“Make the customer the hero of your story.” – Ann Handley.
Organizations have been using evolved marketing strategies and digital platforms like social media, websites, search engines, etc., to understand their customers better to enhance their experience. In addition, they’ve been using monitoring tools like website analytics, social media monitoring, sentiment analysis, and more to understand better what customers expect from their brand.
With time and advancing technology, these tools are evolving, and now organizations have more than enough data to shift their strategies from reactive to proactive. The amount of data that has been collected and is being generated regularly has the potential to help organizations predict customer behaviour.
Predicting customer behaviour can prove to be a game-changer for any organization or industry. Using such insights, organizations can:
- reduce customer churn rate,
- improve and personalize the customer experience with hyper-targeted marketing campaigns,
- reduce their overall spend on campaigns,
- identify and target up-scale customers,
- encourage loyalty,
And much more.
All the above-mentioned usage of data and customer behaviour predictions can be made possible because of artificial intelligence and Big Data advancement.
“AI promises to be the most disruptive class of technologies during the next 10 years due to advances in computational power, volume, velocity, and variety of data, as well as advances in deep neural networks (DNNs),” said John-David Lovelock, research vice president at Gartner.
Gartner predicts AI-derived business value to reach a whopping $3.9 trillion by 2022. Such growth can be attributed to the fact that this technology improves business value at three different sources – customer experience, revenue streams, and cost reduction.
For this article, the focus of our discourse would be the use of AI and Big data for predicting customer behaviour and improving their experience.
According to Forbes, 75% of companies implementing AI and Machine Learning have managed to boost their customer satisfaction by more than 10%. Not only that, but 57% of enterprise executives also believe that the most significant growth benefit from AI, ML, and Big Data would be the improvements in customer experience and support.
Before we get into the nitty-gritty of how organizations can and are using AI and Big Data to predict customer behaviour and improve their experience, let’s quickly refresh what these technologies are.
As the name suggests, it means data that’s too big in size, volume, variety, variability, and velocity and can’t be processed and analyzed using traditional data analytics models. This is where Artificial Intelligence enters the picture.
According to John McCarthy, Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. AI breaks the confines set by biologically observable methods.
Forecasting customer behaviour requires the power of both these advanced technologies. AI methods and tools can break Big Data into processable data sets for predictions and forecasting.
Now that we have a brief understanding of these technologies let’s explore the applications of AI and Big Data for analysis and predicting customer behaviour.
AI and Big Data for Customer Behavior Analysis and Predictions
These advanced technologies enable organizations to predict what their customers will be excited about, will need in the future, and what they will worry about; the future can be 12 hours from now, 12 days, or even 12 months.
Knowing what customers will be talking about or needing shortly can empower organizations to create customized strategies to cater to their needs. For instance, if any AI model or software had predicted this pandemic, apart from countries taking preventive measures to stop it, businesses would have prepared themselves for the surge in demands for sanitisers, toilet papers, gloves, masks, PPE kits, etc. Then we would have avoided the shortage of these daily essentials and the mass hysteria caused by it.
We can let go of the guessing work and the trial and error method by introducing AI and Big Data in the decision-making process. No more depending on limited data gathered from focus groups, surveys, and inadequate customer feedback. Instead, organizations now have the tools and means to collect deeper insights like customer behaviour on their website, web app, or in-app, their different interaction points, clicks, and more. AI software can then break the data collected into small actionable insights, making the decision-making process more data-driven and accurate.
AI/ML models can also predict customer loyalty, estimate customer lifetime value, affinities, purchasing power, etc. Such information can help the marketing teams create personalized, hyper-targeted campaigns for better conversions.
Social media platforms have become an integral part of marketing campaigns for reaching out to customers and monitoring their needs, satisfaction, dissatisfaction, and overall sentiments and behavior. In addition, organizations can also monitor the interactions of their target audience and competitors.
The data collected from the constant monitoring of customer behaviour on social media can be fed into AI models to predict what they will want in the future or how they will react to a particular marketing campaign or product launch. This will enable organizations to create more relevant and creative solutions for their customers, enhance their brand positioning strategies, and increase their market share.
Marketers can also use the data and AI models to create smaller homogeneous segments based on their purchase history, activity on the website or social media account, regions, gender, etc. This would help in adding the personal touch to the marketing campaigns at scale. For instance, Netflix uses AI software for personalized recommendations.
Their AI software is fed the viewer’s viewing history and then recommends the viewer movies and shows according to their taste. They also use AI to power their search results. For example, if I type in ‘documentary’ in the search bar, the recommendations will match their percentage. You can also see the tag that says ‘Because of your interest in:.’ For this suggestion to come in the viewer’s suggestions, AI considers a lot of different factors like viewing history, viewer’s download history, their saved list, their reviews, their clicks, and many more.
Similarly, Amazon uses AI to increase cart values. Right at the homepage, you are greeted with a personal greeting along with personal recommendations based on your purchasing history and browsing history. This not only enhances the buyer’s experience but also saves their time that otherwise would’ve spent browsing around and getting confused.
Since they have the purchasing history of EVERYONE, they also recommend product bundles based on other people’s purchasing behaviour.
Embrace the Change
All the above applications of AI and Big Data for predicting customer behaviour and using the insights to get ahead of the competition sound super exciting. If I had an online retail business, I’d jump on the wagon today.
But… yes, there’s a big but coming.
AI isn’t as widely adopted among organizations just yet. Only one-third of retail organizations are working on the implementation of AI and ML in their processes. As a result, organizations are still reactive, and brand marketers are still forced to use traditional methods to achieve their goals.
Don’t get us wrong, we’ve definitely come a long way in marketing analytics, targeted messaging, and campaigns, but they all are still based on the traditional models.
AI and Big Data in marketing have a lot of room for improvement, and it would be a long time before it is at the mature stage and is the norm among the marketers, but it is the future of marketing.