Demand Forecasting
Machine Learning and Predictive Analytics slash forecasting time and effort by up to 80%, whilst improving accuracy and consistency and even facilitating out-of-cycle re-forecasting.
This is achieved by using historical data for autonomous predictive modelling, executive and SME overlays, and visualisation with adjustments, resulting in consistent forecast scenarios and demand for products with accurate seasonality and consideration for every important predictive factor.
Predictive models are customised to factor in elements internal and external to your organisation including demographics and global economic factors.
ML/AI Based Personalisation for Customer Growth
A personalised customer experience is an invaluable strategy to increase sales, improve customer experience, and boost loyalty. Enabling personalised marketing analytics allows data to empower marketing executives, sales team, and retail specialists to get the most out of customer information.
PE-backed firms can create customer profile, data-driven customer personas to cluster customers and understand their purchasing behaviours. With an ML-based predictive model, understand the customer’s most likely next action. Coupled with personalised marketing analytics, effectively push multi-channel offers and communications where and when the customer is most active.
Customer Experience and Engagement
Key to a PE’s success in transforming a business is to make sure it has a clear picture of the business strength, weaknesses and potentials. There is no better source of these insights other than customer feedback. Customer experience and engagement analytics provides PEs a platform to analyse these feedbacks and establish necessary strategies to turn around results and outcomes.
Enable marketing and customer engagement teams to analyse customer experience across all sites and all competitor’s sites in a single dashboard. Actively identify and address issues before they escalate and surface a new metric for customer experience and marketing effectiveness.
Transform data from these sites into useful insights and predictors. Apply Machine Learning and Text Analytics (NLP) to transform comments to understand sentiment and conversation topics (e.g. Price Rating, Staff Rating, or even topics specific to your organisation like security rating). Augment data with additional attributes for mapping, visualisation and additional insights. Combine with marketing data and understand cause and effect.
Transaction Analytics
Organisations typically make errors between 0.5% and 2% on their accounts payable transactions due to financial leakage, fraud and more. Large individual transaction volumes and the permeation of fraud and processing errors demands secure, scalable solutions, able to handle growing transaction data estates and the need for unprecedented levels of monitoring and testing.
Modern transaction analytics augments operations with super-human memory, processing power and insights to generate timely, consistent, deliberate and reliable analytics for a greater chance at success. Monitoring can be run off-site (for separation of duties) or deployed internally to trusted teams.
It is extremely useful in analysing all transaction data; not just sample testing. Highlighting recoverable savings including duplicate invoices and financial leakage through fraud, overpayments, and GST. Identify areas for controls, training, and process improvement. Identify potential fraud (e.g.ghost employees, fictitious entities, conflict of interest, invoice splitting) whilst using a third party to eliminate the risk of internal data manipulation.
OHS Prediction and Risk Minimisation
Improving safety ratings, minimising risk, and preventing incidents is an important factor affecting cost savings, employee safety and operational efficiency. Through AI, PEs can predict OH&S incidents and minimise risks of portfolio firms. Advanced predictive analytics can determine the likelihood of workplace incidents after significant overtime or surrounding sick and annual leave. This significantly improves the safety of employees and customers, especially in roles that involve physical labour, trades, travel, vehicle operators and all non-desk work.
Similarly, the likelihood of workplace incidents occurring during certain activities, weather events, environmental conditions, time of year and many other potentially predictable scenarios can be assessed by industry so that mitigation strategies can be put in place to reduce the likelihood of incidents. With predictive analytics and optimisation identify hazards and establish dynamic risk profiles to improve working conditions and immediately intervene potential risks.
By creating an integrated and holistic safety program using artificial intelligence and machine learning, have real-time and dynamic safety net to protect employees and assets.
Portfolio Monitoring and Proactive Evaluation
Day to day management of portfolio companies used to be a challenge for most Private Equity firms. Mostly due to lack of access to quality real-time data, delivered through traditional methods of reporting and manual analysis.
Data-driven portfolio monitoring is possible as PEs embrace data cloud platform and employed integration of analytics platform. With these platforms strong data governance, particularly with regard to how information is accrued, stored, analysed, and reported PEs have gained the confidence of managing data access.
Through AI, PEs now have digitized data management, reporting and analysis. This has substantially improved efficiency and accuracy of analysis of portfolio company business drivers, reporting financial performance, KPIs, environment, sustainability and governance (ESG) reporting, and automation of tax and financial statements.
Cost Reduction through Optimisation and RPA
Many PE firms have a 3-7 year horizon for owning the organization and would then look to monetize their investment thus the importance of reducing cost.
By employing Robotic Process Automation (RPA) and Intelligent Process Automation (IPA), entities can automate traditional manual, repetitive processes. This helps organisations to scale the business in alignment with the PEs expectations.
RPA and IPA remove the redundancy of duplicate data entry into different systems. Using RPA, data processes become more accurate and predictable, and, as a result, able to allocate human intervention for more significant tasks such as analyzing and interpreting data.
RPA also reduces human error which is associated to high costs. PRA and IPA improve efficiency and increases productivity, providing employees available time and resources to focus on resolving higher priority concerns and looking for ways to innovate.
In addition, the benefits associated with RPABase → USPs are very easy to quantify thus providing PE firms with a predictable, high value, low risk return for RPA based initiatives.
