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An Introduction to Customer Analytics

Customer analytics refers to the processes and technologies that give organizations the customer insight necessary to deliver offers that are anticipated, relevant and timely.
An Introduction to Customer Analytics

Customer Analytics is a process by which data from customer behaviour is used to help make key business decisions. This information is used by businesses for direct marketing, site selection, and customer relationship management. Marketing provides services in order to satisfy customers. In other words, it is a process that companies use to capture and analyse customer data to make better decision for pricing, promotion and management. As the backbone for all marketing activities, customer analytics comprises techniques such as predictive modeling, data visualization, information management and segmentation.

Forecasting buying habits and lifestyle preferences is a process of data mining and analysis. This information consists of many aspects like credit card purchases, magazine subscriptions, loyalty card membership, surveys.

Data Mining categories

Predictive models

Predictive models or analysis uses previous customer interactions to predict future events.

Segmentation techniques

These techniques are used to place customers with similar behaviours and attributes into distinct groups. This grouping can help marketers to optimise their campaign management and targeting processes.

Importance of Customer Analytics

Customers are more empowered and connected than ever. And becoming more so. Customers have access to information anywhere, any time – where to shop, what to buy, how much to pay, etc. That makes it increasingly important to obtain customer insight to understand how they will behave when interacting with your organisation, so you can respond accordingly. The deeper your understanding of customers' buying habits and lifestyle preferences, the more accurate your predictions of future buying behaviours will be – and the more successful you will be at delivering relevant offers that attract rather than alienate customers.

Customer Analytics Tools

Customer analytics tools are specialised apps used to gain insight into the customer experience, understand customer behaviour and to help tailor marketing campaigns to specific customer segments. Tools from major CRM vendors in the space includes-

  • Adobe Analytics
  • Google Analytics 360
  • IBM Watson Customer Experience Analytics
  • SAP Hybris Marketing Cloud
  • SAS Customer Intelligence 360
Customer Analytics

How do Customer Analytics work?

Customer analytics gives companies full visibility into how customers use their products. Analytics is particularly useful for companies with technology offerings because they can collect step-by-step data on how customers, users, or subscribers flow through their sites or apps. At a macro-level, this exposes major trends such as how users discover their product, which features they like best, where they find value, and what causes them to leave.At a micro-level, customer analytics allows companies to understand who their users are as individuals. They can segment users by demographics, interests, and behaviours and view their unique journeys. This knowledge helps businesses better cater to each customer persona. Elavon, for example, is a mobile payment app that found its users were complaining they couldn’t download the app. Using a customer analytics software, they were able to instantly identify all users trying to download the app on incompatible OS systems, reach out, and suggest a fix.

Which teams have a need for customer analytics?

  • Marketing can create segments and look-alike audiences.
  • Sales can scores leads, prospects, and users.
  • Product can measure features, usage, and customer journeys.

How to implement Customer Analytics?

To implement customer analytics and derive useful insights, companies must do two things:

  1. Capture, store, and organise their data.
  2. Analyse and make decisions with that data.

To capture and make use of data, companies must collect lots of it. They may run surveys, conduct user research, purchase third-party data, and, if they offer a technology solution, passively collect usage data through their site or app. In the case of websites, most customer analytics platforms passively collect all visitor data. With apps, companies may need to define activities or “events” where data is collected, such as login, logout, and user actions. For storing data, it is immensely useful to have a central repository that unites all of your data sources into one single view of your customer. This is a key feature of most customer analytics platform.

Below is the flow chart of describing the important features of Customer Analysis:

Flow Chart of Customer analytics
Flow Chart of Customer analytics

Conclusion

Here we have learnt about basics of Customer Analytics- what is it?, why is it important to use customer analysis?, what are the different customer analytics tools?, how it works and how it is implemented.