Analytics and Its Objective 

Analytics is often associated with understanding why a particular event happened by analyzing historical data. It is more of a diagnostic approach to finding out the reasons for what happened in the past. With the advent of more advanced and sophisticated algorithms, analytics has graduated to a forward-looking approach. This prognostic approach learns the associations from past data to predict future trends and behavior. Such predictive foresight enables business leaders to calibrate their decisions and take the right course of action in line with such predictions. 

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Analytics could be used in multiple initiatives - there are multiple projects that could add business value but not everything can be picked at once. Time is of the essence hence the projects that align with the overall strategic business goal are prioritized first.

Business requirements and goals could focus on improving operational efficiencies, providing a better user experience by learning their preferences, introducing a new feature or product, etc.

Illustrating the Power of Analytics

Let us understand how analytics can be used to learn customer behavior with the help of an example. Search engine marketing requires competitors to bid for their advertisements to land an impression on the search result page. The caveat is to not bid consistently high for all the advertisements across all the regions (we will instead use a generalized form of regions called campaigns through the rest of the post). Typically, the business identifies certain campaigns as more ROI-generating ones than others, hence it would be more cost-effective to design an optimized bidding strategy for different campaigns.

The bid optimization algorithm improves sales as increased impressions lead to more potential clicks which in turn generate conversions. Besides, such an optimization tool brings cost savings by identifying customized bids per campaign instead of one static uniform bid across multiple campaigns. Such analytics projects have the potential to bring a significant positive nudge to the bottom line of the organization.

Analytics Enriches the User Experience

Drawing from the use case explained above, if an organization is able to bid in an effective and efficient manner, then they successfully pass the first stage of landing the impressions. The users click on these impressions and enter the web property of the organization’s ecosystem. The whole new journey of engaging the user starts from here. You have very little time to understand what the user is looking for and serve their requirements and preferences at blazing-fast speed. Amid the rapid rate of digitalization, it does not take a customer drop-off from one application and switch to another platform.

You must understand your customer by answering the following questions:

• What makes the user come to our platform?
• What services and products are they looking for?
• Which page are they spending more time on?
• What trajectory leads to more conversions?
• When do conversions happen more - any pattern in a particular hour, week, or day?
• Which page do the customers drop off often?
• What stops them from checking out and converting to sales?
• What specific information are they looking for and are not provided well in time?

It is very important to not just onboard a customer but to retain them throughout. A happy customer is a repeat customer and vice versa. It takes a lot of hard work to acquire a customer, it would be a nightmare to not be able to devise a successful customer retention strategy. Needless to say, it is more expensive to acquire a customer than to retain one. Businesses understand this very well, hence they want to tap into every single insight that analytics can empower them with, so as to provide a better experience to current customers. 

Leverage analytics to understand your current user base and serve them with better offers and services that would make a long-standing relationship with your product and services. If you are interested to know more about data-driven analysis on customer retention vs acquisition cost, check out this link. It extends to the fact that the new customer acquisition costs seven times more than retaining an old one. Further, it states that the probability of generating conversions from an existing customer is 60-70% which is significantly greater than the probability of 5-20% from a new customer.

People, Process, and Technology

The success of an analytics project depends on three pillars – people, processes, and technology. Let us analyze which one requires more investment to scale up the analytics quotient of your organization. 

Organizations already have a lot of data that they plan to monetize. As part of business strategy, the leaders understand the gap in current technology and expend money to build up the necessary infrastructure to run sophisticated models. However, the top-down approach to the adoption of new technology is not as easy to execute as it may sound. It requires an initial hand-holding phase where a technology expert assists the team in a quick and easy ramp-up.

The process is the next big elephant in the room that needs attention. The leaders may lay down several processes but they fail miserably if people are not put at the forefront of any transformation.

The crux of business success hinges upon the organizational culture that is dependent on its people. 

You need people with the right skills to endorse the processes and become an advocate for your business goals in the organization.

The people need to align with core business goals and onboard this journey of technological advancement. This people-centric culture needs to run throughout the organization. 

Summary

The post explained the power of using analytics to become an industry leader. It focuses on how being customer-centric can accelerate the digital transformation journey of your business. It also explains the key drivers for the successful implementation of strategic business goals i.e. people, process, and technology.

 
 
Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.