Leveraging Automated Analytics to Unearth Customer Insights

Intro

Nowadays, businesses work with large amounts of data, and to stay afloat, they need to know how to make this data work for them. It is a good way to succeed, as when you can analyze the data you produce and collect, you can make better decisions, such as marketing strategies, better user experience on your website, or even check what pages give you the most visitors and find ways to boost them even more.

So, what methods can you use to retrieve this analyzed data from the large datasets? What benefits does this type of analysis give in getting customer insights and predictive analysis? And, finally, what challenges and trends should we expect when starting with automated data analysis? Let’s dive in!

Methods of data collection and analysis in automated analytics

Automated analytics is a powerful tool that also has a lot of methods and ways to collect data. Since each approach is suitable for different ways of getting information, as well as different purposes, it is good to know what each of them employs in the process and how you can use them for your benefit.

Source: Clicdata

Methods of data collection used in automated analysis:

• web scraping. The most popular way of retrieving data from the webpages.

Web scraping can be done both manually and automatically, but if talking about large amounts of data, it is preferred to be automated. For web scraping, you need to have an understanding of Python libraries BeautifulSoup and Scrapy, as well as a basic knowledge of HTML structure to be able to retrieve the correct data.

The use case: an e-commerce website can use web scraping to collect info, such as information about products, prices, and customer reviews from competitors' websites.

• APIs. APIs can be a nice option for retrieving the info from websites (external services, applications) as they allow the automated systems to request the data and get it directly from the other software (service).

APIs are popular options on social media platforms (e.g., Twitter API or Facebook Graph API) as they allow businesses that get them to retrieve analytical data, such as customer interaction statistics, trends, and feedback.

The use case: the APIs allow companies to query social media services and retrieve the needed info in JSON or XML formats that can be easily processed later.

• transactional data. This option means that the data is being gathered from the transactions, which include sales, purchases, and online payments. Advanced analytical tools like SQL databases, data warehousing solutions, or even specialized analytical software (e.g., Tableau or Microsoft Power BI) help here since they are created to manage and analyze this type of data.

The use case: a retailer can use this way of extracting data to retrieve and analyze the data from sales transactions. It can help them find some hidden purchasing patterns, revenue trends, or even customer behavior so that later, they can personalize their marketing strategy to increase sales.

Technologies in automated analytics

There are many ways you can analyze the collected data. In most cases, data analysts use machine learning (ML) for automated analysis, natural language processing (NLP) for sentiment analysis, data mining for analyzing large amounts of data and getting meaningful insights, and predictive analysis for forecasting trends and customer behavior. Let’s talk more bout each of the approaches below:

• machine learning algorithms. ML algorithms are at the heart of automated analytics. They help to analyze data and find patterns that draw predictions based on the analyzed data. For example, if you are in retail, use ML to predict customer buying behavior based on how they purchased their previous ones.
• natural language processing (NLP). Since simple data analysis won't always find good insights, it is nice to try out using NLP. It is an approach that allows you to analyze the dataset, which includes text, such as feedback, social media posts, or even support tickets, to retrieve useful insights on the product. To be more specific, if you want to know if the customer likes/dislikes your product, you can get this data by allowing NLP to search for phrases and words that are emotionally colored to group them by negative, neutral, and positive, and, finally, get the better insight on your product.
• data mining. This one is for those who need to analyze large amounts of data and find meaningful patterns, relationships, and anomalies. This approach is often used for risk management, fraud detection, and market segmentation.
• predictive analytics. This approach means you let the automated systems get the historical data that was stored before, analyze it, and forecast future trends and customer behavior. It is a popular option for businesses working with financial data, demand forecasting, and risk assessment.

Ways of processing and interpreting customer data

When starting to use automated analysis, it is good to know what exactly you want to do with the analyzed data. Here we have created a list of ways of processing the collected data for interpreting customer behavior:

• segmentation – divide customers into groups by specific characteristics (demographics, buying habits, interests). It is a good option for businesses looking for target marketing/personalized offers.
• behavioral analytics – analyze how users behave on your website, so you can have better insight into whether you need to improve your website navigation, better user experience, or make better marketing strategies.
• sentiment analysis – get insights on your product from social media, reviews, and feedback. It can help you to work on the negative aspects of your product or service, improve its quality if needed, etc.

Benefits of automated analytics in understanding customer behavior

Automated analytics comes with many benefits that are very useful, especially if you decide to start using large datasets to increase your customers’ behavior and experience.

It not only helps you process huge datasets in a matter of minutes but also gives you valuable advice based on this data. Here, let's discuss the most prominent advantages of using automated analytics and how it can help your company.

Source: Kanaries

Processing large volumes of data

One of the benefits that helps a lot is the ability to analyze large amounts of data that often come in terabytes or petabytes in size – it is where the traditional ways of analyzing data won't really work.

ML algorithms and frameworks like Apache, Hadoop, and Spark can help you work on the data quickly and efficiently as these technologies work using cloud environments for working on tasks, significantly speeding up the processes.

Efficiency and speed

The analysis that could take weeks or months to complete can now be done in a matter of seconds thanks to AI, which is the base component of automated analysis. For instance, AI can move around customer data across the platforms swiftly, collecting the data – social media, customer support calls, and website interactions. Thanks to this quicker approach, businesses can now have valuable information to make better decisions on the data they have.

Find hidden patterns and trends

Machine learning algorithms are also good at finding patterns and trends that might be omitted when using manual analysis. When using ML, you can get subtle behavior patterns and preferences and even predict future trends. One of the examples is that clustering algorithms can segment customers into groups with similar behaviors or preferences, which might be missed when using fully manual analysis.

Predictive insights

Predictive analysis uses the historical data the business has to predict future customer behavior. It includes such techniques as:

• regression analysis;
• decision trees;
• neural networks.

They are useful for those who want to launch a new product and want to predict the possible customer reaction, likelihood of churn, and lifetime value. It is also a good way to personalize customer interactions and adjust business strategies.

Better customer experience

Finally, the last benefit is the better the analysis is done, the better you understand your customers. With the help of personalization algorithms, you can easily tailor product recommendations, marketing messages, and many more to the specific customer, thus, increasing engagement rate, user experience, and general emotional feeling about your company.

Types of information gained through automated analytics

What are the types of info you can get with automated analytics? There are many, so let's find out here.

Source: Great Learning

Customer demographics and segmentation

Analytics tools use algorithms to find the following information:

• age;
• gender;
• location;
• education;
• income level.

ML uses these and many other factors to create better segmentation, thus helping you make a more personalized approach to each group of customers. To make an example, a decision tree algorithm categorizes the users into different segments so that you can later create better-targeted marketing strategies.

Purchasing patterns and behaviors

Automated analytics employs techniques like association rule learning (e.g., Apriori and Eclat algorithms) to see the relationships between different products and customer buying habits.

For example, market basket analysis is a great way to find out what products are frequently bought together, as well as a time series analysis that allows you to analyze the sales trends over time and anticipate demand fluctuations.

Customer feedback and sentiment analysis

With sentiment analysis, you find out about the feelings and opinions of the customers using NLP. What NLP uses for sentiment analysis:

• customer feedback;
• reviews;
• social media posts related to the product.

NLP algorithms like LSTM (Long Short-Term Memory) networks work on the collected text to categorize it based on the emotion-related words into positive, negative, and neutral. Thanks to such an approach, the company can get a comprehensive understanding of whether the product they have is perceived as a positive or negative one and what exactly they should improve to make it better.

Online interaction and digital footprint

Knowing how users interact with your website/product is essential, and tools like Google Analytics can help you with that. They track user interactions that include website visits, click patterns, and social media engagement to find, for example, the most popular pages or features, time spent on the page, bounce rate, etc. This is all helpful when you want to improve your website but don't know where to start.

Challenges in implementing automated analytics and solutions

With the large number of benefits and use cases of automated analytics come challenges that also need to be addressed. Keep scrolling and learn about what can be an obstacle in your way of automated data analytics.

Source: Millimetric

Data privacy concerns

Data automation often means working with sensitive data. It is one of the reasons why many show their concern. One of the ways to prevent data leaking is to use encryption technologies, such as SSL/TLS for data in transit and AES for data at rest.

The company should also comply with regulations such as GDPR and CCPA (they ensure strict guidelines on data collection, processing, and storage).

Need for skilled personnel

If you decide to use automated data analytics, make sure to have professionals with experience in data science, machine learning, and big data technologies. If you have no time to find a professional data analyst, invest in training and development programs to upskill existing staff.

In addition to this, you can use one of many analytics platforms that provide user-friendly interfaces and are generally more accessible to broader audiences (e.g., AWS SageMaker or Google Cloud AI).

Data accuracy and relevance

The last concert is mostly related to the quality of data that is used. For instance, if the data is not cleaned properly, has too many gaps, or simply is not relevant, the result insights won't do anything good to you.

To make the dataset usable for analysis, regularly clean, validate, and standardize it. Don't forget to use automated data quality tools that can identify and correct errors, duplicates, and inconsistencies in the data. Since ML models are trained on the data you give them, make sure this data is of good quality.

Future trends and predictions in automated analytics

As more and more companies start integrating AI and ML in their workflow, we can say with confidence that the future of automated analysis is bright. From integrating AI/ML to the use of edge computing – everything will work to help get the best insights.

Trends in automated analytics

Here we will explore the trends that will be growing significantly in the future:

• AI and ML integration with IoT. Since IoT devices generate a large amount of data, AI that is integrated into these devices will help process this data more quickly and provide better customer insights.
• predictive analytics advances. With the help of more nuanced predictions, businesses will get more in-depth information about customer behavior, preferences, and insights on future trends.
• edge computing. Edge computing allows data to be analyzed closer to where it is generated (e.g., right inside the device from which it was collected). This approach will help to provide real-time information for applications like personalized marketing and in-store customer experience enhancement.
• data privacy. There is no doubt that with the increased concerns about the security of the data that is used, the future trends in automated analytics will definitely have a place for new encryption methods as well as anonymization of personal customer data.

Final thoughts

To wrap up, using automated analytics to retrieve useful information about customer behavior is a good way not only to get insights for the business itself but also to increase customer satisfaction with your product in the long term.

With the help of ML and AI, you can categorize the users by specific data, find what is the best and worst in your product just by scraping the social media posts, or even have future predictions on whether your customers will like the new product you are going to launch. The landscape of AI analytics is wide, and when you know what to start with, you can do great.