BCNanalytics co-founder, Manuel Bruscas, was in charge of presenting the topic of the day “Analytics and Big Data in Retail”. He explained that in the US alone the retail industry generates $966 billion a year, almost 6% of their GDP. Retail is currently facing several challenges, some of which are the product of technological progress. Through online marketplaces and company websites consumers can now consider a wide range of product sources (online and offline). This has directly led into deep changes within Retail, especially for companies with an offline presence, since they must quickly adapt to changing trends, for example the drop in footfall for physical stores. Technology has also empowered consumers to be more informed than ever, which means that competition will only increase. Therefore companies that wish to remain relevant must learn how to get the most out of their online and/or offline presence through an integrated data-driven strategy.
Jaume Portell (CEO of Beabloo)
Beabloo started in 2008 with the mission of creating new ways of communication between consumers and service providers (e.g. retail companies, government offices, theme parks, etc.). Jaume started his talk with a reflection of how in the early 2000s ecommerce, and the troves of data it was generating, started changing the landscape for physical retail. It was that realization that led Jaume and his colleagues to take action and found Beabloo.
He then took the topic back to present day, where after more than a decade of online growth we are seeing a shift back into offline. A perfect example of this is Amazon, which is now opening physical stores and signaling that this is a real trend, which is being fueled by the fact that still most of transactions are happening outside the web. To fully understand why offline continues to dominate the landscape, it is necessary to understand the customer. Data is the instrument through which we must connect both the online and offline worlds.
He made it a recurrent point that humans have a general need for physical stimulus. While we might not always need it, there will always be occasions in which we will want to see, feel, smell, taste and hear the products we want to buy before we purchase them. This is visible in the statistics of offline and online transactions. After twenty years of e-commerce evolution, still today 90% of transactions in the US and Europe happen offline. This is a gap that the web will hardly ever breach and therefore we need to adapt to it.
His proposed solution starts by recognizing that both worlds are not actually separated, rather they need to be highly integrated and coordinated to ensure that the most value is derived from them. These days technology is adding new capabilities to allow companies to better relate online and offline such as: using smartphones to understand how customers are navigating both worlds and creating campaigns to generate cross-environment awareness. The result of this integrated approach is an abundance of data, processes and metrics that can only be managed by the development of new platforms.
To show what he meant, he shared with us an example which covered the NBA stores in China. Beabloo was hired to solve two problems, the first was related to inventory and how to maximize their physical stores offer while minimizing expenses. The second problem was to develop a strategy of communication to customers visiting the stores. They proposed and implemented a solution that actually attacked both problems which was to develop a kiosk that allows customers to browse information of the store and develop customized products. Through the kiosk the stores offered and almost unlimited stock of products and by tying it to Beabloo’s platform they could have a cohesive communication at a customer level, which would translate into personal promotions, offers or information.
Víctor Martínez de Albeniz
(Professor of Production, Technology and Operations Management at IESE)
Víctor began by reminding us of how difficult the competitive landscape is for Retail. There is a huge level of complexity that companies need to manage that go across many of the departments of a company (communications, design, production, logistics, partner relationships, etc.). Another reality of Retail is the volatility of a product lifespan and the implications that has on planning and investment. If you don’t plan for enough you miss out, but if you over-forecast then you end with unused stock, also known as losses. Therefore, how can Retail companies manage this tough environment? His answer is: use data.
The good news (also, old news) for Retailers is that a lot of data is readily available through the cash register. This will already give them a very good amount of insight into what product is sold and in which quantities. Yet nowadays Retailers have additional data sources that can allow them to get data not just on what they sold, but also on what products caught their customers’ attention but ultimately did not sell. One method he suggested for getting this data is through the use of cameras to track how customers move through your stores. Also wifi can be used to map how customers are moving in a store and how they traverse your physical space. He developed on these methodologies by highlighting that while these systems are anonymous, they still allow for anonymous-CRM which can create powerful offers. For this to actually happen, all these multiple data sources need to be tied up with your internal data sources to create a cohesive customer data ecosystem. That is not an easy problem, but a huge opportunity for those that develop a way to do this with scale.
He then gave an example of an analysis his team is involved in, which is measuring the impact of weather on sales for street and mall stores. This analysis can go to different levels, but starts at overall sales and then moves into individual product groups. As it turns out weather has a measurable effect on how sales will go on a daily level as well as on the type of products that will move. Knowing this can be used to maximize the revenue and sales.
To finalize his talk he gave a second example on how clickstream data can be used to find the optimal pricing for new products. The methodology he proposed is mostly applicable to companies that have a lot of clickstream-data available and would rely on measuring through a test what are the clickstream results for a new product compared to a baseline drawn from the launch of similar products. Through this method, pricing can be set, not blindly, but rather with a good initial insight of where it should stand.
Nick Brittain (Director of Strategic Relationships at First Insight)
First Insight is a company that provides insights to Retailers. The main problem that they try to solve is how should Retailers plan for sales of new products (meaning expected sales volumes by price). He explained how this is an incredibly difficult question to answer, but through analytics Retailers can go a long way.
Nick explained how First Insight is using the “wisdom of the crowd” to help their customers. “Wisdom of the crowd” can be described as independently asking a group of people a question, then translating those answers into a quantitative measure and finally aggregating all the response data to use the resulting distribution as an estimate. As he explained, this methodology relies on a frequentist analysis of the data, that needs to account for several levels of complexity like how to determine a sufficient sample, how to weigh different answers according to what you know of the respondents (i.e. it may be a good idea to weight a happy customer different than an unhappy customer). He continued discussing the complexity of their methodology since analytics is not the only challenge that they face. A big part of the problem is how to collect data in a way that would not bias the customers. While the final challenge is how to visualize the data in a way that allows for the insights to be extracted.
His final point was to highlight how through a methodology like this one, you get the advantage of testing the results from the predictions. This result validation allows for continuous improvement to every step of the funnel, which can lead to very reliable estimates.