Merchants are already tasked with managing countless touchpoints within their customer journeys, in a world where individually personalized experiences are now the expectation. 

From the initial discovery phase to the final purchase and even post-purchase support, each interaction offers an opportunity to engage, impress, and build loyalty. However, the sheer volume of these touchpoints can be overwhelming, stretching resources thin and leaving little room for strategic innovation. 

How can merchants continue to meet the expectations of their market while also innovating and allowing for room to think more strategically?

Enter artificial intelligence and machine learning. 

On the surface level these are buzzwords that sound almost annoying and redundant at this point – but they are undoubtedly here to stay and avoiding them could bode unwell for cynical business leaders; specifically merchants. These technologies, when integrated into the current tech stack, can automate and optimize various aspects of the customer journey, freeing up valuable resources. More importantly, they offer online retailers a significant advantage in a market where speed and agility in adapting to new tech can make the difference between leading the pack or trailing behind. 

This is not a situation of keeping up with the Joneses – this has a significant impact almost as profound as the internet itself.

The hyper-competitive online retail landscape

Merchants face a multitude of challenges, including the need to differentiate themselves from competitors, the pressure to deliver perfectly-personalized customer experiences, the necessity of maintaining high levels of security, and the struggle to keep up with rapidly evolving trends across a broad spectrum. 

While waiting for other organizations to vet these new additions may seem more risk-averse, standing by the wayside and watching your competitors put them through their paces means you’re already behind – especially if they do so and see success. So rather than constantly living in a reactive stance, a hyper-competitive market demands rapid innovation and the adoption of it.

The Benefits of Being First

A proactive approach, as opposed to a reactive one, can yield numerous benefits that extend beyond merely staying ahead of the curve.

• Less competition: Online merchants can position themselves ahead of their competitors by exploring and exploiting the potential of these technologies before others, thereby gaining a first-mover advantage. This advantage can translate into a unique selling proposition that sets them apart from the competition, making it difficult for late adopters to catch up.
• Larger audience and revenue: Tech like AI and machine learning can enable online merchants to offer innovative solutions that cater to the evolving needs and preferences of consumers on any scale. For instance, personalized recommendations powered by AI can enhance the shopping experience, thereby attracting a larger audience. The beauty here is that the tech can then replicate that same personalization in real-time for the new audience as well. This wonderful snowball effect, in turn, can lead to increased customer loyalty, higher conversion rates, and greater revenue while adding no additional employees to the equation.
• Accelerated growth: As technology continues to show, it can quickly boost the operational efficiency of online merchants. It now allows merchants to automate complex tasks, provide instant insights through data analysis, and enhance decision-making processes, opening up the opportunity to move faster than their competition.
• Improved customer experience: What was once seen as a competitive advantage in itself, AI and ML can drastically improve the customer experience and take it to an entirely new level. From chatbots providing instant, human-like customer service to predictive algorithms offering personalized shopping experiences, these technologies can significantly enhance customer satisfaction and retention.
• Data-driven decision making: AI and ML excel at processing and analyzing tremendous amounts of data to extract actionable insights. The best part? They can also automatically apply these learnings, alleviating the amount of employee effort and resources previously required to do this successfully. These insights can inform strategic decisions, helping online merchants to understand customer behavior more effectively, predict market trends, and optimize their strategies accordingly.

Practical advice for early adoption

For online retailers contemplating the adoption of artificial intelligence and machine learning technologies, it's crucial to conduct a thorough assessment of the compatibility of these technologies with their specific business model as the options out there continue to grow in both breadth and capability.

Integrating these advanced technologies into existing systems is also a critical aspect that requires careful planning and execution. This starts with devising a detailed implementation plan that outlines the steps and timelines for integration, but be sure to include those who will be managing each aspect as early in the conversation as you can. This will help employees adapt to the new systems and processes in tandem with the technical teams helping roll out the functions.

Lastly, it's essential to measure the impact of these technologies on business performance. This involves identifying KPIs that align with the business objectives and adamantly tracking them over time. Regularly monitoring and analyzing these metrics will provide insights into their effectiveness and inform any necessary adjustments or improvements. 

Final thoughts

Consumer expectations around online purchasing continue to expand and put significant pressure on merchants. Tapping into the capabilities of AI and ML will soon become required just to achieve the status quo. All it takes is one competitor to set the bar.

Read our latest research report to learn more about Understanding Consumer Attitudes Towards Chargebacks and the AI tools available for merchants to reduce revenue losses due to changes in consumer expectations.