Do you have a good understanding of your customers? Customer analytics may help you obtain a better understanding of your audience, allowing you to make better business decisions and improve customer experience.
The collecting of client data is becoming increasingly important to businesses. During the COVID-19 crisis, business leaders raised their investment in customer data management by a stunning 92 percent, according to a survey.
However, simply collecting customer information isn’t enough. The data you collect must be pertinent. You must also understand the facts in order to draw conclusions and take action.
This is where data analytics for customers comes into play.
Businesses employ customer analytics (also known as consumer analytics) to figure out what makes their customers tick. Data analysis exposes how customers utilise your product or service to solve problems, as well as the stumbling blocks they encounter along the customer journey.
Use consumer analytics to anticipate client wants, and you and your team will be well on your way to providing proactive support and a consistent brand experience.
What is the definition of customer analytics?
Customer analytics is the practice of collecting and analysing consumer data in order to acquire useful, actionable insights into purchasing habits and preferences.
Customers’ data can be collected through a variety of channels, including websites, apps, social media, and surveys. The data can then be analysed and a report created, either manually or via customer analytics software.
Businesses have a greater understanding of their target market as a result of these insights, allowing them to produce better products and services. They also assist organisations in determining the best pricing structure, marketing campaigns that target the right customers, increasing revenue, and improving the entire customer experience.
Customer analytics can be divided into four categories:
1. The use of descriptive analytics
Descriptive analytics entails gathering and analysing information about previous customer actions. While consumer behaviour data like this can help you understand what happened, it can’t tell you why it happened.
Let’s say a large number of consumers submit poor ratings to their support interactions in customer satisfaction (CSAT) surveys. This pattern would be highlighted through descriptive analytics. It wouldn’t, however, inform you why you’re getting low grades.
2. Analytical diagnostics
Diagnostic analytics looks at data to figure out what’s causing patterns and why customers behave the way they do. Diagnostic analytics, for example, can explain why your consumers gave you a low CSAT score.
You can get this information via an open-ended survey question or from reading reviews and comments on social media. After reviewing the statistics, you may discover that long resolution times are the issue.
3. Analytical forecasting
Based on historical data, predictive analytics estimates what your consumers are likely to do. This can assist your support team in anticipating customer demands and identifying patterns, resulting in a better customer experience.
It may look like this: Using data to know when a consumer buys a specific product, a company can forecast when that customer will need the product again and send them a targeted email. Customer satisfaction, retention, and revenue may all improve as a result of this.
You may also use predictive analytics to identify at-risk consumers and prevent churn before it happens. For example, you might see that at-risk consumers use fewer products and contact support less frequently. Knowing whether to intervene can be as simple as recognising these symptoms.
4. Analytics with a predictive component
Prescriptive analytics provides a solution to the question “What should we do?” by advising a plan of action based on prior data. It offers suggestions about how to achieve specific goals. For example, you could aim to cut resolution time by 20% in order to boost client retention by 50%.
Why is consumer analytics so crucial?
Customer activity is fully visible thanks to consumer analytics. You may study how consumers find and utilise your products or services, as well as how they interact with your customer service representatives.
You may discover where you’re thriving and where you need to improve by recording and analysing CX metrics like Net Promoter Score (NPS) and CSAT score. Qualitative customer data, like as comments on social media or responses to open-ended survey questions, might reveal particular causes for customer satisfaction or dissatisfaction.
These crucial insights enable firms to make the necessary changes to boost customer pleasure, and loyalty, and reduce churn.
Consider a retail company that notices customers looking for an out-of-stock product. So that they don’t lose customers to their competition, the merchant can attempt to swiftly supply the goods. For example, suppose a customer service manager learns that consumers are complaining about high wait times. To fix the problem, they can introduce additional self-service alternatives or recruit more people.
How does data analytics for customers work?
Data gathering, cleaning, and analysis are all part of the consumer analytics process. When performed manually, these three steps take a long time. We suggest utilising a customer support software solution that interfaces with a customer data platform, such as RisePath CRM. Customer data collection, processing, and summarization will be more efficient and secure as a result of this.
Obtaining client information
So, where can you get consumer information? You already have a source of crucial data if you employ omnichannel customer support software. Customers’ names, addresses, past support tickets, and purchase history are all stored in this sort of software.
You can also collect data from numerous sources, such as social media, websites, and mobile apps, using your customer service software (or a CDP). After that, the system can construct customer profiles to let you see all of the important data at a glance.
You’ll need to construct a tracking plan when using a CDP: a dynamic document that specifies the metrics you want to track, why you want to track them, and where you’ll track them. This aids with the standardisation of the data, making it easier to sort and evaluate.
Customer surveys are another option. Collect consumer feedback on product and service interactions using CSAT or NPS surveys. Make sure to include open-ended questions to get qualitative data as well.
It’s critical to prioritise consumer openness regardless of how you collect the data. Customers will gain trust and peace of mind if you tell them what information you’re collecting and why.
Data cleaning and classification
The next step is to organise and sanitise the information you’ve gathered. This will assist you in making correct assessments. Your data platform’s analytics tools will clean up any inaccuracies by deleting unnecessary and duplicate data. The CDP will categorise data sets in the central database once you’ve applied your tracking plan.
Assess the data and look for patterns using your platform. Machine learning is used by CDPs to sort through data and uncover trends.
Let’s say you’re trying to figure out what aspects affect user engagement with your streaming service. To track particular behaviours that encourage users to use the service, the CDP will examine data from your app, website analytics platform, email marketing platform, and customer support tool. You might find that your personalised movie recommendations in emails get more clicks than the algorithm’s in-app recommendations.
It’s time to discuss your findings with the team after you’ve acquired, structured, and analysed your data. To display patterns, emphasise trends, or tell a story, use data visualisation tools like graphs, bar charts, and dashboards.
Put consumer analytics to work for you.
What’s next now that you’ve gained a better understanding of your customers?
Make use of what you’ve learnt to go above and beyond their expectations. Do your customers expect quicker responses? For on-demand support, incorporate self-service alternatives into your support channels. Is there a new feature or product that your consumers would like? Inform the product team of their suggestions and concerns. Do they prefer to communicate via social media over email? Engage customers on their preferred channels at the appropriate moment.
Businesses that improve the customer experience proactively will deepen their relationships with customers, resulting in increased loyalty, profitability, and growth.
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