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…
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….
Fueled by the big data hype and the need to extract greater business value from data, investment in business analytics software is on the rise. Many companies have begun to tap into the potential of big data analytics and this number is predicted to increase according to recent reports by the International Data Corporation (IDC). IDC forecasts that the market will continue to grow at a 9.8% compound annual growth rate through 2016 to reach $50.7 billion. Perhaps to a less aggressive extent, interest in Collaborative BI is also on the rise, with top performing companies incorporating collaborative techniques to share knowledge throughout the enterprise according to Aberdeen’s extensive 2011 research report on Collaborative BI. The demands for agile insight and self-service are changing the landscape of BI, driving the need for Collaborative BI, which uses social functionality to improve business decision-making. Separately, the benefits of deploying analytical tools and taking advantage of collaborative techniques are appealing for any organization seeking streamlined operational success – but the payback of merging these initiatives could be even more rewarding.
Analytics is gaining traction in the BI arena due to the need to explore massive amounts of varied information (what we now call big data), extract valuable insight, and quickly deliver these insights to the users who need it. Initiatives geared toward improving analytics utilize technology that gathers and organizes data from disparate data sources and provides a platform for in-depth analysis, yielding benefits such as improved business operations and agility, increased sales, and lower IT costs. So it’s no wonder that organizations are making significant investments in the analytics market.
Collaborative BI, on the other hand, seems to be the new kid on the block…