Customer segmentation analytics suite improves probability of converting users by 30%

Profile

A nanny matching service for employers (think AirBnB for nannies) with minimal data engineering and business performance analytics.

Situation

Our client wanted an analytical suite to understand its customers’ behaviour and optimise the performance of its products. Its business goal was to improve customer retention and lead conversion. When we assessed the client’s current data capabilities, the business had suitable foundational data engineering in place but required our modelling and visualisations in order to derive the desired degree of customer and product insight of customer segments, leads funnel performance and product profitability to achieve the desired degree of analysis.

From this assessment, we built a boutique analytics suite that could simultaneously identify targetable customer segments and monitor sales performance across the leads funnel, supporting tactical decision making for converting leads to paid customers.

Action

Our approach focussed on conducting exploratory analysis of sales and consumer characteristics at each layer of the customer journey. Using statistical analysis, we were able to cluster customers based on customer preferences such as wage, hours, and qualifications.

Through leads profiling, we assisted the business in both characterising successful leads as well as identifying why customers typically left, directly impacting the client’s top line revenues. By further analysing the time lag between lead conversions, we were able to show our client when they should be prompt customers – further increasing the probability of sales conversion. Building on these insights we developed more than 15 dashboards within the analytics suite to monitor and compare the different customer segments, conversion rates and performance over time.

Tools and techniques used in this work

• Exploratory Data Analysis

• Statistical Analysis

• MySQL

• Python

• Excel

Impact

By engineering custom models and visualising performance, the business gained a view at each level of the customer journey, and through statistical analysis, actionable insights which allowed the business to understand what drives success, all delivered within one week. In extracting and developing analytics visualisations, the business now has previously unavailable insights and has the ability to monitor core features which increase the probability of converting users to paid users by 30%.

So what?

QuantSpark’s hybrid strategy and data science consultants were able to design an analytics suite that provided a robust view of the business strategy, answering strategic questions such as:

• Which customers are leaving the leads funnel?

• How do we reduce customer attrition?

• When should we target them?

Our client has since benefitted from a strategic view on client retention and has a sustainable visualisation suite which will provide flexibility to differing business circumstances and clarity on strengths and weaknesses. Whatever the solution we understand that your team needs to trust the results and fully understand the rationale for the recommendations.