As marketers we are lucky to have loads of tools and tricks at our disposal, and new ones are developed all the time. One particular development has been a game changer. The integration of Artificial Intelligence (AI) in business, particularly marketing, means we can analyse vast amounts of data, predict trends, and automate processes – which has elevated and accelerated how businesses can connect more effectively with their audience.
At Virtual Marketers, we embrace the use of artificial intelligence in marketing. And to be honest it’s not a new concept for us – marketers have been using tools like HubSpot and Marketo for CRM, Drift for ChatBots, and in-platform AI-enhanced features for a while now. However AI is now a lot more accessible to the everyday marketer through the likes of ChatGPT, image generation tools like the one in Canva, and ad optimisation tools like those offered by Google and Facebook. It’s reshaping the way businesses connect with their audiences, offering unprecedented insights, personalisation, and efficiency, without the need for massive platform overhaul or specialist skills.
So what does all this AI in marketing mean?
The fusion of real time AI and marketing is entering an exciting stage with loads of emerging technologies to try. As we navigate this phase, staying informed and adopting these advancements strategically will be key to gaining a competitive edge and great results.
By embracing the power of these AI technologies, marketers can create more personalised, efficient, and engaging experiences for audiences, ultimately shaping the future of marketing in the digital age.
It’s getting pretty unreal out there – here are the pro’s and con’s of using AI for personalised marketing
One of the most significant advantages of AI in marketing and sales is its capacity to personalise customer experiences through data analysis. Through machine learning algorithms, AI analyses customer behaviour, preferences, and interactions to deliver tailor-made content, recommendations, and offers. This personalisation helps enhance customer satisfaction, engagement, and done well, ultimately, brand loyalty.
AI personalises experiences by leveraging data, algorithms, and machine learning to tailor interactions and offerings to individual preferences and customer segments based on their behaviours. Checking your email will show you a multiple sea of emails targeted at your most recent online behaviours, or lack of, and some of them just might grab your attention.
Predictive analytics, essentially machine learning for marketing, is taking a leap forward with AI. By analysing vast datasets, machine learning algorithms can now predict customer behaviour with unprecedented accuracy. Marketers can use these insights to anticipate trends, optimise campaigns, and personalise interactions based on individual preferences.
These are all the ways AI is (currently) used to personalise experiences with the goal of fostering customer satisfaction and loyalty to increase the success of the business itself.
1. Product Recommendations:
AI algorithms collect data to analyse customer purchase history, browsing behaviour, and preferences to provide personalised product recommendations. For instance, platforms like Amazon and Netflix use AI to suggest products or content based on users' past interactions.
Watch out: We should be cautious about over-reliance on AI-driven product recommendations, as they may inadvertently limit our audience’s exposure to new and diverse products, potentially missing out on unique and unexpected finds.
2. Dynamic Website Content:
Websites use AI to dynamically adjust content based on user behaviour. For example, an e-commerce site might showcase products similar to those a customer has viewed, creating a more personalised and relevant browsing experience.
Watch out: Again beware that the dynamic adjustment of website content based on user behaviour can sometimes lead to a "filter bubble," where users are primarily exposed to content and products that reinforce their existing preferences, potentially limiting their exposure to diverse perspectives or offerings
3. Personalised Email Campaigns:
AI analyses customer data to personalise email campaigns. This includes tailoring subject lines, content, and product recommendations based on individual preferences, purchase history, and engagement patterns. They will also send emails at times when you are most active (do you notice a lot of your emails come in at the same time of day, and that just happens to be when you usually check your phone…?)
Watch out: Over-automation can lead to a loss of the personal touch in emails, making customers feel like they are interacting with a machine rather than a human, which can harm the customer experience.
4. Chatbots and Virtual Assistants:
AI-driven chatbots have transformed customer service by providing instant, round-the-clock assistance. These bots can answer queries, resolve issues, and guide customers through the purchasing process. This not only improves customer satisfaction but also frees up human resources for more complex tasks.
Chatbots have evolved beyond basic customer service tools. Advanced conversational AI can engage users in more sophisticated dialogues, providing personalised recommendations, product information, and even completing transactions. This not only enhances user experience but also streamlines the customer journey.
Watch out: Overly complex issues may still require a human touch. It's essential to design chatbots that seamlessly transition to human agents when needed, preventing frustration and ensuring a positive customer experience.
5. Predictive Personalisation:
AI predicts customer needs and preferences by analysing historical data and behaviour patterns. For instance, a retail website might use predictive analytics to anticipate a customer's next purchase and proactively offer relevant recommendations.
For example, you buy a water filter every three months, so up pops a water filter replacement reminder when the time’s right to prompt you.
Watch out: While timely reminders can be helpful, customers may find certain AI-driven prompts, like frequent "next purchase" reminders, to be annoying and sometimes even intrusive, which could lead to customer dissatisfaction.
6. Personalised Content Recommendations:
Streaming services, news websites, and other content platforms use AI to recommend articles, videos, or news based on a user's past consumption patterns. This ensures that users receive content that aligns with their interests.
You only need to jump in the video section of Facebook to start to see a long list of videos recommended for you based on your interests and past video engagement.
Watch out: AI predictions are heavily reliant on historical data, which may not always accurately reflect a customer's current or future preferences, leading to missed opportunities or inaccuracies in recommendations.
7. Location-Based Personalisation:
AI can personalise experiences based on a user's location. For instance, a mobile app may use geolocation data to offer personalised promotions or recommendations for nearby stores or services.
Uber Eats’ prompts and special local deals on a Friday night ring any bells?
Watch out: If the historical data used for AI predictions contains biases, the AI system may perpetuate these biases, potentially resulting in unequal treatment or inaccurate recommendations for certain customer segments.
8. Ad Personalisation:
Digital advertising platforms leverage AI to personalise ad content based on user behaviour and preferences. Advertisements can be dynamically generated to match individual interests, increasing the likelihood of engagement.
Got those discount codes from a website you recently visited following you around the web? Bingo.
Watch out: Over-reliance on AI predictions can lead to a less spontaneous and more predictable customer experience, potentially diminishing the excitement of discovery and surprise.
9. Personalised Loyalty Programmes:
AI helps businesses create personalised loyalty programmes by analysing customer behaviour and preferences. This ensures that rewards and incentives are tailored to individual preferences, encouraging continued engagement.
Watch out: Striking the right balance is crucial. Over-reliance on personalisation can lead to privacy concerns and a sense of intrusion. It's essential to be transparent about data usage and provide opt-out options to respect customers' privacy.
AI-assisted marketing has undoubtedly transformed the landscape, providing businesses with powerful tools to enhance efficiency, personalise interactions, and optimise strategies. Our take on it is: you are going to be left behind if you don’t get on board, however, keep an eye on these watch outs and new issues developing every day. With a balance of up to date learning and a critical eye, AI will soon be your marketing personalisation buddy.
Keep an eye on our next blog on the world of AI in marketing, where we look into AI for predictive analytics.
