Highlights of the Case Study
|Client||A global telecommunications company based in the UK partnered with Quantzig to develop hyper-personalized customer services using customer experiences, micro-segmentation, and multi-dimensional data.|
|Business Challenge||The client had a vast network of data pipelines for gathering data from various sources. However, with each new data source, the client’s model had become increasingly complex and unstable, compromising data quality and the decisions arising from the analysis.|
|Impact||Quantzig’s telecom hyper-personalization solutions enabled the client to differentiate its offerings and services from its competitors. Our data governance solutions helped the client untangle data complexities by determining the changes to data flows from new sources and targeting its architecture.|
Game-Changing Solutions for the Telecom Industry
Let’s understand the role of telecom hyper-personalization solutions first. In the current telecom landscape, organizations are often compelled to compete on price or perceived quality to sustain amid competition and retain market share. Since telecom users value the experience above the product offerings, generating a lower price point might not be an alternative in most cases. Therefore, customer retention is determined by user experience with the network provider. Telcos must cover the extra mile at every customer touchpoint and provide personalized offerings. This has led to more organizations improving their customer outreach by making it completely omnichannel, data-based, and hyper-personalized.
Quantzig has been partnering with telco companies on a reiterative basis, infusing extra data and personalizing plans to bring hyper-personalization to action. Quantzig has developed a sandbox testing platform for telco clients that empowers them to spontaneously generate and provide hyper-personalized deals to their customers by leveraging Big Data, AI, and ML models.
The Challenges of the Telecom Client
A global telecommunications company based in the UK partnered with Quantzig to develop hyper-personalized customer services using customer experiences, micro-segmentation, and multi-dimensional data. The client had a vast network of data pipelines for gathering data from various sources into a data pool, which analysts used to generate insights on customer behavior. But with the addition of each new data source, the client’s model became increasingly complex and unstable, compromising the quality of the data insights and the decisions thus derived.
Another challenge the client faced was providing suitable offers according to the user segments. The client wanted to leverage our machine learning model that can match customized offers to the customer group with maximum accuracy. The client approached Quantzig to develop and optimize its services and thus maximize customer engagement and increase profits.
The client approached Quantzig to leverage telecom hyper-personalization solutions and reach their customers where they are.
Quantzig’s Big Data Analytics Solutions
Quantzig leveraged advanced technologies such as big data, AI, and predictive analytics, to develop a hyper-personalized model. This model is based on specific algorithms that capture data points such as customers’ moving homes, the time of the day when data usage is maximum, income to expenditure data, preferences for androids, etc. We first tested the model on a limited number of targeted customers before allowing the ML-based optimizers to target wider audiences. Infusing the above data in our machine learning model helped the client optimize results and design the best possible plans for existing and newly discovered consumer segments.
Impact Analysis of Hyper-Personalization Solutions
Our hyper-personalization solutions enabled the client to differentiate its offerings and services from competitors. Our data governance solutions helped the client untangle data complexities by determining the changes to data flows from new sources and targeting its architecture. This limited the number of data warehouses and fed the pipelines with the properly merged data.
Further, our machine learning models maximized the client’s revenue, increased its conversion rate, and enriched customer lifetime value by creating offers based on customer take-up. This enabled our model to deliver more efficient and accurate results and plan for future deals and promotions. The client offered customized on-demand plans with dynamic content, personalized bundles, and rebates based on users’ segments at the right time.
Quantzig’s intervention helped the client utilize its data bank to understand customer needs and leverage it to create hyper-personalized services. The hyper-personalized services are necessary for telcos to remain relevant and competitive in the industry. The client was thus able to make offers and bundles that catered to the customers’ needs. This increased revenue per customer and ensured long-term association with existing customers and brought new customers to the fold.
Broad Perspective on the Role of Hyper-Personalization Solutions in the Telecom Sector
Executing the hyper-personalization technique narrows down to identifying what triggers the customers and offering deals that match their requirements. Machine learning algorithms use responses from assorted product variations to predict which variation will work best for each customer segment and enrich the customer’s lifetime value (CLV). However, to achieve this level of preciseness, the process needs to be seamlessly integrated across different departments and straightforward to use, which becomes an increasingly challenging task with an increase in new data sources and repetition of data points. The positive aspect is that the foundation needed for personalized marketing is already in place; with machine learning models and quality data, these platforms can be used to scale up the telecom industry’s hyper-personalization journey.
Quantzig’s big data analytics solutions helped the client achieve the following:
- Identify customer needs based on a detailed analysis of customer data
- Reduce customer churn by providing better plans and services
- Match customer groups to specific plans for better adoption success
- Increase sales and revenue from existing and new customers
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