Career Boosters is a monthly e-panel discussion led by Boost Agents. We scout leaders in the marketing, digital, communications and advertising spaces to provide their perspectives on industry topics related to career development, talent acquisition and hiring practices.
For this feature, we spoke to Rushabh Gudka, product manager at Gallop Labs and Paul Raso, associate marketing analytics manager at Boston Pizza about how the world of data and analytics is permanently changing the marketing industry.
In recent years, organizations have put efforts towards finding unique ways to derive value from vast amounts of previously untapped data. What are some unexpected ways in which we will see companies monetizing data?
Gudka: There is certainly a growing challenge many organizations will face as we see greater balkanization of data and services amongst the major internet companies. This one is hard to predict simply because we may, rationally or irrationally, run into privacy issues as we try new ways to monetize data. That said, I think there’s still a lot of runway in using data to build better behavioural insights of your customer base. In effect, we’re filliping the story around instead of asking “who’s valuable to us” we’re going to start asking “who’s going to value my product the most.”
Raso: Organizations will continue to combine technology with analytics to increase the value associated with data. Leveraging technology has enabled companies to not only access untapped data sources, but also create new data sources. Banks are beginning to look beyond demographic information and more towards the psychographic behaviours of their clients to sell products, and retailers are understanding why specific individuals make decisions and what might bring them back to their stores. Predictive analytics is also a hot topic — where retailers are monetizing data by suggesting relevant products and services to a customer, instead of waiting for the customer to come to them.
As analytics technology becomes more advanced and shifts towards automation, how will the role of analytics leaders in organizations change?
Gudka: While I don’t like making predictions about technology advancements, there’s something to be said about taking facts, figures, metrics, charts, etcetera and interpreting them in ways that “connect-the-dots.” If all you’re doing right now is pressing a button and generating a report for someone further up in the organization, you will need to reconsider how you can add value. A good analyst will understand their customer, understand the company, product strategy and most importantly understand how to communicate their findings to everyone within the organization to bring about change. Being good at interpretation means having a great deal of technical dexterity and communication skills to express it.
Raso: In the past, analytics roles incorporated more reporting than actual analytical work. Now that technology has helped us automate most of those processes, the role will be to provide thought leadership on what to analyze, how to analyze a project, which tools to use, and which metrics should be used for measurement. Ultimately, analytics professionals will become leaders in interpreting the results to make informed, strategic business decisions that are consistent with the goals and objectives of an organization.
Should the analytics function within large organizations always be centralized?
Gudka: Clearly there are some significant advantages in centralization — cost effectiveness of IT, a pool of analytical experts that can manage vast amounts of data and even help support organizations that simply don’t have the budget or need for a full time analyst. However, as I mentioned above, interpretation and the ownership of the analysis remain paramount. Centralized resources may not be able to get into the minutia of why one parameter in a data set matters so much, for example. This can have a considerable impact on the success of a project. Simply put, it’s difficult to understand the unknowns as it is, it’s even more difficult to understand the unknowns if you’re not a subject matter expert. Centralization shouldn’t be discounted altogether, done right, a hybrid or matrix approach can be highly beneficial.
With the technology and volume of rich data that is available, marketers can anticipate what consumers want better than ever before. What is your favourite example of data-driven marketing?
Raso: My favourite example is also one of the most common: Netflix. As an organization, they have put a strong importance on using data to suggest and create more relevant content for their viewers. When they decided to invest in the House of Cards original content series, it was all based on data they had collected from existing members, and repurposed to predict what people would like based on their past viewing habits. Without the use of primary research, they used correlation analysis and other techniques to help attract new customers and retain existing members.
