Warning: Despite current readings, few organizations have reached the last stage of data maturity. However ambitious, this is the ultimate goal for most transforming businesses. At this stage:
- Machine learning is mature.
- Data used is clean and regularly updated.
- Operational teams have extensive data science skills.
Earlier, employees were trained to use data for internal and external collaboration. For example, with key suppliers or B2B clients having a contract for periodic statistics. Operational teams adapted accordingly (i.e., BI tools and dashboards, data culture, data source centralization, governance, etc.). They function within a cross-functional structure, facilitating autonomous data use. While machine learning automates low-value repetitive tasks, employees focus on high-value activities.
The Transformed Organization: The Media Example
Looking for a concrete example? Consider customer churn, a key indicator for traditional and electronic media. Churn predicts the likelihood of a media service provider’s client not renewing an offer in the coming weeks or months. Applied to the subscriber database, an algorithm identifies customers likely to unsubscribe soon, based on their respective churn rates. With a known risk client list, targeted personalized offers become possible.
Implementing change is never easy, and using algorithms in your company is no exception. Some employees will inevitably feel destabilized. Here, change management and multiple testing phases could be useful.
Back to the media example: having a list of at-risk clients is not enough. Which promotional offer would keep the most churners at the lowest cost? Testing different offers on various customer segments involves front-line teams and identifies the best solution. How algorithms support your teams will be crucial for future operations.
Webinar on the Transformed Organization
For our final webinar on the data maturity path, our team has big plans:
- Marco Brienza, President of LaData association, will discuss the transformed organization through a use case: media group customer churn. He’ll cover churn definition, the 4 types of media ratios, and describe 2 data approaches [BI & AI] for estimating customer attrition rates.
- Sélim Amrari, Sr. data scientist at Calyps, will demonstrate algorithm treatments (tools used, variables tested, etc.) live.
- A session where you can ask Marco & Sélim any questions.