Originally published by Cameron Koo, a co-founder at SiteFocus, on Pulse
The key to a successful M&A
There is a growing consensus that the AT&T acquisition of Time Warner will lead to more mega-business deals as organizations seek avenues to drive shareholder value and business growth. M&A is about creating synergy through consolidation that will deliver an improved balance sheet and credit metrics. There is risk and reward in every M&A. In hindsight, we often look at successful M&As and easily recognize the benefit or contribute disastrous mergers with a failure in risk assessment and due diligence. The key to driving a successful M&A is foresight that comes from qualitative analytics that complement quantitative analysis — a big job for executives and the board of directors to tackle. To this end, Natural Language Processing (NLP) with Natural Language Understanding (NLU) is an invaluable technology for M&A teams to better understand the context of what’s important & why as they manage timely due diligence and seek compatibility & alignment.
Huntington Bank-FirstMerit: finding the right recipe for success
To give more context into how qualitative Artificial Intelligence (A.I.) can play an important role in M&A, let’s take a look at a concrete merger between Huntington Bank and FirstMerit. In a nutshell, the motions of M&A first started when Huntington Bank noticed a number of FirstMerit executives were scheduled to retire. What followed included a due diligence period involving 500 people over the course of six weeks. The transaction lasted over a period of 15 months from first contact in May of 2015 to announcement in January and close in August of 2016. The success of this M&A was the result of the foresight of Steve Steinour, Chairman, president and CEO of Huntington Bancshares Inc., and adequate risk management and due diligence. Can other executives under different scenarios ensure similar success with their next M&A? How can A.I. be leveraged to complement quantitative analysis?
A retrospective of key factors that drove the Huntington-FirstMerit M&A enables us to see how future M&A transactions can leverage A.I. to drive key business processes.
- Automating research on target segment activity: the initial observation that sparked the M&A
- Intelligent risk/reward analysis of the Customer Experience (CX): monitoring & understanding the context of customer communications and the customer journey — like messaging the right 800-support number, supporting old check stocks, learning & adopting unique user experiences
- Winning hearts & minds with situational awareness: measuring contextual sentiment and its change/progression over time, proactively identifying areas of concern with community outreach
- Enabling intelligent collaboration: monitoring & understanding cultural similarities and differences of two companies, fostering interactive alignment by leveraging qualitative input across a large spectrum of individuals
Automated insights with Autonomous Learning
As discussed in our previous post, the Autonomous Learning Machine (ALM) is an affordable AI system that learns and understands what it reads, automating listening and monitoring of textual data and making it possible to discover insights into relevant subjects, context and relationships that matter. The ALM can be deployed to analyze social network data, generates insight reports based on continuous monitoring of M&A candidates. The ALM can automate the analysis & monitoring of customer reviews and surveys, enabling organizations to open up more feedback channels, encouraging dialogue and communication which the ALM can leverage to help identify customer concerns and issues that directly impact risk and reward consideration. During the due diligence process, the ALM helps analyze textual documents generated from interviews, surveys, email and communications for goals, objective, and behavioral metrics that confirm known variables or shine a light on things that might have missed. These insights can help map out the differences and alignments between operational teams. The ALM’s toolbox of contextual analytics, 1-click reporting, and real-time monitoring makes it easy for any business team to get immediate results and take action without data science. As the business climate warms up, qualitative AI insights with the ALM is ready and available to help organizations meet the M&A demand.
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