The biggest success of the past year is our very low percentage of defaulting clients. We expected this number to be around 25%, but, thanks to this great system, we reached an incredibly low 15% client default rate. This is really unique in the short-term loan segment. Continuous evaluation of marketing investment let us effectively work with marketing agencies, especially during the initial new client acquisition phase. ROIVENUE gave us an overview of marketing investment efficiency, and we made very precise, accurate decisions.”
– Edita Zvařičová, CEO, KOUZELNÁ PŮJČKA s.r.o
How does a loan provider achieve growth, lower client acquisition costs, and decrease the number of defaulting clients? Czech company Kouzelná Půjčka (translated: Magical Loan) faced all three of these challenges. Roivenue helped them succeed in improving all three parameters, thanks to a deep analysis of total customer value, conversion paths, and ad optimization.
Magical Loan offers non-bank, short-term loans. As with any loan provider, Magical Loan needs to analyze every loan application in order to minimize the possibility of clients defaulting. To do this, all applicants fill out a complex questionnaire and are scored based on their responses.
This questionnaire has a high abandon rate – meaning people open the form but do not fully complete it. Magical Loan set up a very strict scoring system to curb the number of defaulted loans, making it necessary to optimize marketing investment. The logic behind this system was optimizing the loans granted, not in relation to submitted loan requests.
We expected that a certain number of loan-seekers, which were targeted with ads, would start filling out the questionnaire. Out of those who finish the form, 70% of the loan requests will be refused outright, and 25% of loans granted will default. If we were to succeed in improving those percentages (conversion rates), it would translate into a significant lowering of the marketing costs associated with getting the initial requests and reduce the burden posed by defaults.
We used the Roivenue Process Explorer to analyze the cost of every conversion step, and measured the performance of the different conversion stages. We then created prediction models to measure the effectiveness of marketing channels from the overall view.
Roivenue can evaluate cost of marketing investment relative to Customer Lifetime Value (CLV), and differentiate between new customer acquisition and retention of existing customers. This simplifies the optimization of campaigns to a very basic level: invest more in effective channels and lower investment in problematic ones.
The next step was to analyze the actual form-filling process – in real time. We used the data-mining algorithm in Roivenue to calculate the probability of a given user’s progress on filling out the loan – uncovering several mistakes in form code and possibilities for improvement of the UX. Most importantly, we were able to classify different types of applicants, predict the level of loss in relation to segments, and discover problematic (potential) clients. This included certain patterns that, in fact, indicated attempts at fraud.
The continuous improvement of performance parameters lead to stable growth and an extremely low level of defaulting clients – only 15%. Thanks to several revisions of the form based on Roivenue data, we succeeded in raising the conversion rate for those who filled the form to 36%, and lowering the cost for new client acquisition to 30% CLV.
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