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M&A analytics – deal valuations and due diligence

August 2019  |  TALKINGPOINT  |  MERGERS & ACQUISITIONS

Financier Worldwide Magazine

August 2019 Issue


FW moderates a discussion between Andrew Robinson, Steve Xing, Chris Woolley and Angelina Kuznetsova at Deloitte on M&A analytics, focusing on deal valuations and due diligence.

FW: To what extent has the proliferation of data and technology in recent years changed the M&A process? How does the current landscape compare to 10, or even five years ago?

Woolley: There has been a fundamental change across all aspects of a transaction, from initial target company identification, through to diligence, through to formulating and identifying the value-creation plan post transaction. We are now seeing M&A as a genuine end-to-end process underpinned by data, the availability of which allows acquirers and their advisers to generate a greater level of insight into target companies than ever before. For example, 10 years ago we would ask for the details of an organisation’s top 10 clients; in some cases we are now being provided with the entire transaction history of a business as part of the diligence process running to tens of millions of lines of data.

There is a risk that AI and analytics outputs are tainted by irrelevant inputs and unconsciously used to support a natural bias towards excessive optimism.
— Andy Robinson

FW: How can M&A analytics, such as machine learning and artificial intelligence, be used to build a more detailed and comprehensive picture of a target company’s operations and enterprise value? What kinds of quantitative and predictive insights can it yield?

Robinson: Coming up with a view on enterprise value requires a point of view on the future for markets and the route to profitable investment by companies into those markets. To the extent that artificial intelligence (AI) and other analytic tools give a clearer picture of both, they naturally will help people form more robust views. This creates the ability to reduce forecasting risk and help managers invest in opportunities with lower expected returns than perhaps previously required. The consequence is an improvement in valuation accuracy and higher prices for assets and early-stage businesses. This is particularly relevant for loan portfolio transactions which continue to be actively traded throughout Europe. We have seen loan book valuations increase as broader and better quality data leads to greater confidence in default and recovery assumptions. However, there is a risk that AI and analytics outputs are tainted by irrelevant inputs and unconsciously used to support a natural bias towards excessive optimism.

Woolley: From our experience it needs to be specific to a given aspect of an industry – for example loan portfolios in financial services or stock keeping unit (SKU) level performance in consumer goods companies, rather than there being a generic M&A ‘AI’ solution. In these situations it can be very powerful in answering some key questions on aspects of the business – such as predictions around performance if particular relationships hold, or if there are particular changes in the macro economy or competitive environment. All of these findings can support assumptions in the business plan.

FW: What considerations should acquirers make when choosing analytics tools and solutions to deploy during due diligence?

Woolley: The first is to have a very clear idea of the question you are trying to answer and why, then work out what data is available to support your ability to answer the question and the timeframe available. From my experience, the tools tend to come last, once there is clarity around the objective. The limiting factor is nearly always the availability of data rather than an ability to analyse it.

The concept of ‘garbage in, garbage out’ is particularly relevant in the high-pressured, high-stakes world of M&A.
— Steve Xing

FW: What challenges might acquirers face when gathering and processing data, including sensitive information? What steps should they take to address related regulatory compliance, for example?

Xing: The two challenges I see many of our clients struggle with when gathering and processing data are quality and control governance. The concept of ‘garbage in, garbage out’ is particularly relevant in the high-pressured, high-stakes world of M&A. Many acquirers are often frustrated by the lack of quality data and information. It is therefore important to establish clear control procedures. For example, the data sets used in the valuation model should always be traceable back to the original data tape, with a clear trail to track any overlays applied. Documentation is key here and it will serve as corporate memory. Second, perform consistency checks. It is likely that data is gathered from multiple source systems at pace. We would always perform two procedures. First, visualise the data and investigate anomalies. And second, run validity checks, for example determine whether all numeric fields contain numeric values. In addition, most M&A deals come with tight deadlines. This is where embedding the modern-day technology can be truly differentiating, and it is here and now. For example, if checking property details on the government’s land registry was performed manually, it would take a team of four 24 hours to get it done and it would be prone to human error. Instead, one person can spend a day developing a robot to perform the task and the robot can complete the task in 12 hours, non-stop. That is a tangible saving and it adds real value. Furthermore, as with any data handled, it is important that all relevant safeguards are adhered to around personal information.

FW: How should acquirers go about filtering results gleaned from M&A analytics in the due diligence phase, into deal valuations and negotiations?

Kuznetsova: M&А analytics can be a significant decision-making tool even before the due diligence phase. A good example is a large corporate which is considering an M&А strategy but is unsure as to what part of the business could benefit most from acquisitions, due to a number of internal conflicting priorities. An analysis of end-customer behaviour and demands, and external economic key performance indicators (KPIs), could assist in identifying the most critical gaps in products and services. Once a deal is underway and due diligence has been undertaken, analytics can provide helpful insights as to whether the target is the most suitable across a number of variables, such as geographic footprint, customer type and cultural fit. A simple depicting of a number of complex data points that M&A analytics offers can indicate to potential buyers the level of strategic fit, and provide an opportunity to reassess the value offered for the target. It is particularly helpful in competitive sale auctions when value expectations could be high, and buyers need all the information they can get to justify the valuation offered and decide on their ‘walk away’ price. If greater than initially anticipated synergies have been identified through due diligence, a strategic value premium can be justified, and vice versa.

Woolley: From my experience, the findings from data analysis are more supportive of enterprise value, including answering questions such as why the asset is attractive and why it is growing, rather than the specifics of getting to equity value. Robust data gives more confidence and if it is supportive of and proves out the target’s attributes – a high-level of recurring business and net revenue retention – then it gives bidders a greater level of comfort in their overall valuation. Diligence is ultimately about facts, evidence and proof – and data obviously gives more comfort around all of these areas.

Diligence is ultimately about facts, evidence and proof – and data obviously gives more comfort around all of these areas.
— Chris Woolley

FW: Are you seeing M&A analytics applied with more frequency to the sell-side of transactions? If so, how?

Kuznetsova: M&А analytics can support sellers of businesses if used as a diagnostic tool in advance of a sale process – to demonstrate any data patterns that may position a business in a less favourable light, show weaknesses in performance and lack of quality in a customer base. The outputs gathered can then be analysed and altered as required over time through a strategy change or by making targeted improvements. Or perhaps a more insightful and quality narrative of emerging trends can be pre-prepared in order to stand up to buyers’ scrutiny, by allowing sellers to own and understand potential problems and value detractors upfront.

Woolley: We are seeing analytics used more in sell-side situations. For analytics to be its most powerful, you need time, access to data and access to the business. All these factors are more prevalent in sell-side situations, and we are certainly seeing a lot more organisations using analytics to diagnose both the strengths of their business and areas of required improvement a long way ahead of sale processes. To be honest, it is just becoming a greater part of corporate governance, control and improvement, rather than necessarily linked to M&A.

We need to provide companies with more modern and technology-enabled tools to support the efficiency of their decision making during M&A processes.
— Angelina Kuznetsova

FW: As digital innovation continues to gather momentum, to what extent do you expect M&A analytics will help companies find better targets, close deals more quickly and capture greater synergies?

Kuznetsova: We need to provide companies with more modern and technology-enabled tools to support the efficiency of their decision making during M&A processes. M&A analytics is one of the solutions that can enable clients to analyse, compare and have a bird’s eye view of very complex data sets – a side-by-side comparison that has never been possible in the past. This is a decision-making tool that can give companies a real competitive advantage when engaging in M&A activities, enabling them to arrive at conclusions faster than competitors. A detailed analysis of the business using the tool can assist in identifying what type of acquisition target can be most beneficial in order to meet its customer demands, pinpoint the most attractive geographies to expand to and highlight proposition gaps. The output can then be used to allocate financial resources and efforts to close the most critical gaps. This will ultimately generate more value from M&A activities by buying exactly what the business needs, rather than relying only on human decision-making power, avoiding any potential bias and overpaying for the wrong assets.

Woolley: Market-sensing tools will allow for a greater reach of potential targets for acquirers, albeit more relevant in certain parts of the market than others, such as start-ups and smaller organisations. After a certain size threshold is reached, it is harder for any organisation to operate ‘below the radar’. In diligence situations, more data and the more rapidly it can be analysed will clearly give bidders more confidence, both on their valuation and their ability to execute quickly. In terms of synergies, data will continue to help support cost savings and synergy identification, but execution will continue to be paramount in achieving the acquisition case.

 

Andrew Robinson is a commercial valuer and expert witness for disputes involving valuation matters. He leads Deloitte’s valuation group in the UK and EMEA, and is a member of Deloitte’s global management executive team for economics, valuation and modelling. He also leads the valuation group’s financial services industry team. His work covers banking and capital markets and asset manager clients. He can be contacted on +44 (0)20 7007 2769 or by email: anrobinson@deloitte.co.uk.

Steve Xing is a partner in the economics, valuation and modelling practice and leads modelling & analytics services for financial services clients in the UK. He has 15 years of experience advising clients in the design, development, testing and implementation of bespoke decision support solutions, leveraging a wide range of analytics platforms and modelling toolkits, typically as part of investment appraisals, business planning exercises, transaction support or strategic transformations, to enhance business-critical decisions. He can be contacted on +44 (0)20 7303 7892 or by email: stxing@deloitte.co.uk.

Chris Woolley is the partner responsible for Deloitte’s transaction services analytics (TSA) business. In this role, he is responsible for supporting the application of analytics to M&A situations, covering all aspects of the M&A lifecycle, from supporting in the identification of transaction opportunities, through to due diligence and the implementation of the value creation plan. Additionally, he is responsible for leading Deloitte’s relationship with a number of private equity (PE) clients. He can be contacted on +44 (0)20 7303 7994 or by email: cwoolley@deloitte.co.uk.

Angelina Kuznetsova is a partner in the Financial Institutions M&A team, focusing on the FinTech sector. She has 13 years of corporate finance advisory experience and advises FinTech businesses on fundraisings and exits, as well as private equity and strategic investors, particularly financial institutions, on their investment into the sector. She has a broad experience across all segments of the FinTech sector, having worked on buy- and sell-side mandates, bolt-ons, market scans, acquisition searches and strategic options analysis. She can be contacted on +44 (0)20 7007 4689 or by email: akuznetsova@deloitte.co.uk.

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