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How to Leverage Data Science For Customer Management


Globally, a wealth of data is collected and stored each day. Currently, more than 2.5 quintillion bytes of data are created each day. By 2025, it is estimated that the world will create 463 exabytes of data each day.

It can bring transformative benefits to businesses and societies around the world if interpreted correctly with the help of data science.

As per Gartner, data science holds the key to unveiling better solutions to old problems.

Also, according to the International Data Corporation, data science is the key for industries to provide analysis and light on best practices for avoiding data breaches and attacks.

In fact, two-thirds of companies with formal customer programs are already leveraging data science to help them make sense of their data.

Let€™s go through some of the best ways to leverage data science for customer management:

1- Customer Segmentation

Customer segmentation is a powerful method for businesses to identify unsatisfied customer needs. It involves arranging customers into homogenous groups based on factors, such as demographics, preferences, or purchase history.

Machine Learning, one of the branches of Data Science, uses clustering algorithms to facilitate customer segmentation. It split your customer base into groups by common interests.

Here are the steps to segment customers using ML:

  • Build a business case: Know the purpose of using ML and AL. For example, your business case can be to find the most profitable customer group.
  • Create data: Find the number of customers you have. More numbers will be beneficial for customer segmentation deep learning. Also, set different measurable attributes based on the best metrics for your business. For example, average lifetime value, retention rate, client satisfaction, etc. Tools such as pandas are helpful for data preparation.
  • Apply K-means clustering: K-means clustering is an unsupervised ML algorithm method. Unsupervised algorithms do not have a labeled data to assess their performance. K-means clustering helps arrange data into more similar clusters.
  • Choose optimal hyperparameters: Hyperparameters are the properties that govern the training process. Hyperparameter optimization helps to find the most rewarding customer group based on past work.
  • Visualization and interpretation: Visualize and interpret the findings once you have profitable customer profiles using the above steps. It helps you improve your marketing campaign, targeting potential customers, and building a product map. You can use Plotly Python for making interactive graphs and charts.

Thus, it makes it easy to cross-sell and upsell business products. When customers receive content relevant to their needs, they are more likely to make a purchase.

Moreover, it builds customer loyalty to your brand as your business adds to their lives.

2- Predictive Analytics for Data-Driven Decisions

Nowadays, every business relies on data. The global market of predictive analytics is projected to reach approximately USD 10.95 billion by 2022, growing at a CAGR of 21%. Skilled data analytics specialists are needed to solve everyday business problems.

The best way to find deep, real-time insights and predict user behavior and patterns are by using predictive analytics tools. The top predictive analytics software and service providers include Acxiom, IBM, Information Builders, Microsoft, SAP, SAS Institute, Tableau Software, Teradata, and TIBCO Software.

Once you have selected the software, use an appropriate predictive analytical model to turn past and current data into actionable insights.

Predictive models using business data generate informed predictions about future outcomes and revamp business decision-making processes. The business data can include user profiles, transaction information, marketing metrics, customer feedback, etc.

The typical business model for customer management that you can use is the Customer Lifetime Value model. It finds out customers who are most likely to invest more in your products and services.

Now, you have to choose a predictive modeling technique. Model users have access to endless predictive modeling techniques. The widely supported technique across predictive analytics platforms for customer management is the decision tree. It determines a course of action and shows a statistical probability of a possible outcome.

In the long term, predictive analytics is more cost-effective than losing a customer.

3- Provide Better Personalized Services

Providing personalized services is a great way to build relationships with your customers. It helps increase sales by offering them products and services as per their interest.

One of the smartest ways to provide personalized services is through artificial intelligence. AI software is efficient, spends less time searching for solutions, and works with multiple customers at the same time.

The AI-powered chatbots and virtual assistants initiate the conversation with a customer, help with routing, engagement, and interaction. The chatbots trained with natural language processing easily answer questions and collect critical customer insights.

More than 80% of chat sessions are resolved by a chatbot, as per Accenture.

Software such as Zendesk live chat offers you the flexibility to reach customers in real-time and build amazing conversational experiences.

4- Analyze the Trends and Sentiments on Social Media

Social media is one of the most crucial sources of data. Data science and big data take advantage of these large volumes of spontaneous and unstructured data.

Social media sentiment analysis allows businesses to learn more about their customers. It helps them understand how their customers feel about their brand or product. ML automatically detects the emotion of online conversations, classifying them as positive, negative, or neutral.

There are three best ways to use data science or ML for effective social media customer management:

  • Social media monitoring: It allows you to monitor and regulate social media content that is necessary for better customer service. There are built-in analytics tools in platforms such as Instagram and Twitter. These tools measure the success of past posts, such as likes, clicks, comments, or views.
  • Sentiment analysis: It judges the sentiments of a text using NLP. It helps analyze social media conversations as positive, negative, or neutral. You can utilize sentiment analysis for customer support and collecting feedback on new products.
  • Image recognition: Computer vision recognizes brand logos and product images without texts. It is helpful when customers upload pictures of your products without directly mentioning or tagging the brand. In that case, with the help of image recognition, you can notice it and send the targeted promotion.

Tools such as Microsoft Automate and Power BI services help you track feedback for your company. These tools have built-in algorithms that consider words, such as good, awesome, or happy as positive sentiments and words like horrible, or worst, as bad sentiments. Moreover, there are social media engagement tools to measure the engagement levels of your content.

You can leverage these tools to:

  • Decide times for the best performance of your company€™s product launches.
  • Predict the best times and audience types for your campaigns.
  • Access and analyze competitor data to improve processes in your company.

Final Thoughts

The world of data analysis is evolving. In the coming years, you will see business disruptions in almost every sector powered by data. It will result in an increasing demand for data science.

Data science and its branches, including AI and ML, allow businesses to track and understand customer data. It helps them communicate with their consumers to increase revenue and make better decisions. The key to leveraging data science for maximum returns is being able to visualize and take action on the large volumes of data.