7 coolest metrics you can predict for your business using AI & ML

Turn your business into a predictable, data-driven machine, helping you to figure out the exact inputs you need to make the right numbers go up. Leverage AI to unlock the true potential of your business by transforming it into a precision-engineered, data-driven powerhouse that monitors and feeds you the metrics that matter.

Sound too good to be true? A few years ago, it might have been.  

Today, however, organizations around the globe are leveraging the incredible power of artificial intelligence and machine learning to run more efficiently and profitably than ever before. According to McKinsey, 76% of organizations reported that they were prioritizing AI and ML projects over other IT initiatives. To maximize return on investment and eradicate wastages, businesses need to have a framework to accurately predict future trends based on key assumptions or inputs. This allows them to make targeted decisions in the present that ensure they will meet the predicted outcome.

Getting started may seem daunting, especially for smaller enterprises that may want to first understand why AI is important for businesses and which metrics impact their business the most that are also convenient to monitor. To start with, we’ve laid the foundation for you by researching metrics you can start predicting for your business using AI & ML and start seeing massive impact right away!  

1. Revenue

Being able to accurately predict revenue by knowing exactly what moves the needle is a superpower for any business. Amazon, the largest e-commerce in the world that started out as an online bookstore, now uses predictive AI modelling to personalise its recommendations for customers – displaying exactly what they know customers want to purchase based on past behaviours. The predictor of revenue here is personalisation using AI, which is what Amazon optimises for to the extent that this AI powered feature accounts for more than 30% of its revenue.

2.Customer Churn

Customer churn is another metric that companies want to watch closely, as it is an indicator of recurring revenue, product-market fit and overall sustainability. Businesses operating online spend thousands of dollars on performance marketing to acquire customers. Losing too many customers rapidly can be a concerning indicator of something being wrong with the business.  

In this case, predictive analysis is an immense benefit of AI for businesses as it can help prevent churn rates from climbing. To be able to predict churn rate, several variables pertaining to user preferences and behaviour can be analysed such as purchase history, engagement pattern, frequency of usage, prices, and ratings. Once churn rate is successfully predicted and customers at risk of churning have been identified, businesses can implement the appropriate call to action messaging at the opportune moment to retain customers.  

Some technology companies take an incredibly proactive approach, such as Netflix and Spotify – both deeply analyse user behaviour on their platforms to share personalised recommendations that help keep their churn rates low. Netflix reported churn rates as low as 2%.

3. Customer Lifetime Value

Customer lifetime value (CLV) is a quantification of the monetary value a certain customer will contribute to a business over the course of their engagement. Predicting CLV by analysing the buying behaviour of customers, for instance, can help a business predict revenue and profitability. This is one of the primary benefits of AI for businesses, as knowing how much value each customer will generate allows businesses to personalize marketing communications and the overall user journey in a targeted manner. CLV is also an indicator of financial health, hence this is an essential metric to be watching for any business.

If you want to find out how this can be implemented for your business, reach out to setup a conversation with our data science experts today!  

4. Conversion rates

The ability to predict conversion rate – how many leads will convert into customers as a result of any marketing effort – is another interesting, and arguably essential, use case for AI & ML modelling. Using inputs such as ad spend, ad type and conversion by channel, a machine learning algorithm can help predict how many leads may turn into customers on any given marketing channel. This can help businesses run more targeted campaigns and invest their marketing budgets wisely.

Realising the potential for value creation in this domain, HubSpot is now offering a neat lead scoring system using machine learning algorithms that automatically scores your leads according to their conversion potential.

5. Supply Prediction

Supply chain optimisation can be a powerful catalyst for business growth, as it ensures businesses are setup to fulfil consumer demand, and this is also easily achieved through the predictive capabilities of AI and ML.  

Zomato, for example, predicts food preparation time using neural networks (a form of machine learning). This helps Zomato know exactly how many orders it can prepare and deliver in a certain window of time, allowing it to set accurate delivery expectations with its customers. Ultimately, this helps optimise for cost as there is minimal wastage and keeps customer retention high as the overall experience is seamless.  

6. Market Demand Prediction

Market demand prediction can be an incredibly important input when it comes to curating seamless user experiences, especially for businesses operating in the logistics and transport industries. This requires some introspection and research into what influences market demand for your product or service. You want to be thinking about prices for alternative products and services, social preferences, macroeconomic factors, and seasonal trends.  

A classic example of this is Uber, which studies factors such as public events, traffic and weather to predict demand for rides in the various locations it operates in. This allows it to optimise supply, ultimately impacting revenue by ensuring that demand is met.

7. Competitor Analysis

You can even use AI and ML to keep an eye on your competitors for you! This may be a lesser-known use case, but its usefulness cannot be underscored. Instead of spending hours poring through your competitors’ blogs, demos, social media and industry papers, you can leverage the swiftness of artificial intelligence to get all the insights you need in one fell swoop.  

Crayon is an intelligence company that has built its entire value proposition around helping companies use AI to monitor and get ahead of the competition. AI will explore all the goings on of your competitors, analyse their next moves and report all the insights to you so you know exactly how your competitors are positioned and what their upcoming strategies are.  

Conclusion

While some of the metrics discussed in this article are universally important, regardless of industry or business model. Anyone looking to get started with predictive analytics needs to first understand their unique business requirements and context.  

Data Pilot’s tried and tested method is to get our consultants involved to run a discovery phase for businesses to assess client requirements and the needs of businesses. The understandings gained in this first step then inform the technicalities of the predictive models to be used and the results that can be expected.

With the constant advancements in machine learning and artificial intelligence, and the new use cases emerging regularly as a result, the opportunity to capitalise on such capabilities will continue to grow. According to Statista, the market valuation of predictive analytics is expected to increase from $5.29 billion in 2020 to $41.52 billion in 2028. Predictive analytics powered by the latest technologies could be the next big driver of business growth!

By Manaal Shuja

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