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:
Tip 1: Focus. Zero in on one place to start
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. My experience is that those with the right data win. The quality of data sources must be the top priority when launching an analytics function. Start small and look to incorporate both internal and external data sets.
Tip 2: Make Smart Hiring Choices to Build The Right Bench
It takes more than data scientists crunching data to be successful. Having the business perspective to develop actionable insights is essential. Build a diverse analytics team with a wide variety of skill sets including data officers, analysts, engineers, scientists and consultants.
As you build a “data bench,” there are three or four roles to consider. The first, a data analyst, who is intimately familiar with how to extract and transform data for its intended purpose. Second is the data engineer, the person who knows how the data is being captured, which servers it is located in, what tools are required to extract data for analysis. Third are the data scientists, who can be used to great effect to create a profile or do clustering analysis on data. Finally, segment experts or consultants who can contextualise the findings and deliver recommendations that are compelling to senior leaders.
Tip 3: Manage Organisational Change
Incorporating analytics into the decision-making process is not always welcomed by all stakeholders, so it’s important to manage the change strategically. You can ensure a smoother transition by:
Encouraging engagement: Pilot programmes and focus groups can help involve stakeholders in the process of building out an analytics function – generally folks want to be part of the solution vs. being given the final output – early participation is a huge lift in driving change.
Enlightening and informing: Even the most sophisticated machine learning models are grounded on some foundational data principles. Exposing some or all parts of a predictive model delivers transparency, which leads to trust, which leads to adoption.
A closing Principle in the CapGemini study reiterates this need to empower your people with insights at the point of action. Sales reps, for example, can be particularly skeptical of data-based decisions because of their often times long-standing relationships with customers. By launching pilot programs that allow sales reps to provide feedback, gathering insight via focus groups and leading discussions with key internal influencers, you can get buy-in. And of course, show them the data.
Every day, I speak with executives and peers in companies of all sizes who are eager to get started, but are overwhelmed by the notion of where to begin. My advice: Start with what you know and have and build from there.
If you’re still looking for where to get started, consider the traditional value benefits of big data – variety, velocity and volume. Look at your current KPI’s, ask yourself what if I could segment my business more effectively? What if I could get these updates more frequently? And what if I had more information to trend? If you answered any of these questions, your first step of inspiration has been accomplished.
And by my estimate, your ability to harness predictive analytics is closer than you think.
John Smits is business analytics and chief data officer at EMC