IBM pushes itself to rely on mergers and algorithms in order to prosper in the market.
IBM Corporation expects to grow from algorithms and mergers. After suffering from declining sales in 14 consecutive quarters, the company is relying on acquisitions and mergers to help it revive its growth, but one of its leading dealmakers is honest about the risks carried by acquisitions.
“If 70% of M&A fails, would you propose spending $20bn doing deals?”, asks IBM executive, Paul Price, as he alluded the widely held belief regarding effectiveness of deal making – and the amount of money IBM allocated to agreements between the five years, starting from 2010 and ending in 2015.
Nevertheless, the technology giant is hoping to attain far over 30% success rate by stripping out what Paul has claimed it as the principal factor responsible for bad deals: human mistake.
Using its experience of signing a number of deals recently, its software’s vault and its huge number of scientists, 5 years ago IBM started to experiment with a computer algorithm that is capable of spotting the risks in target organizations during the due diligence process of M&A.
Like most of the companies, the New York based tech business has a “corporate development” that executes and evaluates transactions in conjunction with the operating units of the company. Its objective in the creation of due diligence algorithm was to align the procedure of acquisition with post-agreement integration plans.
“The focus used to be on the downstream part of the process: target identification,” Mr. Price said. “Integration concerns. . . couldn’t stop a deal.” Now IBM CFO, Martin Schroeter, told the M&A Pro tool has provided the organization with a sharper insight to deal with risk and the capability to complete deals in a faster manner, before rivals could pounce or a target can get cold feet. “We have been able to dramatically speed up and improve the process of acquisitions from identification and due diligence through to integration and execution”, Martin told.
IBM exclaimed that it could now finish and begin a deal in less than three weeks. A study conducted in 2014 by the consultancy McKinsey concluded that the businesses, which had the most impact on M&T typically completed deals at a much faster pace.
M&A Pro is developed as a “machine learning” system – an algorithm capable of learning to make decisions based on precedent data. Its main technology has come from the statistical and computing company SPSS, which the cloud computing company purchased 7 years ago for $1.2 billion.
M&A Pro has also drawn on a number of other tech companies which IBM has purchased including a financial reporting app package ‘Cognos’ purchased for $4.9 billion in 2009.