One of the primary goals of a business is to increase profit. In its simplest form, profit growth can be achieved by increasing revenue and reducing costs. Yet in reality, these could be complex interdependent functions and therefore organizations need to take a closer look to understand the revenue and cost structures and their relationships to manipulate these parameters carefully and increase profit.
There are multiple ways to model profit. An organization should find ways to increase income while protecting the existing revenue, thereby contributing to an increase in profit. An organization should also find ways of reducing costs and avoiding future costs to increase overall profits further. Businesses should strive to achieve sustainable profit growth in the long term, and to that end, data analytics can play a significant part.
Mapping the above framework to data analytics, an organization can implement multiple use cases affecting revenue and cost to realize the value of data to increase profit.
While there are many industry-specific use cases, the following are some generic data analytics use cases that affect revenue and cost, directly or indirectly.
Some of the above data analytics use cases affect both revenue and cost simultaneously thereby strongly affecting profit. Organizations can identify such use cases and prioritize implementing those to realize the maximum value of data. Such sample use cases are described below.
Customers expect intelligent communications from brands that are personalized to individual preferences.
“91% of consumers say they are more likely to shop with brands that provide offers and recommendations that are relevant to them.”
Source: Accenture, Personalization Pulse Check 2018
“52% of consumers are likely to switch brands if a company doesn’t make an effort to personalize communications to them.”
Source: Salesforce Research, State of Marketing 4th Edition
Targeted campaigns are a way of providing personalized offers to customers and one that has consistently proven to improve the campaign results. They can directly increase revenue by delighting the customer with relevant offers leading to more purchases. Targeted campaigns can also indirectly preserve current revenue via protecting the existing customer base by providing excellent service, appealing offers leading to increased customer affinity and reduced churn rates. It can also reduce costs through improved productivity through streamlined and automated target campaign rollouts reducing manual intervention.
The following figure depicts how targeted campaigns can directly and indirectly affect the revenue and costs of the organization.
Recommendation engines have been an essential factor to the success of digital platforms such as Alibaba, Amazon, Netflix, and Spotify. The Recommender System is not just a marketing tool to increase sales but also a platform to provide insights and promote customer engagement to improve customer satisfaction and loyalty, thus increasing customer lifetime value and business growth.
“80% of Netflix’s Viewer activity is driven by recommendation engine.”
“1 Billion USD is saved through reduced churn by Netflix recommendation engine.”
“35% of Amazon’s sales are generated by their recommendation engine.”
Source: Business Insider
Recommendation engines can directly increase the revenue streams of a business by providing relevant purchasing advice and delighting customers, leading to more purchases. It can also increase customer affinity and reduce customer churn thereby protecting the existing revenue base. It also reduces the cost of operation by increasing productivity and automating the process of instantaneously providing intelligent recommendations to the user without manual intervention.
The following figure depicts how implementing a Recommender System can affect the revenue and cost of a business in direct and indirect ways.
In the environment of industry 4.0, predicting machine failures by analyzing and modeling IOT sensory data along with other related data points using advanced analytics techniques is a real use case that can add value to the business. Knowing well ahead that a machine would fail can avoid unplanned downtimes, prevent outages in the factory operation and increase productivity.
“Predictive maintenance typically reduces machine downtime by 30 to 50% and increases machine life by 20 to 40%.”
Source: McKinsey Industrial Analytics
“Predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25%.”
Source: Deloitte Predictive Maintenance Position Paper
Predictive maintenance of machinery can increase the productivity of the operation by reducing unplanned machine maintenance costs, improving machine lifetime and running the machines at their optimum conditions. It can also avoid future costs associated with sudden machine breakdowns that require unplanned break fixes and maintenance of machines, loss of production due to machine downtime and machine replacement due to poor maintenance of machines. In addition, it ensures smooth operation of 24×7 production in the factory, thus enabling the organization to fulfil customer orders on time thereby increasing customer satisfaction, service excellence and loyalty.
The following figure depicts how predictive maintenance use cases can directly and indirectly affect the revenue and cost of the business operation.
The use cases described above may have industry-specific versions that can differently impact the revenue and cost. However, the generic use cases are taken to elaborate on their impact on both the revenue and costs.
In summary, value creation by data analytics use cases can be assessed by looking at the impact they make on the revenue streams and cost structures of the business. This influence can be direct or indirect but ultimately impacts the bottom line of the business in the short term and long term in a sustainable manner. Furthermore, certain data analytics use cases can simultaneously affect both the revenue and cost to improve the bottom line of the business, hence these use cases should be implemented on priority basis to harness the maximum value out of data.