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Market Intelligence

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What is Market Intelligence?

Market intelligence is the information relevant to a company’s market – trends, competitors, customer monitoring, etc. Companies are similarly analyzed specifically for accurate and confident decision-making. Besides this information, companies also determine strategy in areas such as market opportunity, market penetration strategy, and market development.

Why do we need Market Intelligence?

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For example, a company has a vast amount of text data of its users. It has a collection of data talking about their brands. For instance, what their users wrote about their brand is that everything is good or bad across many platforms. But still, the company isn’t able to understand their valuable customers, how their products perform in the market. Just as tracking the number of sales, it can’t conclude what the users think. In addition to this, companies need to analyze customers’ feedbacks and queries.

Companies need to understand the strategies and technologies used to analyze business information and, similarly, make informed decision-making. It helps to create a better understanding of the global market as well as provide an insight into how your company operates. And also, understanding the needs of customers to build better products and services which are welcomed by customers.

AI-Based Market Intelligence System

What Is Business Intelligence? Using Data Analytics in Business Strategy | Ohio University

In this section we will discuss more things How AI improve market intelligent process.With AI’s help, we can build a system where we can track the sentiments, what people are talking about, how they react to the products, etc. To build a market intelligence system, we will have to incorporate multiple models that will give us different pieces of information. Now we will see the process that we will use to build a market intelligence application.

    • Data Collection/Extraction

      Before we start working on the application, we need to collect the data. To collect data, we will use technologies like web-scraping, data extraction from social media like Facebook, Twitter, etc. Using a data versioning like DVC, we will keep track of the data we are collecting.

    • Data Annotation

       

Now once we have collected the data, we can prepare multiple datasets that we will useful to train our models in the Model development part. When we mentioned the above intelligence and talk about other analytics drawn from the data, it’s always important to have well-annotated data. A well-annotated data helps you build models for sentiment analysis, intent classification, etc. but also can help you in content enrichment.

A powerful AI model heavily depends upon the quality of the data you give while training model. And in a system like market intelligence, you have a set of unstructured data with multiple dimensions to annotate for example particular social media comments. You want to have the comment, sentiment related to the aspect, what topics present in that comment, and the intent behind it.

We will require datasets like sentiment analysis, Intent Classification, and topics. Then, we can able to start a hierarchical data annotation process where each text data can be annotated in multiple dimensions. After that, we will arrange our data so that we can easily extract the individual labels as per our requirements. Means we have to divide our data based on different models. For example, for single social media comment, we will prepare structural data as the below:

{“text”: “comment”.”annotation”: {“sentiment”: (aspect_label, sentiment_label),”intent”: “intent_label”,”topics”: “topic”,”keywords”: [“keywords”]}}

    • Data Pre-Processing

      After studying the requirements of the application, we will start to preprocess the textual data which involves things like removal of stopwords, lemmatization, tokenization, etc.

      Now once we have our dataset preprocessed we can start working towards building the model. We will show you how we will use NLP techniques like sentiment analysis (Aspect Based Sentiment Analysis), Intent Classification, topic modeling, etc in the application.

    • Model Development

      When a company has a larger audience it’s always important to keep track of the sentiments of the audience over time. For instance, whether Is their sentiment changing over time or it gets better. So to understand the sentiment you need to build a Classification model that will tell you the sentiments of the customers in real-time.

      It not only tells you what the sentiment is like instead of just saying positive, negative, and neutral. In addition, it will tell you where it is positive or negative. For instance, it will tell you on a particular aspect what should be the sentiment. Similarly, it will tell you the sentiment of a product in the pricing aspect or quality aspect. In this way, the company can understand which aspect they are getting negative reviews and in which positive.

      However, to build such a model we will need to train a model that will first extract the aspects associated with the text. And then find out what that aspect is talking about. By using models like BERT and constructing auxiliary sentences we can extract the aspects and sentiments.

    • Topic Modeling

      Like to understand what the sentiment is, it’s also important to understand what people are talking about. It gives an insight to the company to understand the topics which are frequently discussed among the customers. Also, understand their behaviors towards those topics. We have both probabilistic and deep learning models to extract topics and keywords from a text and we can use our annotated data. We can train AI models with annotated data effectively and extract the related topics and keywords associated with a text.

    • Intent Classification

      In this paragraph, we can understand how intent classification model will helps to market intelligence. Intent classification is a way to understand the intentions behind customer queries, emails, chat conversations, social media comments, and more. It will help us to automate processes and get insights from customer interactions. For example, identifying purchasing intent is pivotal in transforming sales leads into fully-fledged customers. Addressing customer’s requirements as soon as possible always keeps you ahead of your competitors.

      Intent classification allows companies to be customer-centric and understand their needs and solve them, especially in the support sector. We can treat this as a multi-label classification problem and by using Transfer learning methods we can train our annotated data which can help us to come up with good accuracy.

    • Deployment, Visualize and Analytics

      In this paragraph, we will discuss how we will use our trained models.  Then, how we will infer the input data and give us the necessary information by some visualizations. Below we have mentioned some of the examples of it.

      1. Aspect-based sentiment analysis through a drill-down bar chart
      2. Line graph to show the trends of customers either on social media or sells growth
      3. Bar with negative stack plot to show key positive and negative keywords used often through n-gram models
      4. Pie-chart to show the percentage of intents from the Intent Classification model
      5. A polar chart to show aspect-sentiment

Like this, we can have many visualizations and Analytics based on the data and the problem statement.

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