Covering Disruptive Technology Powering Business in The Digital Age

Written By: Hemanta Banerjee, Vice President of Public Cloud Data Services Rackspace Technology

With the end of pandemic-driven digital transformation and over-investment in areas such as staffing, the next cycle of disruption is staring businesses in the face. COVID-19 and lockdowns have been replaced by inflation, a downturn in consumer spending, continuing supply chain issues and rising geopolitical tensions.

Based on the latest pronouncement from the Ministry of Trade and Industry (MTI), Singapore will not be spared from the gloomy economic situation. The local economy is predicted to grow by anywhere between 0.5–2.5%, a significant drop from the 2022 growth rate of 3.6%.

Despite the dark clouds on the horizon, the Singaporean government is optimistic that the adoption of artificial intelligence (AI) and machine learning (ML) will play a crucial part in turning things around. Under the National AI Strategy, the aforementioned technologies are expected to “deliver strong social and economic impact” in fields such as finance, logistics and Smart Cities. Meanwhile, the IDC predicts that by 2026, 75% of large Asia Pacific companies will rely on AI processes to enhance asset efficiency, streamline supply chains and improve product quality.

Roadblocks to ML Adoption

Back in 2016, the Harvard Business Review was already pushing enterprises to adopt ML. Today, the likes of Netflix, Microsoft and Snapchat are all using ML technologies to optimise their operations and change their business models. From highly targeted marketing and customer retention methods to more data-driven and dynamic fraud prevention, it can be argued that the adoption of ML is what separates the biggest names in the business from the rest of the pack.

Even with the explosion of successful ML use cases across many industries, there is still pushback against the adoption of AI and ML. According to our AI/ML Annual Research Report for 2023, 69% of our global respondents still experience resistance to adopting these emergent technologies.

Such sentiments, especially among decision-makers, are understandable. Incorporation of seemingly mysterious technologies such as ML can be daunting, especially at first. But the key to overcoming this is for us technologists to help connect the dots between the technology and how it can benefit an enterprise’s bottom line; and most important how they can take their first steps towards realising these benefits.

“Singapore will not be spared from the gloomy economic situation. The local economy is predicted to grow by anywhere between 0.5–2.5%, a significant drop from the 2022 growth rate of 3.6%.”

Here are three ways businesses use ML to bring their operations to another level.

1.  Optimising Marketing Campaigns

Machine learning is a powerful tool for optimising marketing campaigns and achieving better results. It can help marketers analyse customer data, segment audiences, personalise messages, predict outcomes and automate tasks. Here are some examples of how ML can be used in marketing:

• Machine learning can help marketers design an AI marketing strategy that aligns with their goals and objectives. By using data and analytics, machine learning can help marketers identify the best use cases, applications, and platforms for AI in their marketing mix.
• Machine learning can help marketers improve their digital ad placement and performance. By using algorithms and models, machine learning can help marketers optimise their bidding, targeting, and creative strategies for online advertising. It can also help marketers measure and improve their return on ad spend (ROAS).
• Machine learning can help marketers recommend products and services to customers based on their preferences, behaviour, and feedback. By using recommender systems, ML can help marketers increase customer satisfaction, loyalty, and retention. Machine learning can also help marketers cross-sell and upsell products and services to customers.

2.  Strengthening Customer Retention and Relationships

Businesses have worked hard and spent a lot of time and resources to acquire their customer base. Losing customers is a blow many cannot afford. With ML algorithms, companies can build models using data that is already in their hands to generate customer profiles. Using these, marketing teams can strategise to save those who are in danger of being lost and strengthen their relationship with more loyal customers.

AI can help improve customer experience by:

• Providing 24/7 customer support with self-service options and chatbots
• Delivering personalised recommendations based on customer preferences and behavior
• Anticipating customer needs and potential issues before they arise
• Analysing customer data to predict and prevent churn
• Enhancing customer service agents’ performance with real-time feedback and guidance

3.  Detecting Fraud in Real Time

Organisations can utilise ML tools to identify potential cases of fraud and fraudulent transactions in real time, preventing any damage from being wrought against them. With machine learning, they can enforce verification checks such as phone and email validation, flag suspicious online payments, uncover fraudulent new accounts, and prevent the abuse of loyalty programs and trial offers. With automation, ML models can continuously learn and improve their ability to stop fraud at the source.

Do Not Get Left Behind in the Machine Learning Race

The current economic climate may seem daunting, but opportunities exist for organizations to reap the cost benefits of the latest advancements in AI and ML technology. By using ML strategically, businesses can make more informed decisions when it comes to improving the returns from their marketing campaigns, improving customer experience, and providing better services through more real time fraud detection. Ultimately, this will help them navigate the uncertain economic climate and remain competitive in the future.

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