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Case Study Recommendation Engines

Contract Analytics

Client

Allente

Industry

Broadcast media 

About the Company

Allente is a Nordic TV distribution company offering high-quality satellite, streaming services, IPTV solutions, and fiber broadband to more than one million customers in Sweden, Norway, Denmark, and Finland.

Opportunity

The client was looking for a platform that would recommend to the customers movies, series, or shows based on their view history and personal preferences. The solution was implemented on Knime Server.

The requirements for the platform were the following:

  • The product should be flexible and able to connect with main data sources in the cloud and on-premise.
  • It should be able to utilize and leverage the insight for the benefit of the client, the platform must have an end-to-end user experience.
  • Machine learning models should be able to deploy within the tool and be carefully evaluated, monitored, and returned when necessary.
  • The tool should also have compatibility to use Python-script within the tool.

With the implementation of a data science & machine learning platform, the client aimed to capitalize on the goals set out:

  • Increase loyalty (churn model) and customer lifetime value (“CLV”).
  • Increase satisfaction with their products (“CSAT”).
  • Be more relevant in communications based on insight & machine learning (1-to-1).
  • Improve relevance and conversion in upsell activities.
  • Relevant tips & recommendations of movies, films & series to their customers in their communications.

The data science & machine learning platform will be a central tool for analysis, especially in the Customer Service department. Other departments will also be able to use the tool – depending on what insights the team aims to get.

How Did Our Solution Work

Step 1

On-boarding and installation

The first objective was to get our client’s team familiar with Knime through training and exercises.

The training included:

  • 2-day training in Knime Analytics Platform on-site
  • 1-day training in Knime Server on-site
  • Installation of Knime Server on AWS
  • Discussion of security requirements on Knime server
Step 2

Data Exploration

The objective of this phase was to explore the data and report to Allente any data gaps or any issues. Redfield has been analyzing three data sources provided by the client: event data, a video-on-demand catalog, and sample tables from its legacy Oracle data. This step was an initial milestone to ensure access to the data sources, the setup of the initial Knime workflows, and ensure smooth delivery of the subsequent phases.

  • Data audit and preparation
  • Document data gaps and issues if any
  • Demo Knime workflow of data extraction
Step 3

Content Recommendation

Based on the data explored, the data content recommendation model was implemented in the following steps:

  • Define key segments with the business team
  • Code segments
  • Optimize and refine segments
  • Testing and evaluation
  • Documentation and competence transfer
Why Choose Us

Result

We collected and analyzed enough data to build a recommendation model with partial
coverage. The model would be able to better understand the content that users are engaged in
(vs content they only partially watched) if we can use time spent watching an asset to
distinguish between the use cases.

We will also be able to deliver recommendations to
more users using such metrics as page views to a particular piece of content. This
gave us the basis to recommend content to people for whom we don’t have any viewing
history.

The goal was to create a reference implementation so that Allente data
science team could enhance as new consumption data surfaces.

We use the optimal set of open-source tools to deliver the best results to your project. Our solution is no-code, easy to implement and adapt. At the same time, it is fast and flexible to implement.

Tools

Technologies Used

Knime Analytics Platform
Knime Server
Redfield Managed Service

 

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TESTIMONIALS

Client's Experience

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