What is household identification?
Let‘s meet Miguel, his wife Sandra, and son Carlos. They use 8 different telecommunication services, and share some of these services (e.g., TV, Netflix, Spotify) with each other as a family.
As leasing contracts for Miguel and Sandra‘s will be ending at the end of the month, it would be an excellent opportunity to contact Miguel or Sandra and recommend them a special family bundle with 2 new iPhones, an additional postpaid SIM card for Carlos and Kids TV channel package for additional $10 per month. Highly relevant and customer context-focused offers as these allow for telecommunication services providers to improve the effectiveness of commercial initiatives and accelerate customer base value growth through a lower churn and a higher ARPU.
Due to siloed legacy business systems that contain different customer information, extracting household context tends to be an incredibly complex task for the services providers of telecommunication.
For instance, due to siloed systems, Miguel‘s and Sandra‘s family would be identified as 6 different customers.
The siloed customer understanding serves as the main driver of inelegant and costly customer engagements, like call center agents upselling customers with services they already have. Lack of proper intelligence and contextual understanding often dilutes returns of these commercial initiatives.
Key challenges when building household identification
Working with telecoms globally, we‘ve learned that there are 4 key challenges telecommunication services providers face when building a household identification process:
1.Collecting all necessary customer data
Information about a customer originates in multiple internal & external systems with their unique data structure and logic.
|Internal traditional data||Internal non-traditional data||External data|
Collecting & centralizing this data requires not only infrastructure which would be capable of managing these large data sets, but also implementing complex data pipelines with strict governance processes to ensure data quality.
2. Data normalization and unification
Collecting and centralizing all vital data in one place is not enough. Collected data also needs to be cleaned, normalized, and unified, to identify unique customers and extract insights about them.
In this stage, data engineering teams mainly face data inconsistency and quality challenges. Data inconsistency can be solved with predefined business logic and algorithms or by rigorous customer data validation processes (like surveying customers about their latest contact information).
The next big challenge is to define how and in what format normalized data should be stored in order to meet different analytical needs in the company.
If data will be stored in the original format (e.g., detail event logs), analysts working in the company will always need to carry out complex data processing actions in order to extract simple insights. If data will be pre-aggregated part of important information could be lost in the process. Also, it is essential to clarify what system IDs should be used in aggregating data to have it aligned with complex customer identification hierarchy. In other words, information needs to be processed on different customer views (e.g. subscription, client, household) to ensure efficient analytics processes.
Typically data engineers cope with this challenge by defining data model architecture, which sets clear rules on how the data need to be stored. Over time, such data models become too complex to work with and require significant efforts to be maintained.
4. Activate collected and processed data
However, the biggest challenge occurs at `the last mile` when collected and processed data needs to be exposed for the rest of the organization to implement personalization initiatives. With every new customer-focused initiative engineering team needs to build specialized data load processes or integrations to expose data to other IT systems.
Over time it becomes very challenging to govern all of these processes, especially as they have a direct impact on customer experience and business processes.
Only through the democratization of the customer data, telecoms can transform their current processes, enable proactive customer engagement and personalize the customer experience.
Streamline personalization initiatives with Exacaster Customer Data Platform (CDP)
In order to help our clients to streamline personalization initiatives and cope with these challenges, the Exacaster team has developed a customer data platform – Exacaster Customer 360.
Our platform works as a centralized repository to automatically collect, process and expose all internal and external customer data. It is equipped with out-of-the-box data models and identity management tailored for telecom use cases, which allows us to lower time to market and improve the ROI of personalization initiatives.
Learn how Exacaster „Customer 360“ platform can enable household identification in your company. Contact us for demo!
MEET THE AUTHOR
After implement big-data analytics processes in one of the largest telco groups in South America, Tomas is now driving the development of the Customer Data Platform solution to help Telecoms achieve customer experience excellence. While driving product vision and strategy, Tomas also closely works with sales and marketing.