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Leverage big data
Inflows of private equity (PE) funding is driving ever-higher volumes of acquisition.
PE acquisition however differs from a traditional merger: PE firms focus more on target-specific profitability (more revenue, less cost) and perhaps less on longer-term, organizational structure-based integration.
In parallel, better computational capabilities, or big data analytics provide new opportunities for acquiring companies – mainly PE, but also traditional firms – to improve integration processes, and accelerate value capture. However, much of this new analytical capability and power remains focused on target identification and the subsequent pre- and post-sign due diligence.
According to a HBR study, about 70–90% of companies undergoing integration fail to achieve the desired outcomes of such deals. Critical as this is, a largely untapped opportunity exists to leverage big data analytics in the longest, most complex part of a merger, acquisition, and divestiture: realizing the projected synergies that underpinned the transaction and valuation model.
A ‘hidden prize’ lies unclaimed as acquirers don’t use analytics fully in the post-signature phases of large deals.
The prize is faster realization of revenue synergies. These get ‘second billing’ behind cost synergies, often because CFOs and COOs focus on immediate and tangible results, rather than potential outcomes. Revenue synergies, by their very nature, depend on far more factors, all with an inverse degree of certainty.
Tracking synergy is hard in the maelstrom of deal closure, and a heavy tracking load falls on financial planning & analysis (FP&A) teams. FP&A can generate vast pools of data in systems and processes of a comprehensiveness unthinkable 20 years ago. However, data are often retrospective, operational in focus, and not good predictors of future performance.
Achieving (or even exceeding revenue synergy targets) in valuation models requires a more profound behavioral change. Take for example, a recent financial services merger in Europe. On paper, the deal offered a compelling strategic rationale based on market share consolidation, generating appreciable synergies – both cost and revenue. To ensure a positive return for year one, the initial focus of post-deal integration effort was on driving out the diligently calculated cost synergies.
Revenue synergies would come by cross-selling through a combined, enhanced portfolio. However, 18 months into integration, this growth had not materialized. Merging systems, processes, and sales teams proved far more complex than expected, in turn delaying recognition of actual and potential client losses. Retrospective analysis and time-consuming account reviews revealed corrective actions needed to stem the consumer attrition going forward, but they could not mitigate the damage already done.
In this case, and others we have seen, we believe capture of revenue synergies could have been planned and executed differently.
Existing data and analytics solutions inside firms, primarily in the FP&A space, should be used additionally to speed up and make more accurate synergy realization as well as increase profitability by identifying the ‘magic beans’ of cross-selling to expand share of wallet.
Our big data analytics-related recommendations for maximizing revenues during integration include:
1. Anticipate and prevent consumer attrition
Rather than react to consumer leakage after it is tracked post Day 1, best practice M&A teams should include processes supported by analytics to spot the changes in consumer behavior and drive engagement to prevent loss. Signals such as increased requests for online access to terms and conditions or jumps in customer services calls from specific types of clients could indicate an opportunity to engage sooner rather than later to reduce dissatisfaction or solidify satisfaction.
2. Assess changes in consumer consumption patterns
Joint M&A and FP&A teams should monitor each step of the buying cycle, for both the end and any intermediary consumer. Track variations in completion rates, expressions of interest and even ‘hit’ levels on front-end web pages. Evident changes in consumption patterns will indicate customer willingness (or reluctance) to engage with a firm’s ‘new’ or added portfolio offerings.
3. Prioritize integration activity for greatest growth
Using analytics at a more granular level during the first 100 days, sales leaders can focus scarce integration capacity on products, which can accelerate the revenue synergies in the deal model. This is simple math: earlier, higher revenue inflows translated directly to increased shareholder value. New, targeted marketing campaigns along with agile sales teams and go-to-market processes all supported by scalable systems infrastructure with planned and prioritized change could contribute majorly to faster growth.
4. Thwart the competition
The complex work of M&A integration usually takes 12 months or more to stabilize operations. During this time, incumbents and disruptors in the markets in which the buyer and seller compete typically do not shy away from attempting to take advantage of any perceived or actual changes in the value proposition by the merging organizations’ clients or consumers. In the case of the European financial services merger, local firms previously unable to compete did just that. Deal-specific analytic capabilities designed to identify clusters of ‘at risk’ customers – be they geographic or demographic – based on changed behavior would have enabled proactive, orchestrated efforts to retain them in advance of potential losses.
CxO and integration teams, especially from the private equity sector would benefit from the application of big data analytics to help maximize revenue (and cost) synergy realization. Whether built in-house or provided through trusted technology partners, big data analytics is increasingly a ‘must have’ competence to identify and negotiate diversity of behaviors across industry sectors as boundaries blur and ecosystems change as a result of this increased M&A activity.