Sifting sentiments – How NLP and sentiment analysis transform the direct selling scenario!

When a customer walks into your store, there could be numerous thoughts passing through their mind. Sometimes you can figure it out, from how they behave, their attitude, eye contact, body language, and various other parameters. You could recognize if they are happy, excited, frustrated, or sad with their behavior and mannerisms. According to this, you could talk to them, sell your products, have a chit chat and build your bridge towards a stronger customer relationship.

But, in this fast-paced world, when there are ten other customers in your store at the same time, you might not have time to determine a customer’s emotions and sentiments and act accordingly. Also, in the long run, you wouldn’t be able to keep track of the numerous customers manually to maintain a long-term relationship with them. With online shopping taking over, we never know what your customer, who is shopping virtually is actually thinking. This is where technology would come to your rescue to help you rework on your strategies based on changing customer choices and preferences.

Natural Language Processing (NLP)

Natural Language Processing is a subdomain of Artificial Intelligence (AI) that concentrates on human and computer interaction. It enables computers to recognize, interpret, and generate human language in a very significant way. With NLP, a wide range of real-world applications could be facilitated from customer service, healthcare, finance, ecommerce and many such verticals where it could make computers more accessible and capable of understanding and processing human language thus enhancing the human-computer interaction.

In the direct selling scenario, NLP has made a revolution in the acquisition and retention of customers where, with the help of computers, human intent, innate behaviors, and future activities could be majorly predicted. NLP puts both linguistics and machine learning techniques to use to attain its objectives. Machine learning models involving deep learning models like RNN (Recurrent Neural Network), and transfer-based models fuel the capabilities of NLP to an advanced level. With these models being trained on large datasets of human languages, it can recognize and generate languages in various contexts.

Apple’s Siri, Google Voice Search, Google Translate, and other popular applications use RNN for language translation, NLP, speech recognition, and image captioning. These applications take information from previous inputs influencing both current inputs and outputs.

NLP in direct selling businesses

Having understood what NLP is and how is it beneficial for businesses, let us dig in a little further to find out how can NLP influence the operations and success in the direct selling sector.

1. By analyzing customer feedback – Gathering customer feedback through reviews, surveys, and social media is a common practice in direct selling businesses. With NLP, understanding customer sentiments, identifying common issues and gaining insights into customer preferences can be done effectively.
2. Making use of chatbots and virtual assistants – Providing real-time support to customers with NLP-powered chatbots and virtual assistants can help answer common customer questions, assist them with orders and provide information about products and services. This could ultimately help enhance the customer experience.
3. Giving personalized recommendations – Offer personalized product recommendations for customers by analyzing customer behavior and preferences. Suggesting preferred products aligning with customer interests could ideally improve sales.
4. Analyzing customer sentiments – By inspecting the sentiments behind every new marketing campaign, social media posts and customer interactions, direct selling businesses could gauge the effectiveness of their marketing strategies and formulate new ones accordingly.
5. Customer segmentation – With NLP, patterns of customer communication and their preferences can be analyzed based on which customer segmentation can be easily done. This can help businesses customize their sales and marketing strategies suitable for various customer segments.
6. Monitoring social media activities – Product mentions, business, or even industry mentions across all social media platforms could be monitored with NLP. This could further aid businesses in staying informed about the latest market trends and staying in constant response to inquiries and comments made by customers and potential leads.
7. Predictive analytics – Forecasting sales trends and customer behavior is a technique with which businesses operate in the future from the present. Predictive analytics is the model with which direct selling businesses can derive data-driven decisions and efficiently plan their operations.
8. Risk management and compliance – Direct selling businesses have to stay updated with changing legal amendments and regulations to keep up with industry standards. With NLP, keeping track of customer and distributor communications for compliance and regulation can be done efficiently.
9. Market analysis – Analyzing the market scenario by researching the market trends, competitor data, and customer sentiments could help with product development and marketing of the brands at many levels.
10. Optimizing email campaigns – With NLP, sentiments and content of email campaigns can be analyzed easily. This could help businesses optimize their email marketing to improve open rates, click-through rates, and conversions.
11. Feedback and review summarization – Customer reviews and feedback, which are crucial for direct selling businesses, can be summarized and categorized with NLP making it easier for businesses to prioritize areas for improvement.

From enhancing customer engagement to improving sales and streamlining overall business operations, NLP has quite a lot to do with the direct selling industry. Choosing NLP tools and techniques that align with the specific needs and goals of the business is the first thing to do. Moreover, privacy and data security considerations are crucial while implementing NLP in customer interactions and data analysis.

Types of sentiment analysis and how it could improve sales

With NLP and machine learning, businesses are reinventing new ways to intervene in customers’ thoughts to unlock the way they think. Direct selling businesses have found newer and extraordinary ways to keep up with the pace and deliver an impeccable customer experience through which customer acquisition and retention can be acquired like a breeze.

Gauging customer sentiments to build robust marketing strategies is the smartest way to enhance marketing effectiveness. A sneak peek into different types of sentiment analysis and how it could help improve sales.

1. Graded analysis – One of the most basic and simplest forms of sentiment analysis where a customer gets to grade a product or service on a scale of one to five or ten, or with choices like excellent, satisfactory, average, poor etc. Businesses could take cues from these grading and find what went wrong and where.
2. Emotion-detection analysis – Matching emotions behind words used to gauge the sadness, anger or frustration of customer’s based on their purchase or service acquired. This could help businesses have a deeper understanding of customers' intent behind certain words used to describe their emotion.
3. Fine-grained analysis – Fine-grained sentiment analysis is also widely used to gauge emotions behind opinions shared on various social media platforms more precisely to manage crises. For example, when someone writes that a particular product they use was broken after a particular time period, fine-grained analysis analyzes the product, what went wrong with the product and when, to understand the situation and match it up with the customer emotion.
4. Aspect-based analysis – Similar to fine-grained analysis, this method also looks for positive or negative sentiments based on inputs given by the customer. For example, if the customer is keying their review in a chatbot, the chatbot would detect the customer emotions and accordingly transfer the conversation for assistance if needed.
5. Intent analysis – With this analysis, the intention of the message, whether promotional, appreciative, complaint, or a suggestion, etc could be sorted. Like how our messages and emails are categorized into spam, promotional, transactional, etc., this analysis can help sort customer responses into segments.

Direct selling businesses can alter their sales and marketing plans evaluating social media posts, enhancing their brand strength and improving their crisis management techniques. With NLP techniques, direct selling businesses can vastly improve their distributor experience as well gauging their emotions when they perform well and when they fail to reach their set goals as well. It would be easy to identify what went wrong, where and why a distributor couldn’t perform well.

With this human-centric techniques, direct selling businesses could quickly shift or alter their strategies to find better results, growth and profitability.