Fazhong
Although I agree with the overall spirit of the proposed process I will like to add a couple of caveats:
- The correlation of personally identifiable information with specific network consumption data might be subject to strict restrictions on some geographies (GDPR in Europe for example), there are anonymization techniques to ensure users privacy is protected but this alone will require its own subject of discussion.
- There is not really a good approach to a "Customer Experience" formula, here is where Machine learning is a really great ally to build some understanding, with the consideration that network quality in general only explains between 5% to 35% of the expressed CSAT (by the way, Customer surveys are another vital data point to build understanding around customer experience not listed on the formula before), therefore, the more information that can be added from the customer journey, the better chance the ML models will have to extract meaningful and predictive relationships.
- Finally, not all customer assess value the same way, therefore the model need to be build on top of an actionable segmentation around the customer base as the data volumes allow (most limiting factor tends to be the volume of surveys) to be conducive to significant and actionable results.
Regards,
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Alfonso Miranda
Bell Labs Consulting
Alfonso.Miranda@Bell-Labs-Consulting.com------------------------------
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Original Message:
Sent: Jan 09, 2019 14:36
From: Fazhong (David) Deng
Subject: CEM (Network Probe feed) vs CDR Analsyis - Why CEM
Probe data (xDR) contains the key information for subscriber experience while CDR contains charging and usage information. To build a good solution (use cases) for CEM, my suggestion based on my experience is to correlated data from at least xDR (from probe), CDR, and CRM (user profile). After the data are correlated, you will be able to:
a. define and adjust measurements (index) formula for performance, quality, availability, reliability, usability, etc.
b. define and adjust customer experience measurement formula
c. define dimension for the CEI (customer experience index) so that you can do drill-down/roll-up/drill-cross/drill-through to find the BI in data
d. define reports and dashboards for in-experienced person or person who does not have the necessary expertise to navigate the data to find the answers for questions such as:
i. what customer experience is for overall, by region, by user category, by service bundle, by application category, by application, by APN, etc.
ii. where the customer experience is not good
iii. etc.
If you also want to find out the answer for "why" customer experience is not good, you will then need to combine with OSS system such as iNMS and/or SMS (service management system).
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Fazhong Deng
OSSEra, Inc.
Original Message:
Sent: Jan 08, 2019 09:12
From: thembinkosi ndebele
Subject: CEM (Network Probe feed) vs CDR Analsyis - Why CEM
Good day all , I'm in the middle of an implementation of a CEM solution which the main data feed is probes on majority of interfaces. Comparison of what unique insights does a CEM platform provide ,compared to a CDR Analysis is stalling the adoption of the insights for use-cases.
I'm seeking for any experiences around this
#CustomerExperience
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thembinkosi ndebele
MTN Group Limited
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