John Cradden on how clever use of generative AI (genAI) by SMEs can have a transformative effect on customer service and marketing.
As the potential of generative AI quickly begins to manifest itself in the ever-increasing range of new services and online tools that any business can use, customer service is already among the most popular functions being targeted by those with AI-based solutions to offer.
Whether these services involve the use of chatbots, personalised marketing or predictive analytics, the challenge they all seek to address is help companies understand customer behaviour so that they can (1) create more targeted marketing campaigns and (2) improve the overall customer experience.
“Understanding customer behaviours, preferences and purchasing patterns is ripe territory for the new breed of AI tools that dig deeper into customer data”
Indeed, many businesses are already seeing the clear links between these two aims. A recent survey of senior marketing executives by Red C and revealed that their two top priorities were understanding customer behaviour and showcasing the power of marketing to their bottom lines.
Nearly half (46%) said they want to develop their understanding of how customers behaved, while 41% said they wanted to demonstrate marketing’s return on investment.
Chatbots
Chatbots have been around for some time, but AI-powered chatbots have shown that they can play a huge supporting role to small businesses in handling a wider range of queries from customers, thereby freeing up valuable time for employees to focus on higher priority tasks while still ensuring that no query goes unanswered.
Chatbots powered by natural language processing have the ability to understand the tone and context of messages from customers. This means they can make a better fist of gauging what users really want, and respond with better quality and more relevant information or suggested actions that build trust.
It’s this ability, combined with the capacity to respond instantly, consistently and round the clock, that makes AI chatbots increasingly valuable. After all, no customer wants to be put on hold or waste more than a few minutes of their valuable time in a queue at any time of the day just to speak to a human agent.
These chatbots can also be deployed across multiple social media platforms as well as the company website/app, so they can meet customers on whatever platform they prefer to interact.
Recent research by Intercom found that chatbots boosted sales by nearly 70%, while customer response times improved by a factor of three. Customer support satisfaction also saw a jump of 24%.
Personalised marketing
Understanding customer behaviours, preferences and purchasing patterns is ripe territory for the new breed of AI tools that dig deeper into customer data, the insights from which can then be used to build highly personalised marketing campaigns to win over individual customers.
The type of data such tools would analyse includes browsing history, purchasing behaviour, and interactions with customer services. Armed with this information, businesses can send targeted offers, recommendations and content that clicks with customers’ needs and wants, and build brand engagement and trust.
Platforms like Dynamic Yield use AI tools and A/B testing to customise customer communications via email, text and chat apps. They can also identify the most effective channels, messaging content and timing that maximises the chances of gaining sales. For instance, if email is judged to be the best channel to reach customers, algorithms can determine the best time to send them, craft engagement subject lines that maximise open rates, and segment your audience based on the kind of behaviours they show.
The winner of the ‘Best application of AI in an SME’ category of the 2024 AI Awards was BuyMedia, an AI-powered platform which helps SMEs overcome advertising challenges by simplifying planning, purchasing, execution and learning. It offers data-driven insights, a centralised marketplace and automated campaign management.
Predictive analytics
Predictive analytics also mines historical user data to help companies anticipate or predict their customers’ needs and behaviours. Where it differs from personalised marketing is that the information gleaned from the analytics is used to implement proactive measures to better engage with customers.
For example, AI tools like DataRobot and RapidMiner can predict when a customer might repurchase a product or when they might need more support. Using the alerts generated by AI, businesses can opt to take certain actions to increase the chances of the customer engaging, such as sending a reminder, offering a discount or even addressing potential issues before they arise.
Predictive analytics also helps with inventory management by forecasting demand. With this information, small businesses can ensure they have the right products in stock, thereby reducing waste and preventing missed sales opportunities. The insights can also inform long-term business strategies, such as emerging trends or new customer segments.
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