Based in Ottawa, canada, Tuesday Standard is a blog about consumer insights, digital measurement, online engagement and marketing.

How Retailers became Data Companies + How Amazon Humbled me as a Data Analyst

How Retailers became Data Companies + How Amazon Humbled me as a Data Analyst

With The Tuesday Standard, I have tried to focus on posts that were less in the weeds of analytics and insights, (i.e. how to manage tags in the data layer of your website), and more on the strategy around measurement, etc. This week will be a bit of a departure with the focus being on an experience I recently had that have put things in perspective. Or confused me even more.

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I recently had the opportunity to discover how analytics and insights are managed at one of NA’s largest online retailers (or maybe we just call them a retailer now). Through a series of meetings and research, it became clear that this was an organization where data and analytics were not just a department or team that filled requests from Marketing, Product Management, or Finance. Data was not just something that was checked during a decision-making process or reviewed for weekly meetings. The business had in fact figured out a way to turn data (behaviour, state of being information, responses to stimuli, purchasing decisions, transaction optimization, shipping information, etc.) into a business model, that by extension, happened to use online and bricks and mortar to delivery products and services.

This experience was quite humbling. My personal (work related) belief system has been built around this notion that my career, which went from marketer - to start-up founder focused on digital engagement - to insights and analytics professional measuring behaviour on one of Canada’s biggest websites - with a pit stop as an implementation analyst for Salesforce.com CRM, gave me confidence in saying I had some competence as an data analytics professional. Even before this was a big thing, working with data has been just something that came with the work I was doing.

I remember the point when I realized I quite enjoyed telling stories with data (cue flashback). It was while running Ideavibes. There I managed to win a small project from Kellogg’s in the UK. Yes – my little start-up managed to bag a large CPG company for a research exercise. When we were working through the project brief, I was told that what Kellogg’s was really looking for, were the stories that came out of the open innovation exercise. They were using crowdsourcing to hack breakfast.  When I built the Ideavibes crowd engagement platform, I had a notion of what data customers would want. Working with a large CPG company helped me realize how little I knew. With the help of my developer, we opened the data coming from the platform to the data coming from Google Analytics and had the ability to mash and filter the results for the express purpose of being able to tell new and interesting stories. 

Kellogg’s loved the work – the speed with which it could be carried out – and how my plucky little firm managed to not break their budget. I knew I should have charged them more. But I also figured out that data was going to be the underlying value proposition of almost anything I did from that point forward.

This seems like a lifetime ago. But I look at the level of competency many of us had, and that many of the tools available back then, and look at what is in front of me now and it is astonishing. The field of analytics and marketing has changed unbelievably over just the past 6 years. While there are disparities based on organization size and industry, data is driving all sectors of business, education, government, not-for-profits, entertainment and healthcare.

Spending time getting to understand how Amazon is positioned and evolved into a data company, was an interesting discovery. In fact, Amazon launches new divisions to respond to the opportunity around data. Amazon Web Services is a perfect example.

So ultimately, I still have much to learn about the practice of analytics and how to use data to help organizations make better decisions. There has been much in the news about the unethical use of analytics and data, particularly around elections. Certainly there have been lapses in judgement taking place and, referring to my previous articles on governance, can be dealt with through policy. That said, let’s not go overboard. There is still much happening that is providing consumers with customized online experiences that are a benefit to those that want a focused interaction with a brand, store, etc. The pendulum will go back and forth on this issue and time will tell how consumers and policy makers end. It will be interesting to see how other retailers make that shift to data centric organizations – similar to what we see with companies like Amazon.

How Canadians Search for Real Estate - 2017 REALTOR.ca Insights Report

How Canadians Search for Real Estate - 2017 REALTOR.ca Insights Report

Pattern Theory and Effective Analytics Programs

Pattern Theory and Effective Analytics Programs