We’ve seen an explosion of new tools and techniques for predictive customer analytics in the retail sector. This is not surprising considering the advantages of using predictive customer data to plan, segment, target, and personalize marketing campaigns. Predictive analytics is proven to show measurable lift in conversion rate and margin dollars, but only works as well as the accuracy of the predictions. To help you decide which is the best predictive analytics approach for your brand, we highlight three options to consider.

#1 Do-It-Yourself

Investing in an in-house data science team can have far-reaching long-term benefits, but requires an investment in people with new skills, changes to data infrastructure, collection and integration processes, and time for trial and error. In the long run, this option may offer great improvement around your company’s unique set of problems and circumstances if you can afford it.

Pro’s

Flexibility
Focus on specific needs
Continuous improvement

Con’s

High cost
Patience required

#2 Pre-Packaged Platform

Many marketing automation and engagement platforms now include some level of machine learning. Some generate product recommendations or segment customers for specific content or marketing journeys but many of them are actually simple rule-based systems and are not the same as predictive analytics. Typically these platforms observe digital engagement and then trigger customized messages, offers, or digital experience. These platforms can boost results from engaged customers without much extra effort, but flexibility is limited.

Pro’s

Easy to implement
Real-time responsiveness
Analytics included

Con’s

Limited metrics use cases
Harder to customize and improve
Limited value for customers not recently engaged

#3 Managed Data Service

Specialized service providers are emerging that offer more flexibility than pre-packaged platforms plus automation to reduce costs and simplify implementation. By combining best practices and proven techniques for retail marketing with configurability and managed adaptations to the unique requirements of each retailer, a suite of consistent, accurate, highly useful predictive metrics on each customer can be regularly updated and always available to marketers. With these predictions, campaigns on any channel can be planned weeks in advance, targeted for maximum lift, personalized based on customer propensities that accumulate over months or years, and measured not only for the campaign itself, but on its influence on each customer’s long-term value and risk predictions.

Pro’s
Flexible use cases
Available across any channel
Simplicity and low cost
Proven and customizable

Con’s
Not for real-time engagement response
Marketers need to learn metrics
Campaign execution is not automated (data is loaded into existing systems)

Ultimately the most critical test is to evaluate whether the option you choose meets business needs, resources, budget and whether it adds value to the business.

 

Peter Moloney is CEO of Loyalty Builders, a cloud-based predictive analytics service enabling marketers to get revenue lift from more relevant communications to their customers.

Tags :