Understanding your customer is essential to get more leads and more business. It is the key to giving your customers a good service that increases strong customer relationships and new sales through positive word-of-mouth recommendation.
It is important to know what your customers want and the most effective way to make your product or service available.
Understanding Your Customer Is Not Easy
To understand your customer’s psyche, you need a thoughtful analysis to know their preferences or purchase patterns to anticipate their needs and exceed their expectations.
The Depth Of Knowledge Is Crucial
To have a deeper understanding of your customers, you have to collect data beyond their names, ages, genders, and incomes. Hobbies, tastes, and interests play an important role, along with what your customer prefers to watch, listen, and read. Knowledge of these can be a profitable advantage for you.
Think about the following questions related to your customers:
- What are their reasons for being interested in your product or service?
- How often are they going to buy from you? Is there a way you can always reach them at the right time when they’re in need of the service again? You must not allow your customers a chance to look elsewhere for their needs by always being there at the time of need.
- Which of your customers are also the consumers of your product? A customer is the one who makes a purchase but may or may not necessarily use (consume) your product. A customer may purchase from you for someone else’s use. In this case, your messages and promotions should be relevant to the interest of the consumer.
- Where are your customers more likely to purchase? If you are a gym owner, and you hear from your customers that they would prefer an online at-home fitness service from you, then you should improve your business model by investing in a website.
Customize Customer Experience To Create Loyalty And Repeat Business
Companies that know what exactly their customers want and what they expect can customize the experience for each customer to win their loyalty and provide them a reason to come back to you again next time.
Companies can personalize the customer experience by employing advanced machine learning engines to build actionable customer segments. With more knowledge and clarity of each segment, marketers can talk to each customer in a tailored and relevant way, and strengthen the customer’s trust in your company. Such a customer is likely to bring you more revenue over time.
Artificial Intelligence And Machine Learning
Artificial Intelligence (AI) is a computer science field focused on making machines that seem like they possess human intelligence. The “intelligence” of these machines is artificial because humans create it, and it does not exist naturally.
Machine Learning (ML) is a subset of AI. ML algorithms are computer-implementable instructions that take a dataset as input and perform calculations to find out patterns within that dataset that were previously undiscovered.
These algorithms improve their performance over time as they encounter more data. They learn by experience and self-correction. The more data they encounter, the more experience they gain.
Predictive Analytics And AI
Predictive analytics comprises statistical techniques from data mining, predictive modeling, and ML that analyze current and historical data to predict the future. It is a tool to predict customer behavior, using historical customer data to foresee your customers’ probable future actions.
- When is your customer likely to make their next purchase?
- Is a customer likely to end their relationship (churn) with you?
- Which of your prospects best “look like” the prospects that were successfully converted into customers in the past?
You can use ML to obtain answers to such important questions. In supervised ML, the dataset that your model takes as input is always labeled. Labeled data has a target variable or value that is intended to be predicted for a given combination of feature values.
Once your model is trained on a sufficient training dataset, you can give it unlabeled data as input and it would predict the label for each unlabeled record.
Let’s develop a proper understanding of how supervised ML can help you by using an example. Suppose your dataset contains your customers’ data, and each customer is classified as either “churn” or “active”.
Customers who have ended their relationship with you are classified as “churn”, and those who are still loyal to your business are classified as “active”.
Your ML model would figure out how much weight each feature has put on a customer’s decision to churn. You can employ a Decision Tree Classifier or Random Forest Classifier for this problem, two common examples of supervised ML algorithms.
After figuring out the importance of each feature in the classification of each customer, your model would be able to discover the patterns that lead to a particular label.
If you would input the dataset of your current customers in future, your model would be able to identify which of the current customers have developed feature values resulting in patterns that led past customers to churn.
In this way, your model would predict the label for each customer either as a (potential) “churn” or an “active” customer.
Transform Into A Data-Driven Company Using AI
Netflix is one famous company that makes the most use of predictive analytics. Making large investments in movies and TV shows without the certainty of their high profitability is risky. Therefore, everything Netflix does should be based on data, from the shows it creates to the movies it promotes.
A company like Netflix has to collect enormous amounts of data on each user to put into an AI-powered algorithm that predicts what they would probably prefer to watch next.
A user’s data could be of their demographics, watch history, ratings, and preferences that allow the algorithm to make predictions with high accuracy. Around 80% of the content on Netflix is watched due to the recommendations.
A strong system like this saves Netflix $1 billion a year in customer retention.
Conversational Artificial Intelligence
Conversational AI is any machine that a human can talk to. It includes chatbots on any website or social messaging app, a voice assistant, or any other interactive messaging-enabled interface.
Chatbots
Chatbots are your virtual customer assistants. Chatbots play a crucial role in understanding customer intent, which then becomes a stepping stone to providing effective customer queries solutions, ultimately leading to total customer satisfaction.
In our daily lives, you engage in a conversation with people to understand them. Likewise, chatbots have conversations with your customer but in a more evolved manner, largely using Natural Language Processing (NLP).
Customers Can Purchase And Receive Recommendations From Chatbots
Customers may feel difficulty in choosing what to buy. Therefore, they need advice. Consumers in the US are interested in receiving chatbot recommendations, and chatbots perform this task quite well.
They do not provide a long list of irrelevant recommendations; rather they analyze data associated with each specific customer and render relevant choices on its back.
Predictive Analytics And Chatbots
Even simple observations to your chatbot data can reveal a lot. These observations can address business queries like what is happening in the context of customer behavior. Moreover, the analytic allows discovery and understanding of why it is happening and what may happen next.
Chatbots allow you to go deeper and broader in data analytics. A prevalent practice these days is to integrate advanced behavioral analytics technologies to chatbots.
Conclusion — Predictive Analytics Powered By AI Does Wonders For a Great Customer Experience
Predictive analytics is a very powerful tool. If powered by AI, it can solve and prevent plenty of problems for your business by allowing you to foresee the future. Companies should use it strategically and often to create amazing customer experiences.
