The background

Cetelem is a subsidiary of BNP Paribas Personal Finance, specialized in consumer credits. Cetelem provides responsible financial solutions for individuals, such as personal loans, credit cards, and instalment loans. Cetelem has a unique customer-centric approach, and uses modern technological solutions to serve its customers. They play a dominant role in the Hungarian market.

The goal

To understand the role of each communication channel in the various stages of the customer journey and to have a clear view of the different journeys customers make before making an online personal loan application. We also wanted to validate some hypotheses regarding customer behaviour with facts and data.

Our approach

Moving away from last click attribution and campaign efficiency, we wanted to build a 360-degree customer view and thus proposed a journey-based campaign measurement and efficiency analysis, with an extended attribution analysis as a first step.

Since Cetelem does not have a Google Analytics (GA) 360 account, we proposed a solution – bearing in mind the account’s limitations – where we did not rely solely on GA reports and settings but used some offline mapping tools, as well. We started the analysis with a Google Analytics measurement audit to map the potential gaps/ limitations of the settings (if any), that could affect the outcome of our analysis.

After the audit, we worked with Assisted Conversions and Top Conversion Paths reports, custom segments and some advanced settings to export data and be able to analyse them outside of GA. Thanks to data exports and offline analysis, and with the help of our data analysts we were able to map the tables, aggregate and visualize the data in a way that helped us identify deeper relations/correlations between channels and conversions.

The following analyses were prepared with custom channel grouping to define the role of the channels in each step of the customer journey:

Conversion path analysis – we worked with GA export, and after text parsing we divided the journeys into steps to analyse them. We checked which parameters of the journey affect the conversion the most. (Analysis of one and multi-step journeys from different angles such as loan application types, credit risk, the goal of the loan application and duration.)

Typical channel switches analysis – from the divided journey steps we created a heat map where we could see the most common channel switches based on device or customer type.

Attribution model comparison – to detect which channels have the highest impact on conversions at various stages of the journey and to check if there are any channels over or undervalued. GA only supports these models in an aggregated view, while we were able to examine a number of combinations of data from journeys divided into steps along the dimensions mentioned above to draw the final lessons.

The results

We are convinced that campaign spending should not be re-weighted based on one single analysis. Instead, after implementing the new custom attribution model and the missing tracking methods in GA we should analyse another period with similar depth analysis so that we have enough data to be able to improve campaign spending through a better understanding of conversion pathways.

The campaign journey-based efficiency analysis is just the first step towards our long-term goal to connect all campaign clicks of a user with the onsite loan application journey and the offline loan placement process, in order to get a 360-degree customer view.