Covering Disruptive Technology Powering Business in The Digital Age

This article was originally published by retailcustomerexperience.com and can be viewed in full here

As 2015 came to a close there was a crescendo of news on Big Data and data collection and analytics in the retail environment. Not only does data help retailers determine everything from traffic patterns to inventory movement and consumer activity in the store and online, it plays a pivotal role in marketing, operations and pretty much any strategic move a retailer takes.

The good news is that most retailers are, and have been for years, collecting data. The bad news is that much of that data are not being optimized, and the use of mobile devices within the retail scenario is piling on more data.

Getting a grip on data management and analytics and discovering how to eek out as much value and insight is a critical task and for retailers getting started it can be overwhelming. For retailers already moving forward there are likely some challenges still remaining and optimizing such a critical strategy is something that needs to be done on a consistent basis.

So Retail Customer Experience reached out to a data analysis company, Algebraix Data, to talk about why it’s such a crucial strategy, how to get a good strategy in place and some missteps to avoid. Here’s valuable insight from Peter Chen, director of data science. Chen has more than 15 years of experience, primarily in data science, business analytics, statistical modeling, forecasting, and quantitative and risk analysis at companies such as Mitchell International and Petco. He leads the Algebraix Analytics team, providing data science as a service to help retailers achieve the benefits of advanced analytics without the learning curve or upfront technology investment.

RCE: What are the three reasons retailers must embrace analytics?

Chen: In a nutshell:

1. Customer Analytics: To better understand consumer behavior, retailers can use analytics to improve customer acquisition and retention. Plus, by segmenting target customers, analytics can provide tailor-made offerings per individual.
2. Marketing Analytics: With advanced analytics, retailers can optimize multi-channel campaign effectiveness, market spending, and recommend next best offers according to customer profiles.
3. Demand & Supply Chain: Retailers can use demand forecasting to optimize supply chain, store stocking and determine cannibalization effects. Additionally, analytics can help with inventory planning and replenishment analysis.

RCE: Big Data and just plain data often just seem overwhelming for retailers, big and small. What planning or strategy first steps do you recommend to get them moving and on a good track?

Chen: Big Data, like any new technology, requires careful planning and expertise. Before investing in shiny new Big Data technologies, retailers must understand that advanced analytics can readily use existing data infrastructures or standard relational databases, without the need of new Big Data technologies (e.g. like Hadoop). Big Data technologies come in handy when retailers want to integrate all sorts of data beyond just transactional. The advice is fitting the technology to the business problem at hand rather than dive into Big Data without a business case justification for its costly implementations.

RCE: What are some “no-no’s” or missteps that retailers tend to make with data analytics?

Chen: A common misstep is not planning ahead for the data analytics initiatives. Advanced analytics requires owners and executives to champion, support and socialize the power of analytics, and operationalize the resulting insights. Secondly, advanced data analytics requires a team of specialists that include data analysts, data engineers, and data scientists to execute on the analytics project. Unless the retailer already has such a team in place, this can be quite a substantial investment in human capital. Let’s not forget the investments in hardware and software to build and run these advanced models.

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