Data-driven products with purpose (Part 2/3)
Data science is part of product management. It can be used to validate product concepts, experience design, pre-launch refinement, and optimal scaling approaches. As I mentioned in Part 1, it can also be the primary value proposition of the product.
When product managers and data scientists work together, some very cool applications of big data analysis and machine learning can come to fruition.
Case Study: Loio — Legal tech powered by data
“Data is the fuel of the 21st-century technological products,” explains Serge Sotnyk, Senior Data Scientist behind AI-powered contract review and editing software Loio.
He added that Loio in particular is fueled by data as it tackles the data-abundant contract review process using market-available data analysis automation tools. They use machine learning (ML) to optimize outcomes:
Machine learning is definitely the most efficient tool in this regard. With traditional software, developers write instructions for computers on how to process the data. Every new case requires a new rule. This makes such a solution hard to maintain with time and increases its risk of making a mistake. With machine learning, developers don’t create any rules but teach by showing examples. This way, a computer crafts rules by itself.
In other words, data is central to ensuring Loio delivers value for its customers. Serge noted, “The beauty of this approach is that the more data ML-powered software processes, the better it gets at doing it.”
What does this mean for the end-users of the tool? “They save a considerable amount of time when doing the routine job of reviewing and editing contracts as they get all the necessary data to make the right decision incomparably faster.”
The product team assessed pain points experienced by legal professionals in the contract review process, and they are using ML to get to the best outcome for the customer much faster.
Note: I have acted as a legal+tech subject matter expert to Loio’s product management team, and I have written for their blog.
Case Study: Impactoria — Credibility and reputation of nonprofits driven by data
Anton Vitaly and Ruslan Raspopov, co-founders of Impactoria, shared details with me on how their platform helps donors find and donate to the most impactful nonprofits based on regular independent expert assessment.
Anton explained how Impactoria aims to enable charitable organizations to segment and better understand the motivations and behavior of their donor base.
He went on to note that they leverage data to target the right donors, volunteers, and partners with optimized messaging through the right channels. Additionally, through the targeting:
This helps commonly resource-constrained nonprofits to achieve two things: reduce overheads and increase donation revenues, which in turn help them to achieve a bigger impact on their cause and end beneficiaries in both the short and long term.
Diving further into the data, Ruslan expressed how funnels can be created through tracing and measuring actions taken by users on their platform — or anywhere else — as a testament to the power of data:
We can then tweak and tailor these funnels with a combination of digital and physical instruments to help the user achieve their goal in the most optimal possible way. Be that selecting a new pair of shoes, deciding on the next travel destination, or choosing a movie to watch, all online solutions rely heavily on data to help us make those choices as quickly and precisely as possible.
In the context of Impactoria, he mentions that funneling allows charity donors to connect with organizations that best suit their philanthropic style. To make this process even stronger, he added that they “plan to eventually develop an automated charity-donor matchmaking engine, which would process various data points to match the donors with the most suitable charity depending on the cause area, charity size, specific end beneficiary group, etc.”
Starting to see a pattern? Products that value data enable product managers to add value for the customer with relevant features.
Note: I am on the Advisory Board of Impactoria.
To learn about my “product fail” and thoughts from a determined data scientist, read Part 3.
This is a personal blog. The views and opinions expressed in this article are those of the author and do not represent those of people, institutions or organizations that the author may or may not be associated with in a professional or personal capacity. All information is provided on an as-is basis.
