The problem for many brands when they launch a marketing chatbot is a lack of data. Data about their unique customer base. How they speak and engage in conversations. What the customer’s intent is in specific scenarios and conversations.
44% of consumers will become a repeat buyer after having a personalized shopping experience. How can you better understand customers’ intent and questions? Natural language processing, a form of machine learning, is one way. It is the practice of understanding how humans speak through artificial intelligence.
NLP chatbots are one of the best sources of customer intent data. Simple chatbots often only do the bare minimum. They guide customers through basic decision-trees but don’t improve marketing ROI because they aren’t able to understand and respond to customers.
For example, you wouldn’t expect a brand like Nike to have the same set of customer intents as Vogue. They have completely different customers, receive unique questions, and require niche data on how people engage with their brand.
Advanced NLP marketing chatbots are capable of identifying and classifying domain-specific intent to solve this problem. This customer intent data is the foundation for optimizing your chatbot over time.
Generative adversarial networks (GANS), a form of machine learning, generate variations to create more accurate data faster. This helps marketing teams offer delightful customer experience without needing a treasure trove of data to start with.
Here’s a deep dive into how domain-specific NLP and generative adversarial networks work.
What is domain-specific natural language processing?
Natural language processing can be general or domain-specific. General NLP is good for simple tasks like text recognition, autosuggestions, and understanding semantics. However, this is not good for improving marketing ROI and understanding your customers’ language or intent.
Marketing chatbots that use domain-specific NLP, on the other hand, learn how your individual customers speak. The customer intent data specific to your business, customers, and goals are used to continuously improve your chatbot based on how your customers actually speak.
This niche data is not easy to come by and you may not have large amounts of data to train the bot with initially. Domain-specific NLP collects intent that is developed strictly for your business and classified by analyzing each unique conversation. This enables it to match intents with the best response for maximizing conversation flow and conversions.
The consumer healthcare company HRA Pharma, for example, wanted to educate women seeking information about the morning after pill. However, it needed to reach people in a comfortable and engaging manner. HRA Pharma also needed to understand the intents of customers to provide sensitive and helpful responses in times of need.
HRA Pharma used Spectrm’s conversational marketing platform to create Ella, an award winning healthcare support bot. It offered personalized advice to women and identified highly nuanced intents in many different contexts. This also helped HRA Pharma automate more of its customer service, provide relevant answers, and drive engagement with a unique chatbot.
What are generative adversarial networks?
Once an NLP marketing chatbot has collected customer intent data, it can use generative adversarial networks (GANS) to generate up to one million variants and map each intent to the most relevant response.
These are systems of machines designed to train one another. This creates large amounts of data very fast and helps to better classify intents.
54% of executives say AI has increased productivity in their businesses. NLP-powered chatbots that use generative adversarial networks can save marketers precious time and drive more growth with their customer intent data.
RedBull, for example, wanted to drive user engagement on social media and generate extensive insights into customer intent. They used Spectrm’s conversational marketing platform to build a Facebook Messenger chatbot and achieve this.
Proprietary machine learning and NLP analyzed conversations and mapped customers to exclusive content based on their intent.
This helped RedBull improve its products and marketing by understanding what customers wanted and how they searched for it.
How Spectrm’s Hybdrid NLP engine works
Spectrm’s Hybrid NLP Engine combines pattern matching with proprietary NLP technology. The process begins by helping marketers generate domain-specific customer intent data with a syntax helper.
Entities are the name of persons, places, and organizations. Synonyms replace each other but keep the same intent. Optional words are those that may or may not be present in a customer conversation. The quality score determines the accuracy of the intent pattern.
Spectrm has taken the GANS model from image recognition, evolved it, and applied it to train each intent pattern.
The generator presents fake images and the discriminators determine if it’s real or fake. A score is given based on its accuracy and the generator changes the data to make it better the next time. A new score is then given.
Spectrm expanded this to include a head discriminator.
Real data is generated by extracting mandatory elements out of intent patterns to create possible sentences. Noisy data is generated by parsing intent patterns and extracting all elements. Finally, negative data is composed of all the sentences extracted from the other intent patterns using the one versus others approach.
The discriminator is trained to distinguish between noisy data of one intent class to create an AI template. The generator is trained to fool the discriminator by transforming noisy data to appear as real and at the same time learn from the head discriminator to make negative data look different from real data.
The head discriminator is trained to distinguish noisy data of one intent class with all of the other intent classes.
If user input matches the intent pattern, the intent is classified. If not, Spectrm’s proprietary GANs model is used to predict user intent. Input is transformed into word vectors, fed into the generator/head discriminator pairs of all intent classes, and a prediction score is calculated.
The intent class with the highest prediction score is selected and if above the threshold it is classified.
Marketers further train the chatbot to be more accurate through supervised validation. This requires no engineers or reiterating upon every change.
The result is a cost-effective and efficient way for your brand to develop conversational AI that is optimized based on your customers’ unique intent patterns.
Final thoughts on domain-specific NLP and GANS
Simple chatbots guide customers through basic decision-trees. That’s all. They don’t map responses and information to each customer’s unique intent. Nor do they generate more accurate data faster and improve the shopping experience.
NLP powered chatbots built with Spectrm, on the other hand, use a form of machine learning known as natural language processing (NLP). This artificial intelligence makes sense of human language. However, you need to understand your unique customers’ language and intents when talking with your brand. Not just how they talk with everybody.
Spectrm focuses on domain-specific NLP. An approach that learns how your individual customers speak, ask questions, and what they’re actually searching for. You can easily classify intents and train chatbots as more data is collected. This makes it increasingly accurate. Marketers can use domain-specific customer intent data to offer personalized recommendations and lift conversions.
But there are hundreds of ways to say the same thing. What can you do? Generative adversarial networks take a brand’s limited customer intent data and predict over one million variations. You get much more accurate data faster to classify each intent and match them to the best response.
Reach out to our experts to learn how Spectrm can help your brand collect domain-specific customer intent data and leverage it to have a large impact on marketing ROI.