19 April, 2017
Since 2013, analysing consumer behaviour with In-Store Analytics’ technology has rapidly become more sophisticated in brick-and-mortar.
According to CB Insights, from the second term of 2013 to the second term of 2015, a huge number of operations to fund companies that develop and sell software for retailers were executed. The biggest investments were done to start-ups of the In-Store Analytics sector, representing a 64% and equating to $280 million. Afterwards, investments have continued to increase but at a slower pace.
The big expectation on this sector is explained by different variables, but the main one is the e-commerce boom. On the one hand, people started buying on a large scale through the internet, forcing retailers to understand these new consumers. On the other hand, it was necessary to analyse in a different way the purchasing process, with tools such as Google Analytics. As a matter of fact, Counterest and a lot of the start-ups we mentioned before declare ourselves as the Google Analytics of the physical world.
Nowadays, implementing technology to know consumers’ behaviour in physical stores is a reality. In the 2014 Brickstream Global Retailer Survey “Retail Analytics: what’s in store?”, 71% of respondents said they were planning to use People counting by 2015, while 68% said they would introduce in-store Wi-Fi and loyalty systems.
But, although technology is widely implemented, only 33% of the retailers consider themselves highly data-driven when it comes to making decisions, according to “PWC 2016 Global Data and Analytics Survey” carried out to 2.100 managers. And, even though 90% of respondents use data at different levels, when it is time to take decisions, they still rely more on human judgement than machine algorithms. Moreover, the data is mainly used for diagnostic purposes, followed by a descriptive usage, what means it is limited for understanding the past.
Thus, even though In-store Analytics technology to understand shoppers in brick-and-mortar exists; and has been improved during the past few years, partially thanks to investors’ trust, it seems like there still is a long way to go for retailers to incorporate these tools daily and exploit their full potential.
A plausible explanation is the one obtained from the EMC Corporation report, “Information Generation”. This suggests that despite all information businesses have access to, they struggle to sort through all the data to discover solutions. Particularly, the report pointed out that 38% of the 3.600 respondents answered that the abundance of information available to their business was helpful, but they were struggling to determine “actionable results” from this data. Moreover, 14% also described the abundance of data as “information overload” and that it was difficult for the company to even make any decisions.
We can say In-store Analytics solutions are evolving to an avalanche of information, which becomes difficult to assimilate by retail professionals. Data is accessible and provided to retailers, and now the challenge is to treat it so it is beneficial and provides insights. Somehow, it is important to distinguish what indicators can be considered Key Performance Indicators and what is the detail level of information needed for each situation and user. So, it is essential, retailers shift from collecting data they hope will be valuable to collect data that they know is useful and will help them improve operations by impacting their performance.
For these reasons, we believe that what is important is to provide insights and predictions to retailers. They already have a lot of data, but the key is to keep in mind that not all interesting data can be used to understand customers, so avoiding noise and having insights is what will help them improve their performance.