First, let’s understand the term “360 degree view of the customer.” Customer data comes from various sources. It is possible to track whether a customer opens a particular email – and presumably then reads it – or how many visitors have clicked through an ad on a blog or website, how many times a post has been shared on social media and so on. The problem that arises is that such data cannot be studied independently of other data. Every bit of data has a bearing another. For example, a customer who opens a particular email may then click through a link in it or may not. Each behavior gives insight into the customer’s psyche. Having clicked through a link in the email, does the customer then take action, such as sharing the information on social media, or does he or she simply read and move on? If the information is shared on social media, which medium does the customer prefer?
A 360 degree view of the customer gathers all of this data about the customer and his/her behavior and uses well-tested heuristics to form an actionable profile of the customer. By using a 360 degree view, service providers can refine or even re-design their services.
That doesn’t mean a service provider has to analyze each customer individually. For one, that would take too long, even if the customer base is in the few hundreds instead of the hundreds of thousands that is the reality for all big businesses. For another, human bias is a very real danger in customer analysis. Instead, software can objectively analyze the behavior of each individual customer, aggregate the data over all customers and provide a road map for the service provider that will tell him / her what exact tweak of service will benefit most of the customers.
So what’s the problem with 360 degree analytics? Plain old 360 degree analytics deals with past data. It’s an excellent way to tell how a customer would have reacted to stimuli in the past. However, the problem is that customers in the present are constantly being bombarded with information; information that may influence future behavior regardless of past statistics.
Enter Predictive Customer Analytics
Predictive Customer Analytics goes beyond mere analysis of customer behavior. It factors in the probable influencers of the customer that will then determine future customer behavior. Based on the existing customer profile, Predictive Customer Analytics determines the messages that the customer will probably be exposed to and seamlessly factors those messages into predicting future customer behavior. This offers a truly 360 degree view of the customer.
There are several factors to be considered when implementing Predictive Customer Analytics, including:
- Peer review: A large percentage of customers evaluate a product or service according to reviews posted by unbiased users of that product or service. According to the MarkMonitor report on online shopping (https://www.markmonitor.com/download/report/MarkMonitor_Online_Shopping_Barometer-q4-2018.pdf?cid=pr) 63% of respondents checked up on a website through online reviews. Predictive Customer Analytics will take such peer reviews into consideration in making suggestions for improvements in a product or service.
- Grammar and spelling: According to the earlier mentioned report, grammar and spelling are very important. 39% of respondents evaluated the authenticity and value of a product or service based on the grammar and spelling of the text that described that product or service on a website. Predictive Customer Analytics will analyze the grammar and spelling of an offering, suggesting areas where the copy needs to be changed to cater to a specific audience or prospective customer base.
- Social media posts: This is by far one of the most important sources of data on products and services, since individuals tend to vent on social media without thought as to whether they are actually evaluating a product or service. Posts on social media, whether Facebook, Instagram, Twitter, etc., usually reflect the genuine feelings of users. So, if a user expresses frustration over a product or service on social media, it is usually in an attempt to gain sympathy and not necessarily to influence buyer behavior. Thus, it is seen as a genuine plug or complaint about that particular product or service. By analyzing social media posts, 360 degree customer analysis through Predictive Customer Analytics can point to a direction that the company can take to increase customer interaction and engagement.
- Trimming of “gaming” posts: Let’s face it, a lot of companies “game” social media by creating identities and postings as individuals, even though such posts are automatically generated that are designed to influence buyers. Predictive Customer Analytics, through the use of Artificial Intelligence, recognizes such automated posts and filters them out of the analytics, so the customer gets an unadulterated view of the actual customer base.
The benefits of Predictive Customer Analytics for a 360 degree view of the customer are many and varied. By embracing its potential companies can create memorable customer experiences that will ensure repeat sales!