Seizing the Low-hanging Fruit in Business with Data Science

This article was also published on Towards Data Science.

Today I want to share a seemingly common sense yet often ignored data science finding.

Companies will gain tremendous growth opportunities by simply cutting back budgets on initiatives that are not working - the low-hanging fruit in business.

I once worked as a data scientist in a startup. Most of the startup's revenue came from inbound leads. Although there were satisfying numbers of leads coming in every day, the conversion rate was not ideal. Curious about how to improve conversion and revenue with the limited resources, I started a lead scoring project to find the prospect cohorts most likely to convert.

I expected to spend a few weeks building the data pipeline and running the machine learning model before I could get any meaningful insights from the data. Because, as you may be aware, a lead scoring project takes non-trivial efforts.

Surprisingly, shortly after I aggregated the data, I found something interesting - the data indicated that the company should ignore at least 80% of their inbound leads. These leads often came from prospects who clearly could not afford the product - once the sales team saw all visitor attributes in one place, they could quickly tell who would never convert.

For example, metric A was proved to be a key metric in predicting conversion. While a high metric A didn’t guarantee closed won, a low metric A indicated closed lost, as shown in the diagram below.

However, the sales team spent 50%, if not more, of their time chasing the orange cohort. Why? The team didn't have a prospect's complete picture in front of them.

Once the company implemented the lead scoring system to guide sales efforts, sales productivity tripled within three months. The sales team moved efforts away from those low-quality leads and closed more deals and bigger deals.

Later on, as I worked on more data science initiatives with a wide range of companies, including both B2B and B2C, I noticed this repeating pattern. For example, many companies’ ad campaign performances look like the diagram below: a large proportion of the budget is spent on campaigns that generate little to no return. These campaigns should be turned off immediately.

Once a company cuts back on underperforming marketing campaigns or other initiatives, it immediately gets a longer runway for high-potential pilots.

In the current economy, where there is considerable uncertainty, generating more revenue with fewer resources is more relevant than ever.

You may ask why companies would continue to spend money on something that is not promising. Well, they wouldn't. Most executives would quickly take action to trim the spending on the stalling initiatives as soon as they saw the complete picture of the performance. However, the issue was that they didn't have such a picture until they had already wasted tens of thousands, if not millions, of dollars. Take marketing data, for example; the data flow looks like the below in a company actively running marketing campaigns.

Without dedicated minds and efforts in data strategy, most companies don't have a holistic view of their business performance. Therefore, the opportunities to reduce resources wasted on underperforming initiatives never reach the executives. Many companies treat data science as a nice-to-have, and don’t invest in analytics until executives can no longer wrap their heads around what’s working vs not and why that is.

Conversely, data science is on every company’s critical path - customer acquisition.

Monitoring business performance in real-time and swiftly halting initiatives with negative returns is the low-hanging fruit for every company. It will give a company more time and cash to survive the unstable economy.

You can read Connect the dots in data strategy to learn more about how to seize the low-hanging fruit. In the following articles, I will discuss how to run experiments and identify what works in businesses with data. Stay tuned!