This blog will highlight the importance and implications of customer engagement analytics for companies to outperform their competitors, backed up by strong evidence across various industry segments. Before taking a deep dive into the content, let's understand the context of customer engagement analytics, after all, context is the King!
Customer engagement analytics facilitates organizations to make data-driven business decisions for direct marketing, site selection (measuring the needs of a new project against the merits of potential locations) and customer relationship management (CRM). The outcome is a 360-degree viewpoint of customer behavior that enables to find out who they are, what they want, what they do, and when & how to best reach them – in order to create personalized customer experiences that drive customer loyalty.
It provides a great opportunity for companies to understand customer preferences and allows them to “think like a customer”, which in turn help them to deliver the best possible service that matches their preferences. A typical example can be an online retail customer surfing across sites like Amazon or Flipkart to buy a smartphone. The company has huge data of searches made by previous customers from a certain geography, thus enabling them to produce the best results for the customers to choose.
The online world has become huge and today customers are everywhere – mobile phones, social media, email, etc. Companies can reach customers via all these communication channels; hence it is imperative for companies to create a 360-degree customer engagement platform to stand out in the competing market place.
This is crucial for companies, to understand the best use of customer engagement analytics. Generally, customers are segmented based on demographics (age, race, gender, religion, ethnicity, income, education level, family size), psychographic (lifestyle, social class, and personality characteristics), geography (where they stay and work), behavioral (spending, usage, consumption). The best customer analytics tool generally considers all these segmentation and segregates data, which can then be manipulated to understand the user behavior for better customer experience.
Jeff Bezos, the CEO of Amazon believes that customer experience is the number one priority and therefore obsessing over your customers is the key to understand them better. For instance, Zappos – the leading online shoe retailer famous for its best customer service doesn’t believe in call time tracking, rather it allows reps to have extra time with customers to understand their requirements. Apart from Customer segmentation, there are few other factors which are important to create a best in class customer engagement analytical system.
Is customer segmentation alone enough for creating the best customer engagement analytics tool? The answer would be no. Well, one needs to consider other factors as well to make a comprehensive analysis, that includes:
These factors would allow companies to create a brand/product persona to resonate with their audience choices and can utilize their resources effectively.
Customer Satisfaction Analysis: Customer loyalty is directly proportional to customer satisfaction, hence happier the customer – more likely for the customer to buy from you again. Customer satisfaction analysis based on feedbacks, ratings and reviews helps to understand the satisfaction level at a faster pace and quickly alter the product and service specifications to address the need.
Customer Lifecycle Value Analytics: This evaluation helps the company to understand how long a customer has been associated with a company considering the time spent on the site, purchase history, products or services availed etc. By analyzing these records, the company can invest its marketing efforts fruitfully by targeting the right customers. It also provides a way to increase the length of the relationship with customers and in turn the customer value.
Sales Channel Analytics: A company might have various sales channels to sell their products and services, but a company will fail to grow if they don’t understand what channels are most profitable. Hence, this part of the customer engagement analytics facilitates an organization to discover which are the most profitable sales channels and how the resources can be used effectively. Since this blog is concerned about online sales and analytics, some of the online sales channels are a general market place (Amazom.com), auction market place (ebay.com), social media (Facebook, Twitter, Instagram etc.), SEO marketing etc.
Web Analytics:This is the predominant form of customer engagement analytics which is highly prioritized when comes on online sales. Web analytics is the process of evaluating the customer behavior on a website such as the number of visits by a customer, time spent on the site, first time or a returning customer, through which channel the customer visited the site (via social media or google search or bitly links), purchase habit etc. By analyzing these customer behaviors, the company can focus better on the spend management which in turn improves the cost involved for their marketing efforts.
Social Media Analytics: : The highlight of social media analytics is that it is real-time and allows your company to know what people are talking about your products and services. In this form of analytics, the tool gathers text data from social media posts and blogs to do text analytics and sentiment analysis.
Customer Churn Analytics: : Retaining existing customers are much cheaper than finding new customers. On that note, customer churn analytics helps an organization to understand the retention rate and to find out how many customers a company is losing over a period. By keeping the track of customer churn in the past, evasive action can be taken in the future to improve customer retention.
Customer Acquisition Analytics: : This is another very important form of metrics that allows your business to generate more profit. If a company cannot acquire new customers, it is certain to fail among the compelling market place. Acquisition analytics helps to acquire new customers from the open market as well as to pinch them from your competitors. The metrics generated will allow a company to understand the cost per lead and the conversation rate through the marketing efforts.
McKinsey Report (Dec 2018) : According to a recent study conducted by McKinsey & Company on Dec 2018, though everyone is diving into Big Data, not everyone is succeeding by using it. The study also helped them, to discover that 50% of the companies that use customer analytics are more likely to improve their sales performance significantly higher than their competitors, and companies which make the best use of customer analytics are 6.5 times more likely to retain the customers, 7.4 times better feasibility to outperform their rivals on making sales to the existing customer, and 19 times increase in above-average profitability.
MIT Sloan Management Review Research Report (2018): A research conducted by the Information Systems Department at the Carroll School of Management at Boston College on using analytics to improve customer engagement has come up with few findings for the agriculture industry. For many farmers in the U.S, improving agricultural productivity while meeting consumer demand to reduce the use of pesticides and chemicals on crops became a challenge during the 2000s. Many apps and data services company came front to help these farmers to manage pests, plant diseases, weather conditions, and yields. But most of them failed to provide the desired results as data alone proved insufficient, since farmers also needed guidance to analyze and interpret the data.
By 2016, a new company developed a tool which provides recommendations to farmers based on the data interpretation gathered from different customers, thus it helped the farmers to address the needs of the customers in a more productive way than ever before.
To help farmers manage pests, plant diseases, weather conditions, and yields, dozens of startups emerged to offer apps and data services — part of a precision agriculture boom. Many of these companies failed or struggled as data alone proved insufficient; farmers also needed help interpreting the data. By 2016, a new variety of data-oriented service providers was helping farmers apply their harvested data.