By: Helena Schwenk, Principal Analyst, MWD Advisors
Published: 1st August 2013
This work is licensed under a Creative Commons License
One of the most enjoyable parts of my work at MWD involves working with enterprises on ‘best practice insight’ case study reports (an example of which can be found here). These reports are designed to highlight key best practices, maturity and perceptions amongst enterprises in their customer analytics initiatives. Having completed a few already this year, I thought it would be worth reflecting back and in particular highlighting the similarities, key ingredients and factors that can impact analytics success. None of these may necessarily be a big shock to you but nonetheless it’s worth underlying the role they play in helping organisations drive value from their data and analytics investments.
Here are my top five ingredients.
Data: In the organisations I am speaking to, being able to manage and exploit expanding data volumes—particularly those emanating from digital channels—is a core focus area for their customer analytic programs. One retailer is finding that by incorporating granular web session data alongside offline data within a centralised data warehouse, it’s able to develop a more accurate and richer view of customer activity across its digital channels, used as a basis for improving the online customer experience. As we know, paying attention to the management and maintenance of a reliable and trustworthy data foundation is fundamental to customer analytics success since good analytics are nothing without good data.
People: Finding and harnessing the right skills is a common trait amongst the organisations I’m speaking to. The difficulty for many of them centres on getting the right mix of skills to enable customer insight functions to explore, analyse and find insights within their data and, more importantly, align them with a particular business problem or opportunity. Not surprisingly many find these individuals in scarce supply and hence are looking for other ways of sourcing analytical talent. As an example, an insurance company I’ve been speaking to found success by bringing together a multi-discipline team with both business and technical skills recruited both internally and externally that collaborated to drive value from its data and analytic investments. The team combined offline and online business domain expertise; other industry best practice; A/B and multivariate testing; and coding skills that in unison could be utilised to work on projects for improving the relevancy of its website content for online visitors.
Process: Another key ingredient prevalent in the organisations I am talking to is their shared ability to manage analytics as an on-going process. What these organisations have established is an understanding of what to do with the insights they are generating and how, where and when they should apply those insights to help support and guide operational decision making processes. This isn’t something they have taken for granted; it is a multi-faceted challenge that involves integrating the insights generated into business process, systems and applications to help maximise the impact analytics has in the organisation. Furthermore, these organisations have looked further than just technical process integration. They have extended this to look at the impact on organisational structures, roles and resources. One telco we have been speaking to, for example, views their customer base management initiative as a change management program rather than just a technology implementation program; one that involved them re-thinking how they were structured and aligned organisationally, and how using analytical insights would impact employees and the way they worked.
Technology: Of course you’d expect to see technology as a key ingredient here. Given that many organisations we speak with are now starting to frame their customer analytics initiatives alongside their ability to harness and exploit Big Data, it’s not surprising to see that scale and speed are common data management technology requirements quoted by organisations. Similarly utilising data capture and integration capabilities that enable them to bring together data—especially data from online and offline channels—is high up on the list of technology priorities. This in turn is also underlined by a need to have a responsive, agile and robust analytics environment that can integrate with and exploit their expanding data infrastructure.
And finally, the last ingredient focuses on having the right culture and mindset. It’s become apparent through my research that organisations successfully driving value through their data and customer analytics initiatives have a strong ethos and culture built around the use of analytics. More often than not they see analytics as a strategic consideration that can not only bring about organisational efficiencies, but also drive growth and innovation. In other words they see analytics as a way of managing the company’s operations, growth and value and, in doing so, place it as a key element in their overall corporate strategy. Above all these organisations possess the right level of buy-in, commitment and sponsorship to make their customer-focused initiatives succeed, they are comfortable with making fact-based decisions (rather than relying on gut instinct), they are able to collaborate across the business and they are able to effectively communicate the benefits and impact analytics is having on their business.
As with any recipe, getting the right blend and balance of ingredients is critical for success and this certainly holds true for customer analytics projects. However as always I’m really interested to hear from you and understand what you think—are there ingredients you would add or remove from this mix, or do you have an alternative recipe?
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