Business Intelligence Blog from arcplan

3 Ways Analytics Help Retailers See Black Friday More Than Once a Year


retailers-black-friday-arcplanPeople 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…

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Poor Data Quality – Part 2: Who Should Be Held Responsible?


We’re almost half way through the year and by now more than half of us have forgotten or become complacent about our New Year’s resolutions. But it’s never too late to get back on track! The same is true about data quality management; it’s never too late to restore order to your company’s data and combat the consequences of poor data quality. Data quality management should be an ongoing process since bad data affects business intelligence systems and ultimately the decisions based off of BI. It’s a big job and someone has to take responsibility for it. Who should that be?

“Data quality is not solely an IT issue…success depends mostly on involvement from the business side…Business professionals must ‘own’ the data they use.”

–  Gleanster Deep Dive: How Top Performers Improve Data Quality for Better Business Intelligence, January 2011

The knee-jerk reaction to the question of who should be held accountable for maintaining data quality is “the data steward,” “the data quality manager,” or any variation of that role. But who is the data steward? I believe that each organization should have several data stewards and that they should be the content owners or really, the people who most care about data quality. Here are a few examples:

The marketing director who scrubs the CRM system to ensure that lead information is correct often wears the hat of data quality manager. Data quality is important to marketers because good data (email addresses, mailing addresses, and other segmentation fields like revenue and industry) is necessary to avoid fail points in communication and to ensure that the target audience receives your message. With a 2011 Experian QAS research report revealing that 90% of organizations believe as much as 25% of their departmental budgets were wasted during the last year as a result of inaccurate contact data, you can bet that your marketing team has a CRM data clean-up project in the works. Sometimes that means using an appending service to fix bad email addresses and sometimes that means manual research and data entry, but there is true ROI for marketing data quality initiatives.

The account manager who oversees a territory and enters sales and account information in the CRM system is also responsible for data quality…

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Big Data in Retail – Big Ideas for Better Retailer Performance


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

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Uncovering 21st Century Consumer Behavior with Business Intelligence


This post on BI for retailers is co-authored along with Raj Kutty, CEO of iVEDiX.

Retailers are frequently challenged with a new definition of multi-channel marketing. The marketing landscape includes more than the traditional components of print advertising, direct mail, and Customer Relationship Management (CRM). It also rolls email, social media, mobile and web (e-commerce) into the marketing mix. Customers engage with brands and make purchasing decisions on a new array of platforms, which has increased the amount of consumer behavior data available for retailers to manage. This subsequently makes the marketing campaign management process much more complex—from budgeting and planning to predicting consumer behavior to providing superior customer service.

With the rapid advent of new, innovative technologies, Business Intelligence (BI) has seen a great deal of change over the past few years. BI has reached a state of sophistication where it is being adopted as a key strategic initiative by retailers. BI solutions aggregate information and provide retailers fast and easy access to data for business reporting, analysis, planning and decision support. By transforming data into actionable information, BI helps retailers make better fact-based decisions at every level of an organization.

Social media, an influencer of consumer behavior
Most retailers are aware of who their customers are. They are equipped with the technology to reasonably ascertain demographics, buying patterns and influencing behaviors. However, the proliferation of numerous social media channels in the consumer market—like Facebook, Twitter and Foursquare—has exponentially amplified the challenge of identifying and understanding target markets. Next generation Web 2.0 communication has altered the frequency and intimacy with which retailers interact with their customers.

Retailers, more often than not, have data on their customers’ online and in-person shopping habits stored in separate repositories—a CRM system and an ecommerce database. For a complete analysis, this information can be combined with social media data—customers who “like” a particular store or product or who tweet about a specific brand or product—as well as fundamental demographic information such as income level, gender and age. These layers of information can be superimposed on a geographical map to create very powerful campaign segmentation visuals. Going even further, tying in customers’ actual receipts can give the retailer an incredible perspective on the customers’ buying behavior and thought process leading up to their purchasing decision.

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