It is increasingly important to predict how customers will behave when interacting with your company so that you can respond accordingly.
The worldwide market for analytics is big business. Gartner Research states that analytics will remain a top focus for CIOs throughout the next few years yet despite the importance of data in making business decisions, more than half of all analytics projects fail. Projects that do not give an immediate return are also considered to be failures and the questions “Are you getting value?” can be hard to answer in these cases.
In our experience of working with customers to unlock their data by providing advanced analytics to enhance and refine a customer engagement model, we have seen the good, the bad and the ugly. We’ve put together the top three reasons why customer analytics projects fail and how to overcome them.
Budgets and Schedules
Every project is unique and provides a different set of challenges but one of the biggest reasons customer analytics projects fail is because the investment in time and money is largely underestimated. To overcome these problems it is crucial to have an upfront plan, but also manage the project throughout so that you can identify problems as they arise.
Keep the schedule realistic and invest in truly understanding the functionality and business expectations sought from the project. Stay on top of costs and have realistic contingency budgets available should you need them.
Bad Data
The quality of available data is key to the success of a customer analytics project. Data that is poor or difficult to use needs to be moved from old operating systems to new information systems slowing down a project and creating further costs resulting in many projects failing.
To avoid bad data causing problems for your analytics project make sure that all of the old data elements are correctly mapped to the new system. Rigorously test the file extracts before proceeding to the present data. Sometimes data may have to be manually cleaned.
Lack of Communication
Business users are the owners of what they would like to know about their customers and data scientists are the experts at crunching data and applying supervised and unsupervised statistical modelling on a titanic amount of data.
There is a gap that exists in terms of the business users understanding what can and cannot be achieved and in what amount of time and data scientists capabilities in understanding the business requirements, mapping that in technical terms and managing different pieces of technical implementation efficiently to deliver the desired and expected insights back to the business users.
Another major reason customer analytics projects fail is because of a lack of communication between teams. To avoid this problem there needs to be regular meetings between all teams working on the project. Read more about what it takes to be a Customer Analytics expert.
An effective customer analytics project manages different pieces of technical implementation efficiently to deliver the desired and expected insights back to the business users. While there are many reasons customer analytics projects fail, there are just as many reasons why they can be hugely successful.
If you would like to know more about how Acrotrend can help you with your customer analytics data science, please contact us