Divestment and predictive modelling

Divestment and predictive modelling
A Case for Shedding Weight More Strategically

John Hagerty: Vice President, Product Management for Business Analytics at Oracle @jfhagerty


Why analytics is the key to better divestment strategies

The link between horticulture and businesses is stronger than you might think. Just as the conscientious gardener thins out their seedlings or strengthens their favourite rose bush by pruning the weaker stems, companies improve their financial health by divesting themselves of underperforming assets.

In spite of the post-crash recovery, not everything is rosy in the garden of business. Continuing political and economic uncertainties are leading to a bloom in divestitures. This was highlighted by a recent report from E&Y, which revealed that four in five companies are more likely to divest parts of their business in the next year to manage macroeconomic volatility.

 

“Divestment decisions are critical, and need to be made with a “root and branch” understanding of the business.”

When gardening, it’s important to know which cuts to make. Our instinct is to prune stems that look unhealthy, but sometimes these just need more air and sunlight while more vibrant stems are actually dying at the root. Similarly, many companies tend to drop their less profitable or early-stage product lines when they need to cut costs. This is short-sighted however, as over time it might be those outliers that would help the business stay current and continue to flourish.

Divestment decisions are critical, and need to be made with a “root and branch” understanding of the business. It’s important to consider how any changes to products, services or operations will affect the organism as a whole.

Getting strategic with data

Modern Finance Webcast Series

Modern Finance Webcast Series

As with more traditional planning, the cues that will help companies make the right divestment decisions lie in their data. The early applications for analytics were about getting better understanding of customers and operations, and these continue to deliver major returns, but we’re now seeing more businesses rely on data when it comes to forward planning.

More specifically, finance leaders can model different scenarios based on a mix of historical, customer, and market data so they can predict the impact of change on their company’s performance. What this means is that any combination of events, from unlikely election results to a dip in customer demand to poor weather, can be factored into divestment models. Applying a Monte Carlo-style analysis to this many data points allows businesses to map more potential outcomes and determine the best way forward.

 

“What this means is that any combination of events, from unlikely election results to a dip in customer demand to poor weather, can be factored into divestment models.”

The stronger a company’s foundation is around advanced analytics, the more likely it is to ask the right questions and uncover new patterns that will drive continuous improvement. The ability to see how a divestment will affect customer relationships or reshape service delivery and manufacturing is invaluable.

Three steps to building more complete models

Modern Finance Webcast Series

Modern Finance Webcast Series

With negations continuing around the UK’s relationship with the EU and persisting uncertainty around how America will tax foreign imports, it’s no surprise companies are cutting costs, and many will be tempted to divest themselves of products with margins that are threatened by higher shipping fees.

This may indeed be the best approach, and planning models might agree. However, a longer lead view of the challenges ahead will inspire companies to rethink their approach and take some strategic risks that could also lead to growth opportunities.

What follows are three fundamental steps to developing a robust analytics-driven approach to modelling:

Modern Finance Demo

Moving to Planning, Budgeting and Forecasting in the Cloud?

Step 1: Bring together as much data about your business as possible, including operational, human, customer, and industry data in addition to external influences. The more information you have to work with, the more comprehensive your scenario modelling will be.

Step 2: Once the data has been collected, ask questions that will help you understand how different decisions and external scenarios will combine to affect each part of the business. It’s as important to look at how the connections between different pieces of the model will change as it is to track specific outputs.

Step 3: Finally, use the insights uncovered from your data to build predictive and prescriptive models. Enhance these even further with machine learning algorithms that can crunch data and make intelligent predictions about what will happen next. Combine these with your own strategic assumptions to decide whether divestment is the right move, and what its impact will be.

 

“The more information you have to work with, the more comprehensive your scenario modelling will be.”

While it may be tempting to cut one’s losses early and move ahead with a ‘fire sale’ of business assets, it is always wiser to see the big picture before committing to a major decision. Success in today’s uncertain market is about the ability to react quickly when things do change, and companies that have the tools to decipher the tea leaves in their data will be best prepared for any eventuality.


Learn More

Jsme zde, abychom pomohli

Kontaktujte odborného prodejce

Rychlá seznámení

Požádat o ukázku 1:1

Přihlášení podle tématu