Demystifying Artificial Intelligence – Using Machine Learning to Increase ROI

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Kitae Kim

Principal Sales Engineer

There is no doubt that Artificial Intelligence (AI) is part of the next wave of Digital Transformation and for organizations to remain competitive, using solutions that leverage AI is essential. We are already seeing and experiencing real-life benefits and successes of those companies who are already embracing AI. They are using AI to automate routine tasks, discover valuable insights from big data to make increase their bottom line. Considering that, business leaders must make informed, yet agile decisions on where and how to apply AI in their business. At Apttus, we believe that the first step to start this initiative is knowing and understanding AI essentials.

What is Artificial Intelligence?

· Artificial Intelligence is “intelligence demonstrated by machines”. It allows machines to imitate human intelligence including the use of logic.

· Machine Learning (ML) is a subset of Artificial Intelligence. Machine Learning makes a machine to detect patterns and learns how to predict, forecast, or recommend by processing data, not by explicitly programmed instructions.

· Deep Learning is a part of Machine Learning where a machine can train itself to do tasks such as text, voice, and speech recognition using multi-layered neural networks.

AI Layers

What is Machine Learning?

When machines can mimic human intelligence without receiving explicit instructions, we call it Machine Learning. That differentiates machine learning from artificial intelligence. Also, that’s the reason why ML allows machines to become smarter and smarter as they receive new data.

The following diagram shows an intelligence/analytics maturity chart. Machine Learning focusses on the top three: Predictive, Prescriptive, and Cognitive.

AI Diagram

1. Descriptive analytics

o Descriptive analytics helps people understand what has already happened. It’s done by laying out relevant summaries and manipulating data into certain formats that can be easily consumed. Analytics at this level is a “rear-view” exercise.

2. Predictive analytics

o Predictive analytics helps people understand what will happen. It’s fundamentally probabilistic. This helps recognize patterns and detect meaningful trends.

3. Prescriptive analytics

o Prescriptive analytics delivers recommendations on what to do. That is, it provides deeper insights and forecasts showcasing what is likely to occur, accompanied by relevant, system-driven recommendations on the next best actions and tactics to adopt.

4. Cognitive analytics (Machine Driven)

o Cognitive analytics exploits machine learning to refine trend and pattern analyses in an on-going, unsupervised basis in order to constantly evaluate processes and associate data.

Using Machine Learning in a Quote-to-Cash process

By nature, there are many machine learning use cases in Quote-to-Cash (QTC). QTC is the most important business process as it covers everything from creating initial offers for prospects to collecting cash. It’s a highly complex process for large enterprises and has direct impact on companies’ bottom line. Given that, it’s not surprising that automating Quote-to-Cash requires lots of computational power and it is the perfect place where business leaders should apply AI. Here are some of the machine learning use cases in a Quote-to-Cash process.

AI Revenue

Using Machine Learning to Increase ROI

Consider the experience of a global company with a subscription business, that leveraged Machine Learning to increase the number of products per cart and margin per product.

Challenge: The company had grown through multiple acquisitions, leading them to have a high number of SKUs. The sales teams had a hard time figuring out what products, services, or bundles to sell or add for their prospects and customers. Additionally, the company had a limited customer base, so cross-sell and up-sell to their existing customers was essential. However, the process of determining what products or services to cross-sell or up-sell for their customers was completely manual. The loss related to the issues was estimated at about 0.25 ~ 0.5% returns on sales.

Solution: The company needed a mature CPQ solution that leveraged Machine Learning to optimize their quoting process. As Apttus provides a single platform where users can manage the entire Quote-to-Cash process, all sales activities and data can be easily shared and leveraged by Machine Learning. Furthermore, the combination of Machine Learning and Apttus CPQ Guided Selling can provide insights and product or service recommendations to sellers at the moment of action. The cross-sell and upsell engine using Machine Learning evolves continuously as it gets new data, resulting in managing the process more effectively and efficiently.

Apttus’ expertise in Machine Learning in the CPQ space for large Enterprise, made it the number one choice for this particular company and it is now way ahead of its competitors in the AI race.

In my next blog, you will learn what Deep Learning is and the difference between Deep Learning and machine learning, see business use cases in Quote-to-Cash, and how Apttus products leverage the technology.

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Leveraging AI For The Quote-To-Cash Platform

 

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