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…
Sixty-four percent of organizations are already investing in or plan to invest in big data soon according to a recent Gartner report.* That equates to a huge number of individuals who now have to research how to embark on a big data deployment. The prevalence and benefits of big data analytics are undeniable, but there are some considerations to keep in mind before jumping in:
1) Identify a specific business need
Big data projects reap the most benefits when they address specific business needs. Having a use case in mind will help determine what data you need to analyze – social, machine or transactional data. Gartner recommends researching use cases and success stories in other industries; why not get inspired by what’s worked for others? Gartner analyst Doug Laney recently shared examples of big data at work in various industries: using big data analytics, the department store Macy’s was able to adjust prices in near real time for 73 million items based on demand and inventory; Wal-Mart was able to optimize search results and increase web checkouts by 10 – 15%; and American Express used sophisticated predictive models to analyze historical transactions and forecast potential churn. Once you’ve identified the analytic need not met by “small” data analysis, you have the first green light for considering big data technology.
In the wake of the BYOD movement, organizations are challenged to support multiple devices for accessing business information while providing the best mobile experience for end users. Seamless mobility is now an expectation for many knowledge workers who rely on smartphones and tablets to do their work. With arcplan 8, our latest release, we offer unparalleled flexibility for mobile business intelligence deployments for developers and users alike. arcplan 8 was designed with the principles of Responsive Design in mind. Developers can use our HTML5 client to build state-of-the-art BI applications that only need to be designed once, yet can be deployed on any mobile device. Let’s examine the 5 principles of Responsive Design – design principles that are simple and effective, and can be used as a guideline for developers to create responsive mobile BI applications:
1. Design with mobile in mind.
Designing with mobility in mind leads to a better user experience across all devices and platforms. When designing a BI dashboard application, think of the charts as modular tiles. These tiles will need to be rearranged depending on the device’s screen size and orientation, so it helps if they are designed with similar widths and heights. Desktop monitors and tablets in landscape orientation can accommodate all the tiles arranged in two rows, but smartphones and tablets in portrait mode will be better served by tiles stacked on top of each other so the charts are large enough to be understood without too much zooming.
2. Start with the smallest device first.
Business intelligence is the key to unlocking insights from data and empowering company leaders to make impactful decisions, act swiftly even in volatile market conditions, and plan strategically for the success of the organization. arcplan is celebrating its 20th anniversary this year, and BI has been around at least as long as we have. Over the last 2 decades, we’ve seen companies make similar mistakes – mistakes that undermine the success of their BI initiatives. Those new to BI should learn from their predecessors. Here are 5 common BI worst practices and how to avoid them:
1) Blindly buying technology without considering your analytical requirements
BI projects do sometimes fail; it’s not something anyone likes to talk about, but most of the time these failures can be blamed on a lack of requirements gathering. Vendors like us have to ensure that we understand our customers’ requirements inside and out in order to deliver a solution that will be successful and demonstrate concrete ROI. But the truth is, some companies don’t have a thorough understanding of their users’ needs before they start evaluating solutions. Too many organizations start “feature wars” with vendors and end up buying the solution with the most perceived bells and whistles – features they barely understand and will never have a use for.
This is a much of a problem for customers as it is for vendors; it’s our job to ensure that what we’re selling you will have value to your organization, and a lot of that comes down to understanding your users’ needs. But if you don’t understand your users’ needs, how can we?
The first thing you must understand before you try to purchase a BI solution is the analytical problems your company is trying to solve. Don’t get side-tracked by fancy bells and whistles that will not solve your business problems. Avoid the feature wars and make your shortlisted BI vendors prove that their solution is a match with a custom demo or proof-of-concept application.
2) Using BI as a gateway to Excel
Unearthing previously unimaginable insights from massive data sets is the premise of all the big data hype. Over the past few years as more and more stories come out about how companies are finding competitive advantages in their data, big data has moved beyond the buzz. Enterprises are deploying big data projects at a faster rate every year, and even more plan to do so within the next 2 years.
The extent to which a company can take advantage of big data analysis is determined by the amount of resources and infrastructure it has available. The good news is that now the barriers to entry have been lowered, making it possible for more organizations to meet their goals to transform operations with insights gained from big data. Here are three approaches that companies of any size can take based on their particular situation.
One thing to note is that these are underlying infrastructure approaches, and that you’ll still need an analytic engine like arcplan on top in order to interact with, visualize and distribute your insights.
Lots of resources and lots of infrastructure
Before big data was “big data,” Teradata was the only game in town. They’ve been at it for so long and their functionality is so robust – some of their capabilities are second to none. Now other vendors like SAP (with HANA) and Kognitio have their own massively parallel analytic databases. They offer robust processing and querying power on multiple machines simultaneously, enable near real-time MDX (Multidimensional Expressions, for OLAP querying) and SQL (Structured Query Language, the standard way to ask a database a question) queries, and in the case of SAP HANA and Kognitio, are fully in-memory. Not surprisingly, Teradata and SAP HANA come at a high price, but for that high price, the insights you achieve can be very near the speed of thought.