Does an initial purchase translate into a continuum of sales? No. It doesn’t even hold the promise of customer retention like it used to in the yesteryears. Customers now have much higher expectations than they used to, in order to remain loyal. What can you do to help them come back to you every time?
It takes a customer-first mindset and a company-wide commitment to improve the customer experience to make them come back every time. In addition to that, organizations that excel at customer experience have been leveraging Artificial Intelligence (AI) and Machine Learning heavily, to produce immersive, authentic experiences across every customer touch point.
Some facts to show the shift in consumer behavior:
- The cost of attracting a new customer is five times the cost of keeping an existing one, hence businesses need to pay as much attention to retaining customers as they do to acquiring new ones.
- According to Bain & Co, increasing customer retention by just 5% increases profits by 25% to 95%.
- 96% of unhappy customers don’t complain and 91% of those will simply leave and never come back- Colin Shaw, author and pioneer of customer experience.
How can Machine Learning help organizations to tackle Customer Churn?
Some churn is inevitable but AI can lead efforts to minimize the churn rate by creating an effective win back strategy, drive retention, loyalty, revenue and cost-effectiveness.
Video streaming giant Netflix recently claimed to have saved almost $1 billion by retaining customers using predictive algorithms. This is one of the most commonly known success stories and has encouraged the rising popularity of predictive customer churn models over the last decade.
Keep track of customer data; they expect you to
It is expected by most customers that the companies they buy from or do business with will make the experience easier for them by using all the data they have available. To retain your customers, you should keep track of their data, utilizing cookies and user logins, product telemetry, purchase history and previous digital interactions. This can help ML models know who the customer is and predict what they will need.
For websites, advanced analytics can be used to personalize specific aspects such as: predicting the types of questions they may have, the types of content that might be helpful, the types of products they may want to find and the type of support they may need.
Make an inventory of available data
Predictive models are known to be data guzzlers. The more data you have, the higher the accuracy of the predictions. Most companies have more customer data than they realize. When you make an inventory of the kind of data you have and take stock of the existing data sources, there can be multifold benefits. You would improve the model’s output and also put the assets which you considered redundant, to good use.
Use multiple use cases to build models and focus on insights as well as actions
Data science can be used to deploy machine learning for customer retention and to create the models needed to create the multidimensional view of churn risk and opportunities. ML models can also help to predict at-risk customers and be used to uncover opportunities to save the customer. If you are using more than one model, ensure that all the models work in tandem to integrate the right insights with the right customer touch points and drive timely retention actions on a large scale.
ML models deliver exceptional experiences to customers to build attitudinal loyalty to your products, services and company. This also gives you the rare opportunity to create customers for life.
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