The modern customer-centric business world is no longer about simply understanding your customer – it is about being able to predict customer actions before they occur, and Customer Intelligence is here to help with this overwhelming challenge. A wide variety of customer data available can be used to improve your customer engagement strategy and act upon it.
Modern solutions for customer intelligence make it possible to create a strong profile of exactly who your customers are, what they want and how they are likely to act. You can use universal solutions by industry leaders like SAS and IBM or choose one that is focused on a more specific area. For example, MoneyGraph by InData Labs or Crimson Hexagon that provide customer insights from social media.
Such insights can be used to provide an in-depth understanding of how to effectively engage customers and manage their experience with your products and services.
Customer intelligence provides consistent and effective experience across all the channels, whether it’s marketing outreach, sales, or customer service, and across all the touchpoints: call center, mobile applications, in-store, social media and website activities.
The ability of such analytical solutions to identify both profitable and costly customers empowers companies to maintain and enrich profitable customer relationships while trimming excess costs from unprofitable ones, which can favorably impact customer response rates, conversion into sales and ROI, but how exactly is this accomplished?
Gaining Customer Intelligence through advanced customer analytics
Social media has become one of the major sources of customer intelligence data that provide insights about features of brand’s products directly from the customers.
In recent times it was enough to simply count the number of stars on each customer review and calculate the number of times it was shared. But in order to understand how exactly the customers felt about your company and who they are, it was necessary to read thousands of internet reviews and manually identify their sentiment.
Modern data analytics solutions allow to fully automate the process. In order to do it, such platforms take the data that companies already collect and combine it with customer data and sentiments from their social media posts. All this data is driven through a powerful predictive analytics engine. It builds models from customer data, and as a result, companies get ready-to-use insights that open endless possibilities for customer re-engagement and personalized communication, churn prevention, recommender systems implementation, and shopping carts abandonment reduction.
Advanced customer analytics provides a wide range of opportunities, allowing companies to:
- Target customers with the most relevant offers through the most appropriate channel at the right time, resulting in higher customer satisfaction rates;
- Detect and predict emerging trends for product demand and development;
- Analyze buying behavior in order to learn how customers react to price and service range changes, or what happens when a new product appears on the market;
- Better understand their customers’ individual relationships with each brand;
- Predict which customers are at risk of churning and why, and take actions to retain them;
- Turn new customers into loyal ones through personalized up-sell and cross-sell offers.
This way, customer intelligence serves as a secure base for various customer engagement techniques, making all the communication between companies and their customers matter.
Using machine learning, AI and Big Data technologies InData Labs helps tech startups and enterprises explore new ways of leveraging data, implement highly complex and innovative projects, and build breakthrough AI products. Our core services include Data Strategy Consulting, Big Data Engineering, Data Science Consulting.
