Highlights of the Case Study
|Client||A global telecommunications company operating in the US sought to drive hyper-personalized customer plans by mining customer engagement in real time.|
|Business Challenge||Our client wanted a mobile marketing solution to enhance the CX of its existing customers by generating hyper-personalized customer plans.|
|Impact||Quantzig leveraged its real-time insights to enable hyper-personalization at scale across various touchpoints such as mobile push, in-app, and social messaging platforms.|
Game-Changing Solutions for the Telecom Industry
Over the last decade, the telecom industry has undergone unprecedented changes and has transitioned from traditional approaches centred around call centre operational efficiency to data-driven decision-making. Telecom operators and communications service providers (CSPs) possess enormous customer data that can be leveraged to draw insights and enhance customer experience (CX). They use techniques such as personalization to develop tailor-made products and interactions for a specific customer group. However, even these techniques are becoming irrelevant amid the rising customer expectations.
Hyper-personalization has become an essential tool in the telecom industry to enhance CX, in turn, improving customer retention. It involves collecting real-time data through all customer touchpoints and processing it through artificial intelligence (AI) and machine learning (ML) algorithms to develop a unique profile for each customer at an individual level.
Quantzig has developed an AI-powered framework for our clients in the telecom industry and uses a data management platform to predict CX in real-time accurately. We empower our clients to deliver hyper-personalized CX through highly tailored customer plans, offers, and deals by leveraging Big Data, AI, and ML technologies.
The Challenges of the Telecom Client
Quantzig was approached by a global telecommunications company operating in the US to push hyper-personalized customer plans by mining customer engagement in real-time. Our client had designed a mobile app for its users to manage their accounts, access data plans and deals, pay bills, and collect rewards. However, engagement and retention campaigns such as recharge reminders, special offers, and new bill notifications were conducted manually. Owing to its large user base, our client found it challenging to scale hyper-personalized marketing campaigns with optimal RoI.
Our client also targeted its users through e-mail marketing and SMS systems, which hindered their persistent efforts to develop an omnichannel strategy. As a result, our client failed to provide hyper-personalized plans and offers through a suitable medium, which led to users uninstalling its app. Our client wanted a mobile marketing solution to enhance the CX of its existing customers by generating hyper-personalized customer plans.
Quantzig’s AI Hyper-Personalized CX Strategy
The consultants at Quantzig developed a hyper-personalized CX strategy for our client to analyze contextual data such as the user’s preferred device, the location from where the user accesses the device, the time of day when the user is most active, and the industry with which the user is associated. In addition, we provided persona-based recommendations and customized plans for our client’s existing customers built on various customer attributes, such as service usage patterns, including call durations, data consumption, streaming patterns, and rebate history.
Quantzig’s AI-based recommendation engine used ML, predictive analytics, and Big Data. This tool helped our client gain a better understanding of its customers and thus, enabled it to transition to automated and real-time insight-based action. Our client delivered hyper-personalization at scale across various touchpoints, such as mobile push notifications, in-app announcements, and social messaging platforms.
Impact Analysis of Quantzig’s AI-based Recommendations
Our client used our individualized marketing solutions and implemented our hyper-personalized strategy to improve its customer knowledge and offer hyper-personalized services to enhance CX. The benefits delivered by our client to its customer base included the following:
- Personalized digital data plan report: Users of post-paid data plans were provided with data consumption details, usage history, a comparison with similar users, and bill forecasting.
- Personalized alerts: Users were proactively informed about abnormally high usage of data, an upcoming high bill, and peak period usage alerts.
Our client achieved the following benefits by using our hyper-personalized CX strategy:
- Reduced the churn rate 25% 25%
- Improved the click-through rate (CTR) for the onboarding campaign 10% 10%
- Enhanced the CTRs for re-engagement campaigns 7% 7%
- Improved scores of different CX key performance indicators 15% 15%
- Improved scores of different CX key performance indicators (KPIs) such as Customer Satisfaction (CSAT), Customer Effort Score (CES), and Net Promoter Score (NPS).
- Improved the click-through rate (CTR) for the onboarding campaign by 10%
- Enhanced the CTRs for re-engagement campaigns by 7%
- Used just-in-time (JIT) discounts and offers to increase average revenue per user (ARPU)
- Reduced the churn rate by 25%
Our tools enabled access to 100 predictive scores, which were generated and automatically updated by our solutions. This was then used by the client to deliver hyper-personalized customer plans, leading to significant improvement in customer experience and a reduction in customer churn by 25%. In the highly competitive telecom industry, understanding the customer’s needs is imperative to retaining them and ensuring business growth.
Broad Perspective on Artificial Intelligence Hyper-Personalization in the Telecom Sector
For telecom marketers, hyper-personalization is a data problem that needs to be addressed with a robust analytics technique. Marketers now use third-party data, including media spending and retail footprint, and integrate them into the data set to picture the customer journey better. In addition, advanced algorithms are being used to identify previously hidden variables and predictors of customer engagement. Such advancements in data analysis techniques will better analyze customer behavior to offer highly accurate real-time insights.
- Implemented ML, predictive analytics, and big data to understand customer needs
- Minimized potential loss in revenue caused due to poor CX
- Helped optimize the custom-engagement plan to attract new customers
- Enhanced communication with customers via mobile push notifications, in-app announcements, and social messaging platforms
- Enabled the implementation of large-scale hyper-personalized marketing campaigns to optimize RoI
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