Disruptive technologies, in this digital era, are driving transformation at an exponential rate, introducing innovative tools that are redefining the business dynamics like never before.
The rapid emergence of revolutionary technologies like the Internet of Things (IoT), Cloud Computing, Machine Learning, Analytics, and Artificial Intelligence (AI) – terms which seemed alien even a few years back – have forged a hyper connected world sooner than expected.
These next-generation technologies are making significant inroads into our lives, touching us, influencing us, and increasingly changing the way we make decisions in our day to day affairs.
Today, it is the age of tablets and smartphones. These internet connected shiny gadgets are nowadays our best friend, assistant, fitness tracker, transaction maker, and a lot more.
However, every single thing we receive and send out, leave our digital footprint in the connected ecosystem in the form of data. This data influx, coming from millions of connected devices worldwide, is ‘gold’ for enterprises.
Business organizations, through proper mining of this ‘Big Data’, gain crucial insights about their business and customers, which help them make improved decisions that maximize customer engagements and ROI.
Machine Learning and Analytics Are Providing Enterprises the Needed Framework to Unleash the True Value of Enterprise Data
Powered by the cloud, these modern-day business tools deploy algorithms to offer leading-edge insights into dirty, bulk, unstructured data sources, such as call logs, e-mail records, social media, Internet of Things sensing data, etc.
Machine learning and analytics, by filtering and refining this unstructured data, provide excellent predictive accuracy across all channels that enable organizations to accomplish a variety of business-critical tasks.
Irrespective of whatever the nature of work is – be it sales, marketing, finance, operations, legal, or management – machine learning and analytics can help enterprises automatically initiate policies and actions, forecast risks and profits, create personalized experiences for customers, and detect future business trends in near real-time.
Welcome to the Era of Quote-to-Cash
To optimize business performance, efficiency, and profitability, there is no better place to bring into play the potential of machine learning and analytics than the Quote-to-Cash.
The Quote-to-Cash is an end-to-end business process that starts with the customer’s intent to buy and ends with the revenue in the bank. It encompasses core business principles like contract management, contract renewals, revenue management, quoting, and billing.
Enterprises, by focusing their ‘intelligence’ around this process, can deliver the right products to diverse customer segments, expedite sales, drive margins, understand customer relationship cycles, and accelerate the time to market of their products.
Global organizations use analytics and machine learning insights to improve their Quote-to-Cash operations. The Quote-to-Cash data is used to identify patterns, optimize resources, predict outcomes, and to improve decision-making capabilities that increase revenues.
Analytics Infuse Intelligence Into Quote-to-Cash Data to Recommend Practical Actions Like –
- Cross-Sell / Up Sell – The tool analyzes user behavioral patterns and recommends personalized products and services that customers are more likely to buy.
- Pricing Intelligence – By analyzing the Quote-to-Cash data, businesses can effectively set the optimal price, or the discount amount, for each quote and renewal.
- Quote Scoring – Analytics predicts the chances of quote conversion in advance, and at the same time, recommends appropriate actions to amplify the conversion probability.
- Contract Cycle Time Prediction – Machine learning intelligence forecasts the ideal time to execute a contract, based on previous approval history and similar deals.
- Contract Risk Scoring – The use of analytics in Quote-to-Cash data can assist organizations to formulate the right ‘Terms & Conditions’ through historical contract data.
- Alternate Clause Recommendations – Analytics can be used to compare almost identical clauses and contracts, which help manage business risks in a more efficient manner.
Quote-to-Cash converges with every channel, every department, and every customer. If managed accurately, Quote-to-Cash can empower businesses to quickly adopt innovative business models, predict outcomes, and meet the increasing customer demands without risks and delays.