Every year, organizations are creating data at an unprecedented rate. By 2025, the global amount of data being created, stored, and consumed will exceed 180 zettabytes.
All that data is giving businesses an unparalleled amount of insights about their customers. Now, analytics has reentered the spotlight as a critical tool set to understanding and acting on valuable data sets. Many corporations are using a centralized data management solution, like a customer data platform (CDP) to collect, integrate, and unify their customer data to tailor messaging and customize the customer experience.
So, this begs the question – what are the top CDP use cases for customer data analytics today, and how are they helping marketers understand, target, and message their most valuable customers more efficiently and effectively?
1. Customer Segmentation
Customers want personalized experiences from the brands they interact with, but only if those experiences deliver value to them. Being able to aggregate customer data from any source, and sift through that data to find common demographics, attributes, and behaviors, allows you to group customers into smaller audience segments for activation.
A CDP with customer segmentation capabilities will allow you to identify key attributes from your most high-value customer segments. Then, you can display that information in an easy-to-use analytics user interface for marketers to make intelligent, data-driven decisions.
When enriched with first, second, and third-party data, customer segments can help marketers understand common user interests and how user behavior represents broader business and industry trends.
2. Customer Sentiment Analysis
Advanced analytics are all about understanding your customers better. Being able to understand the subjective feelings of your customers is what sentiment analysis is all about. Customer sentiment analysis, also called opinion mining, is the automated process of identifying emotions in digital interactions to glean how your customers feel about your products, services, or brand overall. This level of insight into customers allows brands to message their customers as fully-rounded people.
Customer sentiment analysis leverages natural language processing (NLP) and advanced machine learning algorithms to detect patterns in text, and classify customers feelings as positive, negative, or neutral.
Algorithms can go further by distinguishing opinions, whether they may be subjective or objective, comparative or direct, or, explicit or implicit. By applying customer sentiment analysis, unstructured data is turned into structured information.
This data on customer likes and dislikes, can be unified with other customer data and used by marketing, sales, product development, and customer service to develop more customer-centric products and services. Other use cases for customer sentiment analysis include brand strategy optimization, monitoring of brand reputation, and tracking customer sentiment over time.
3. Personalized Omnichannel Marketing
Global brands that want to execute successful omnichannel marketing campaigns need advanced analytics solutions to make sense customer interactions across channels. Brands need to be able to track complex user behaviors anywhere they are, and then make that data available to other applications to affect the customer experience. CDPs help brands manage customer data so they can engage in personalized omnichannel marketing activities.
CDPs are able to gather data on customers from any channel and combine it together so you have a single source of truth for your customer data. A customer unified profile can be managed globally, so customers can be marketed to appropriately.
Enterprise-grade CDPs will have robust analytics capabilities that allow marketers to glean actionable insights that can make their omnichannel marketing campaigns more effective.
4. Predictive Analytics
Predictive analytics uses artificial intelligence and machine learning algorithms to anticipate future outcomes based on data. There are a variety of predictive analytics models, including classification models, clustering forecasts, and time series. These models help predict future variables based on the insights and data.
The top CDP use cases that leverage predictive analytics include:
- Reducing churn
- Improving retention
- Improving customer lifetime value
- Customer segmentation
- Next-best action recommendations
- Identifying upsell and cross-sell opportunities
- Predicting buying behavior
- Demand planning and inventory forecasting
5. Price Optimization
How something is priced is a critical variable in how it affects a business, and its customers, overall. Pricing affects revenue, profitability, demand, satisfaction, and customer purchasing behavior. Pricing, however, can be quite complex, as it includes a variety of variables that can impact demand planning and inventory forecasting.
Analytics give marketers and brands the insights they need on past purchasing behavior. By defining optimal pricing based on historical data analysis, brands can develop promotional campaigns, or implement dynamic pricing. Markdown optimization can also be applied to suggest optimal discounts rates.
Data Analytics with a Customer Data Platform
Analytics and data go hand-in-hand. To be a data-driven company, you must have advanced analytics solutions to understand your customers.
Advanced analytics have come a long way from the days of simple KPIs and standard campaign performance measurement. Today, analytics are broad and varied, and robust analytics applications and algorithms are standard capabilities for the most essential CDP use cases.
