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[悬赏]机器学习可以帮助您找到理想的客户 (已翻译0%)

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英文原文:Machine Learning Can Help You Find Your Ideal Customer
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admin 发布于 2017-06-26 11:05:34 (共 3 段, 本文赏金: 12元)
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Finding customers based on traditional marketing tactics is now old hat. Now, when it comes to finding customers,machine learning is the main attraction. Old solutions required sales and marketing teams to create generic personas that would then be assigned to customers by focusing on broad similarities. But with the advent of big data mining, this one-size fits all just isn’t cutting it anymore. Customers and clients all want a more personalized experience and businesses are serving just that by switching over to a more customer-focused and personalized offering model.

Today’s organizations and businesses are being driven by customer experience, and those who make it their number one priority are the ones who will really stand out from the competition. Businesses can give more personalized offerings to their customers and ultimately drive higher conversions by building ideal customer profiles. These profiles can answer questions such as “which customers will buy next month?”, “what marketing content is the best for a particular client?”, and “what customers will be our largest spenders?” Answering these kinds of questions will lead businesses to target the right individuals and ideal customers all through the use of big data mining and predictiveanalytics through machine learning. And on the other hand, customers feel as if these businesses can predict their every need such as when you might run out of toilet paper.



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Whether you sell a product or offer a specific service, you will need to find people who will purchase your goods, satisfy their expectations and then keep them coming back for more. But customers go through a type of journey from when they initially connect with your company to when they become lifelong customers. At the start, they have different needs than when they purchase an add-on component or need help from the support team and they have even different needs years down the road when they are long-time customers. By focusing only on initial sales and transactions, companies can lose out on solidifying loyal customers. But with big data analytics and machine learning, businesses can now gather extensive amounts of data to predict customers’ future needs.

There is a myriad of data points that can be collected and analyzed to determine a customer's’ future needs from their demographic information to where they live, their social media engagement, purchase history and frequency, personal contact through the help desk, and even in-person information that can be mined from intelligent video analytics (think facial reactions to advertisements in-store). Machine learning gathers all of this information--big data--and then learns from the past to predict the future behavior of each customer.



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Machine learning works 24/7 to sift and analyze massive amounts of data and then pinpoints minute patterns and contrasting bits of data. The end result can be a report that offers statistical foundations for real-time tactical decisions for marketing and sales teams or even for customer support who needs to react to requests as quickly as possible. They also need to anticipate customers’ needs in order to influence future actions. This can all be accomplished by machine learning systems that will discover patterns in customer’s historical interactions with the company allowing you to create more relevant campaigns, offering your customers more personal interactions from Day 1 of being a customer to Day 1, 293, 568.

Based off of a Gartner report, in the next three years nearly 60 percent of digital commerce analytics investments are to be spent on customer journey analytics. Gone are the days of relying upon manual surveys and collections by sales teams to determine generic customer profiles. With more and more work going into machine learning, we are beginning to see computers that aren’t needed to be specifically programmed but are based on continuous learning giving us a foundation to sky rocket revenue. All through the creation of ideal customer profiles.


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