Just over a week ago we welcomed some of Canada’s best-known retailers to our Tango Leadership Series Roadshow. We launched the Tango Leadership Series last year as a way for real estate and store development executives to get together and discuss topics and trends that are dominating the industry.
Our latest session, The Changing Face of Retail Customer and Location Predictive Analytics, was held in Toronto, and featured of panel of industry leaders including Bruce Mooney from
Loblaw Companies, Chris Curry from Canadian Tire and Tony Hernandez from Ryerson University. Tango’s Pranav Tyagi and Paul Thompson also joined in the lively discussion on data, modeling techniques and innovations in tools.
A few major themes emerged:
- There has been an explosion in the availability of data, which has created significant opportunities for retailers.
- Modeling techniques remain largely the same and are now tasked with trying to deal with all the data noise.
- A new skill set is required to manage and make sense of all this data.
With the dramatic increase in the number and variety of data sets available, as well as the speed at which new data opportunities are emerging, panelists agreed that it is challenging to understand what you are looking at and to really understand the relevance of the information. Retailers need time to diagnose what the data is saying, to see trends and act on the information. Additionally, evolving forms of retailing have produced new data sets that are presenting unique challenges– as Tony Hernandez said “Omnichannel is the wild west of data.” When dealing with new data sets, in general retailers have to assume they will be less efficient at the start, but with time and increased focus, will become more efficient in their data management and processing.
Modeling is now faced with coming up with new ways of solving the same problem. One of the main challenges with modeling is that the shelf-life of data is so much shorter now – with a constant stream of new and more relevant information, models may have to be recalibrated more often.
Additionally, with all these new data sets, retailers are faced with the challenge of how to actually take advantage of these data advances. McKinsey Global Institute estimates that by 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions. During the discussion, Pranav Tyagi addressed this issue and explained that the traditional geospatial analytics skill set is not necessarily suited to manage new data types, and that it will need to be augmented with new analytical capabilities and experience in order to meet the opportunity presented by new data sources. These new sources of data will take a “hybrid data scientist type”– an individual who possesses both GIS and a “mash-up” of computer science, modeling, statistics, analytics and math skills.
Some universities are addressing this by developing new curriculum for this role, combining database management with geospatial, statistical and computer science skills. However, one of the challenges that students face is not knowing what is ahead for them. With such a new and rapidly changing field, there isn’t an active set of role models and it’s hard to understand their career trajectory.
The session ended on a high note, with the panelists agreeing that the tools now available are doing a good job at addressing this new world of data – and ensuring that it is leveraged effectively to help retailers make key decisions cross the entire real estate and store development lifecycle.
The lively, engaged discussion during and after the session showcases the importance of retailers getting together to discuss areas of common concern and opportunity. Stay tuned for information on our next Tango Leadership Series session later in this year. To learn more about how Tango leverages data to help retailers make smarter real estate and store development decisions, download our Data datasheet below.
