Meet Ivy the Innovator and Charlie the Copycat on Their Journey to Financial Inclusion
Enabling financially excluded populations to open a bank account is a challenge for many organizations and policymakers. However, the bigger and more difficult problem is to ensure that account-holders actually use these accounts. One study based on data from India showed that six months after opening the bank account under a financial inclusion drive, nearly 80 percent of account-holders had not made any transactions.
In this blog, I discuss my research, using two representative first-time account holders – Ivy the innovator and Charlie the copycat – to help us understand how different behaviors affect adoption rates.
How does behavior change over time?
We must start by appreciating that, from the perspective of Ivy and Charlie, using a bank account for the first time represents a momentous behavioral change. For starters, Ivy and Charlie have to become comfortable with handing over the cash in their pockets to the bank, and trust that they will get it back when needed. They have to learn to deal with a formal institution and to read and understand bank statements. As their comfort with these new behaviors increases, their usage of the account will increase.
But, how do Ivy’s and Charlie’s comfort levels increase? Is it just a question of time? Or are there other factors which can increase the pace of adoption of different transactions, or, to put it more formally, “the pace of diffusion of innovation?”
I studied these questions using data collected over three and a half years from India’s large-scale PMJDY financial inclusion program, in which more than 400 million basic bank accounts have been opened for low-income and less-literate consumers at the bottom of the pyramid. I developed mathematical models to understand the process of adoption of deposit, fund transfer and ATM usage in these accounts. The results of my research point to interesting dynamics of adoption for different types of bank account transactions.
The S-shaped curve of adoption
When you mathematically fit a curve to the adoption data collected over a period of time for a large number of products, it turns out to be shaped like an S. Adoption starts out slow. Then, as the number of total users reaches a critical mass, the rate of adoption shoots up. Later, as most of the target population has adopted the product, the rate of growth plateaus. Over a period of time, this process forms an S-shaped curve of adoption.
According to the Bass diffusion model, this curve is the combined effect of two types of people – innovators and imitators. Ivy, one of our first-time account-holders, is an innovator – she is more knowledgeable and alert and is the first to adopt an innovation after evaluating its utility and matching it to her needs. Charlie is an imitator - he becomes aware of the innovation later. His behavior is driven by observing the behavior of Ivy the innovator, obtaining information from his other friends and family and then copying the behavior.
Using the Bass model, I calculated the relative effect of innovator and imitator behavior on the adoption rate of deposits, fund transfers and ATM usage. In the table below, the higher the ratio, the more important the role of imitation in increasing adoption.
Table: Ratio of the effect of imitation behavior (Charlie) to the effect of innovator behavior (Ivy)
Thus, we see that imitation behavior plays a far more important role in adoption of fund transfers (16.3) and ATM usage (35.4) as compared to the adoption of deposits (2.5). This implies that a much larger number of people behave like Charlie the copycat when it comes to adopting fund transfers and ATM usage – they need to see someone else use it before they are ready to use it themselves. On the other hand, more people are comfortable adopting deposits on their own without waiting to see what their friends are doing. For this type of transaction, they behave more like Ivy the innovator.
What do these results mean for expanding financial inclusion?
How should financial inclusion practitioners interpret and use these results?, and points to a few key lessons we can apply in promoting financial inclusion.
Firstly, we’ve seen that the level of complexity of the product influences whether more people will behave like Ivy or Charlie. The more complex the product (such as ATM usage), the higher the number of people who will behave like Charlie and imitate. For such products, it is important to have a higher focus on communication at the launch so that innovators like Ivy can adopt the product early and start influencing imitators like Charlie more quickly.
Understanding how innovators and imitators behave and affect adoption rates can help financial inclusion practitioners fine-tune their efforts and convince more people to use the financial services they have available.
Second, people like Ivy are generally the opinion-makers in their social circles. This means that getting an endorsement from innovators for the product can help greatly in convincing others. Initial marketing efforts should target early adopters, and then, once they have adopted, marketing campaigns should advertise this fact. For example, on a local level, opinion-makers can be featured in audio clips or posters used for marketing. In the case of higher marketing budgets, national level influencers can also be used.
Thirdly, some products, like fund transfers, have network effects which can be exploited by targeting innovators. The more friends and business associates an individual account-holder has, the higher the possibility that they’ll use the banking network for fund transfers and bring their contacts into the system as well. For such products, organizations can provide incentives to early adopters to initiate network effects and thus bring about wider adoption at a faster rate.
The goal is to go beyond access to adoption, with people at the bottom of the pyramid using services which help them manage their financial lives. Getting both Ivy and Charlie on board is key.