Machine learning: what is it and how can you use it in your research?

Machine Learning is used in many disciplines and has very different applications, e.g. B. in the areas of data security, finance, healthcare, search algorithms and even smart cars.

Nowadays it is also used in market research. That's why we've written an article to teach you a little more about this topic so you can incorporate it as part of your research methods.

What is Machine Learning?

Machine learning is the process by which an artificial intelligence learns from data and adapts without the need for explicit programming.

Machine learning occurs through the use of algorithms that convert a data set into a model to analyse patterns in the data and draw conclusions from them.

Machine learning makes it possible to extract information from large amounts of data (big data). For example, a large amount of existing data about a product or service can be transformed into a detailed list of insights in the language of customers.

Advantages of machine learning

Some benefits of implementing machine learning in a growing company include:

Developing more products and services.

With data in hand, companies have much more information and the ability to predict what customers want before they even know they want it, allowing them to create new products and services based on the conclusions of machine learning software develop.

Companies benefit from AI by using data to tailor their services to different types of customer needs.

Optimization of content

Machine learning can help marketers create content strategies by discovering new content ideas based on research, identifying the top-performing topic groups, displaying the most relevant keywords in their niche, and automating performance measurement.

Improving customer experience

Machine learning can improve the online customer experience in many ways, such as:

• Guide the shopping experience by providing personalized product recommendations that help the customer find what they are looking for;
• Ensuring your online store never runs out of stock or offering alternatives when supplies are low.
• Provide customers with a 24-hour help desk.

4 types of machine learning

There are 4 types of machine learning:

Supervised machine learning

Supervised machine learning is characterized by using labeled data sets to train algorithms that accurately classify data or predict outcomes. As the input data is fed into the model, the model adjusts its weights until it is set correctly.

With supervised machine learning, the machine learns through examples. The operator provides the machine learning algorithm with a known data set with the desired inputs and outputs, and the algorithm must find a method to determine how to arrive at those inputs and outputs.

As the operator knows the correct answers to the problem, the algorithm recognizes patterns in the data, learns from the observations, and makes predictions. The algorithm makes predictions and is corrected by the operator, and this process continues until the algorithm reaches a high level of accuracy/performance.

Semi-supervised machine learning

Semi-supervised machine learning is similar to supervised machine learning, but uses labeled and unlabeled data. During training, a smaller labeled dataset is used to guide classification and feature extraction from a larger unlabeled dataset.

Semi-supervised machine learning can solve the problem of not having enough labeled data for a supervised learning algorithm. It also helps when it is too costly to label enough data.

Labeled data is essentially information with meaningful labels so that the algorithm can understand the data, while unlabeled data lacks such information. This combination allows machine learning algorithms to learn to label unlabeled data.

Unsupervised machine learning

Unsupervised machine learning uses machine learning algorithms to analyse and cluster unlabeled data sets.

In this case, the machine learning algorithm examines the data to identify patterns. There is no answer key or human operator to give instructions. Instead, the machine determines correlations and relationships by performing an analysis of the available data.

In unsupervised machine learning, the machine learning algorithm has to interpret large data sets and process them accordingly. The algorithm attempts to organize this data in some way to describe its structure.

Reinforcement machine learning

Reinforcement machine learning is about regulated learning processes in which a machine learning algorithm is provided with a series of actions, parameters and end values.

By setting rules, the machine learning algorithm attempts to explore different options and possibilities and monitor and evaluate each outcome to determine which is optimal.

In reinforcement machine learning, the machine learns by trial and error. She learns from previous experiences and begins to adapt her approach to each situation to achieve the best possible outcome.

Machine learning algorithms

The algorithms that belong to machine learning are:

• Neural Networks: Neural networks simulate the functioning of the human brain, with a large number of interconnected processing nodes. Neural networks are good at recognizing patterns and play an important role in applications such as natural language translation, image recognition, speech recognition and image creation.
• Linear regression: This algorithm is used to predict numerical values ​​based on a linear relationship between different values. For example, this technique could be used to predict house prices based on historical data for the area.
• Logistic regression: This supervised machine learning algorithm makes predictions for categorical response variables such as: B. “Yes/No” answers to questions. It can be used in applications such as spam classification and quality control on a production line.
• Clustering: Using unsupervised machine learning, clustering algorithms can detect patterns in data and cluster them. Computers can help data scientists by identifying differences between data elements that humans have missed.
• Decision trees: Decision trees can be used both to predict numerical values ​​(regression) and to classify data into categories. Decision trees use a branching sequence of linked decisions that can be represented in a tree diagram. One of the advantages of decision trees is that they are easy to validate and verify, unlike the black box neural network.
• Random Forests: In a random forest, the machine learning algorithm predicts a value or category by combining the results of multiple decision trees.

How can you use machine learning in market research?

Here are 7 steps to using machine learning in market research. The process of using machine learning to uncover customer or consumer insights is as follows:

1. Identify data sources and extract content

Machine learning can be used to identify data sources to examine and extract relevant content from the sources. The data is then prepared for analysis, which includes breaking it into individual sets and other tasks to clean the data.

2. Data cleaning

Clean data does not exist in nature. To be useful for machine learning, the data must be aggressively filtered. For example, you must:

• Check the data and exclude columns with a lot of missing data.
• Reexamine the data and select the columns you want to use for your prediction.
• Exclude rows that are still missing data in the remaining columns.
• Correct obvious typos and merge equivalent answers. For example, USA, USA, USA, USA and America should be combined into a single category.
• Exclude rows whose data is out of range. If you e.g. For example, if you're analyzing taxi rides within New York City, you'll want to filter out rows whose pickup and drop-off latitudes and longitudes fall outside the city limits.

3. Data encoding and normalization

To use categorical data for automatic classification, you must encode the text labels in a different way. There are two common encodings.

One is label encoding, where each text label value is replaced with a number. The other is single-point encoding, which converts each text label value into a column with a binary value (1 or 0).

4. Selection of the algorithm

There is only one way to find out which algorithm or group of algorithms produces the best model for your data and that is to try them all.

If you also try all possible normalizations and feature selections, you will be faced with a combinatorial explosion. Because it is impractical to test everything manually, machine learning tool providers have put a lot of effort into developing automated systems.

The best systems combine feature engineering with a sweep over algorithms and normalizations.

5. Algorithm training

Train word embeddings and apply a convolutional neural network to filter out uninformative sentences from informative sentences. Informative sentences are those that contain important information about the consumer or their wants and needs.

6. Machine execution

The machine then clusters the phrase embeddings and selects phrases from different clusters to create a final database of phrases.

The result is a list of approximately 2.000 informative phrases that contain various insights.

7. Analysis by a qualified professional

Finally, it’s time for a professional machine learning analyst to review the sentences and identify a unique set of insights.

Use of machine learning and AI in surveys

QuestionPro has several tools that make it easier for you to use machine learning and AI technology in market research surveys. Some examples are:

1. predictive answer options.

For example, with QuestionPro's predictive answer options, our machine learning AI engine automatically predicts and fills in the answer options based on the question text you enter.

2. Generative surveys with artificial intelligence

With the appearance of Chat GPT We also introduced QuestionPro QxBot, a generative AI survey generator that allows you to create questionnaires in seconds.

QxBot leverages ChatGPT's AI capabilities as well as our own secret sauce based on our own question libraries to create surveys.

QxBot is currently available in BETA phase. If you want to use it, contact your account manager to get early access and enjoy all exclusive features.

3. Sentiment analysis

The sentiment analysis tool allows you to use artificial intelligence to classify open data into three categories: positive, neutral or negative. This allows you to analyse the qualitative data from your surveys more quickly.

This is one of the reasons why Tekpon named QuestionPro the best survey software company.

Take advantage of all the advantages that QuestionPro has in store for you!

Learning to use machine learning from scratch can be a daunting task for a market researcher or CX professional, but with the right tools, you can start using it to make your work easier, faster, and more efficient.

Now that you know that QuestionPro is one of the ways to achieve this, we invite you to get a first-hand look at all the tools that use artificial intelligence and cutting-edge technology to make researchers' lives easier.

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KEYWORDS OF THIS BLOG POST

Machine Learning | ML | Artificial intelligence