Use Cases

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Benefits of Analytical Customer Relationship Management

There are a number of key benefits that arise from gaining a deeper knowledge of your customer base. A significant benefit is an increased ability to manage customer churn; that is, the measurement of the number of customers lost over time. Understanding the reasons why customers discontinue a product or service enables organizations to effectively anticipate and manage risks. Being able to predict shifts in customer interests is a key component of customer migration to lower cost channels — this enables companies to maintain a customer relationship, even if it means decreased revenue, as opposed to losing them altogether.

Conversely, the ability of analytical CRM solutions to detect both profitable & costly customers empowers companies to maintain and enrich profitable relationships while trimming excess costs from unprofitable arrangements. CRM analytics can make a significant impact on your company’s bottom line in terms of efficient promotion, price, product, and distribution management — but how exactly is this accomplished?

Using DSS to Perform CRM Analytics

Analytical CRM software combines the engagement aspects of Customer Relationship Management with the robust capabilities of predictive analytics. The use of analytical CRM techniques is about more than just understanding your customer — it is about being able to predict customer actions before they occur. Data Science Studio (DSS) is a comprehensive solution in which you can use a wide variety of customer data (e.g., lead, customer, contact, touch-point data, and so on) to improve your customer and lead knowledge strategy. DSS connects to a wide variety of data sources and enables you to use advanced cleaning, processing, wrangling, and modeling processes to create a powerful profile of exactly who your customers are and how they are likely to act.

These analytical CRM techniques can be used to provide an in-depth understanding of how to effectively engage and proactively manage your customer’s experience with your products and services. For example, CRM analytics enables your company to track and analyze buying behavior based on customer history (from shopping membership card tracking data), profiles, and varied dimensional data (e.g., length of commute, income area of residence, time of year, etc.). This data is formatted, cleansed, and modeled according to your specific marketing goals, such as the likelihood of buying decisions based on events or perhaps store product placement based on time of day. DSS uses advanced algorithms, machine learning, and predictive analytical CRM techniques to bring clarity to complex marketing scenarios.

Business Benefits of Building Analytical CRM Solutions with DSS

Using analytical CRM methods enables organizations to enjoy a wide array of benefits, such as:

  • Trend Detection and Prediction: Use DSS to understand historical buyer trends and, more importantly, predict customer trends before they happen. Powerful algorithms and machine learning techniques enable you to refine your models and explore exactly how new trends will develop so that you can configure your business efforts accordingly;
  • Highly Customized Campaigns: Cookie-cutter campaigns become relics of the past as your marketing initiatives become intelligent, customer-centric strategies. Whether it’s branding or promotions, DSS gives you the tools you need to understand what your customers are looking for and to customize your campaigns based on expected purchasing decisions;
  • Customer Satisfaction: There are not many limitations in terms of the kinds of data that you can include in your DSS-derived forecasting. Of particular interest is customer satisfaction: understanding, and even predicting, how happy, or unhappy, your customers are with the products/services they are purchasing. This type of data adds an important element to the models you'll build in DSS, as it enables you to quickly refine your them based on direct customer feedback;
  • Buying Behavior: How will customers react when you change your price-points or services? What happens when a new product is introduced in a highly-competitive retail environment? Traditionally, organizations had little recourse but to just “try it and see what happens.” After all, the variety of disparate marketing factors, coupled with the costs of pre-market testing, can make predictive analysis a near-impossible task. Of course all of this reflects a world in which predictive analytics is a non-factor. Fortunately, the reality is that data-science fuelled predictive analytics empowers organizations to understand and predict customer buying behavior based on multiple types of varied content sources, from historical trends to dimensional data.

Contact our sales team to find out how DSS can help you build your team's in-house CRM Analytics Solution.