Customer experience is becoming a CMO responsibility, and the top marketing channels are those that lend themselves to personalizing that experience through effective use of data analytics and insights. In fact, customers have increasingly high expectations of brands regarding the value they are getting from sharing their data.

According to Aimia’s 2016 Loyalty Lens study, 61 per cent of consumers expect better service and individualized offers in return for sharing personal details. Furthermore, 51 per cent of consumers get annoyed when companies don’t use what they know about them to offer better, personalized products and services, and 56 per cent have taken steps to limit brands from tracking and advertising to them online.

CMOs and their teams are challenged to find ways to identify and supply their organizations with useful insights from data, to enable fast and informed business decisions. However, sometimes the flow of data can be overwhelming if you’re not looking at the right data sets. The marketing industry is on a journey to create a 360-degree view of the customer, leading to data-overload for many CMOs.

There are three areas where the smart CMO can avoid an overload on data:

1.Make sure you identify the question you are trying to answer
Since the advent of Big Data, many companies have spent too much time focusing on new datasets and looking for what value they can derive from the data. This can be the wrong way to go about it. You need to approach it first by identifying the question you are trying to answer, and then prioritizing the data sets that will add the most value. For example, Aimia owns and operates Aeroplan, Canada’s premier coalition loyalty program. To better serve Aeroplan’s millions of members and its growing network of more than 75 partners and 150 brands, we have focused on defining the customer lifecycle, creating more than 250 customer lifecycle touchpoints. By starting with the question, you can then avoid ingesting data “for the sake of it”.
 
2.Not all data is equal
It’s important to first list out the data sets by value priority.  Aeroplan has driven significant value from focusing on the key transactional datasets around points accumulation and redemption. When looking at other datasets, we have prioritized which data will add the most value in order. For example, while something like geo-location data is a hot topic in the industry, it is not the most important dataset for many of the questions we are trying to solve. In developing lifecycle management programs, we have prioritized understanding our customer based on who they are and how they like to receive their offers ahead of knowing the place for the offers.
 
3. Invest in analytical resources
You need a team of people who know what they are doing to support you. There is no point in having all this data at your fingertips if you don’t have the people who know how to use it – an analytics team and a data team. The analytics team makes sure you can see the trees from the forest of data – and improve business decision-making through the adoption of data throughout an organization.


Mike Poyser is ​Vice President, Analytics, Americas Coalition at Aimia.
 

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