Don’t be left guessing the future – use predictive analytics

This is massively valuable if your business decision making and business problem resolution is still based too much on guesswork and too many blank canvases. Predictive analytics or PA, gives you a best assessment of what will happen in future.

Where is predictive analytics’ greatest value?

PA has moved from the domain of mathematicians to business analysts and line of business chiefs because of growing volumes of data, faster, cheaper computing, easier to use software and tougher economic conditions generally. It’s everywhere and it’s mainstream because the sort of business challenges it tackles are wide ranging:

Fraud detection – PA can improve pattern detection and prevent criminal behaviour. And to improve cybersecurity, high-performance behavioural analytics can examine all actions on a network in real time to spot abnormalities that may indicate fraud and advanced persistent threats. Commonwealth Bank uses analytics to predict the likelihood of fraud activity for any given transaction before it is authorized – within 40 milliseconds of the transaction initiation!

Marketing campaign optimisation – Predictive models help businesses attract, retain and grow their most profitable customers with:

• Predictive Scoring:Prioritizing known prospects, leads, and accounts based on their likelihood to take action. This adds a scientific, mathematical dimension to conventional prioritization relying simply on speculation and experimentation to derive criteria and weightings. It helps to identify productive accounts faster, spend less time on accounts less likely to convert and initiate targeted cross-sell or upsell campaigns.
• Identification Models: Identifying and acquiring prospects with attributes similar to existing customers. Accounts which exhibit desired behaviour (made a purchase or renewed a contract) serve as the basis of an identification model. This helps to find valuable prospects earlier in the sales cycle, uncover new markets, prioritize existing accounts for expansion, and power account-based marketing (ABM) initiatives by bringing to the surface accounts that can reasonably be expected to be more receptive to sales and marketing messages.
• Automated Segmentation: Segment leads for personalized messaging. B2B marketers have traditionally been able to segment only by generic attributes, like industry and with such manual effort that personalization applied only to highly prioritized campaigns. Now attributes used to feed predictive algorithms can be appended to account records to support both intricate and automated segmentation. This helps drive outbound communications with relevant messages, enable substantial conversations between sales and prospects and inform content strategy more intelligently.

Operations and business process improvement – Predictive analytics enables organizations to function more efficiently:

• Many companies use predictive models to forecast inventory and manage resources better. Airlines use predictive analytics to set ticket prices and hotels try to predict the number of guests for any given night to maximize occupancy and increase revenue. Salt River Project, the second-largest public power utility in the US, uses PA to analyse machine sensor data to predict when power-generating turbines need maintenance, minimising breakdown or performance failures.
• PA is also used extensively for software testing. It can identify inefficiencies and issues in current business processes and enhance both testing effectiveness and client engagement.

Risk reduction – Credit scores generated by a predictive model are used to assess a buyer’s likelihood of default for purchases, incorporating all data relevant to a person’s creditworthiness. Other risk-related uses include insurance claims and collections. In software development, PA helps teams leverage appropriate technologies that will give businesses the capability to better hedge investments or price products more appropriately. In addition, it can also help leverage appropriate technology to predict profitability more accurately and deploy resources proactively, thereby improving risk management.

Identify a business problem, but let the team solve it

To realise the value of PA for your business, it’s not a case of simply clicking the ‘predictive analytics button’ on your dashboard. Integrating into your business decision making means going through an analytical life cycle such as the following:

• Identify the problem to solve – ask yourself what you want to know about the future based on the past?
• Have sponsors in place – executive sponsors can help make the dream a reality. Don’t start a project or work in a vacuum where you’re the only person who appreciates the value of PA.
• Prepare your data – You’ll need someone with data management experience, to help you cleanse and prep the data for analysis. It also requires someone who understands both the data and the business problem.
• Conduct predictive modelling – Increasingly easy-to-use software means more people can build analytical models. You’re likely to need a data analyst to help refine your models and come up with the best performer. You then might need someone in IT who can help deploy your models.

So it will require a team approach to bring predictive analytics effectively into the business. But you can be sure we’ve only just scratched the surface, both in the ways different industries could integrate PA and the depths to which PA tools and techniques will redefine how we do business in concert with the evolution of AI. And as we inch closer to truly mapping an artificial brain, the possibilities are endless!