An Exploratory Study of the Utiva Data Analytics Fellow — Ololade Odunsi.R., Gideon Adeshina .O., Victor Adedeji .A. and Oluwaseun Salisu .Y.
Product Management Versus Data Science
Before we can begin to determine the relationship between Product Management and Data Science, it is only necessary to understand that both areas are separate fields.
Product Management helps “businesses” for an organization, firm or company to set product visions and road maps, establish goals and strategy and drive execution on each product throughout its lifecycle. At a high level, a product manager (PM) is the single cross-functional owner directly responsible for the success of a product.
Data Science, on the other hand, is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Data science practitioners apply machine learning Algorithm to numbers, texts, images, video, audio, and more to produce artificial intelligence (AI) systems to perform tasks that ordinarily require human intelligence. In turn, these systems generate insights which analysts and business users can translate into tangible business value.
In September 1994, BusinessWeek published a cover story on Database Marketing —
“Companies are collecting mountains of information about you, crunching it to predict how likely you are to buy a product, and using that knowledge to craft a marketing message precisely calibrated to get you to do so… This goes to show that, gone are the days where the A/B Testing method is being used to determine what works in the market, what would work or what people want. Data science can be used to gather data, analyze it and be able to interpret the data in knowing how a product would perform and how an existing one is performing which saves cost, time and energy.
How Should Product Management Interphase with Data Science
There are some good arguments for a data scientist in the product manager position. Both product managers and data scientists use data to make decisions and specific metrics to measure the outcome of those decisions. A product manager needs to know what success looks like for a product or a feature, while a data scientist chooses evaluation metrics that define the outcome of an experiment. Both product managers and data scientists then need to be able to explain their decisions to stakeholders on other teams clearly. They need to be technical, business-oriented, and creative enough to communicate with everyone from engineers to designers.
Leo Quin explains Data Science and Product Management as chocolates and peanuts combinations. So goes the old commercial for Reese’s Peanut Butter Cups, which makes the point that some things are just better when they’re together. The same can be said for data scientists and product managers — not only do they benefit from working closely together, but effective data scientists serve as the product managers of their inventions.
Strategic Models Attached to Product Management and Data Science
As a data scientist, it is vital to understand that incrementalism is the best option for delivering value. Rather than building a model to perfection and releasing it only when ready, instead align periodic releases with enhancements in the delivery of value. Remember that for most problems, no current solution exists, so any solution is better than the status quo. By delivering something quickly, you gain the trust of your stakeholders, and follow-on enhancements to your solution make that bond even stronger.
Here are a few practical Features to consider in product management and Data science modelling. Whilst product managers are asking the following questions;
▪ What will solve the customer’s problems?
▪ What are our delivery challenges?
▪ Where do we have blind spots that could help us operate better?
▪ How can we provide greater insights to our customers?
The data scientist gives insight and clear visibility with the following querying Models to provide clues to the answers with the following;
▪ A quick fix to product repackaging/rebranding as analysed from customers feedback data generated.
▪ Automating or Engineering working processes to address delivery activities by utilizing real-time data.
▪ Launching deep into historical, present and projected future operations with effective burg fixing and monitoring systems.
▪ The saying ‘customers have power’ is very valid with the data scientist providing some-what relatively wholistic clues on which customers yearning or agitations are hinged on, thereby helping the product manager re-engineer customers satisfaction.
Product Manager’s and Data Scientist Deliverables
The process flow of Product Management and Data science is broken into three Broad parts
- Brainstorming and product strategy sessions
- Data generations and execution with engineered or automated running processes
- Report generations for Growth analytics patterns
Brainstorming and Product Strategy Sessions: The product manager also referred to as the product CEO carries out full brainstorming sessions on new developments and feasibility studies strategies from; Historical which helps study trends of product patterns over time by comparisons. Behavioural which addresses user’s actions such as visits, clicks, time on site, and conversions. Demography which addresses user’s social data, gender, birthday, language, location, or income, Filmography like demographics, but for businesses: age, employee count, revenue, industry, and business model Technography are Technologies a company use, such as CRM provider. Psychographics which addresses a user’s interests, beliefs, and affiliations.
Data generations and execution with engineered or automated running processes: The Product Manager or product CEO liaises with the data scientist in implementing some of the features mentioned earlier such as fetching raw records with metrics in its distorted (dirty data) form and transforming it to something understandable and relatable. This gives more insight into the next line of action on trackable management procedures;
- Transaction metrics: This manages Monthly recurring revenue (MRR), Average order value (AOV), Gross merchandise volume (GMV), Customer lifetime value (CLV), Ad click-through rate (CTR), Ad cost per million (CMP), Cost per acquisition (CPA)
- Engagement Metrics: This manages Average daily active users (ADAU), Net promoter score (NPS), Customer satisfaction (CSAT), retention or churn, Stickiness, Event Frequency, Referrals, Product searched, visits, bounce rates, Ratings, Uninstalls session per user etc.
Report generations for Growth Analytics Patterns: Product managers can use a variety of reports to understand their metrics. Here are a few of the most useful product analytics reports that data scientists implement for effective product management;
- Segmentation: A report that allows teams to divide users by the characteristics they share, such as behaviour, signup date, or marketing source. Data-driven product teams can compare the metrics and KPIs of different groups and draw distinctions between them.
- Cohort analysis: A cohort is a user segment that has been named and saved for future comparison. For example, a news site might save two cohorts: one for it’s paying subscribers and one for its free visitors. Each has different behaviours and interests, and the team can cater to each without offending the other.
- Retention: Retention reports measure how well digital products keep users coming back. Every company measures retention differently, but it’s typically tied to repeat actions. Funnel analysis: Funnels measure a series of steps users take towards a desired outcome such as a purchase. Funnels help reveal the health of processes like onboarding and show teams where users drop off or get lost.
Conclusively, these texts leave us with the convictions that Data science should now be part of every product manager’s general education, not so they can get into the details of “how,” but rather, so they can understand “what could be”. This understanding gives product managers a sense of the kinds of questions data science can answer, so they begin to think more creatively about solutions that would benefit their biggest stakeholders: the customers. Continuous development, and microservice architectures, we now see AI, ML, and data science as en vogue, and for good reason
there is a ton of potential value in harnessing the power of data science to solve critical business challenges. Algorithms can be used to make sense of a massive amount of data, and both machine learning and AI can automate tasks that humans find tedious. To realize these benefits, engineering teams are starting to add data scientists to their teams. Data scientists are able to build data models suited to the organizations’ needs, have an understanding of SQL and Power BI, and are adept at translating data into insights.