Funnel Drop-Off Analysis with Sankey and Bar Charts: Visualising the Customer Journey

For any business, mapping the customer journey and pinpointing the exact stages where prospects disengage is essential to driving growth. This is where funnel drop-off analysis comes into play. By visualising the different stages of a process (such as website visit, product selection, checkout initiation, and purchase) and mapping the flow of users through them, we can pinpoint areas that need improvement. This article examines how Sankey and bar charts can be utilized for effective funnel drop-off analysis, offering actionable insights to enhance conversion rates.

Imagine a marketing campaign designed to generate leads. A traditional approach might only focus on the total number of leads generated. However, funnel drop-off analysis dives deeper, revealing where those leads are getting lost. Are they abandoning the landing page? Are they having trouble with the sign-up form? Identifying these bottlenecks allows for targeted interventions.

The Power of Visualisation: Sankey Charts vs. Bar Charts

While both Sankey and bar charts visualise funnel data, they offer distinct perspectives:

  • Sankey Charts: These diagrams use flowing ribbons to represent the movement of users between stages. The width of the ribbon indicates the number of users flowing from one stage to the next. Sankey charts excel at showcasing the entire flow, revealing not just drop-off rates but also where users are going after abandoning a stage. For example, are they going back to a previous stage? Are they navigating to a completely different part of the website? This provides valuable context.

  • Bar Charts: Easier to interpret at a glance, bar charts represent each stage as a bar, with the height indicating the number of users at that stage. Comparing the heights of consecutive bars clearly shows the magnitude of the drop-off. While they don't directly show the flow between stages like Sankey charts, they are excellent for quickly comparing drop-off rates across different funnels or segments. For instance, comparing drop-off rates for mobile vs. desktop users can reveal device-specific issues.

Building Your Funnel Analysis: A Practical Example

Let's consider an e-commerce website. The funnel might consist of these stages:

  1. Website Visit: Initial landing on the site.

  2. Product View: Viewing a specific product page.

  3. Add to Cart: The action of placing a product into your online cart.

  4. Checkout: Proceeding to the checkout process.

  5. Purchase: Completing the purchase.

By tracking the number of users at each stage, we can construct both a Sankey chart and a bar chart:

  • Sankey Chart Insights: The Sankey chart illustrates the flow. We might see a thick ribbon connecting "Website Visit" to "Product View," indicating many users are browsing the catalogue. However, a much thinner ribbon connecting "Checkout" to "Purchase" suggests a significant drop-off during the payment process. Further investigation into payment gateway issues or confusing checkout forms might be warranted.

  • Bar Chart Insights: A bar chart visually compares the number of users at each stage of the process. A steep decline between "Add to Cart" and "Checkout" immediately highlights the checkout process as a problem area. We could then use additional bar charts to compare checkout drop-off rates across different product categories or customer segments.

Data Science Tools for Funnel Analysis

Popular data science tools, such as Python (with libraries like Plotly for interactive Sankey charts) and R, offer powerful capabilities for building and analysing funnels. These tools allow you to:

  • Clean and transform raw data into a format that is ready for a funnel.

  • Create dynamic and customisable visualisations.

  • Perform statistical analysis to identify statistically significant drop-off points.

Becoming a Data Science Expert: The Role of Training

To effectively apply these techniques, a solid grounding in data science fundamentals is essential. For those seeking to develop analytical expertise and enter the data science field, a data science course in Kolkata provides the ideal starting point. These courses prioritise practical learning, covering core areas such as data visualisation, statistical modelling, and machine learning.

Alternatively, a more general data scientist course can equip you with the necessary knowledge, regardless of your location, and even allow you to specialise in specific areas, such as marketing analytics or customer experience optimisation using funnel analysis techniques.

Actionable Insights and Continuous Improvement

The ultimate goal of funnel drop-off analysis is to derive actionable insights. Once you've identified bottlenecks, you can experiment with different solutions, such as:

  • Simplifying the checkout process.

  • Improving website navigation.

  • Offering incentives to complete purchases.

  • Personalising the user experience.

By analysing your funnel activity in real time and refining strategies based on data, you pave the way for improved conversions and business expansion. Remember that choosing the right visualisation tool, whether it's a Sankey chart for flow visualisation or a bar chart for comparative analysis, is key to effective funnel drop-off analysis. By leveraging these techniques, it opens the door to better decision-making.

BUSINESS DETAILS:

NAME: ExcelR- Data Science, Data Analyst, Business Analyst Course Training in Kolkata

ADDRESS: B, Ghosh Building, 19/1, Camac St, opposite Fort Knox, 2nd Floor, Elgin, Kolkata, West Bengal 700017

PHONE NO: 08591364838

EMAIL- enquiry@excelr.com

WORKING HOURS: MON-SAT [10AM-7PM]

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