Every day, millions of people make buying decisions based on search – products to buy, restaurants in the neighborhood, and tons of other choices. According to the Nielsen Report “Global Trust in Advertising” however, while consumers rely on online opinions or price comparisons, more often than not, it’s word-of-mouth recommendations that are the most effective. The most credible advertising comes straight from the people we know and trust, and over 83% of respondents completely or somewhat trust the suggestions of friends and family.
So when we make a final decision to buy, it’s reasonable to assume that we ask a spouse, relatives or close friends for advice. After all, they are the ones who know us, our tastes, preferences, sense of fashion, etc.
But what if a computer was able to get to know you even better than your close ones?
What Computers Know About You Now
The digital era has made purchasing journeys more accessible, but an increasingly complex one. Choosing from hundreds or even millions of options make it harder to make a decision. Online recommendations systems change the way we browse and choose products – they narrow our decision-making process by bringing us closer to what we’re looking for, suggesting complementary or even alternative products.
This “knowledge” about your shopping persona usually comes from what you have purchased or viewed in the past, what shoppers with similar profiles have viewed or bought, as well as the date and time of viewing. Recommendation technologies listen to what you’re looking for and suggest products. They gather and analyze millions of datapoints about your preferences to serve ultra-precise suggestions.
It sounds simple, but these technologies require massive volumes of data to deliver accurate predictions. And, of course, the more information, the better. This is where deep learning comes into play – an innovative branch of artificial intelligence that solves problems by imitating the work of the human brain in processing data and creating patterns of decision making.
AI Will Predict What You Want
Most of us already have experience with data-based suggestions as shown above. We’ve purchased new products on Amazon recommended under the “Frequently Bought Together” section, or added new people on LinkedIn after seeing “People you may know.” Even watching a movie on Netflix exercises our familiarity with AI-based recommendations.
And now engines are only getting smarter. They employ deep learning tools that personalize a user’s experience by trying to figure out their habits even after just one or a few visits – sometimes during the first visit. Paired with real-time analytics, self-learning algorithms can enhance suggestions up to the point of prediction. Services like Spotify can predict the next song suggestion, while YouTube queues up recommended videos based on the current one you’re watching.
Ultra-precise deep learning is used in all sorts of digital industries, none more so than advertising. Self-learning algorithms help to achieve super-accurate recommendations that make advertising activities up to 50% more efficient. But how does it work in practice?
How Deep Learning Works With Recommendations
Let’s take the example of shopping for a new dress. When a shopper clicks on anything within the website, the recommendation mechanism captures every piece of information. It checks the color of the dress, details you were focused on, the price range, sizes and dozens of other actions points. It then connects as many interaction patterns as possible. By measuring and analyzing them (in real time) the system can understand the history, taste, interests or even mood – and then make accurate predictions of interesting products. Matching heels and jewelry selections, date-night outfits, or summer wear, could be recommended based on what is predicted as most effective. This all happens without any human input on the advertiser’s end. In the field of purchase prediction the self-learning algorithms have already obtained so much knowledge, that it has rendered manual intrusions unnecessary, if not straight up misleading.
Typical models of recommendations cannot do this. Most early recommenders simply gathered information and then selected products to show with rules predefined by a human, such as for example “Show jewelry only to those which visited female clothing, since they are most likely women”. Now, this can be substituted with “Our system knows having visited female clothing is some predictor for buying jewelry, but has also learnt to detect men who intend to buy jewelry for themselves or as a gift”.
Deep learning algorithms simulate our way of thinking, but learn by practicing outcomes without any human touch. A machine will analyze countless data sets relentlessly, without getting tired or bored, and will produce super logical, risk-proof decisions without stress, doubt or emotions. It will obey the general rules of the advertiser, but most importantly, it is able learn and write new rules with proactivity and performance unachievable by human work. This is the essence of the self-learning algorithms and why they are so effective for the ad industry.
Moving Towards AI-Personalized Experiences
According to Janrain & Harris Interactive, 74% of online consumers become frustrated by content that is irrelevant to their needs on a website.
What is more, Infosys found that 86% of consumers say personalization plays a role in their purchasing decisions.
Using ultra-precise recommendations strengthens the brand’s relationship with its consumers and in fact, accelerates retailers sales, improves conversion rates and increases revenue – no matter it is movie, music or ad industry. Better accuracy and more persuasive approach in turn make deeply targeted suggestions a must-have not only for e-commerce, but also banking, insurance, travel and even in everyday grocery shopping.
Steve Jobs is often quoted as saying that “People don’t know what they want until you show it to them.” The deep learning industry may make this feat an ordinary, automated experience for every user in the digital era.