October 7, 2016
The buying process begins and ends with consumers and what they go through as they recognize needs, figure out ways to solve their needs, make purchase decisions, process information, and implement their plans. While there are plenty of variables in this process, big data and machine learning are significantly affecting the ways consumers behave and make decisions about what they want and need to purchase.
Here we explore why machine learning and big data analysis are so important to the contemporary consumer-buying journey:
1: Consumers Make Decisions in Real Time
The only way to really understand your customers is to get better insight into how they really behave. You can turn to surveys to find out more and generalizations based on demographics can be helpful, too. In reality, though, today’s businesses need to collect the most data on how customers behave in real time and how that affects real-time decision making.
Nearly 60 percent of executives feel big data is important to the success of their businesses, and 70 percent realize big data is vital to stay competitive. Many brands realize the power of machine learning, yet few have turned the key to unlock its potential. When you understand how to guide customers based on their actions in a real-time situation, you can better address their needs and improve your bottom line in the process.
2: Consumers Are Better Matched With Products
Technology such as machine learning can lead to an extraordinary shopping experience. Retailers can capitalize on the accuracy of algorithms to determine product availability or recommend a price point and quantity to access a specific customer who is located in a certain area.
Applications similar to this are being seen by companies such as PNC, which uses a learning system that helps prevent shoppers from becoming weighed down by the array of choices. “Decisioning happens in one central place, making it easy for PNC to provide a consistent customer experience across all channels,” says PNC Customer Management SVP John DeMarchis.
The PNC example is just one of many ways big data is being collected. Another way is through self-learning models that guide consumers to make better decisions faster. Stop dictating what you think your customers want – instead utilize the information obtained during their interactions with various assets and teams. In this case, the program is informed by real consumer information, which causes the system to refresh its recommendations instantly. This improves results through relevant and timely actions that encourage adaptable, real-time decisions.
Consumers Get What They Want Before They Ask for It
Companies are able to make the experience seamless for customers due to big data. Past behavior allows business owners to anticipate what customers will want based on history, making customers more satisfied and more likely to make purchases.
Companies gather a lot of data on their customers, from what they have purchased in the past to what websites they visit – even where they live and if they interact with their brand on social sites. This data, which may seem overwhelming and unrelated, allows companies to offer a more personalized touch than ever before.
Find out how long customers stay on your website and what links they are clicking. Figure out what triggers customers to share your content via social media. The businesses that are making strides are the ones that utilize automated data gathering and analysis technologies to collect real-time customer data to detect patterns and predict what customers need before they are even aware of what they want.
More retailers see the benefit of tracking consumer shopping habits by utilizing data sources like purchase history, social media, market trends, and customer demand. Relying on big data technology helps you gain a better understanding of customers and buying trends, and in turn allows retailers to make the most of customers’ spending and promote loyalty.
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