Face-to-face negotiation and human chemistry still play a key role in how M&A transactions are brokered. However, that is changing as cold hard data becomes an increasingly important factor in deal-making.
Traditionally, ahead of a deal, an acquirer conducts due diligence around four pillars – legal, operational, commercial and technical- in line with its particular needs – whether that is to plug a gap in its provision or to give it access to new audiences, new markets and so-on. It will be looking to benchmark the target’s current performance against that of its peers and it will also seek to pinpoint where the opportunities for future growth lie.
Savvy acquirers are also advised to look deeper into a target’s data set-up to account for how it captures, stores and uses data to inform strategy as a key measure of potential (read about missed opportunities in digital due diligence here).
In a digital-first world revenue figures provide just half the story. The path to long-term growth depends on a business’ ability to call on data to understand where this growth will come from at a customer, cohort, product or industry level.
This requires not only the means to use regular data reporting to react to customer and competitor activity as they arise, but also the ability to use predictive modelling to forecast and plan for growth.
Consequently, a target should be able to demonstrate a firm handle on unit economics at a granular level; for example, to track gross margin, conversion rate, CPA, customer lifetime value and so-on.
Acquirers should work on the assumption that the commercial application of data is a given in the current landscape. This has profound implications for the traditional three-step acquisition process as data transparency renders the marketing phase as little more than a formality.
Due diligence tools are now arriving to market that enable acquirers to plug into the APIs of their target’s ERP and related cloud-based systems and review its (anonymised) unit economics directly and on a ‘live’ basis.
As such, it has become very possible to interrogate the data to qualify any assertions, thus making digital due diligence purely about checks and measures.
A high value business should already have data best practice in place, or at least have the foundations in place to transition to a data-led growth model. Not all do though, there are still plenty of lazy qualitative assumptions being made about audiences, markets etc.
As such, there are a number of red flags to look-out for (read more about investor red flags here).
First, how often data is reviewed in order to take advantage of emerging opportunities or to identify and act upon anomalous trends that could result in lost sales. If this doesn’t happen daily it raises questions around a business’ agility – and its leadership.
Data should clearly guide the marketing strategies for both customer acquisition and retention so outreach strategies are balanced with ROI. The particular metrics to seek out are repurchase rates and lifetime value metrics. Not taking these into account highlights poor business acumen – particularly if the cost of acquisition is more than the purchase.
Also ask for evidence on how data science is used to inform growth strategies for the mid and longer terms; these should be aligned to a clear understanding of the needs of high-value audience segments. Ideally, the target should be able to demonstrate how predictive modelling informs product diversification to grow share in the domestic market, and/or to guide internationalisation.
Understanding where a business is on its data-transformation journey helps the acquirer understand if the target has been valued fairly and whether it’s a safe acquisition. The data processes and thinking in place can be as valuable as the data itself in reaching a final decision.
Hygiene factors can be strong indicators; so pay attention to how data is stored and quality control within that set-up. This means no data duplication, no inaccuracies and certainly no trying to game the system.
Any business should be measured by its potential. We have all heard many times that data is the new oil, but even businesses with an abundance of data need to possess the means to extract, analyse and act upon it to justify the valuation.
The right infrastructures can be undermined if the target does not have a demonstrable data culture in place that is led from the top. As such, look at how growth targets are defined; data needs collaboration and KPIs should thus be set on an organisational, rather than a departmental or divisional basis. Data held in siloes could be the clue you’d be buying into a business that is riven by internal politics.
The value of data can be unlocked but the time, energy and costs in doing so can be significant and impact return on investment. Sometimes it is more sensible to invest in a smaller data-savvy challenger business that can be scaled up than a market leader that does not have to nous to make data a priority.