Hearing your customers’ voice is crucial for any business that wants to provide competitive products or services. In the age of social media when decisions are made instantly upon other people’s reviews, you don’t want to risk getting negative feedback that may sink your ship. Act preemptively to understand what problems and obstacles your clients may face when navigating your website using the power of digital customer analytics.
Analytics is the discovery, interpretation, and communication of meaningful patterns in data. Especially valuable in areas rich with recorded information, analytics relies on the simultaneous application of statistics, computer programming and operations research to quantify performance.Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include predictive analytics, prescriptive analytics, enterprise decision management, descriptive analytics, cognitive analytics, retail analytics, store assortment and stock-keeping unit optimization, marketing optimization and marketing mix modeling, web analytics, call analytics, speech analytics, sales force sizing and optimization, price and promotion modeling, predictive science, credit risk analysis, and fraud analytics. Since analytics can require extensive computation, the algorithms and software used for analytics harness the most current methods in computer science, statistics, and mathematics.
Analytics is multidisciplinary. There is extensive use of mathematics and statistics, the use of descriptive techniques and predictive models to gain valuable knowledge from data analysis. The insights from data are used to recommend action or to guide decision making rooted in business context. Thus, analytics is not so much concerned with individual analyses or analysis steps, but with the entire methodology. There is a pronounced tendency to use the term analytics in business settings e.g. text analytics vs. the more generic text mining to emphasize this broader perspective. There is an increasing use of the term advanced analytics,typically used to describe the technical aspects of analytics, especially in the emerging fields such as the use of machine learning techniques like neural networks to do predictive modeling.
Marketing has evolved from a creative process into a highly data driven process. Marketing organizations use analytics to determine the outcomes of campaigns or efforts and to guide decisions for investment and consumer targeting. Demographic studies, customer segmentation, conjoint analysis and other techniques allow marketers to use large amounts of consumer purchase, survey and panel data to understand and communicate marketing strategy.Web analytics allows marketers to collect session-level information about interactions on a website using an operation called sessionization. Google Analytics is an example of a popular free analytics tool that marketers use for this purpose. Those interactions provide web analytics information systems with the information necessary to track the referrer, search keywords, identify IP address, and track activities of the visitor. With this information, a marketer can improve marketing campaigns, website creative content, and information architecture.
Analysis techniques frequently used in marketing include marketing mix modeling, pricing and promotion analyses, sales force optimization and customer analytics e.g.: segmentation. Web analytics and optimization of web sites and online campaigns now frequently work hand in hand with the more traditional marketing analysis techniques. A focus on digital media has slightly changed the vocabulary so that marketing mix modeling is commonly referred to as attribution modeling in the digital or marketing mix modeling context.These tools and techniques support both strategic marketing decisions and more tactical campaign support, in terms of targeting the best potential customer with the optimal message in the most cost effective medium at the ideal time.
People analytics, also called HR analytics, is the application of analytics to help companies manage human resources. The aim is to discern which employees to hire, which to reward or promote, what responsibilities to assign, and similar human resource problems. For example, an analysis may find that individuals that fit a certain type of profile are those most likely to succeed at a particular role, making them the best employees to hire. HR analytics is becoming increasingly important to understand what kind of behavioral profiles would succeed and fail. While HR analytics is done for employees within the organization, Customer Segmentation techniques are used in the market to study customer profiles and identify which customers most likely form the target market.
A common application of business analytics is portfolio analysis. In this, a bank or lending agency has a collection of accounts of varying value and risk. The accounts may differ by the social status of the holder, the geographical location, its net value, and many other factors. The lender must balance the return on the loan with the risk of default for each loan. The question is then how to evaluate the portfolio as a whole.The least risk loan may be to the very wealthy, but there are a very limited number of wealthy people. On the other hand, there are many poor that can be lent to, but at greater risk. Some balance must be struck that maximizes return and minimizes risk. The analytics solution may combine time series analysis with many other issues in order to make decisions on when to lend money to these different borrower segments, or decisions on the interest rate charged to members of a portfolio segment to cover any losses among members in that segment.
Predictive models in the banking industry are developed to bring certainty across the risk scores for individual customers. Credit scores are built to predict individual’s delinquency behavior and widely used to evaluate the credit worthiness of each applicant. Furthermore, risk analyses are carried out in the scientific world and the insurance industry. It is also extensively used in financial institutions like Online Payment Gateway companies to analyse if a transaction was genuine or fraud. For this purpose they use the transaction history of the customer. This is more commonly used in Credit Card purchase, when there is a sudden spike in the customer transaction volume the customer gets a call of confirmation if the transaction was initiated by him/her. This helps in reducing loss due to such circumstances.
Digital analytics is a set of business and technical activities that define, create, collect, verify or transform digital data into reporting, research, analyses, recommendations, optimizations, predictions, and automations.This also includes the SEO (Search Engine Optimization) where the keyword search is tracked and that data is used for marketing purposes. Even banner ads and clicks come under digital analytics. A growing number of brands and marketing firms rely on digital analytics for their digital marketing assignments, where MROI (Marketing Return on Investment) is an important key performance indicator.
Learn From Your Customers to Offer Great Customer Experience
There are many tools businesses can use to receive adequate feedback from their customers, including various feedback forms and questionnaires. However, not infrequently the harm is done before you know it: your customers may find that navigating your website is too complicated and leave without giving it a second thought. Negative feedback is valuable, but not many of them will bother to provide you with it.With digital customer analytics, you will be able to map your customers’ journey on your website to understand what features or pages may cause troubles, as well as how much time they spend on filling in forms and how long it takes for them to make the decisions.
It is vital to know how your clients experience your product. Session replays provide you with valuable and tangible metrics that enable you to see your customers’ journey through their eyes. Everything is important: you need to know how fast the pages load on the customer’s end, what ratio of graphics to text is optimal, how your clients respond to the layout of your website, how many lines of the drop-down menus they are willing to scroll, to name just a few.Knowing the source of the problem enables you to react instantly and quickly fix the issues you clients may face to provide them with great customer experience.
Strengthen Your Competitive Edge With Digital Customer Analytics
The relevant data enables you to both fix the problems your customers may experience on your website and plan a stronger digital strategy for the future to make your business more competitive.Knowing what works and what doesn’t and understanding your customers’ behavior can determine whether they continue using your service or go back to the search engine looking for greener (and more user-friendly) pastures elsewhere.It is also vital that the collected data is made available not only to your IT department but also to your marketing team and everyone involved in the business cycle. The customer is king, and in this competitive age, your clients can be unforgiving if their issues are not addressed in a timely manner. On the other hand, great customer experience is a sure way to secure loyal customers and receive good reviews to help you win new clients.