The Importance of Analytical Marketing Strategies

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We all know the marketing world is becoming more data driven, and customer experience (CX) is no exception. In fact, you might be tempted to think data-driven CX is the norm.

However, that might not be the case.

In a recent survey of global customer experience leaders, just over 40% of them were using customer data and insights to improve contact center routing, marketing personalization and omni-channel marketing analytics. This begs the question: why aren’t CX leaders using data and analytics to optimize their efforts?

Our survey shows a few reasons that stand out.

Lack of Talent

Thirty-five percent of all respondents stated that finding relevant insights in a sea of big data remains a struggle. This challenge ultimately speaks to the scarcity and expense of data science talent in today’s marketplace. But there is hope: there are a growing number of tools that empower the so-called “citizen data scientist.” At the recent Tableau conference, Clarity Insights unveiled a solution that blends three technologies to allow almost any marketer to build a marketing analytics model.

Lack of Direction

A bigger issue may be actually turning data into actionable insights, something which almost half (48%) of our CX leaders said was a challenge. In our experience, this results from two factors:

  • A data and analytics program that is not aligned with the business
  • A culture that is not data-centric

The first can occur when data and analytics are not aligned properly with the business units in a company, often reporting into IT without incentives to act as an internal service provider. As a result, resulting analysis may not be answering questions the business is asking (or perhaps not as quickly as the business needs). Another issue may be data accuracy. Our experience shows that taking an agile approach to data and analytics—where a business stakeholder (e.g. a marketing director) acting as product owner—throughout the process is one way to solve this.

Culture is harder, but not impossible, to overcome. It’s also a prevalent challenge: in our survey only 38% of respondents said their culture was data-centric. Often this is because it’s too easy to “overrule” the data with intuition and professed experience. Creating a data-driven culture means undertaking the change management processes seen in other transformations: aligning incentives, communicating, training, and celebrating wins. At Clarity Insights we embed change management as part of our process to specifically overcome this issue.

What about Big Data?

Another reason CX leaders are not using the full potential of their data may be connected to attitudes and use of so called “big data.” Big data refers to the set of technologies that enable huge data sets to be analyzed all at once, originally created by Facebook and Google to analyze their vast stores of customer information. These tools now enable any company to integrate previously siloed data sets and perform advanced analytics on them in near real time.

Even though these capabilities have been out in the market for some time, 52% of the CX leaders we surveyed do not use big data technologies and 13% of those that do have been doing so for less than a year. This is despite the fact that 35% of those surveyed cite data silos as being one of the biggest challenges hampering CX executives—exactly the kind of issue big data is good at solving.  One reason for this could be that the concept of “big data” has received a fair amount of hype but has not always generated the returns people expected.

Our survey found suggestions of this phenomena. Even those that do use big data are not that happy with it: 56% of those surveyed say they are somewhat or very dissatisfied with the quality of their data and analytics. Why might this be?

In our experience, big data instances can be rolled out without sufficient planning. A team may decide to implement big data, but without a roadmap, clear goals tied to business needs or proper data governance and security considerations, the project will fail to meet expectations. In these situations, big data becomes a big expense and pain.

In conclusion, more customer experience could be leveraging data to improve performance by, increasing personalization or improving cross-sell. However, our survey results show this is not easy. That being said, the problems are surmountable.  Between tools for creating the “citizen data scientist,” building a data-driven culture that addresses real business needs and a big data instance that is properly built and maintained are straightforward, there are practical ways to make data- and in particular big data – work.

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