Month: May 2015

AE Impact Story: Forecasting the future of the Barbadian Insurance Industry

Aim of the Project

The great recession that began in 2007 was a period of significant uncertainty. Economic activity in most Caribbean countries contracted, resulting in rising rates of unemployment and reduced sales for most companies.

Our client was a company within the Barbadian insurance industry, which is particularly sensitive to changes in the labour market. The Managing Director believed that the uncertainty within the local and international economy warranted a more rigorous approach to their annual strategy development sessions.

Antilles Economics was tasked with helping the MD understand the economic developments currently affecting his company, as well as anticipating how these developments could impact the insurance company’s revenue stream.

What we did

Antilles Economics has a proprietary macroeconomic forecasting model for Barbados. We used this model to provide macroeconomic forecasts for the next six years. In addition, a new module was constructed to forecast premium income and other company-specific information.

This forecasting exercise correctly predicted the reduction in real GDP in 2008, 2009 and 2010. Using this information and other predictions from the model, we provided additional information on the likely path for both insurance claims and premiums. The model also accurately forecasted the falloff in revenue premiums as well as the reduction in new policyholders.

Impact of the Project

The results of this forecasting exercise formed the foundation of the company’s strategic plan for the next few years, and our client was able to plan for the downturn before it arrived.

The MD presented the projections to the Board of Directors and successfully argued the need to implement revenue-enhancing and cost-control measures as a matter of urgency. Being able to anticipate what has turned out to be one of the longest economic slumps in Barbados in decades allowed our client to develop strategies to minimise its negative impact on the company’s bottom line.

Our Work in the Consumer Finance Industry

AE Consumer FinanceConsumer finance is one of the biggest industries in the Caribbean, encompassing commercial banks, credit unions, finance houses, insurance companies, auto dealerships, store cards, hire purchase agreements and building societies. It is also one of the most competitive. Across the globe information is the linchpin of winning the competitive race, and here in the Caribbean is no different.

At Antilles Economics, we aim to be the intelligence partners for consumer finance companies, providing economic forecasts through our dashboards, market insights through our market research solutions, and timely advice through our ongoing strategic support.

Our team has relevant, practical experience. We have been trained by national regulators, have worked in economic and strategy advisory for commercial entities, and have been researching on consumer finance for more than a dozen years.

To learn more about our Consumer Finance practice and how we can help your organisation, click here to view our brochure.

Data Limitations – What to Do When the Data You Need Isn’t There

logo-antilleseconomics

At Antilles Economics we often encounter data limitations both when doing work for clients as well as when conducting our own independent analysis. Not all of these limitations are within our power to fix. But some are. Here’s a selection of common limitations and how we’ve fixed them.

 

There are times when a particular variable is not explicitly available

Either it is available but not published or it has never been estimated. In some of these cases, there is sufficient data available to estimate it ourselves. This solution may not result in the exact same numbers that would have been published by the authorities, but it usually gives us a good idea of trends. We check for reasonableness to make sure it is in line with our expectations, and focus our use of the information on the direction and magnitude of the changes we observe in the trend, rather than on the exact number.

Another work around we have used for non-existent data is to collect it ourselves. For example, we conducted our Use of Data survey to understand how data was used in corporations because there was no dataset or insights available on this topic for Barbados. In another example, we conducted a survey for a client because there was no data available on the topic they were interested. Companies use surveys all the time to collect their own data on topics such as customer satisfaction, brand loyalty and consumer preferences.

There are times when the data exists but is inaccessible for some reason

For example, many companies have multiple data collection systems depending on the data. There may be one system for financial accounting, another for tracking sales, another for inventory management, another for customer management, another for social media statistics and yet another for production. Many of these systems do not ‘talk’ to each other. So if you want to know the percentage that one particular sales assistant contributed to overall revenue in any given time period, or whether a particular marketing campaign was more effective in one region/branch or another, it is almost impossible though theoretically the data exists. Some companies employ a useful workaround: an overarching business intelligence solution that captures all of the data from all of the various sources into one ‘super database.’ This is the ideal solution. But most companies are ignoring these types of questions because it is too complicated to attempt to answer them and too expensive to purchase a business intelligence solution. We have encountered this problem and solved it in two ways. The first was to use a business intelligence solution that captures data from innumerable sources into one easy to use system. The other was to request the necessary data from all the different sources as we needed it, and combine them manually in a simple software such as Excel. We obviously prefer the first option.

Some databases were not designed to capture information in formats that are useful for analytical purposes.

Another example of inaccessible data can occur depending on database design. For example, a company may have one client that uses three of their services, but the database captures this client as three separate clients because each time the client chooses a new service, a new account must be created. Imagine an analyst wanting to know how many clients are using multiple services or which services tend to be used together, it would almost be impossible with this setup. One solution would be to sample the company’s clients. You could ask a group of relationship managers to analyse their pools of clients and create a simple table with the name of the clients as the rows and the type of service they use as the column headings. Aggregating this table across relationship managers would give you an idea of how many clients use multiple services and which ones. You wouldn’t need to do this for all clients, just a sample, to get a reasonable estimate.

 

This is just a selection of some of the data challenges we’ve encountered and how we’ve chosen to solve them. We’d love to hear about your experiences, so please leave a comment after this post telling us what challenged you faced, and if you solved it, how.