The company has now combined the capabilities of its Customer Data Platform (CDP) and Communications Platform as a Service (CPaaS). This provides an integrated customer engagement data solution that offers an incomparable customer experience and allows financial institutions to shift from a product-centric to a customer-centric approach. This enterprise solution drives deeper engagement and consistent user experiences across all omni-channels.

According to a Harvard Business Review survey, 64% of respondents from financial services companies say that improving CX is a top business priority. Furthermore, a McKinsey report found that companies that excel at personalization of customer content, generate 40% more revenue from those activities than average players.

As banks typically have fragmented data across various communication touchpoints, the delivery of exceptional personalization and customer experience requires robust technologies which unify this disconnected data which makes the solution by Terragon a timely one. 

Terragon CDP takes advantage of Artificial Intelligence (AI) and Machine Learning (ML) to harness the wealth of raw and unstructured data derived by banks from their interactions with customers across multiple online and offline channels.

These channels include brick-and-mortar branches, social media, call centers, mobile, internet banking, USSD and WhatsApp, and translate these into coherent customer correspondences and actionable insights. The data generated is stored using cloud technology and is anatomized to accurately identify and segment customers across multiple channels, producing hyper-targeted and consistent engagements. 

These efforts help to significantly reduce the pain points faced by customers who often face communication gaps and disjointed experiences across different banking verticals, contributing to a leading decline in customer trust and loyalty. These pain points often include:

• the inability to seamlessly continue conversations with customer service personnel across multiple channels
• receiving irrelevant offers and promotions
• the inability to receive relevant information and support as and when needed 
• a bank or payment solution provider’s inability to resolve customer queries or complete transactions

Speaking on the establishment of the first-of-its-kind banking solution, Deji Balogun, CTO and Co-Founder of Terragon, stated, “Our team has been focussed on one common goal; to deliver unparalleled customer experiences by helping businesses better understand their customers and offer the relevant services.

We are thrilled with our latest iteration for banking clients. The combination of the value of both platforms is the ultimate game-changer for financial institutions as it provides them with unprecedented data-driven marketing capability for intelligent and omnichannel customer experiences.

As customers continue to embrace digital transformation, we are glad to have provided this outlet for banks to create meaningful and value-adding long-term relationships with their customers”.

The consolidated solution also automates processes such as loan propensity scoring, next-best-action recommendations, and product recommendations, helping banks save time, reduce costs, and increase profits.

Founded in 2009 by Elo Umeh and Deji Balogun, Terragon leverages data and technology to help brands intelligently reach, engage and deliver more meaningful experiences to African consumers on their mobile devices. The company has spent almost a decade building technology capabilities that allow businesses to merge their customers’ online and offline data, digital footprints and activities to gain marketing intelligence.

The company has, since its launch, expanded its solutions to cater to the nuanced and specific needs of companies across various industries and verticals spanning FMCGs, banking institutions, SMEs and more.

The recent update for banking clients is poised to galvanize financial organizations in keeping up with global trends by incorporating disruptive technologies to generate real-time behavioural insights and take omni-channel messaging to new heights.

MTN Benin and Ericsson have partnered on the deployment of an Artificial Intelligence (AI) and Machine Learning (ML) solution in Africa to address throughput challenges.

A network management system utilizing AI and ML was designed to address throughput degradation ultimately providing improved customer satisfaction.

This is building on the existing Ericsson Operation Engine designed to provide data driven network operations including state-of-the-art AI-enabled Cognitive Software for network optimization.

The ongoing rise of connected devices has led to an increase in spectrum requirements putting a strain on certain sites.

Ericsson devised its latest algorithms to act on highly accurate predictions of future lower throughput based on historical data.

As a result, MTN Benin can maximize the use of available spectrum in turn improving user experience through reliable, and best-in-class network experience for MTN Benin’s subscribers.

Uche OFODILE, Chief Executive Officer of MTN Benin said: “Throughput degradation, one of the most common issues in network management, has been successfully solved thanks to our joint efforts with Ericsson. The most crucial aspect we are looking for is speed, to service the increased use of connected devices and the corresponding rise in network traffic. Ericsson has offered us the best available solution with their proactive AI and ML solutions, identifying the issue before it materializes. By utilizing Ericsson’s solution for designing creative network solutions, we hope to provide a more individualized approach to network management, thereby boost customer satisfaction and successfully contribute to government ambition to be the leading provider of digital services in West Africa.”

Hossam Kandeel, Vice President and Head of Global Customer Unit MTN and Customer Unit MTN Africa at Ericsson Middle East and Africa said: “It is a pleasure to support accelerating MTN Benin’s automation journey, with remarkable positive effects on end-user experience, network performance and operational efficiency. We are able to provide highly accurate predictions of future limited throughput – anticipating throughput challenges and offering zero-touch corrective action. This collaboration is aligned with our goal of transforming network operations and optimization to a proactive data driven approach – building on Ericsson’s AI-Based Cognitive software and Operations Engine.”

The AI and ML solution is multi-faceted in its benefits as it addresses throughput degradation in the most efficient manner by anticipating it, offering zero-touch corrective action, increasing throughput for Service-Level Agreements (SLAs), and making the best use of network resources.

There has been a tremendous transition in Nigeria’s financial services industry’s dynamic terrain in recent years since AI and machine learning (ML) emerged as catalysts for revolutionising digital product marketing.

Today, we explore the tremendous influence of AI and ML on digital product marketing in Nigeria’s financial services sector, revealing their enormous potential for driving growth, personalisation, and customer-centricity. With their unrivalled capabilities, these ground-breaking technologies are altering how financial institutions communicate with clients and advertise their goods.

AI and ML’s primary benefits are their ability to analyse vast amounts of data with incredible speed and accuracy.

In digital product marketing, this translates into powerful insights and data-driven strategies. Financial institutions can leverage AI-powered algorithms to understand customer behaviour, preferences, and needs on a granular level. Using this knowledge, they can design targeted marketing campaigns that tailor product offerings to specific customer segments and maximise the chances of conversion.

Furthermore, AI and ML enable personalisation at scale. Gone are the days of generic marketing messages that fail to resonate with customers. Financial institutions can now deliver highly personalised and relevant content to their target audience through sophisticated algorithms.

AI and ML empower marketers to create meaningful connections with customers, fostering loyalty and enhancing customer experiences through customised product recommendations, personalised emails, and targeted social media advertisements.

Also, AI-powered chatbots like Sparkle’s Indy and UBA’s Leo have revolutionised customer interactions in the Nigerian financial services industry.

These intelligent virtual assistants, equipped with natural language processing capabilities, are available 24/7 to address customer queries, provide support, and deliver personalised recommendations. By enabling swift and efficient problem resolution while reducing operational costs, this technology enhances customer satisfaction and allows human resources to concentrate on more complex tasks, optimising productivity.

The impact of AI and ML on digital product marketing in the Nigerian financial services industry extends beyond customer engagement. These technologies have also proven instrumental in risk management and fraud detection.

Machine learning algorithms can analyse patterns and anomalies in real-time, detecting fraudulent activities with greater accuracy and speed. Fostering trust and confidence in the industry, financial institutions protect customers and safeguard their reputation and integrity.

While we cannot deny that the benefits of AI and ML in digital product marketing are significant, it is crucial to address their potential challenges and considerations. When implementing these technologies, prioritising data privacy and security becomes essential.

Financial institutions must actively adhere to robust data protection protocols to ensure customer information’s confidentiality and integrity. Additionally, emphasising the ethical use of AI and ML becomes crucial in building customer trust, where transparency and fairness take precedence.

As Nigeria’s financial services industry embraces AI and ML, collaboration and knowledge sharing among industry players become imperative.

Sharing best practices, experiences, and insights can accelerate the adoption and optimisation of these technologies, benefitting the entire ecosystem. Regulatory bodies also play a vital role in providing guidance and frameworks that ensure responsible AI usage while promoting innovation and customer protection.

In conclusion, artificial intelligence and machine learning have unleashed a new era of digital product marketing in the Nigerian financial services industry. Through data-driven insights, personalised experiences, and enhanced customer engagement, AI and ML are reshaping how financial institutions market their products.

Leveraging these technologies allows for targeted campaigns, improved customer experiences, and effective risk management. However, ethical considerations and a focus on data privacy must remain at the forefront of this technological advancement. By embracing AI and ML responsibly and fostering collaboration, the Nigerian financial services industry can unlock unprecedented opportunities and drive sustainable growth in the digital age.

About the writer:

Okwuchukwu Udeh is a highly accomplished Growth and Product Marketing Manager with a strong focus on fintech and financial services. She uses technology to drive business success and offers invaluable financial advice. Okwuchukwu’s op-eds inspire readers and provide insightful perspectives to navigate the dynamic landscape of financial services.

With exponentially more ransomware incidents being reported than there are skilled cybersecurity professionals available, progressive businesses are looking to Artificial Intelligence (AI) and Machine Learning (ML) to close the gap.

Every day more and more people and businesses are held to ransom as exposed vulnerabilities are exploited by criminals.

Even those with robust cybersecurity strategies in place are concerned by the sheer number of business attacks, which increase in number every year.

The cost of attacks was estimated to rise to USD$10 trillion annually by 2025 according to a 2020 report, and may yet exceed that amount.

Given the stark reality of everyone connected to the internet potentially becoming a victim of a cyberattack, managing cyber threats manually and still having time to actually do business is no longer possible, says Patrick Evans, Chief Executive Officer of SLVA Cybersecurity. 

“Protecting all end-points, integrations, APIs and every other threat vector is, quite literally, an insurmountable task. A staggering 93 percent of companies can be penetrated by cybercriminals; the consequences of which to the unprepared are dire indeed. Consequences that span from service disruptions, the holding of information and systems to ransom, through to destruction of physical infrastructure which, beyond the inconvenience, the financial and reputational implications, and potential threat to life, can easily result in the total loss of an organisations ability to remain in business,” explains Evans.

This is where artificial intelligence (AI) and machine learning (ML), both of which are being used with great success in reliable and resource saving robotic process automation, become so interesting for the management of our overloaded and under resourced cyber threat environments. Machines have always processed data faster than humans can, but not always any better. Which is why ML that can use the most proficient and effective techniques to analyse massive amounts of data is becoming mainstream and widely used today. “More importantly, as the latest developments in AI now start to reliably deploy systems that can start up and fully learn its environment in the way a skilled and experienced human does, AI can now deliver reliable and repeatable analytics with immediate accurate and automated responses for those the high volume repetitive security event monitoring tasks that cannot retain or even attract, the level of human skills required for reliable results.” says Evans.

“It goes further.  AI/ML can operate at scale which was not possible before.  What this means is that for the first time, one can continually ingest and analyse data from all relevant cyber security and IT tools to present a unified and cohesive view of an organisation’s investments in real time.  Now enterprises can report cyber risk in business terms and in rand terms, which is traceable to underlying operational reality, which delivers multiple benefits to any organisation.

This use of AI has long helped alleviate the skills gap throughout the global ICT market by automating highly manual processes, aggregating feedback, and taking over massive and mundane tasks to free up humans to focus on implementation and further research.

This IT and cyber skills shortage is even more stark in South Africa where local demand far surpasses even international supply. AI tools however do not just provide a replacement for missing skills, they have also become critical teaching components in growing new cybersecurity and risk skills.

Emigration and remote work arrangements now mean local employers compete for local talent against international corporates with deeper pockets.

“AI and ML build not only build up a database of vulnerable threat vectors and then based on discovery can continually test and learn new ways of attacking systems, they can also use their understanding of how to manage threats to teach the next generation of cyber risk managers. They do this 24/7/365, and as a result, are extremely effective,” he says.

“Bespoke cybersecurity solutions that leverage AI and ML are easily the most effective testing and discovery engine for companies looking to defend their systems. Key insights provided by the ongoing threat detection systems allow highly skilled and scarce resources to prioritise their time in more meaningful ways,” Evans adds.

With the average cost of a ransomware attack now standing at USD$1,85 million, every sector in every industry is at risk. While breaches at large firms make headlines, Evans cautions that small and medium-sized entities are often not aware of the extra risk they carry and are often an easier target for hackers because of SMEs’ lack of resources and security expertise. And it’s not just tech companies being attacked. Any business running a web application and those with operations in education, manufacturing, and finance are finding themselves to be frequent and popular targets for criminals.

Says Evans: “It’s simply not feasible to address all your cybersecurity requirements with a single vendor let alone a single team or a single person. The landscape is far too vast and ever-expanding for that.

Considering the current environment where skills are scarce, threats are multiplying, and businesses must contend with the very real possibility of complete closure because of cybercrime, the only answer is the adoption of smart ML and AI tools aligned to the threats specific to your business that will leverage on others understanding to manage the most aggressive threat environments you can expect.

Artificial intelligence and machine learning are not silver bullets that can just be used without thought, but in the hands of cybersecurity and IT professionals are tools that can be readily and reliably deployed to bolster existing cybersecurity solutions that allow any business to resiliently avoid damage from the all but inevitable cyber attacks that are today’s reality.”