The success and sustainability of a business is dependent on having customers. To acquire, grow, and retain customers, it is essential to know their needs; what they like, and what they dislike. Customer analytics is the systematic examination of a company’s customer information and customer behaviour to identify, attract, and retain the most profitable customers. The more the business understands their customers’ buying behaviour and lifestyle, the more accurate the business can classify their customers and get business insights. Targeting customers with the right product at the right time and with the right price improves the overall customer experience.
What are the benefits of customer analytics?
- Increase response rates
- Customer loyalty
- The opportunity to contact the right customers with relevant offers and messages.
- Reduce campaign costs by targeting those customers most likely to respond.
- Decrease attrition by accurately predicting customers most likely to leave and developing the right proactive campaigns to retain them.
- Deliver the right message by segmenting customers more effectively and a better understanding of target populations.
Brands are constantly evolving and changing how they interact with their customers. Customers are the foundation for the success of any brand and learning new methods of connecting with them forms the basis of success and failures for companies. That is why brands need to keep continuous track of how their target audience is perceiving their customer service and customer relations.
There are different types of customer analytics methods that are used by different brands to keep a continuous track of their customers. Some of these methods are discussed below.
Customer acquisition analytics
If you don’t have enough customers your business will fail, and the same applies if you spend too much money acquiring those customers. Customer acquisition analytics seeks to establish how effective you are at acquiring new customers, including how effective you are at pinching customers from your competitors.
Customer satisfaction analysis
Customers who are happy with your product or service are much more likely to buy from you again. Customer satisfaction analysis is the process of assessing whether your customers are satisfied or unsatisfied. Companies usually use a combination of both quantitative and qualitative surveys to measure customer satisfaction. The first step in the process of accurately measuring customer satisfaction is to ask customers how satisfied they are. Solicited feedback, usually collected through surveys, is useful to determine if your customer satisfaction is high or low. However, you must keep in mind that mildly dissatisfied or mildly satisfied customers often don’t bother to take surveys. You need to intentionally assess all groups of customers because, if you are only hearing from the extremes, your satisfaction scores are not reflecting reality. Through aggregating feedback from each group, you'll be left with a somewhat accurate representation of how customers feel about your service or products. If you were working in the dog food or dog treats business, you might see this as an opportunity to diversify with a range of private label dog treats that meet the demands of your existing customers based on what their feedback stated. Making small runs of each product can help you to gauge how well each one is selling and further how they are received by your regular customers.
Customer segmentation analytics
Seeking to sell all things to all customers via the ‘shotgun’ approach doesn’t work. Customer segmentation is the practice of dividing a company’s customers into groups that reflect similarity among customers in each group. The goal of segmenting customers is to decide how to relate to customers in each segment to maximize the value of each customer to the business.
Being able to assess your customers and split them up into various segments that might buy more of one product than another or buy more often allows you to tailor your marketing and communication efforts. Customer segmentation has the potential to allow marketers to address each customer most effectively. The internet is a vast source of useful customer data, helping companies identify clear segments. An additional approach to customer segmentation is leveraging machine learning algorithms to discover new segments. Machine learning customer segmentation allows advanced algorithms to surface insights and groupings that marketers might find difficulty discovering on their own.
Sales channel analytics
Is your company in full control when it comes to understanding the effectiveness of the various sales channels, gauging channel growth, and comparing channel margins, along the distribution chain? Unless you know how your sales are made and what channels are most profitable then you may be wasting time and money on sales channels that don’t work. Sales channel analytics are aimed at identifying relevant players, in-depth price levels, and profit opportunities in modern, fast-paced, volatile markets while caring to improve customers’ satisfaction, return on investments and gain a competitive advantage in the market. For this analysis, you need to identify all the sales channels that you currently use or could use, then attribute each sale to a channel and subtract the relevant cost of sales for each channel.
Customer lifetime value analytics
Customer lifetime value is a primary metric for understanding your customers. It’s a prediction of the value your relationship with a customer can bring to your business. This approach helps companies demonstrate the future value they can generate from their marketing initiatives. That’s why your evaluation of investments should be done based on long-term profits rather than short-term wins. Instead of looking at transaction profitability, you look at how long a customer is likely to stay a customer, how often they are likely to buy during that period, and therefore how valuable they are across that timeframe. This allows you to focus marketing attention on the most valuable customers. Done well, this analysis can also potentially identify ways to increase the length of the relationship and the value of the customer.
Social media analytics
If you don’t know what people are saying about your company or products, you can’t resolve any issues that arise. The core of social media analytics is the gathering and analyzing of marketing and audience data upon which you'll base business decisions. It's the only sure way to access insights that you can use to optimize your marketing efforts and strategy. In social media analytics, text data from social media posts and blogs are gathered and mined for commercially relevant insights using text analytics and sentiment analysis.
Customer engagement analytics
Collecting customer data is no longer enough to achieve success in the customer experience. Companies need to put the behavioural and feedback data they capture across multiple channels into action by delivering personalised, timely, and consistent messages within marketing, sales, and service activities.
Customer engagement analytics is a rapidly evolving field where businesses are trying to map the entire customer interactive journey on- and off-line. It is defined as the use of analytical tools to analyse structured and unstructured customer and operational data captured across multiple channels. Ways of measuring customer engagement include surveys and social media analytics.
Customer churn analytics
Keeping your existing customers is always much easier and cheaper than trying to find new customers. Customer churn (also known as customer attrition) refers to when a customer ceases his or her relationship with a company. Online businesses typically treat a customer as churned once a particular amount of time has elapsed since the customer’s last interaction with the site or service. The full cost of churn includes both lost revenue and the marketing costs involved with replacing those customers with new ones. Reducing churn is a key business goal of every online business. It also allows you to predict customer churn in the future and take evasive action before you lose those customers.
To succeed at retaining customers who would otherwise abandon the business, marketers and retention experts must be able to (a) predict in advance which customers are going to churn through churn analysis and (b) know which marketing actions will have the greatest retention impact on each particular customer. Armed with this knowledge, a large proportion of customer churn can be eliminated. Churn isn’t always straightforward to calculate, especially when it’s measured based upon past data. The future may resemble the past, but nothing is certain. Unforeseen events, from the emergence of new competitors to black swan market fluctuations, can prove old models wrong and lead companies to take the wrong actions. It’s also difficult for teams to apply the findings of churn analytics to individuals. Customer churn can be assessed using KPIs such as customer retention rate and customer turnover rate.
Online sales in just about every industry are increasing. Web analytics is the process of analysing online behavior to optimise website use and increase engagement and sales. There are two types of web analytics: off-site and on-site. Off-site web analytics is useful for assessing the market and opportunity whereas on-site is useful for measuring commercial results. There are many web analytics tools and service providers available, such as Google Analytics.
Customer analytics is the basis for gaining important and valuable insights from the customer. Adapting to the changing market scenario and investing in a good customer relationship is the only way in which brands can grow and prosper. The future of marketing is rooted in personalization and meeting the needs of every customer; customer analytics has the potential to take brands to that stage in an effective fashion.