Year after year the hype surrounding data analytics becomes louder. Thought leaders and research organizations have sung the praises of analytics as a means for generating much-needed insight into business operations, and companies that have embraced analytics have been able to translate insight to better operational productivity and faster, more accurate decision-making. In a competitive business environment where your competition is just as hungry as you are to reach and secure new customers and where business leaders need to make accurate, fact-based decisions about the company’s future, analytics can be the game-changer that makes the difference between success and failure. Here’s how:
The beauty of analytics is that it can serve as a guideline for transforming sub-par business performance to one that is efficient and profitable. Whereas reports provide a historical view of what transpired, analytic output is forward-looking, and plays an integral role in helping executives plan for the future.
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People do crazy things, especially during the holiday season. On Black Friday – and even on Thanksgiving evening – customers will wait in line for hours to grab the best deals and knock items off their wish list. Retailers offer ridiculous discounts on high-priced items and keep doors open 3 days straight to cater to buyers around the clock, then web retailers kick in their own Cyber Monday promotions. Black Friday has remained the number one shopping day for the past decade, accounting for most of the sales that businesses reign in during the holiday season. But smart retailers don’t have to wait until Black Friday to ramp up their bottom line. Your analytical or business intelligence platform can keep a pulse on operations year round and help increase sales before the 11th hour. Here’s how:
1) Use analytics for in-store promotions
Analytics can help guide store layouts by tracking which products perform best on an aisle shelf vs. an endcap in various cities, and what products should go on sale in a given month and region based on sales history and inventory levels. Using business intelligence, you can run scenarios based on historical data and make predictions about programs designed to drive in-store business. For example, if you increase the circulation of your direct-mail flyer, how much additional business can you expect it to drive? Similarly, if you offer in-store coupons for a certain timeframe, what kind of sales uptick can you expect? Essentially, you can analyze past customer purchasing behavior to determine how to best influence future purchasing behavior.
arcplan’s retail customers use our platform to track KPIs and run what-if scenarios to ensure that products are priced appropriately. For retailers, one important KPI is the cost of goods sold (COGS) – the price paid for the product, plus shipping, handling and other expenses to get it ready for sale. By keeping track of COGS…
How BI facilitates a decision-making process that saves millions
At the core of every business decision is the desire to drive value for the company – whether that’s increased sales, higher margins, elevated profits, or return on investment. Decision makers should use all the resources at their disposal to drive this value, including their business intelligence software, which may include guided analytics (i.e. dashboards), ad-hoc analysis and collaboration capabilities that contribute to informed decision-making. Today I’ll explore how BI software facilitates decisions in a retail scenario. But this article isn’t just for retailers – anyone can extrapolate this information to their business to see how BI can provide concrete ROI.
arcplan serves a number of customers in the retail industry, including two of the largest grocery chains in the United States. Retailers are well-known for the small net revenue margins – on average, 3% across the globe for all types of retailers – which pose significant challenges on process controls and efficiency in supply chain decisions. One of the key areas of interest for all retailers, especially grocery chains, is the reduction of shrink – the loss of inventory due to product spoilage, waste, theft and other causes. It’s estimated to account for 2-3% of overall sales. Perishable shrink even goes up to 5% within a typical grocery chain. So for one of our customers, whose revenue reached $6.25 billion in 2012, a reduction in shrink of just 0.1% means $6.2 million to their bottom line.
So a simple question that would catalyze a decision-making process at this grocery chain might be: How can BI help reduce my shrink by 0.1% while balancing availability of goods and customer satisfaction? They would want to meet high customer expectations without over-ordering, which leads to shrink through spoilage.
Everyone is throwing around the term “analytics” – about as much as they’re throwing around the term “big data.” While I might put big data on my list of the Most Overused Phrases, analytics gets a pass. As companies realize the amount of insight and value they can glean from their ever growing volumes of data, there has been a surge in analytics initiatives. The goal of these projects is to use data to analyze trends, the effects of decisions, and the impact of scenarios to make improvements that will positively impact the company’s bottom line, improve processes, and help the business plan for the future.
In order for analytics to remain relevant and always provide value, organizations must continually up their game. One way to do this is with predictive analytics, which is becoming more mainstream every day. If you stick around to the end of the article, I’ll tell you a simple way to bypass its complexity and still get the predictions you need.
Gettin’ Predictive With It
Predictive analytics involves making predictions about the future or setting potential courses of action by analyzing past data. A 2012 benchmark study by Ventana Research revealed that predictive analytics is currently used to address a variety of business needs, including forecasting, marketing, customer service, product offers and even fraud detection. While predictive analytics used to be in play in only a small number of companies, two-thirds of companies participating in Ventana’s survey are using it, and among those, two-thirds are satisfied or very satisfied. These results indicate the maturity that predictive analytics has undergone over the last few years, as technology has advanced to make it less expensive and more approachable, and therefore easier for more areas of the business to make use of. At this point, it’s safe to say that most Fortune 500 companies are churning out predictive insights on a regular basis, but that doesn’t mean smaller companies without “big data” can’t do the same thing. They can supplement their internal data with external data from social media, government agencies, and other sources of public data to get the insights they need.
Let’s take a look at finance institutions, which have predictive analytics down to a science….
According to industry forecasts, the world’s volume of data doubles every 18 months, and all forms of enterprise data will grow 650% over the next five years. The talk around big data is more than marveling at the mass of information we’re creating. As analysts and data scientists, we’re trying to find the good stuff – the trends, the data that allows us to make better decisions now and in the future, to predict the moves that will make our business more successful down the line.
Big data (explained in our previous article here) might be new to you, but I’ve seen some analyst reports referencing big data ideas as far back as 2001. However, the BI world is talking about it more and more as data volumes grow and we begin to see the potential knowledge to be gained in these data sets.
So maybe you’re thinking, how can big data benefit my company? It’s hard to think conceptually about it, so let’s take a look at some concrete examples of how companies are using big data today. We’ll start with the retail industry. Keep in mind that many of these ideas can be used on a smaller scale for retailers of any size.
Wal-Mart sifts through massive amounts of unstructured social media and search data to find out what products consumers are talking about. They use that information to set their ad buying strategy on sites like Google, with the goal of competing for e-commerce sales – currently dominated by Amazon.com.