A key challenge, no matter what the industry, sector, or organisation, is the pressure to reduce costs and do more with less; in other words, efficiency gains. This is all set against a background and minimum requirement of maintaining a quality service without disrupting the end user whether this is a patient or medical worker, a customer or a client, an office employee or offsite worker, or even a member of the public. Typically, however, cuts can only go so far before the effects are felt by everyone.
Imagine if you could identify an area or a process that, with some attention applied, could be altered to yield a saving of just 1 or 2%; and that once scaled up across an organisation, could save hundreds of thousands of pounds.
These changes don’t have to be applied to a prominent or obvious areas such as clinical procures, offender demographics, underachieving in certain schools, or consumer behaviours.
Although the obvious areas are important, they often turn into large cultural or organisational change programmes that take significant time and effort to deliver. Delivering multiple smaller initiatives, on the other hand, can bring immediate benefits.
Using Predictive Analytics to focus on smaller operational changes can help you find efficiencies quicker, allows you to make greater cost-savings, and helps you divert resources to more critical areas of your organisation. By using Predictive Analytics you could, for example:
- Reduce the number of ‘do not attends’ for appointments by allocating more appropriate times for certain demographics – i.e. a Wednesday afternoon instead of a Monday morning for an 18-year-old male student.
- Reduce ambulance fuel consumption by identifying ‘hotspots’ and station vehicles closer to them, or creating best routes to take to A&E depending on the time of day.
- Reduce pressure on operating theatres by making the most of existing theatres taking into consideration things like non-clinical preparation times and standard operation times per surgeon.
- Improve tax collection by identifying those who are unlikely to pay i.e. those who won’t pay and don’t care about consequences, versus those who can’t afford to pay.
- Improve police resourcing at an event i.e. football match by taking into consideration things like weather, opposition, distance to travel of away fans, the day of the week and times.
- Reduce the need for street cleaning by placing more litter bins in places with high footfall, to identify most common areas for fly tipping.
- Save money on transport by reducing refuelling times, or improving the planning of maintenance based on vehicle usage and parts wear.
- Increase the competitiveness of your insurance company by using customer data to give a more personalised underwriting using more detailed factors.
- Increase your number of online sales by identifying where people most commonly drop out and either do not purchase from you or have to contact customer service to continue purchase.