Learn why customer analytics has become very important in today's world, and why you'd benefit from using it.
- [Instructor] Every business needs customers to survive. They are the source of revenue. Needless to say, the success of a business is directly proportional to its ability to acquire customers, nurture them, make them happy, solve their issues, and consequently make more money from them. But for that to happen, the business needs to identify the right potential customers. They have to figure out the who, what, why, and how. Who are the potential customers demanding their products? What do they want? Why do they want this particular product? And how are the customers making their buying decisions? How does a business go about doing this? Well, typically all businesses have customer-facing people, like sales, marketing, and support who keep talking to their customers.
They become the face of the company, but remember that it's impossible for a business to contact every potential and current customer individually from time to time to understand their asks. You can imagine that there are serious challenges in keeping tabs on what customers think. When the target markets are large, say, a million individuals or more, it is difficult to show one-on-one attention. Also, with most businesses going online, the business do not have any direct contact with the customers.
They are scattered all over the world. You should also consider the competition that can reach out to customers much faster than before. The traditional barriers of geography and language are gone. A business in China can easily sell to a customer in the US, say, like Alibaba. The competition is getting bigger and smarter. Customers today have more options for any product or service, and the barriers to switch to vendors is becoming smaller. This brings businesses to a situation where they need to understand and plan for what their customers might do in the future.
For that, they need to predict customer behavior. Therein comes predictive customer analytics. To be ahead of the curve and act proactively, businesses need to know what their customers might do in the future. Will they buy your product? Will they switch? Are they happy with the product? Are they going to be dissatisfied? Will they buy more? Businesses need answers to these questions in order to identify the right customer, the right channel to contact them, and the right way to offer to help them.
They need predictive analytics. Predictive customer analytics uses customer data to build models. These models help predict future behavior. It helps businesses to target prospects who will convert and identify additional products the customer might buy. When customers have problems, predictive customer analytics will help businesses to identify the right resources to solve the problems. It will help identify customers who might switch and make them offers to redeem them.
And with predictive customer analytics, businesses can achieve this with lower cost and higher effectiveness than traditional means. The opportunity for doing predictive customer analytics today is better than ever before. There are rich data sources that are widely available, such as web blogs, social media, chat, transactions, and voice transcripts. Also, today's big data technologies are capable of doing large-scale data processing, integration, and storage in cost-effective ways.
We have multiple ways to understand what the customers think and feel, and be able to mine the data to build effective models. We can then use these models for driving business towards the right customers and making them stay with your business.
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Use big data to tell your customer's story, with predictive analytics. In this course, you can learn about the customer life cycle and how predictive analytics can help improve every step of the customer journey.
Start off by learning about the various phases in a customer's life cycle. Explore the data generated inside and outside your business, and ways the data can be collected and aggregated within your organization. Then review three use cases for predictive analytics in each phase of the customer's life cycle, including acquisition, upsell, service, and retention. For each phase, you also build one predictive analytics solution in Python. In the final videos, author Kumaran Ponnambalam introduces best practices for creating a customer analytics process from the ground up.
- Understanding the customer life cycle
- Acquiring customer data
- Applying big data concepts to your customer relationships
- Finding high propensity prospects
- Upselling by identifying related products and interests
- Generating customer loyalty by discovering response patterns
- Predicting customer lifetime value (CLV)
- Identifying dissatisfied customers
- Uncovering attrition patterns
- Applying predictive analytics in multiple use cases
- Designing data processing pipelines
- Implementing continuous improvement