Top 5 Deep Learning Implementations in Marketing
Deep Learning systems continuously work in the background of famous companies such as Amazon, Facebook, and Google. Recently, Deep Learning paved its way deeper into marketing. Moreover, solutions previously available only to big corporations have become affordable and accessible to medium and small-sized businesses. These algorithms can improve overall marketer performance in tasks such as generating branded content, extracting and classifying relevant information, customer communication, etc.
Be aware that many marketing technology companies are focused on digital marketing, but claim that their solutions are “AI-powered.” We want to help you to see the difference between the two. Here are the top 6 applications of Deep Learning in marketing:
1. Multi-Arm Bandits - Content Optimization
AB testing is an experiment where two or more content options (email, web page layout, visual elements in an ad, etc.) are shown to the customer at random, and statistical analysis is used to determine which option performs better for a given target. The downside of A/B Testing is that it involves a period of “regret,” a time where you lose revenue while using the less optimal option. So it is purely exploratory approach because test options are randomly assigned to each group with the same probability. The overall result equals to the average effect of all the variants.
In contrast, bandit tests decrease opportunity loss with dynamic optimization. The idea is to simultaneously explore and exploits options, gradually and automatically moving towards the better choice. In a nutshell, the “exploration vs. exploitation tradeoff” can be explained as acquiring new knowledge and maximize the reward at the same time. Regret is defined as a decrease in reward due to the learning period instead of behaving optimally from the beginning.
So Multi-armed bandit algorithms are an alternative to A/B testing. It balances exploitation and exploration during the learning process. It uses actual results from the experiment to allocate more time to the options that are performing better and vise versa. In theory, multi-armed bandit algorithms should produce higher overall results while still allowing to gather data on how users interact with different variations of the campaign.
There are several Multi-armed bandit algorithms. The most popular ones are Epsilon Greedy, Thompson Sampling, and Upper Confidence Bound.
2. Text Classification - User Insight & Personalization
Every marketer’s dream is to be an economic prophet. They strive to look at people’s and businesses’ wants and needs trying to outgun one another. Not so long ago, a new technology burst on the scene - Natural Language Processing. Similarly to human understanding, it allows deriving meaning from a broader context around an entity. Marketers can now directly get info on their audience’s buying intent. The technology offers new ways of determining the attitude of the target audience toward your brand, products, or services. The speed at which insights can be generated by is incomparably superior to what human-enabled processes can offer. They can probe text or voice-based content, then classify each content based on different variables to make consumer insight in a matter of seconds.
Identifying B2B & B2C Buying Intent
If you are a marketer whose task is to identify individuals or businesses interested in your product or commodity, with this technology, the sky is your limit. The system will be capable of finding all mentions of your product in the social network via you promote your products. The system will analyze the context in which the product occurs. You can also identify potential buyers. Similarly, if you are in B2B, you can browse vast numbers of websites to identify your potential clients. You can search for a tender, partnership opportunities, and more.
Identifying Customer Sentiment
Natural Language Processing technique called Entity Recognition allows you to split out the Named Entities from any other kind of textual information. These Entities can represent individuals, brands, or organizations. The system than can processes all textual information associated with a found Named Entity and explores the related context.
Increasing your Email Marketing Campaigns’ Performance
It is possible to build a predictive model that will take into consideration the extracted entities and other essential features from your previous campaigns and use them in new ones. Sentence structure, thematic word groups, style, and the quality of grammar are all taken into consideration. With this approach, you can achieve a higher Open Email Rate and making your email marketing efforts more efficient.
3. Chatbots & Customer Experience
The prime intention when you are running an organization is to increase product performance and reach the targeted audience. For every question raised by your audience, you should be able to respond, manage, and keep them in your network. If you do not handle well, you will surely lose. To overcome these failures, you need Chatbots.
The Chatbot is a smart way of interaction between your client and machine. Your clients can always receive messages for every question requested. Since it is a programmed operator, there is no need for any human powers to manage all the time.
Be aware that most marketing bots you see these days are entirely scripted and use minimal Natural Language Processing and Deep Learning. More sophisticated ones can reference external knowledge bases, adapt to unusual questions, and also escalate to human agents when required.
Chat with your Clients - 24/7
Whenever a customer reaches, it is business time. Every minute is a business hour for an organization. For every raised question, a prompt response is required. If a delay occurred, there are vast chances of losing the customer. The Chatbots will help you tackle these situations very well with a 24/7 client base coverage. They increase overall customer satisfaction and make the clients reach you again.
Increase your Lead Count
Chatbots help you know more about your customer preferences. When a user enters your webpage, you can sort out their interests. This information can be useful for sending customized marketing updates via our Chatbot. If any discounts or exclusive coupon codes appear, which is intended to specific user type, you can share them with customers and improve your lead generation. With this approach, you can always be customer-centric and improve your sales.
Personalized Messages
With the help of Chatbots, you can connect with your customers and do the crucial first step with personalized messages. With this customer-centric approach, you can check customer preferences easily as well as help them know more about your business.
4. Computer Vision - Branded Object Recognition
Computer vision is a rapidly advancing field in Deep Learning that lends itself to a wide range of applications. Marketers can use these solutions for product recognition and extraction of user insight as well as identifying when their brand logos have appeared in user-generated content and quickly calculate earned media from video analysis.
A special section of the object recognition is logo recognition. It is defined as the process of grouping and identifying logos. When we detect the brand logo on the image, the goal is to classify that image into a specific group. This way, logo recognition can be considered as a classic object detection task specialized in marketing.
From a technical perspective, we are using end-to-end Deep Learning methods based on Convolutional Neural Networks. It allows the detection of logos on different scales, colors, and rotations.
From the business perspective, the main goal is to find mentioned brands in social media posts with no words in it. This is the latest breakthrough in modern digital marketing, which means new opportunities for the brand to communicate with its consumers and to understand their needs. On the other hand, only a select few know more than just a couple of primary use cases apart from fun. This technology offers new opportunities for next-level digital marketing:
- See user reviews via implicit brand mentions
- Track negative sentiment towards your brand
- Discover “Instagrammable” people and much more
5. Generative Adversarial Networks - Original Media
Nvidia shocked the business community by creating a buzz around its methodology for generating photorealistic images of fake celebrities. While these images make look like photos of real people, they were entirely created by Deep Learning algorithms-GANs.
Generative Adversarial Networks (GANs) are a part of Deep Learning that operates by pitting two Neural Networks against one another. One Neural Network generates new data instances, while the other, evaluate them for authenticity.
These Generative Adversarial Networks are optimal for producing, evaluating, and reworking new creations. That is the main reason why it has been put into the creative-side of Deep Learning.
Robbie Barrat code was used to trained the GAN on 18th-century portraits and eventually sold one of their digital creations at Christie’s auction for $435,000.
GANs’ potential is enormous. They can learn to mimic any distribution of data. They can be taught to create worlds similar to our own in any domain: images, music, speech, etc.. If you want to give it a name - robot artists with outstanding output.
Comunity is experimenting with GANs on sneaker designing and high fashion industry by creating entirely new clothing color palettes and augment the role of creative director at a fashion house. They are experimenting with GANs in creating a description for their products or a new type of logos.
Apart from this, we have Pose Guided Person-Generated Network. The idea is to manipulate a subject within an image into different poses. Imagine that photographers could focus on taking one great picture and allow the GAN to recreate all the other product angles. Great time and cost savings for an E-Commerce Business.
Apart from all of these obvious economic benefits, there is a feeling that we still miss the bigger picture.
Teaching a GAN to produce ANY convincing text or image, just by giving it a set of parameters, is a future that we are heading for sure.
Bare in mind that the theory behind GAN is only four years old.
Takeaways
This was an overwhelming journey, and you must have asked yourself, what now? Well, you should put yourself in learning mode and collect as much information as you can about current experiments and projects with Deep Learning. The crucial thing is to learn what Deep Learning is capable of and how we can benefit from it. Talk to knowledgeable people, ask questions, learn from blogs and forums, and have one important thing in the mind-this industry is so dynamic that conventional education won’t be enough. You have to explore by yourself.
The next big thing that will be the differentiating factor between companies that successfully implement Deep Learning Solutions and those that fall behind is proprietary data.
What data are you collecting today that is unique to your organization? If the answer is “none,” you better do something before it is too late?
