Despite the fact that in Colombia all workers are obliged to contribute to the social security system, in 2018 35% of employees of private entities and 67% of self-employed workers did not contribute to their pension. This, in part, due to the fact that 44% of Colombians have incomes below the minimum wage and, therefore, the minimum contribution to the system represents a very high percentage of their income. Without sufficient income, many Colombian workers do not have an automatic mechanism to save for their old age.
To improve coverage for this population, in 2015 the Colombian government created the voluntary pension savings program Periodic Economic Benefits (BEPS). When the intervention was designed, in June 2018, BEPS had 1,107,383 linked, of which 352,192 (31.8%) had saved at least once in the program. Moreover, only 19% of those who saved did so consistently: either they had saved in 2017 at least 147,500 Colombian pesos (approx. $115 PPP), or they had made more than six contributions in that year, requirements to access life insurance subsidized by the program in 2018.
From this perspective, we proposed the implementation of Single Customer View (SCV) to allow us to create a complete view of each customer. Integrating all relevant information about their behavior and activity throughout their customer lifecycle.
The Client
From the IDB retirement savings laboratory, the Inter-American Development Bank asked us to develop several processes. Such as, improving the results of linking new savers, increasing the recency of savings, increasing the frequency of savings transactions and improving the average value saved in each transaction. These were the 1.3 million people linked to Colpensiones’ BEPS program (Periodic Economic Benefits).
The program consists of offering people a voluntary savings mechanism for old age, the majority of those linked must be people of low socio-economic levels with an income of less than 1SMMLV (One Minimum Legal Minimum Wage in Force). At the time of the pilot, 68.20% of the portfolio had no savings and 31.80% were considered savers. These are populations with little or no banking coverage, mainly underemployed and informal employees.
The point of focus
The IDB needed to have a comprehensive understanding of its savers, as well as the reasons why other people involved in the program did not save, in order to encourage savings in the most efficient way for both groups.
There are multiple reasons that limit voluntary long-term savings in low-income populations. On the one hand, low experience with the financial sector and lack of knowledge of the program (which in some cases leads to distrust). They may lead many people to prefer to save in financial instruments that are more familiar to them, such as investing in their home, even though the returns on these investments may be lower and more volatile.
On the other hand, psychological biases, most notably inattention, decrease individual willingness to save.
It has been identified that individuals do not have an image of their own old age, especially young populations do not consider it; there are life cycle conditions that facilitate the appearance of this imaginary, so these variables become relevant in the analysis and approach to potential savers.
In order to motivate those linked to the BEPS program who did not save (inactive) to start doing so, and to encourage those who did save (active) to do so more, a strategy of savings incentive campaigns was proposed, executed through different channels, including text messages and high-touch telephone calls, in segments chosen by analysis of massive data (big data) with medium and high propensity to save.
Project Challenges
Low contactability and the impossibility of building multichannel in these groups, a population that, for the most part, changed their mobile number and physical address between two and three times a year.
In addition, we had to take into account a limited budget and the presence of users in more than 1,100 municipalities nationwide. This considerably increased the costs of any face-to-face commercial action that we wanted to propose.
GBA Smart Marketing® Solution:
Single Customer View (SCV).
The task was to increase the number of annual transactions, the average amount saved in each one of them, and to get those who did not save to pick up the habit again. However, we had little knowledge of the customer, which is why all campaigns were segmented in a traditional way and their communication channels were limited to the use of their Contact Center and SMS messages.
We performed a data quality process on the information received from the entity in order to detect outdated and/or inconsistent information. We then updated, complemented and standardized the physical addresses of savers and non-savers, which allowed us to geocode 95% of the database.
Based on the transactional and socio-demographic information and attributes built by GBA Smart Marketing® for the project. Among them, the savings propensity score, income estimator and credit risk score stand out. We were able to segment 100% of the savers and non-savers with more than 51 characteristics for each one.
With the knowledge acquired, we planned and executed differentiated marketing campaigns, through more channels, with messages and frequencies appropriate for each group, maximizing the return on marketing investment (ROMI).
To study the impact of the campaigns, 24,000 program participants were randomly assigned to the pilot, creating a treatment group and a control group, each of which consisted of 6,000 active and 6,000 inactive participants. Since the participants were randomly selected, it was possible to assess the impact of the call campaign on the total number of linkados.
The result:
- We increased the value saved by 9.47%; the average transaction by 4.11% in the first two months of implementation; the historical number of transactions from 3.31 transactions/year to 5.22 transactions/year; and the contact data update rate by 17.03%.
- We identified and corrected 17.21% of the database of related parties that presented inconsistencies in the information registered, which affected the results of all marketing actions.
- We implemented at least 51 new descriptive characteristics that significantly increased the knowledge of the affiliate.
- We located the areas of concentration of non-members with the highest propensity to save, so that the commercial area of the institution could carry out a more efficient linkage work through its face-to-face brigades with advisory managers.
- By automating the affiliate journey, we increased the number of campaigns carried out and optimized their management speed.
- We excluded from marketing management 11.70% of the portfolio of affiliates who had no real possibility of saving.
- Among the active affiliates, those assigned to the treatment group were 14% more likely (18.41 pp vs. 16.14 pp of the control group) to make contributions in a month and saved 9.4% more per month (approx. US$2.34 vs. US$2.13 of the control group). Thus, among active affiliates, the treatment generated 1.6 dollars of savings for every dollar invested.
- In the inactive affiliates, although those assigned to the treatment group had a 41% higher probability (0.86 pp vs. 0.61 pp of the control group) of making contributions in a month and saved 46.9% more per month (0.13 dollars vs. 0.09 dollars of the control group), the differences we found are not significant. In other words, the treatment did not generate a significant increase in savings in the inactive affiliates.
Conclusions
High-contact call campaigns in selected groups, using analytical techniques and in accordance with their savings potential, increase the savings of independent and low-income workers, such as those linked to the BEPS program in Colombia. However, their effectiveness depends on whether these workers have already saved voluntarily.
Among those who had already saved, the treatment generated $1.6 in savings for every $1 invested. Among those who had not yet saved, the treatment did not generate significant savings.
One determinant of cost-effectiveness in this case is the price of the calls. Targeting those with a high savings potential allows minimizing the cost of calls while maximizing the potential impact. The savings potential, however, was estimated based on voluntary savings without intervention.
Knowledge of the client through the use of non-conventional variables, or variables that go beyond demographic data, makes it possible to generate personalized campaigns that involve differentiation in the message as well as in the channel and the recipient, maximizing the benefit obtained with the investment. Saving is always possible if it is based on financial education and personalized advice based on an imaginary aligned with the characteristics of the financial product.
Organizations can save money and improve their indicators with a strong investment at the beginning to improve their databases and customer-facing processes, but with a more economical operation in the medium and long term based on updating profiles by observing their reaction to campaigns, their life-cycle changes, emerging events and economic evolution.
For GBA Smart Marketing®, the value and interpretation of data plays a key role in the development and innovation of our portfolio of tailor-made solutions for each of our clients.
Contact us for more information about our services.
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