Sisu is a fairly new, relatively unique tool that applies a user-friendly interface to robust and deep-diving business analytics, such as the example of big data analytics in the telecom industry we’ll cover in this blog post. With well-defined KPIs and a strong grasp of the business decisions relying on the analytics, even non-technical users are able to confidently answer questions using the power of machine learning through Sisu.

Below, we’ll detail the process of using Sisu to uncover the main drivers of customer churn for a telecom company, showing you what kind of data is appropriate for analysis in Sisu, what analysis 2nd Watch has performed using Sisu, and what conclusions our client drew from the data analysis. Read on to learn how Sisu may offer your organization the competitive advantage you’re looking for.

What is Sisu?

Sisu uses a high-level declarative query model to allow users to tap into existing data lakes and identify the key features impacting KPIs, even enabling users who aren’t trained data analysts or data scientists. Analysis improves with time as data increases and more users interact with Sisu’s results.

Sisu moves from user-defined objectives to relevant analysis in five steps:

  1. Querying and Processing Data: Sisu ingests data from a number of popular platforms (e.g., Amazon Redshift, BigQuery, Snowflake) with light transformation and can update/ingest over time.
  2. Data Quality, Enrichment, and Featurization: Automated, human-readable featurization exposes the most relevant statistical factors.
  3. Automated Model and Feature Selection: Sisu trains multiple models to investigate KPIs on a continuous or categorical basis.
  4. Personalized Ranking and Relevance: Sisu ranks facts by several measures that prioritize human time and attention, improving the personalized model over time.
  5. Presentation and Sharing: To dig into facts, Sisu offers natural language processing (NLP), custom visualization, supporting statistics, and related facts that illustrate why a fact was chosen.

How does Sisu help users leverage data to make better data-driven decisions?

Sisu can help non-technical users analyze data from various data sources (anything from raw data in a CSV file to an up-and-running database), improving data-driven decision-making across your organization. A couple of things to keep in mind: the data should already be cleaned and of high integrity; and Sisu works best with numerical data, not text-based data.

Once the data is ready for analysis, you can easily create a simple visualization:

  1. Identify your key variable.
  2. Choose a tracking metric.
  3. Select the time frame, if applicable.
  4. Run the visualization and apply to A/B groups as necessary.

With Sisu, users don’t need to spend time on feature selection. When a user builds a metric, Sisu queries the data, identifies high-ranking factors, and presents a list of features with the most impact. This approach subverts the traditional OLAP and BI process, making it easier and faster to ask the right questions and get impactful answers – requiring less time while offering more value.

Simplicity and speed are key contributors to why Sisu is so advantageous, from both a usability standpoint and a financial point of view. Sisu can help you increase revenue and decrease expenses with faster, more accurate analytics. Plus, because Sisu puts the ability to ask questions in the hands of non-technical users, it creates more flexibility for teams throughout your organization.

How did 2nd Watch use Sisu to reduce customer churn for a telecom company?

Being able to pick out key drivers in any set of data is essential for users to develop specific business-impacting insights. Instead of creating graphics from scratch or analyzing data through multiple queries like other analytical tools require, Sisu allows your teams to query their data in a user-friendly way that delivers the answers they need.

For our client in the telecommunications industry, group comparisons were crucial in determining who would likely become long-standing customers and who would have a higher rate of churn. Filtering and grouping the demographics of our client’s customer base allowed them to outline their target market and begin understanding what attracts individuals to stay longer. Of course, this then enables the company to improve customer retention – and ultimately revenue.

Sisu can also be employed in other areas of our client’s organization. In addition to customer churn data, they can investigate margins, sales, network usage patterns, network optimization, and more. With the large volumes of data in the telecom industry, our client has many opportunities to improve their services and solutions through the power of Sisu’s analytics.

How can Sisu benefit your organization?

Sisu reduces barriers to high-level analytical work because its automated factor selection and learning capabilities make analytics projects more efficient. Using Sisu to focus on who is driving business-impacting events (like our telecom client’s customer churn) allows you to create user profiles, monitor those profiles, and track goals and tweak KPIs accordingly. In turn, this allows you to be more agile, move from reactive to proactive, and ultimately increase revenue.

Because feature selection is outsourced to Sisu’s automated system, Sisu is a great tool for teams lacking in high-level analytics abilities. If you’re hoping to dive into more advanced analytics or data science, Sisu could be the stepping stone your team needs.

Learn more about 2nd Watch’s data and analytics solutions or contact us to discuss how we can jumpstart your organization’s analytics journey.

By Sarah Dudek, 2nd Watch Data Insights Consultant