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Forrester Report on the State of Customer Analytics 2018

AvatarBraeden Daly | February 27, 2019

Key Takeaways from Forrester Report on the State of Customer Analytics 

The findings of the 2018 Forrester Report on the State of Customer Analytics are based on an online survey in which 144 North American analytics and measurement pros, from a broad set of industries, took part. A vast majority of respondents make or influence decisions at their organization. 

This report looks at what worked in 2018 in customer analytics and measurement. It can also help organizations take a more informed stand on how they would like to approach analytics, based on current trends and guide them towards framing a more data-driven analytics framework for 2019. In this blog, we will discuss the key highlights of this report.  

Let’s dive right in and find out what the report has in store for us.  

More firms choose to adopt analytics to increase customer acquisition: 

‘Increasing customer acquisition and targeting’ is the top driver that encourages organizations to adopt measurement and analytics, with 57% of respondents opting for it. The next top driver for organizations to adopt analytics is customer retention which came in at 41% and 27% chose customer engagement as their top driver.  

Customer Analytics and its diverse applications 

Customer analytics has steadily been extending into the various departments of organizations, unlike the traditional approach where it would only be correlated with marketing.  

  • Distributed Model is the most popular Analytics organizational Structure 

This is evident as the “distributed model” organizational structure sees an increase among the analytics teams. 47% of respondents say their analytics team is organized with multiple measurement and analytics teams. 29% of respondents said they had a centralized analytics team and 24% had the hub-and-spoke model. 

  • Increasing use of analytics for product development  

With growing maturity among firms in their application of analytics, we see more firms opening up to the idea of using analytics in areas that are beyond marketing and CX like product development, enhancement, operations management, pricing decisions, etc.  

  • Retailers find other ways to use analytics too 

The Retail industry has started using analytics to improve product merchandizing and planning, optimize supply chain logistics as well as for demand forecasting.  

Data Challenges that persist 

While there has been a significant increase in the maturity across the board, there are still certain challenges that remain to be overcome. Some of the data challenges reported by the respondents are:  

  1. The quality of data and access to diverse sources of data are the top two challenges today facing analytics teams, as they have since 2014.  
  1. The inability to bridge the gap between IT teams and business teams, when it comes to analytics is another top challenge that prevents analytics projects from delivering results.  
  1. Finding, hiring and retaining top analytics talents or data scientists continues to be a challenge that is relevant even in 2018.  

Customer Analytics is gaining more maturity and sophistication 

As of 2018, Customer Insights Pros mine customer data from an average of six different sources for insights, employing eight analytical techniques! This speaks volumes on how Customer analytics maturity is on the rise.  Some of the top indicators of growing maturity are:  

  • Predictive Analysis is gaining popularity:  

Studying customers’ past behavior is no longer enough. Nine out of ten respondents report that their companies use predictive analysis to inform decision making, beating basic reporting. 89% of respondents use predictive analysis as opposed to the 83% who use basic reporting. The study also shows greater analytics sophistication apart from maturity in that 28% of respondents say that their companies use some form of Artificial Intelligence.  

  • Open source data science platforms:  

In addition to statistical software such as SAS Enterprise Guide and SPSS Statistics, respondents indicated that they also use open source statistical programming languages like Python and RStudio suggesting that data scientists are comfortable taking complete ownership of the firm’s analytical projects.  

  • Advanced analytics techniques:  

Other predictive techniques such as forecasting models, customer lifetime value, propensity models, customer lookalike targeting, customer churn, etc. are being adopted by CI pros to understand their customers. This is yet another validation that predictive analysis is now a mainstream approach which allows companies to understand customers at multiple parts of the customer life cycle.  


Based on the level of maturity and sophistication of customer analytics employed in companies, three groups were formed- Leaders, Followers and Laggards.  

2018 sees a significant rise in the number of companies that fall under the ‘Leaders’ category. In 2017, 26% of them were leaders while this year, it has grown to 37%.  

The Laggards, on the other hand, see a 15% decrease in 2018 meaning more companies are now in the Leaders and Followers bucket.  

This year’s study establishes a clear connection between maturity level and partnership with service providers and consultancies. 51% of Leaders partner with service providers while only 17% of Followers and 29% of Laggards do so. Contrarily, 42% of Followers and 35% of Laggards say they don’t work with any external organizations for analytics and measurement.  

This year’s study also suggests that Leaders use more complex data. They leverage data from an average of nine different sources to perform their analytics. Image, Video, Speech data from call centre recordings, in-store video, customer emails, phone calls and survey comments are the different sources that Leaders make use of.  

The findings of the 2018 Forrester report on the State of Customer Analytics could serve as a solid benchmark for you to build your company’s analytics framework. There is always scope for every organization to do better and earn a deeper understanding of their customer, whether you are a Leader or a Laggard.  

Some of the methods suggested in the report, that will help CI pros hone their analytics maturity are:  

  1. Understanding current maturity level- Determining whether one would qualify as a Leader, a Follower or a Laggard and then diving deeper to understand the number of data sources used, types of analytics performed etc., is a good start.  
  2. Partnering with analytical service providers- This will help firms in kickstarting their analytics projects as well as with training nascent teams in best practices.  
  3. Identify and prioritize insights projects: Take a close look at your company to see which areas demand insights and which don’t. Next, prioritize projects that need attention. In optimizing each interaction with the customer at every touchpoint, companies deliver on their ‘Customer-first’ promise. This activity also helps bridge the inter-departmental gaps and silos, which as we just saw can hinder the success of analytics and measurement projects.