Analytics with Purpose
It hasn’t been long since I decided to make my career pivot into analytics. Like many others, the concept of working in analytics was vague to me at first. Most people picture a data analyst behind a screen all day crunching numbers to build a miraculous model that will help a company make millions. While it’s nice to believe it’s as smooth as that, it’s better to face reality. It’s far from being a one-person sport and meaningful analytics relies heavily on communication. More-so, an understanding of the business is crucial. Without domain knowledge, there is an increased chance of producing suboptimal results. So, it’s best to always determine the business purpose behind using analytics.
In my excursion to this new field, I began with familiarizing myself with tools used by data analysts. I started applying them on projects that were publicly available. I was writing SQL queries & coding in Python to investigate datasets through an entire data analysis process by asking questions, wrangling data, exploring data analysis to ultimately draw conclusions. While learning the tools was necessary, I was still struggling to find how analytics drives value in an actual business setting. So, I decided the best way to do that was by positioning myself in a primal location that harnessed analytics: San Francisco.
Fast forward to today, I’m working with a team of students to help an early stage Software as a Service (SaaS) technology start-up as part of our MSBA Practicum Project. Currently, the start-up helps their customers monitor their products listed on major e-commerce websites. Through their platform, customers are able to view aggregated data such as reviews and pricing on a user-friendly dashboard and learn more about their product’s history timeline through customized reports. Initially, our main objective as incoming data analysts was to understand the start-up in depth and later find out how analytics could bring value to their business.
Understanding the SaaS Model
The success of any SaaS business is immediately tied to its ability to retain customers and create recurring revenue. Software companies in particular rely heavily on quick growth to survive. In a study released by Mckinsey, if a software company grows at a 20% annual rate, it has a 92% chance of ceasing to exist within a few years. Our partnered start-up is heavily involved in understanding the market requirements to cater for existing customers and potential new ones. By building a comprehensive tool, they are able to help their customers have a complete overview of how their products are doing on e-commerce websites. Ultimately, the continuous development of this tool is a differentiator which helps them retain their customers and revenues through offering innovative solutions. So, how will data analytics help our start-up?
Moving onto Analytics
Depending on who you ask these days, there are three or four different types of analytics: Descriptive, Diagnostic, Predictive and Prescriptive.
Initially, our partnered start-up utilized descriptive analytics to help their customers better understand how their products are doing through dashboards and reports. Through our partnership, we hope to drive insights using other branches of analytics. To achieve that, we must position ourselves in the eyes of the consumer and think of solutions they could find meaningful. Only after that, were we ready to explore the data for the right questions:
- WHY are products performing poorly?
- WHAT will help drive ultimate customer value?
Instead of having the customer know what has negatively impacted their brand image, through diagnostic analytics we wanted to inform them why it happened. To take it a step further, through building prediction models, we could generate key insights that will help them understand what features best contribute to improved sales performance.
There’s No Reward in Life Without Risk
While, there is certainly value that comes from applying new forms of analytics, it can sometimes be difficult to quantify its impact. Therefore, the proper integration of these new solutions into the existing platform is crucial in order to be able to assign metrics that may measure revenue performance. This could be in the form of providing it as a separate paid service or perhaps including an additional charge for report types highlighting these new insights.
My project experience so far has been quite enriching. While many caution about common mistakes behind building predictive analytics, there are still those who end up driving towards eminent failure. Analytics is becoming a crucial business requirement to maintain a competitive advantage, however, it’s important to always establish a path before operational deployment. The objective is not always clear which is why it’s crucial to maintain a line of communication with your cross-functional team. This helps ensure your interests are aligned in the same direction.
