“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. It 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 to achieve growth, lower client acquisition costs, and decrease the number of defaulting clients? Czech loan provider
Kouzelná Půjčka (translated: Magical Loan) faced all three of these challenges. We succeeded in improving all three parameters,
thanks to a deep analysis of total customer value, conversion paths, and ad optimization using Roivenue.
Problem: High acquisition costs and defaulting clients
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 client default. To do this, all applicants fill out a complex questionnaire for scoring purposes.
This questionnaire has a high abandon rate – meaning people open the form but do not fully complete it – which deters many
lendees. Magical Loan set up a very strict scoring system to curb the number of defaulted loans, making it necessary to optimize
marketing investment. The idea was to optimize relative to loans granted, not in relation to submitted loan requests.
Challenge: Optimizing acquisition costs per predicted loan-seekers’ success
We expected that a certain number of loan-seekers, which were targeted with ads, would start filling 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 moving 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.
Our solution: Optimization of ad investment relative to the probability of loan repayment
We used the Roivenue Performance Monitor to analyze the cost of every conversion step and measure the performance of the
different conversion stages. We then created prediction models to measure the effectiveness of marketing channels from the
Roivenue can evaluate cost of marketing investment relative to Customer Lifetime Value (CLV), and differentiate between new
customer acquisition and retention of exisiting 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 R 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 were in fact attempts at fraud.
Result: Stable growth and extremely low level of loan defaults
The continuous improvement of performance parameters lead to stable growth and an extremely low level of defaulting clients
– just 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.