Implementing operational KPIs in the world of analytics to drive strategic decision making

By Paul Reading and Kenneth Ingram

KPIs are just the tip of the iceberg when it comes to analytics. Beneath the surface, CEOs and CFOs of private equity (PE) backed businesses are organising their teams, technology and data sets to uncover and deliver deep insight that provides a competitive advantage as the availability of data explodes. This starts with a data centric mind-set to collect, organise, analyse and commercialise the data assets, embedding data driven decision making into a business's value creation strategy.

We recently held a break-out session on this topic with a number of leading PE-backed CEOs and CFOs at the EY Private Equity Portfolio Forum.

Reductions in the cost of computing power, the increasing number of flexible, plug-and-play suites of analytics tool-sets and an increasing pool of new data science talent, has led to the democratisation and increased accessibility of the capabilities required in the market.

For CEOs and CFOs, this represents a unique opportunity to transform how KPIs are designed, derived and implemented to drive strategic and operational decision making, without the need to invest in full-scale enterprise resource planning (ERP) solutions. Analytics enables data to be collected, and metrics operationalised; this provides enhanced insight and enables faster decision making to support accelerated business growth.

At EY, over the past 18 months, we have also embedded the use of analytics across the transaction lifecycle, in areas such as market and competitor analysis, financial, commercial and operational due diligence. Most recently the PE Value Creation service offering has been embedding analytics to provide deeper insights, in faster timescales using increasingly larger data sets that traditionally would not be possible in short duration diligence engagements.

The application of analytics to drive value insight has developed from being descriptive in nature (e.g. understanding reasons for past performance) to more valuable predictive analytics incorporating information to build new rule sets from multiple internal and externals data sources enabling complex, multivariate models to predict future scenarios. This predictive analysis is equally applicable to modelling commercial outcomes (e.g. market share or pricing), operational metrics (e.g. the optimal supply chain or logistics network) and financial outcomes (e.g. the impact of improving commercial and operational levers on EBITDA and cash).

A simple example can be thought of as an ice cream van business. Looking at historic sales of May 2018, we may understand we had a peak in sales on a hot summer’s day when the royal wedding took place in the United Kingdom. A predictive model would apply the rules of a hot summer's day and royal events together, to predict an increase in sales, and therefore indicate (i) which locations we should park our ice creams vans and (ii) what stock we may want to pre-order to ensure we were able to meet the demand increase. In this example, external data sets like weather forecasts and royal event timetables are levers we can use to unlock additional value in the business.

We can further increase the value insights we deliver using prescriptive analytics in our ice cream van example. As we add additional data points such as school locations, school holiday time tables, hourly traffic data and transaction level sales, we are able to understand how each variable impacts our revenue lines for each ice cream van. When we operationalise this data we are able to predict (i) where our drivers should park their vans, (ii) at what time and (iii) the optimum price to charge for each stock keeping unit (SKU); we are using our collected data and analytics to prescribe the action that is likely to result in the highest revenues. In doing so, we are scenario modelling outcomes and operationalising the optimal scenario to enable revenue to be maximised.

Applying these concepts using analytics tools to accelerate the selection, testing and ranking of multiple variables that significantly impact different lines of sales and cost, allow CEOs and CFOs to make strategic and operational decisions that drive additional value that is measureable in the P&L.

In our experience, the transformation of data into information that drives value insight and intelligent actions is the one of the biggest areas of opportunity for CEOs and CFOs in PE backed businesses. But, as ever, there are pitfalls, and there is no substitute for careful planning before any implementation. Between the data, technology and data science capability, the commercial drivers must be clear, which fundamentally requires the combination of a data-mind-set and commercial acumen, which senior executives must consider with their counterparts and in developing their business model

In getting these things right, you may start to challenge your very view of the traditional business model; that of ‘people following processes supported by technology’, moving towards ‘technology driving processes supported by people.’ 

For a more detailed exploration of this topic, please see the EY articles What makes data valuable? and Optimising across the business or please do get in touch if you would like to discuss further. If you are interested in future events for CEOs and CFOs of PE-backed businesses, please contact us at privateequity@uk.ey.com.

The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.