The Crystal Ball – Exploring a Walmart POS Seasonal Forecast #CPGBI

One of the most common uses of machine learning in analytics is to forecast time based data. It’s the quintessential sales question – what will my sales look like next month, or next quarter, or next year even – the proverbial crystal ball, if only it were that simple. Something that we were fairly quickly put together using MicroStrategy’s visual insights and R-Integration is an “Ordinary Least Squares” regression algorithm to fit the best curve that captures the general trend and seasonal variability of Walmart POS data to predict future sales.

The formula is:

Y = bTrend*Trend + Σ (bSeason_i*Seasoni) + bIntercept


  • Y is a numeric metric (called the Dependent Variable)
  • Trend is a numeric metric that’s an arithmetic sequence of monotonically increasing values
  • Seasoni is a binary indicator metric derived from Season, a numeric or string metric that represents each season. Binary indicators have a value of 1 for the i-th season and are 0 for all other seasons. For n seasons, there are n-1 XSeason_i variables
  • bTrend, bSeason_i, and bIntercept are coefficients determined by the regression algorithm.

2015-09-28 09_32_23-WM POS Seasonal Monthly Forecast. MicroStrategy 9

As sales drop in for the coming months, we should be able to gauge the accuracy of our prediction for the rest of the year. If this hold true, we could use it for some of our business decisions going forward. We could also look at just the latest complete months, so we would not see that monthly drop in month 201402. We could also look at this weekly by switching out just a couple of metrics.

Something else we could do is create a variance against actual POS sales, and if the variance exceeds some number, like 10% difference plus or minus, we could create an alert and send out warning emails to key people in our business so that they can plan for unanticipated high sales, or research a drop in sales.

Please contact us to see how we can help you leverage regression analysis with your data to help predict your future!

Business Intelligence vs Analytics vs Big Data vs Data Mining #CPGBI

The business intelligence landscape is rapidly changing, and there is a lot of confusion on what the difference between BI, Analytics, Big Data, and Data Mining is. Whats more, you turn your head for just a minute and then there are whole new classes of terms that you’ve never even heard of before.

In the below article from Dennis Junk at Aptera’s blog, he breaks it down into four main categories to help you understand. As a CPG company supporting Walmart, I believe it is important to have a strategy for all of these concepts – especially in the wake of their new supplier terms and squeezing they are employing. If you don’t, we can help you with that, as we are a full service Business Intelligence company that can deliver Analytics from Big Data sources and use the R statistical package to mine the data for meaningful insights.

Business Intelligence:

This is the broadest category and encompasses the other three terms here (at least as they’re used in a business IT context). BI is data-driven decision-making. It includes the generation, aggregation, analysis, and visualization of data to inform and facilitate business management and strategizing. All the other terms refer to some aspect of how information is gathered or crunched, while BI goes beyond the data to include what business leaders actually do with the insights they glean from it. BI therefore is not strictly technological; it involves the processes and procedures that support data collection, sharing, and reporting, all in the service of making better decisions. One of the trends in recent years has been away from systems that rely on IT staff to provide reports and graphs for decision-makers toward what’s called self-service BI—tools that allow business users to generate their own reports and visualizations to share with colleagues and help everyone choose what course to take.


This is all the ways you can break down the data, assess trends over time, and compare one sector or measurement to another. It can also include the various ways the data is visualized to make the trends and relationships intuitive at a glance. If BI is about making decisions, analytics is about asking questions: How did sales for the new model compare to sales for the old one last month? How did one salesperson do compared to another? Are certain products selling better in certain locations? You can even ask questions about the future with systems that perform Predictive Analytics. Some companies treat analytics and BI as synonymous—or simply rely on one to the exclusion of the other. But analytics is generally the data crunching, question-answering phase leading up to the decision-making phase in the overall Business Intelligence process.

Big Data:

This is the technology that stores and processes data from sources both internal and external to your company. Big Data usually refers to the immense volumes of data available online and in the cloud, which requires ever more computing power to gather and analyze. Because the sources are so diverse, the data is often completely raw and unstructured. Since you’ll probably be using this data for purposes it wasn’t originally intended to serve, you’ll have to clean it up a bit before you can garner any useful insights from it. The systems you put in place internally to track KPIs are obviously the main source you turn to when you need to answer a question about your business, but Big Data makes available almost limitless amounts of information you can sift through for insights related to your industry, your business, your prospective customers. Big Data is the library you visit when the information to answer your questions isn’t readily at hand. And like a real library it allows you to look for answers to questions you didn’t even know you had.

Data Mining:

Finding answers you didn’t know you were looking for beforehand is what Data Mining is all about. With so much information available, you can never be sure you’re not overlooking some key fact pointing the way to better performance. Data Mining is the practice of sifting through all the evidence in search of previously unrecognized patterns. Some companies are even hiring Data Scientists, experts in statistics and computer science who know all the tricks for finding the signals hidden in the noise. Data Mining probably fits within the category of analytics, but most analytics is based on data from systems set up to track known KPIs—so it’s usually more measuring than mining.

Not everyone will agree on these terms, as Dennis points out in his article, but it’s a good start. As a core strategy I believe your BI should encompass all three: easy to use analytics that allows your users to ask their own questions, big data to capture MORE than just sales data, and data mining so that you can leverage all of your data for the best insights possible.

Please contact us to see how we can help you create a strategy in all of these areas that might unlock a competitive advantage you didn’t know existed!