- Data analytics can help transform a business, but it can be difficult to accurately predict the impact using traditional thinking.
- Depending on what they uncover, commercially focused data analytics projects are capable of providing considerable returns on the initial low-cost investment.
- A key strength of data analytics is the ability to measure and improve customer experiences.
Across developed economies, data is fast becoming a precious resource, yielding many benefits for those that can tap into it, analyse it and yield actionable insights.
Data has certainly played a significant role in the success of technology companies such as Google, Amazon and Microsoft. However in most businesses, it’s not easy to accurately quantify data’s role. Every organisation is likely at a different level of analytics maturity, with some performing better than others.
This uncertainty raises the question: how does a business’s data analytics capability stack up against current metrics of success, such as revenue, shareholder return, customer experience or profit?
Point of significant
It may initially be difficult to accurately predict the effect of data analytics on a company’s bottom line but, as PwC’s 2016 Big Decisions report argues, it’s not far-fetched to anticipate significant returns for any company that builds an analytics capability.
This capability can be established in-house (an effective but slower-paced solution), co-sourced with a partner (a better and faster option), or outsourced to a third party.
Depending on the insights produced, commercially focused analytics projects can commandeer extremely high returns, potentially outstripping most other projects or initiatives by a factor of ten. These returns are made possible by leveraging financial benefits from what is only a very modest investment in data capability.
Despite its inherent value, data doesn’t cost very much: it’s often not expensive to obtain externally, with many organisations such as the Australian Bureau of Statistics, State Governments, city councils, public libraries and social media platforms making large repositories of data publicly available.
As well as data becoming cheaper to get hold of, the cost of crunching the numbers has also come down considerably, too – especially compared to the recent past. This has been principally led by the arrival of low-cost software applications such as Alteryx, the open source R programming language, Amazon Redshift and Tableau, all of which can analyse and visualise large databases quickly and effectively.
Many organisations might already have access to considerable business intelligence tools but not realise it. A Microsoft enterprise licence, for instance, grants access to the organisation’s SQL Server software, which features analytics and data warehouse capabilities.
Combined, these conditions mean that with only a small investment of time and capital, you can generate insights that lead to direct improvements in an organisation, whether it’s customer acquisition or the customer experience, workforce productivity through deployment of human labour, or better targeted promotion pathways for staff members.
Indeed, if a monetary value could be calculated for an analytics project, it would not be uncommon for a $1 million investment in analytics to receive a $10 to $50 million payback.
the customer experience
It’s one thing to outline the potential for data analytics to unlock exponential returns, but what do these scenarios look like in the real world?
I recently visited a trucking company that had invested in a pricing and profitability ‘data mart’, in which analytics capabilities are provided to a specific department or team. Across its 15,000 Australian customers, the data mart allowed the company to deploy predictive analytics on processes such as contract renewals or customer margin responses, filtering the results through specific metrics such as market segments or geographic locations.
It also identified opportunities for better fee structures and customer incentives. Previously, such opportunities were clouded by a labyrinth of disparate systems and the entrenched views of managerial experience.
As a result of the data mart, within a year an additional $28 million of recurring bottom line was captured from the $2 million investment in data analytics – not bad at all considering there were more than 40 other managed projects in the business that collectively delivered less margin.
Other companies have also successfully leveraged analytics to initiate a pivot, refocusing service offerings around a popular but currently underserved part of their service offering. In 2011, the founders of the streaming platform Justin.tv discovered that a significant portion of user traffic revolved around the live streaming and consumption of video games¹.
In response, they decided to launch a spin-off service, TwitchTV, to cater to this audience segment. The platform was a success, eclipsing the original service before being acquired by Amazon for over a billion dollars in 2014. Without traffic analytics uncovering that audience activity, the founders may never have made the strategic decision that would eventually become a billion dollar idea.
In spite of their small upfront costs and many potential returns, Australian companies remain hesitant to invest in data pilot projects. But why are Australian executives still so slow to invest in predictive analytics capability?
It’s likely that much of this is related to a holdover from the IT industry, where it’s common for projects to become saddled with lengthy delays and cost overruns. This has left leaders – across both the private sector and government agencies – relatively gun-shy when it comes to experimenting with new technologies or capabilities.
It needn’t be this way, however. The cost of analytics capabilities has come right down, while the potential value they could uncover has skyrocketed.
Don’t be afraid
Companies shouldn’t be afraid to experiment or attempt specific use cases that could become high value. The point is not to avoid failure altogether, but to test, learn and fail fast to ensure a swift recovery.
Any new opportunities, once identified by these analytics capabilities, can then be industrialised and made part of the organisation’s operation.
Ultimately, companies are slowly learning to not be afraid of data, and to invest more confidently in its capture and analysis. The barriers to entry are low, and the potential returns are large and numerous.
Don’t be timid – it’s never been a better time to get stuck in.