Hubspot says that billions of people use social media globally. Do you agree with the fact that if your business is not online, sooner or later it will run out of business? With such overwhelming engagement of people on social media, undoubtedly it has emerged as one of the best channels to get new leads and promote your business. However, how do you catch your audience’s attention citing such huge competition?

Why Storytelling?

What is it about your business that drives people crazy? What is it that forces them to press that “like” button on your post or leave a comment in the “comments section below”?

Photo by Lukas from Pexels

Well, the goal is to make a human connection. The goal is to stand apart from the crowd mechanically leveraging social media handles. It’s a well-established fact that all of us love stories. We relate to a well-told story like nothing else. Stories help your prospects make sense of the decisions they are about to make. Whether it’s the decision of subscribing to your email list or buying your product’s annual subscription. Your story is “why” you are doing “what” you are doing and “how” it makes a real difference to the world.It’s about standing out and not blending in! But, how do you tell great stories? What does a great story look like?

What Do You Need to Tell a Great Story? 

A Great Story = Visualization + Context + Content

Data, visuals, and graphics have been used to tell great stories for long. However, over the past couple of years, the “greatness” has disappeared from the stories. Plain stories are left behind. A graph chart displaying some statistics related to business intelligence, and analytics, that pretty much sums how data is leveraged to tell stories. Telling stories using data and analytics, which people can actually relate to, demands rich and intuitive data visualizations.

Overwhelming information with flashy and non-targeted visualizations (hard to decipher) ruins almost everything a great story has to tell. Telling stories with data and analytics has a lot more to it than creating a bar chart and uploading it to a dashboard or PPT. A context and an accompanying content are indispensable. 

A great story using data and analytics must convey why the information being shown is relevant to a business’s strategies and operations or how it solves the pain point of its consumers and potential customers.

What else does a great story take? It takes detail! Detail to an impressive level. The seemingly insignificant details must paint a picture in someone’s mind to truly make the story complete. Including data and analytics in your marketing strategies should enable you to tell the stories crucial for successful campaigns and customer journeys. However, understanding all the data correctly, extracting the key takeaways, and turning it into a great story is not easy. A lot of organizations struggle hard with this.

How to Tell a Great Story Using Data and Analytics?

It’s All About Engagement

Some of the great minds working in the data and analytics can comprehend huge data sets but they often fail when asked to help someone else understand the findings. Stories are all about engagement. One of the must-have skills for those working with data analytics is the art of presenting data in an engaging way. Your story should be digestible and should get people asking questions. Commonly people turn to data visualizations for this, but your creativity lies at the heart of finalizing what visualization brings the best out of data.

Because, not all graphs are easy to understand! Brainstorm hard and finalize how you want to present the data. Whether you want to go for charts, graphs, infographics, etc. Studies indicate that audiences prefer visual elements to numbers in presentations and will remember information more accurately and till long when presented to using visualizations. You can turn to tools such as Taswira for a new way to report and tell effective data stories. The tool helps you turn data into amazing visualizations with a context, driving the idea home properly.

It’s Not an Art Project

Your business story is not your art project. Period. Yes, colors enhance engagement, but they should be pleasing for the eye, not distracting. Under no circumstances and at no cost should your story distract the audience from the key takeaway. Don’t use a pie chart just because you see everyone using it, or you personally like it. The design element should help people relate to the story and find it engaging but it should not be the best and the only thing about your story.

It Must Have a Context

If your story has plain figures, facts, and data, it may essentially not be a story. Understand your audience well and structure your story in phases for them. Help them have a context. Help them understand “why” you are saying “what” you are saying and “how” it would help them.

 Odds of a significant section of your audience needing help understanding where you are coming from are pretty high. If you have multiple visualizations to share, have a linear approach. Start with the essential background they may need to grasp what’s underway.

While some of the phases of the story may be more relevant and important, it is important that you pay due attention to each without taking forever to complete it! Work on telling it in such a way that the listeners feel like they are there, living the situation.

Have a Timeline

Finally, your story must have a timeline. You should seem like progressing onto something. Don’t make it all about haphazardly conveyed, bland facts and figures. Tell your story in a clear beginning, middle and end order. Remove everything extra.

While studying data sets you may have a lot to share, but you must filter what’s essential and what’s not. Filter your findings and work on ways to present them in a linear fashion, progressing gradually from the beginning to the end. Stories are how audiences remember what you said.

Storytelling in Action

One of the best examples of brands that have experienced the difference storytelling makes is Unthinkable Media. The brand worked hard for years to gain capture the audience’s attention. Surprisingly, when traditional methods failed, storytelling came to their rescue. The company shifted its focus from impressions to subscribers. Things changed drastically when the firm focused on creating a community and making people a part of their story! In an era where a lot of people are selling what you are selling, it’s your story that differentiates you and provides you with an identity of your own.

And, that unique identity is your USP, that is what sells! When you tell the “Why”, you are making a human connection and engage people well and that works without fail. Happy storytelling!

Mathematical models have existed for the longest time ever known and for many reasons. Key and most important, they have been used to predict the future. As impressive as it sounds, it’s not always easy to make a perfect model as the outcomes vary a lot from reality. Often, there are many factors involved which change and are controlled by many secondary variables.

Today I want to briefly explain how challenging it might be to create a model that predicts pandemic outcomes. This is not meant to discourage anyone; no single model is wrong. Any measure that will reduce the fatality rates by even 0.1% is worth trying. The number of people who die is not statistics. Each death leaves behind a story. A story that affects lives and leaves emotions rolling down hundreds of thousands of families.

Getting started

I want to believe; this brief can help you understand the logic between modeled numbers and an accurate picture we are seeing.

To start off, let us explore some of the factors that determine the fatality rates resulting from COVID 19? Like many other predictive exercises, coming up with smart predictors early enough could guarantee quick success. However, picking the right predictors requires lots of problem understanding and personal intelligence. Ideally, one would run a model with all possible predictors only to remove those that are not significant. When there are many predictors, we need some strategy for selecting the best predictors. The complexity of modeling COVID 19 lies in the unavailability of descriptive predictors.

Before we look at the predictors, what is the fatality rate of COVID 19? A simple question, is it! To get the fatality rate, divide the number of people who have died from the disease by the number of people infected with the disease. Let us explore some variables that may go into predicting fatality rates for COVID 19. You are right to say fatality rate is a factor of:

• The daily rate of the number of people already infected
• How many people could eventually become infected
• The mode of spread and how quickly we can prevent the spread
• Number of people the virus can kill
• Availability of a cure or preventive medicine
• Availability of funds to procure PPES and conduct mass testing
• How quickly the authorities can identify, track, and trace possible contacts of an infected person.

This may sound straight forward to many; however, each variable mentioned above could be or depends on several secondary variables. As we usually say, lack of data is data.

The inaccuracy of the dependent variable

Fatality rates is our dependent variable which is a challenge to get. We do not have a single confirmed fatality rate for COVID 19. Countries have their own calibrations. The fatality rate varies with age, demography, race, location, etc. As quoted above, to get the fatality rate, the number of infected people if a factor. Unfortunately, it is not known how many people have been infected. The best way to determine this number is by testing the 50M people in a country such as Kenya. This is unfeasible given the challenges experienced in testing.

To get an accurate tally, it might be important to emulate one of the cruise ships that got quarantined after a COVID-19 outbreak. Nearly everyone on board was tested. The close confines help the virus to spread, but closed environments are also an ideal place to study how the new coronavirus behaves. Unfortunately, the world isn’t a confined ship. When COVID-19 was detected among passengers on the cruise ship Diamond Princess, the vessel offered a rare opportunity to understand features of the new coronavirus that are hard to investigate in the wider population. Some of the first studies from the ship — where some 700 people were infected — have revealed how easily the virus spreads, provided estimates of the disease’s severity, and allowed researchers to investigate the share of infections with no symptoms. The results of this unusual setup suggest that there are many people walking around with COVID-19 who don’t know it — and, consequently, that the death rates are lower than other data has suggested. Lack of accuracy in calculating the predictor breaks the York. Have you ever boiled an egg with a cracked York?  Well, if you are hungry, it doesn’t matter.

Messiness of data

At the moment, there have been rumors and blame games over cases of under-reporting or misrepresentation of the impact of COVID 19 in China. This is a typical example of how messy the data situation is Global. When data is extracted from disparate databases, the inevitable result is data inconsistencies, and nobody trusts the numbers. A lack of centralized global process, data management, and inadequate data strategies towards combating COVID 19 has contributed widely towards inaccurate data. Countries and regions collect data in different ways. There’s no single spreadsheet everyone is filling out that can easily allow us to compare cases and deaths around the world (fivethirtyeight).

The “nature” of things

The challenge of foreseeing the future of such a pandemic guided by data has many inconsistencies. Apart from being structural in nature, for some countries, testing is stratified. Kenya is testing people in isolation camps as well as targeting areas with a high potential of community infections, while other countries are testing everyone.

The virus itself discriminates in nature. Africa whose majority population consists of the youth has recorded high rates of infection among people between ages 10 and 60 yet in other western countries, COVID 19 has killed the aged.

There are many tracked and untracked factors that affect the fatality rates because of a pandemic some of which are:

• Hospital Capacity - Ability to prevent death once someone is grievously ill which depends on hospital capacity
• Infection rate - This depends on the willingness of the population to wash hands, maintain social distance, and report suspected cases.
• Rate of contact - how many people an infected person interacts within a given time period
• Rate of transmission per contact
• Symptomaticity ratio
• How long the virus can survive on a surface
• How far it can be flung through the air
• Duration of infectiousness

Our team in Nairobi is working hard to create a centralized database of the COVID pandemic and hopefully, in due course, we shall make it public.

Over the years, Fintech has grown tremendously.

Last year, global Fintech funding rose to over $100 billion, thanks to huge, successful rounds of financing. 

Worldwide Fintech investments were expected to grow even more this year, but COVID-19 hit the entire world like an unwanted, unpleasant surprise and the industries, including Fintech, were forced into a never-ending state of uncertainty.

However, there always is light at the end of the tunnel.

Hoping that pandemic will live its due course and get eradicated totally by 2021, it’s high time that we start visualizing a post-COVID-19 world and devise strategies to march out of this victorious.

What will Fintech look like once the pandemic is over? How soon would it be up and running?

What’s the way out of all this and what post coronavirus changes need to be adopted to have a sound, functioning world?

Let’s approach all of this one after the other and try understanding how Fintech will fare through the coronavirus crisis.

Also, we shall explore an interesting possibility and see how AI, and particularly Machine Learning and predictive analytics can help Fintech be its best post the pandemic.

How Will Fintech Fare Through the COVID-19 Crisis?

Financial technology has been revolutionary for finance.

However, the demand for Fintech services across the globe is dependent on economic activity.

With everything on hold and close to zero economic activity due to strict lockdowns and social distancing measures in place, payment revenues are expected to drop by anything between 8-10%.

Spending is decreasing and so is the overall number of transactions in the Fintech space, hence a decline in the demand for services.

A significant section of Fintech relied on scaling up the customer base and earning small profit margins from money transfer and payment services. This section will suffer a blow.

In addition to this, the Fintechs that depended on international transactions including travel spending or other forms of payment would also suffer because of global restrictions on travel and trade.

There is a widespread lack of funding as well. Several budding Fintech startups will not be able to cope with this and would either fail miserably or witness a massive decline in their valuations.

The Flip Side

Seeing the other side of the coin, COVID-19 times are also the times when a lot of Fintechs, with a little smart work, can position themselves strongly.

How? With social distancing and restricted movements of people, Fintechs can assume an important responsibility.

If approached in an innovative manner, Fintechs could be the bridge between a failing economy and the recovering economy.

Across the world, the governments are injecting cash in the economies to keep the companies from defaulting and helping them run the minimal operations necessary or to start afresh post months of lock down.

The circulation of these large amounts of cash through various coronavirus special schemes in such a short period of time calls for a strong financial infrastructure and supply chain in place, preventing people from actually having to stand in long queues outside the banks.

Here, Fintechs can step in and facilitate these credit requests from businesses and funding to individuals who need it.

Using Customer Segmentation for an Enhanced Outcome

The Survival of the Fittest

For Fintechs, the new normal means the survival of the fittest.

Quality is the key metric for success.

It won’t be wrong to say that the future of Fintech has never been brighter actually but only if they are able to raise the standard and emerge as a strong player, leveraging technology and modern innovation.

AI in Fintech Post Coronavirus

Source

Technology, especially AI, can be of immense help here. Understanding your customers better through customer segmentation using machine learning has immense untapped potential.

Smartly deploying and accelerating the integration of big data and Artificial Intelligence (AI) powered solutions can be a game-changer.

Fintechs can employ these smart solutions to store, process, analyze, understand, and drive insights from huge sets of data about their customers' behavior and their social and browsing history.

Furthermore, customer segmentation provides insights into what trends are underway. Leverage this to answer critical business questions for the future and make smart, data, and logic-based decisions. 

Customized Financial Products

Empowered with accurate customer trends, data, and preferences, Fintechs would be able to invent highly customized financial products and services that traditional banking never could.

Yet another opportunity here for Fintechs is to serve those customers who can’t approach banks for credit due to lack of reliable collateral.

Targeted Marketing

The integration of AI-powered solutions makes the delivery of a targeted marketing experience possible.

An abundance of financial products and services in the market today makes it difficult to judge which product would cater to which customer segment the best.

When you are struggling to survive as a firm, you can’t take too many risks. This becomes even more relevant post coronavirus.

Talking about risk management, AI can help you examine data points from credit bureau sources and assess credit risk for consumer and small business loan applicants. That way, you can assess the loan applicant beforehand and point out defaulters, if any.

Such AI empowered platforms like Underwrite collect portfolio data and use machine learning and customer segmentation to decipher and understand patterns to classify applications into good and bad applications.

This can highly help Fintechs to reduce their default rate.

Furthermore, there are platforms such as Ayasdi, that provide anti-money laundering detection solutions to help Fintechs understand and manage risk, and well anticipate the needs of customers.

Having accurate information about customer trends and preferences is more useful and relevant than anything else these days.

Customer segmentation helps decipher previously hidden patterns and get hands-on insights on behavior-based information.

Powerful AI solutions offer accurate results, which you can further use to target specific groups with financial products that will best resonate with their needs after the coronavirus crisis ends.

Macro-Economic Trends

Financial institutions across the globe have impressive amounts of data about macro economic trends.

This information can be life-changing for Fintech investors and policy-makers. However, due to the lack of necessary technology, it remains as it is with its immense untapped potential.  

Machine Learning and AI in general, can propose models and solutions to harness the benefits of this and provide the world with a roadmap to better Fintech services and products to cope with a dwindling economy due to the coronavirus crisis.

An Ever Agile Industry

There is no denying the fact that the Fintech revolution emerged partially from the ashes of the previous global financial crisis.

Key players in Fintech are habitual of dealing with such losses and uncertainty. They are used to surviving in an agile, uncertain environment and this would be their greatest strength in the times of this crisis.

Probably it’s too early to comment on anything about it but yes we are sure that irrespective of the economic hardship expected to accompany the coronavirus crisis, now is the time for investors to come forward and support Fintech, whose services are expected to enhance the end consumer’s financial wellbeing in a post COVID world.

With everyone going contactless, digital financial services are the future. These are more necessary than ever in an increasingly digitized, socially distancing world!

Data science in the new acronym everywhere I go. Organizations around the world are anxious to crunch data with the hope of being more competitive and innovative. Unfortunately, data science talent is rare. There is a high demand for data science skills; all companies are fighting for the few individuals in the market.

Hire a good data scientist and in less than 12 months, he/she is gone.

Some companies opt to build capacity internally an exercise that is not only time-consuming but expensive. Companies are trying to convert software developers and data analysts to data scientists. While this is true and good in the long run, software developers only make good data engineers and mathematicians, on the other hand, make good data analysts.

For a data science team to operate efficiently, a business needs a minimum of 3 key roles in place : data engineers, data analysts and a data scientist.

While you seek to build a team, outsourcing this component can speed your organization's path to developing data science capability. An outsourced relationship will help your organization build capacity to fill important skill gaps that would have taken years to realize.

Vendors such as Nakala Analytics(Kenya) may come in handy to assist build a data team. Here are a few reasons to consider outsourcing.

1. Save costs of up to 50% on hiring, training, operations, etc.
2. Increase the speed of delivery. You have a team of highly skilled and experienced experts whom you can rely upon to answer your data questions within SLA. The entire team is carefully selected and managed to handle existing challenges at the beginning of the contract.
3. Outsourcing your data science team would free your energies and allow you to focus on building your brand. All you may need to do with an outsourced data science team is to ask the right questions.
4. You no longer need to invest in recruiting and training expensive resources for your business.

Outsourcing data science allows you to start running from day 1. It allows you to focus on deliverables that matter to your business. An ideal path to getting started is to follow these steps. If this idea sounds interesting to you, please reach out!