Predictive analytics has come a long way, and in an era defined by the ever-increasing influx of data – and heightened customer demands – businesses can no longer deny its strategic importance.
Industries such as insurance, financial services and retail have used predictive analytics for decades, while others are just getting started.
So what’s new? Predictive analytics is now being used to support day-to-day business operations and decision making rather than special, retrospective projects. Companies that effectively use it can glean forward-looking insights that enable them to spot new business opportunities and innovate more quickly.
So why aren’t more companies doing it? Many simply don’t know where to start. Becoming a ‘big data dynamo’ in your organisation does not require a complete rethink and change in how things are done. However, while business leaders agree on the importance of a data driven approach to survive the next decade — an overwhelming number admitted that at the core, they still struggle with information overload and deriving actionable insights from data they already possess.
While difficult, it is possible and increasingly necessary to use predictive analytics to effectively compete today. In fact, in a recent study by Capgemini, 65 per cent of respondents agreed that their business risks becoming irrelevant if they do not embrace big data.
Here are three tips to position your company for success:
With a sea of potentially useless data, narrow down your options and find the right area of your business to get started. CapGemini studies confirm that the number one guiding principle to harnessing success with big data is to focus on solutions supporting your primary business objectives. Depending on your function, some considerations are market planning, account intelligence and optimizing operations to streamline processes.
Market planning: To spot the right market opportunities, use simulations to blend both economic data with your business performance data to determine who is most likely to buy so you can focus efforts and build and deploy resources most effectively.
Account intelligence: Consumer brands born on the internet, such as Amazon or Alibaba, excel at market basket analysis – analysing customer purchasing behaviour to figure out what they might buy next – but it is relatively new in the B2B space. Basically, you can determine which accounts have the greatest propensity to buy based on which purchases are likely to go together. Retailers use it for promotions and targeted recommendations. Similarly, B2B companies can use internal and external tools for such analysis to amp up traditional lead-generation efforts.
Operationally smart: Which of your day-to-day operational tasks can be done smarter? At EMC, for example, we noticed that there were more contract renewal opportunities than we had sales reps to make phone calls, so we use analytics to prioritise the highest value renewals for them to focus on.
Collect the right data: Some believe those with the most data will win.