BIG Data, BIG Deal

Everywhere we go, it’s there. Big data. In some form or another. And in every business sector it’s there. Let’s add machine learning, artificial intelligence, Internet of things, omnichannel, and digital to the list. It is not going away. And that’s good. Why? Well, mostly it’s because I work in the retail industry like many of you reading this and there are yottabytes of data. Knowing that is a good thing but finding it and knowing what to do with it are two different things.

Challenge One: FINDING IT

Literally, finding the data I need really shouldn’t be that difficult because really all I need to do is call my IT

professional and tell them.  Ok, I am oversimplifying here but let’s assume you are interested in setting up a data hub philosophy in your organization whereby a user-friendly hub acts as a repository for all of your user’s data needs. The users should be able to query, analyze, drill, correlate, export, report…etc. from that hub. So I really need the right technology to harness all of this data.


The data that I need has to be associated to the problem I am trying to solve. Yes, I want a data hub. But what immediate problem am I trying to solve? Well, like most retailers these days I want to increase my sales, but as a first step, I want to increase my comp stores sales. I need data that will help me do that! Seems like a very daunting prospect. I mean, if I could just tell you what data you need so that you could increase your sales (and then how to do it), one of two things would happen to this blog posting: It would either 1) Get laughed at because if we all knew the ingredients to increase sales we all would be doing it or 2) This posting would go viral



It may seem obvious but don’t want to assume this, to start to understand the sales implications of an organization we must begin by analyzing the data elements over the past 2-3 years available to us through Point of Sale (POS). Most data hubs available to retailers today are capable of not only consuming these data elements but also marrying them to hierarchies and other data sources such as an organizational or product hierarchy and an item master feed. This will be critical in the both the aggregation of this data as well as the

details that sit behind it. But let’s explore many of the crucial data elements available in POS and why we want to start there (available but not limited to, depending upon POS system):


The dollars, transaction counts, and item counts of the following attributes: store sales, item sales, customer sales, cashier sales, department sales, register/terminal sales, all tender type sales, layaway sales, personnel sales, promotional sales, new item sales, exit item sales, scan sales, keyed sales, refunds, exchanges, line voids, error corrects, transaction voids, post voids, store coupons, electronic coupons, manufacturer coupons, price modifies, transaction discounts, item discounts, tax exempt, training mode, suspend transactions, resume transactions, and manager override transactions.


POS data elements such as the ones listed above are really all that is needed to do a complete sales analysis for a retail organization especially comp store sales year over year. And, this is the only data that you will need to



tell your leadership what it will take to increase your sales without purchasing new inventory, cutting in new items, creating promo specials, or discounting prices. Really?? Really.


Challenge Two: WHAT TO DO WITH IT

This is really where the proverbial rubber hits the road. We have established that the data we need can be found in POS. Part two of our earlier proclamation was to discuss what to do with the data once we found it and this is where data analytics and data science comes in to play. And this is where you call the professionals at tSCG. We will take you through the process of:

  • Developing key comparable data relationships (KPI-key performance indicators and SRA-sales reducing activities)
  • Building variance indexes from KPI and SRA results
  • Correlating KPI and SRA data relationships
  • Benchmarking critical KPI and SRA across various hierarchies
  • Establishing data relationship groupings and ranges
  • Develop visual representation of historical performance
  • Drive outlier strategies for improvement
  • Designing executive and performance reporting
  • Creating return on investment scorecards

These are just a few major components that tSCG will build, implement, measure, and teach to your organizations to improve overall comp store sales. And not only improve but to maintain moving forward. We pride ourselves in not only developing these strategies but also creating a mechanism to help organizations to sustain their performance.