertisers and the tactics they employ. It’s already streamlining operations to enable advertisers to make more informed decisions quicker.
AI will be capable of more accurately predicting consumer needs, thanks to the extensive data it accesses. Consequently, your campaigns will achieve greater success. Human mistakes will become a rarity as AI advances, and the strategic choices we make as advertisers will be more well-informed. Let’s explore its capabilities and potential applications further below.
Deep Learning: what it is, what it does
A branch of artificial intelligence, deep learning holds the promise of revolutionising the marketing landscape by enabling companies to forecast consumer actions. This approach to machine learning employs complex, interconnected neural networks, akin to those in the human brain, to acquire new abilities and tackle intricate challenges far quicker than humans. It equips machines, whether they be computers or robots, with the capability to perform tasks that traditionally required human intervention, such as identifying objects, recognising voices, and translating between languages.
Deep learning offers a method for training artificial intelligence to forecast outcomes based on provided inputs. It might seem straightforward, but it’s anything but: Although it demands less manual data preparation from humans compared to traditional machine learning methods, deep learning necessitates a substantial amount of data and considerable computational resources. Nonetheless, with access to these essential components, a deep learning system can achieve a high level of accuracy in predicting human behaviour.
Predicting Human Behaviour
Think about the “Predictive Vision” study. Scientists from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) equipped the deep learning model to forecast the likelihood of characters in episodes from series such as “The Office” engaging in activities like hugging, kissing, shaking hands, or giving high-fives. After analysing over 600 hours of YouTube content, the model achieved a prediction accuracy of 43 %.
This is a significant improvement over the existing algorithms, which could only achieve a prediction accuracy of 36 %. A more famous instance of deep learning’s capability to forecast human actions is seen in the realm of autonomous vehicles.
Researchers from Cornell University and Stanford University have created a “Brains4Cars” system, which incorporates cameras, sensors, and wearable technology to track a driver’s body language and the surrounding traffic. This system issues a warning when it senses the driver is heading towards a potential car accident. The system’s algorithm can predict the driver’s actions up to 3.5 seconds before they occur.
Predicting Consumer Behaviour
These studies yield impressive outcomes, but what does this mean for advertisers? As deep learning technology advances and enhances, companies can now utilise the vast quantities of data they’ve accumulated on existing, past, and potential clients through various online and offline platforms. Moreover, deep learning will emerge as an increasingly vital resource for advertisers as the Internet of Things expands, and a huge volume of information on consumer behaviour is amassed whenever they visit an ecommerce site to shop.
Combining extensive data with deep learning will enable companies to develop targeted marketing strategies that will attract anyone who might be interested in their products. (To grasp this potential better, take a look at this article by Jeremy Fain, the CEO and co-founder of Cognitiv, a provider of neural network technology. His analogy of a hot dog stand illustrates how a deep neural network algorithm, based on data on current and other customers, could identify potential future customers.)
Solutions are available for immediate installation, where specialists in AI consumer behaviour, offer the ability for ecommerce retailers to take advantage, of specialist knowledge that delivers immediate returns.
We have compiled a list offering the distinctions between the top 30 hyper-personalisation providers to enable ease of consideration.
AI, Consumer Behaviour, and Marketing
Reshu Rathi, a digital marketing specialist, discusses in the article “how deep learning can uncover hidden patterns within data“, aiding companies in grasping the true desires of their customers through marketing automation. She points out that deep learning enables hyper-personalised marketing and customer experiences by considering the customer’s intentions, rather than just their past transactions or interactions.
For instance, a study by researchers at Renmin University of China revealed that by incorporating details on consumers’ hobbies and employment situations into a deep learning algorithm, it’s possible to forecast the likelihood of individual preferences of each consumer for the next imminent purchase.
The capacity to accurately anticipate a customer’s needs and fulfil them is invaluable for marketers. With the assistance of sophisticated AI, marketers can shift their reliance from guesswork and assumptions to a more evidence-based approach in predicting customer actions, even in the future. Steve King, the CEO of Black Swan Data, elaborates in a Deloitte video that the progress in AI and analytics is ushering in a new phase of “social prediction.” By utilising social data, such as sentiment analysis from social media listening tools, marketers can spot trends that help in predicting consumer behaviour several months ahead.
AI is a Trillion Dollar Opportunity
This significant investment and progress in deep learning are sure to open up thrilling possibilities for marketers. Indeed, experts at McKinsey believe that the majority of applications for artificial intelligence in business will be categorised into two main sectors: supply chain management and manufacturing, marketing, and sales. They project that applications in these sectors will represent two-thirds of the total potential of AI. The potential value for marketing and sales, as per their study, could reach $1.4-$2.6 trillion globally.
They also predict that 40 % of the total value that could be generated by analytics today will be from AI methods that are part of the deep learning category. Although deep learning is still in its early stages, it’s poised to grow quickly due to the pace of technological advancement.
The capability to accurately predict consumer behaviour using AI trained with deep learning is not a distant possibility. Deep learning is already transforming marketing strategies and approaches, and failing to adapt to these changes would be a grave error for any marketer.
Predictions for AI beyond 2024
Given its full potential, the opportunities for AI in marketing seem limitless! It’s unlikely that AI will completely take over the role of humans. It cannot understand humour, creativity, or the crucial skill of sarcasm, which are essential for crafting content that engages your audience. Nonetheless, AI can lay the groundwork for marketers to leverage.
Traditional manual A/B testing is set to become obsolete as AI can collect data from various factors simultaneously and deliver instant feedback. We’re already familiar with AI-powered conversational assistants like Alexa from Amazon or Siri from Apple, where we interact with them by asking questions or making requests.
Picture an advanced marketing AI technology that informs you about the latest trends, the outcomes of your projects, and recommendations for improvements to your ongoing campaigns, and better still conducts them for you autonomously, which has already become standard operating procedure.
