The ROI of Predictive Analytics for Marketing

Chart of the Day: Predictive Analytics Users Get Better Results

Predictive Analytics isn't a new marketing technique, but interest in it has increased with the hype around using Artificial Intelligence for Marketing. This typically involves using Machine Learning, a key AI technique for marketing, by analysis of historical results of campaigns to inform future targeting and creative.

In this research, analysts Aberdeen Group review the impact of Predictive analytics on different ROI metrics as presented in the chart below.

But, before we take a look at the results, what is predictive analytics? Aberdeen define  predictive analytics as:

'A technology allowing firms to analyze structured and unstructured data, be it captured in the past or in real time. Such analysis reveals key trends and correlations while also predicting the likelihood of things such as customer churn'.

This is a broad definition since most marketing applications focus on analysis of structured, historical data to inform targeting of future marketing activities. This definition includes unstructured data, but the research doesn't give examples of what those might be, but one is analysis of creative like subject lines in email to develop copy for future emails, for example in the Phrasee service. Others could include review of customer service queries or social media monitoring to inform future responses.

The more general Wikipedia definition of Predictive Analytics is closer to the general understanding of predictive analytics in marketing:

"In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions".

So, turning to the research, the chart shows how users of predictive analytics achieve improved return on investment measured by different KPIs of business impact.

Specific technologies covered in more detail in the research include:

• Database management (e.g. for better targeted emails)
• Recommendation engines (e.g. for on-site personalisation)
• Real-time decision assistance and guidance (e.g. for customer service)

About the research

• Source: Aberdeen Knowledge Brief : Predictive Analytics in Customer Service
• Sample: 154 - details not given unfortunately

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