A study released by Deloitte Canada a few months ago showed that 82% of more than 300 CMOs they surveyed have been asked to interpret consumer analytics data and admitted that they felt unqualified to do so based on previous experience. This is a problem made bigger by the fact that 70% of respondents in the study said their companies expect marketing decisions to be based on analytics and that they are expected to treat data as the “voice of the customer.” It is no wonder, then, that almost 70% of the same respondents reported that they expect agencies to play a key role helping them analyze and interpret data.
How does this relate to IoT analytics? If consumer analytics data provides insight into how consumers feel about a company and its products, IoT analytics data provides insight into how consumers use the products themselves.
The combination of this data provides greater insight than was possible before. For instance, if social media analysis rates a certain product one way but IoT data analysis refutes it, the gap is a revenue opportunity. To use a specific example, the Chicago Cubs lost 101 games in 2012. The operational data is irrefutable: This is a product that could use some fixing. But they drew almost 2.9 million fans that year. The social data is also impossible to ignore: The Cubs are lovable losers and can therefore still sell seats. The gap between those two indicators is revenue – they sold more seats than a normalized correlative model keyed to performance might predict. The lesson here for a product company is that consumer analytics data does not necessarily accurately correlate to product, which does not necessarily correlate to future sales because it does not necessarily fully inform the effectiveness of a campaign.
For this reason among many others, one of the biggest consumers of IoT data is the marketing department. One of the key roles of marketing is about to become considerably more technical than it was before: Marketers – and agencies – will need to understand how to analyze and interpret data analytics from multiple sources and synthesize them into a cohesive strategy.
A media strategy uninformed by data today is not a strategy at all, and a media strategy for an IoT company that doesn’t take IoT data into account is missing the most important component. The good news is that adopting a tools-based approach to do the heavy lifting of gathering and analyzing IoT and other data makes the marketer’s task considerably easier.
Perhaps the biggest opportunity at the intersection of marketing and IoT analytics is for the massive community of agencies to reinvent itself as technologically-savvy, not just on the media deployment side of the equation, but also on the input side. If you asked the average CMO what they use agencies for, not many would say data analytics. But that sentiment is going to change when IoT companies driven by IoT data look to the marketing department to fulfill the role they have always had; as an integrative function between product, finance and customers.
Give it another year and the average CMO is going to expect data-driven insights from the agencies they hire to satisfy their organization’s needs. Understanding the importance of IoT Analytics in the company value chain will help position the winning companies of tomorrow to communicate the importance of IoT data in the marketing value chain.