What is customer analytics and how it works?

Customer analytics is a strategic process that uncovers valuable insights into customer preferences and behaviors. It facilitates the development of products and services that resonate with customers, enables smarter communication strategies, and delivers tailored experiences. It's like having a competitive edge, as evidenced by a McKinsey study showing that businesses utilizing customer analytics are three times more likely to achieve above-average revenue growth and twice as likely to achieve higher ROI on marketing compared to their competitors.

What is customer analytics?

Customer analytics encompasses the collection, validation, interpretation, and analysis of data to extract valuable insights regarding customers' preferences, behaviors, and requirements. It employs diverse tools and data science methodologies to comprehend and forecast customer interactions, sentiments, and perceptions towards a company's brands, offerings, and marketing endeavors.

What motivates companies to utilize customer analytics?

Companies employ customer analytics to inform strategic decisions based on data, enhancing customer experiences and fostering business expansion. Utilizing these insights not only enhances customer engagement and loyalty but also enables the anticipation of customer actions, discovery of fresh market prospects, and promotion of innovation that meets customer expectations. Ultimately, this leads to augmented market presence, customer satisfaction, revenue, and profitability.

How do customer analytics work?

As previously mentioned, customer analytics involves a series of steps, starting with data collection, followed by processing and analysis, and concluding with actionable insights to support our business objectives. But what does each step entail? How do we progress from gathering raw data to taking meaningful action? And what skills must we develop to effectively utilize customer analytics? Let's delve deeper into these questions.

Establish clear objectives

While customer analytics is invaluable for addressing challenges and answering complex questions, it's not a magical solution. It cannot reliably address subjective inquiries like "Does Coke taste better than Pepsi?" However, it can provide insights into questions such as "Which marketing channel offers the best ROI for Coke or Pepsi?" or "Which customer segment presents the most significant growth opportunity this year?"

Collect and analyze customer data

Every customer action can be quantified in some manner. For instance, simply logging into a website can generate numerous data points, including login time, device type, and browser. Additionally, existing customers contribute data such as age, gender, and height.

Broadly speaking, there are four distinct types of customer data, each offering unique insights into customer behavior and preferences.

• Behavioral data aids in comprehending and forecasting customer actions, including recent transactions, product usage patterns, and feature engagement duration.
• Personal data furnishes individual customer details, such as their name, birthdate, occupation, and credit card information.
• Engagement data illuminates customer interactions across diverse marketing channels, encompassing activities like website visits, social media engagements, and customer service inquiries.
• Attitudinal data provides insights into customer perceptions and sentiments towards your brand, including measures of satisfaction, sentiment analysis, and purchase preferences.

While the process of collecting and cleaning data may seem straightforward in theory, it poses challenges in practice. Firstly, you need to identify the personal, engagement, behavioral, and attitudinal data relevant to your query or issue and determine their sources.

However, the complexity arises when aggregating, validating, and cleansing this data. This involves extracting necessary data from various systems like CRMs, data warehouses, web analytics tools, and social media platforms, rectifying errors, addressing missing values and inconsistencies, integrating datasets into a unified database, and converting them into a usable format.

Essential skills to consider

You don't necessarily need to be a professional data scientist or coding expert to utilize customer analytics effectively. However, familiarity with these fields significantly enhances your analytical capabilities. If acquiring these skills independently is challenging or undesirable, consider hiring individuals with the requisite expertise or outsourcing to service providers.

For those interested in practicing customer analytics, proficiency in basic data science techniques, Excel proficiency, and familiarity with coding languages like Python or R are beneficial. Mastery is not necessary, but being able to navigate through problems using online resources is essential.

How might customer analytics benefit my business?

Once your data is gathered, processed, and transformed into a usable form, you can begin analyzing it for valuable insights and developing strategies to meet your goals. Let's examine some examples of how customer analytics can benefit your business.

Utilizing customer analytics for customer segmentation and personalization

Customer analytics enables you to categorize your customers based on various data points, including personal details and their interactions with your products or services. By segmenting your customers, you can create customized experiences and messages that are more likely to resonate with each segment.

For instance, let's consider a scenario where you operate a streaming service and have set two objectives for this quarter:

• Increase current subscriber engagement by 10%
• Increase ROI on digital marketing by 5%

After analyzing subscribers' viewing habits, genre preferences, and interactions with the service, we identified three subscriber groups: TV fans, movie fans, and sports fans. To boost subscriber engagement, we plan to recommend content based on each subscriber's group, encouraging more platform interaction and reducing subscription cancellations.

Our analysis also revealed age as a key factor in segmenting subscribers. Older subscribers lean towards movies, middle-aged viewers favor sports, and the youngest audience segment prefers TV shows. Additionally, age predicts the most effective communication channels: email and LinkedIn for older subscribers, Instagram for middle-aged, and TikTok for the youngest.

Using this data, we tailor a marketing campaign to target non-subscribers based on their segments. By delivering messages that align with their interests through their preferred channels, we aim to improve conversion rates and lower customer acquisition costs.

Predict and prevent customer churn with customer analytics

Customer churn is the rate at which customers stop using a company's products or services over a specified period. Minimizing churn is crucial because acquiring new customers costs more than retaining existing ones, and existing customers tend to spend more. Customer analytics can predict when a customer is likely to churn, allowing businesses to take preventive action. For instance, imagine you manage a gym and notice that members who decrease their usage for three consecutive months and don't participate in weight-loss programs are more likely to cancel. Conversely, those who join such programs tend to increase their usage. By identifying these patterns, you can offer incentives, like a free month of the weight-loss program, to prevent churn.

Effective cross-selling and upselling with customer analytics

Customer analytics, such as purchase history and behavior, can help businesses increase revenue by identifying upsell and cross-sell opportunities. For example, as an IT equipment reseller, you might aim to boost accessory sales by analyzing purchase and browsing history. If customers who buy a laptop often purchase accessories like carrying cases and headsets, you can suggest these items when they add a laptop to their cart, increasing the average transaction value.

Smart product development with customer analytics

Customer analytics can guide product development by highlighting necessary adjustments to existing products and indicating new product areas to explore. For instance, as a multifunction printer (MFP) manufacturer facing a chip shortage, you can use usage data and service logs to determine which features are underutilized. If Wi-Fi connectivity, a standard feature, is rarely used, you can make it optional to free up components for other models. Additionally, if many customers only use the scan function of your MFP, you might develop a standalone scanner to cater to this specific need, increasing both customer spending and market reach.

In summary, leveraging customer analytics removes uncertainty from decision-making and future planning, giving your business a competitive advantage. Through analyzing customer data, you gain insights into customer and market trends, uncovering issues that may not be apparent otherwise. This analysis enables you to anticipate customer behavior and market shifts, allowing you to develop strategies that attract new customers, enhance average transaction values and customer lifetime value, and boost overall business performance. Moreover, it helps you proactively address challenges to enhance customer satisfaction and prevent negative experiences that could lead to increased churn.