For the rapidly evolving digital financial services industry, data
analytics holds a lot of promise. It can produce necessary business
intelligence for service providers to fine-tune product development, sharpen
marketing efforts, and improve the strategy to better reach the unbanked. Big
data analysis may be particularly useful to better target potential new users.
Big data is a big topic. Rarely a day
passes without news of innovative applications of the data we all produce through
our frequent use of technology. It is also increasingly recognized that
effective analysis of large amounts of technology-generated data can support
efforts promoting development. By the very nature of the business, the (Digital
Financial Services) DFS industry produces a multitude of data that can be
helpful in advancing its business goals and financial inclusion. Most DFS
providers in Sub-Saharan Africa specifically target the previously unbanked –
consumers the financial industry has previously known little about. MNOs are
particularly well placed to leverage their data, producing large volumes of
both call and transaction records that so far have been surprisingly underused.
To improve the understanding of existing
and potential DFS users in Africa, big data projects in partnership with Mobile
Network Operators (MNOs) can shed light on opportunities for new markets.
Leveraging an MNOs mobile money transaction database and call-detail records,
data analytics can answer questions such as: What characterizes active mobile
money users? What drives inactivity? Is it possible to identify behavior
patterns among customers and to use that information to stimulate better uptake
of the service? Is it possible to better target potential new customers that are
more likely to be active DFS users? The objective of analytics can help answer
important questions such as how to improve uptake and active usage of services.
In Africa, such studies have been carried
out, for example, using six months of call detail records and mobile money
transactions data, together nearly one terabyte in size but this can vary
depending on the size of the MNO. Users were segmented into three categories:
‘voice only’, ‘registered but inactive mobile money’, and ‘active mobile
money.’ The results showed that these segments had very distinctive patterns of
usage in terms of voice calls, social network structures and geographical
mobility. Active DFS users, defined as customers who use digital financial
services consistently at least once per month, made on average not only almost
twice as many phone calls than customers not using mobile money, but these
calls also lasted significantly longer.
The same pattern was true for text
messages: active mobile money users sent and received the most SMS, followed by
inactive mobile money users and then by non-users of DFS. Whilst active DFS
users call and text their friends, families and business partners more often,
they also have a much larger social network with than other users, and these
contacts are geographically more spread. Non-users of DFS seem to move around
much less than active users, as evidenced by a lower number of cell towers
picking up their phone signal. Active DFS users moved around ten times more
than voice-only users. Factors such as lifestyle and mobility between rural and
urban areas may account for this.
While more research is needed to better
understand these customers and determine whether regular usage of mobile money
increases usage of other services, it is clear that they constitute a specific
and valuable customer segment that is geographically mobile and socially well
connected. For example, it is possible that some customers have small
businesses for which they use their DFS accounts, which could explain both a
large network of contacts and high mobility.
In one such research, the data analysis
revealed a strong correlation between high usage of telecoms services and the
potential to be an active, regular DFS user. The big data did not contain any
socio-economic or demographic information however, and to overcome this limitation
the project combined the big data analysis with classic market surveys. The
surveys included age, gender, and education analysis, as well as income and key
poverty index metrics. The MNOs users represented all demographics,
but tended to be more male; married; in their early 30s; high-school educated;
and living in lower-middle tier housing.
The overall research showed that many
telecoms-only customers had a demographic profile similar to highly active DFS
users. The team, therefore, scored all telecoms subscribers according to the
extent to which users are similar to the profile of highly active DFS users,
using a model that captures some dominant usage variables to predict whether a
subscriber is likely to become users of mobile money. Based on this predictive
model, an MNO can translate this to maps that identify areas of the market with
the highest concentrations of likely adopters and to concentrate marketing
efforts there.
A couple of interesting general insights
have also been gained from this research. Whilst younger people are the most
active users of voice services, for example, they also have the largest share
of registered yet inactive DFS accounts. This suggests there is room for
improvement of the services offered by DFS since these mobile-savvy younger
customers are not being engaged by the current offering despite using their
phones regularly for other reasons. The study also confirmed that DFS usage
leads to loyalty and increased usage of same-brand services.
DECLARATION OF ORIGINALITY
I
affirm that the attached work is entirely my own, except where the words or
ideas of other writers are specifically acknowledged according to accepted
citation conventions. This assignment has not been submitted for any other
course at Robert Kennedy College or any other institution. I have revised,
edited and proofread this paper. I certify that I am the author of this paper
and that any assistance I received in its preparation is fully acknowledged and
fully disclosed in this paper (examination). I have also cited any sources from
which I used data, ideas, theories, or words, whether quoted directly or
paraphrased. I further acknowledge that this paper has been prepared by myself
specifically for this course.
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