Predictive Analytics is presently being defined as the process of using a range of data sources along with algorithmic analysis to identify patterns and predict future user behavior. The 3 important stages of analytics maturity is elaborated here. The earliest stage of competency in analytics was characterized by data sources that could not be compared easily, very little description and lack of dedicated human resources. Next in line was a change in culture required to prioritize actionable insights. Last of all, a capability in predictive analytics is built on one big data reservoir and support for data driven decisions throughout the organization. Theoretical models are good indicators, but challenges faced by marketers in establishing capabilities with predictive analytics or the ease with which marketers are able to move between the stages of maturity are to be given due thought. As organizations discover more possibilities with automation and machine learning, there is a need to substantiate the time and resources required for predictive modelling and compare this against the potential benefits.
4 crucial aspects of effective predictive modelling are-
1. An appropriate source of data
A fundamental point to consider is whether the data available is capable of providing answers for all questions of the organization. As there may be data gaps, experts ask “While building a new product, whether software or a physical item, will you be able to find the required data? Sales data and buying patterns are available, and hence strategies are built on that, and that’s as complex as it gets.” Many factors determine the appropriateness of a data source and these include volume , velocity, regulation and privacy. caution is urged while branching out after first party data. Third party data is usually less valuable as it is not exclusive to your platform.
After deciding on the appropriate sources of data, it’s cleanliness and usefulness should be assessed. Experts warn about how incorporating too many data sources may defeat the purpose it is intended to achieve. New data sources feeding the model can add value, but at the risk of hitting the law of diminishing returns after a while. Prioritizing data sources along the customer acquisition journey helps optimize predictive models towards conversion. If you start with a wide consumer base on a platform where media was aimed at prospecting buyers, you would require more data to realize a purchase. There is value in third party data, particularly in anonymous data at the top of the funnel to target paid media. But it is important to get your own data in order first as you can be sure of it’s efficacy.
3. Machine Learning and Automation
Martech vendors today trumpet machine learning as a part of their software, but making use of these learning algorithms in an analytics team is just not plug-and-play. The scale of this task is defined by how most people look at machine learning and try to figure out if they can find resources for it. The resources being referred to here would be data scientists, technologies and the amount of storage space required. As you may have learnt, it is just not about tossing large amounts of data into a warehouse and accessing it. Machine learning is a massive undertaking and having all data sources talk to one another and then making it all actionable in real time is a prominent challenge. New data protection rules, like the GDPR (General Data Protection Regulation), effective May 2018, is affecting plans to skill technicians n this area. Consumers should know how their data is processed and they will be entitled to contest the ‘black box’ style analysis that is completely automated. Many marketers, including us, had great plans, but now with the GDPR coming in we may have to redesign our strategies. Identifying the potential difficulties while working with partner data and not knowing what is okay under GDPR makes moving forward a challenge. 18 months from now when there is more clarity, agencies that can prove they have a clear understanding will be at an advantage.
4. Meet Business Objectives
The finishing line for effective predictive analytics is determining whether the activity meets all business objectives. Predicting customer purchase behavior is great but marketers must consider the bigger picture. When using predictive modelling for the right reasons, it is done to build the best customer experience. Problem arises when personalized experiences are designed with the idea that all you want to do is sell more goods. Then you would much rather give the customer a great offer.
What we would like to say is predictive modelling requires immense management for something that would move the needle slightly. The agility of leadership required to make use of predictive analytics can be summarized as leaders knowing and believing in their strategy. If the model shows something of interest, then quick decisions have to be made on whether to pursue it or not. Change is great when it is appropriate but one shouldn’t jump on things that just appear interesting.