Bridging the Gap: How Good Growth’s Customer Insight Leverages Science and Technology


In the contemporary digital era, companies have access to an abundance of data, supported by large insight and analytics teams. However, many businesses still grapple with understanding their customers on a profound level.

This deficit in customer insight can result in a flawed decision-making process, with organisations resorting to percieved “best practices”, expert opinions, or at times, the views of the highest paid individuals to shape their digital strategy and activities. Consequently, businesses find themselves mired in ambiguity, unsure of where to concentrate their efforts and thus, incurring significant expenses without witnessing the desired performance improvement.

A business’ need to understand its consumer is critical, but the successful translation of this aspiration into meaningful insight is a rarity. Various factors contribute to this disconnect:

1. A deficiency of expertise or capacity within the analytics team

2. Misplacement of personnel – data scientists do not necessarily excel as customer insight specialists

3. Substandard data practices and an absence of codified analytical methodologies

4. Failure to ask pertinent questions, leading to a focus on the ‘what’ rather than the ‘why’

5. Organisational disunity, where internal teams often work at cross purposes or maintain a narrow view of the customer

To address this predicament, Good Growth advocates for a separation of data (which furnishes an understanding of ‘what’) from insight (which provides an understanding of ‘why’). Data describes performance, such as monthly traffic volume, year-over-year revenue changes, conversion rate trends, bestselling products, and most valuable customer segments. On the other hand, insight delves into explaining this performance, probing into the reasons behind traffic variations, revenue shifts, user behaviour, and platform preference.

Good Growth’s approach of integrating data with insight is implemented across the digital system through continuous cycles of ‘Test & Learn’. This process applies the scientific method to digital data, paving the way for a customer-led strategy that highlights the most lucrative avenues for growth and innovation in sales and marketing.

For the generation of these insights, Good Growth merges quantitative and qualitative data from three sources – Customer Behaviour, Customer Voice, and Customer Engagement. No single data source, in isolation, can provide answers comprehensive enough to generate the desired insight.

These data sources are guided by rigorous standards. They must be accurate, unbiased, describable, directional, and reproducible. The amalgamation of the three aforementioned data sources into a cohesive ‘single version of the truth’ facilitates the production of one or more hypotheses for testing.

A hypothesis is a proposed explanation rooted in evidence and insight, forming the starting point for further investigation, testing and innovation. It is not a solution but a tentative explanation for an observation or problem. A valuable hypothesis is measurable, articulates the relationship between major variables, the direction of this relationship, and it must be logical and precise.

In essence, Good Growth’s methodology deftly bridges the gap between science and technology, enabling businesses to gain a deeper understanding of their customers. It provides a model for using data to inform, validate, and direct strategy and action, ensuring that businesses are equipped to grow in the most effective and efficient way possible.