If you’re creating a new brand (or trying to revitalize an old one), pick brand elements that consumers can instantly recognize and recall. It’s also a good idea to choose brand elements that are descriptive of the products under that brand. Ask yourself, “If the brand elements are all a consumer knows about my brand, how would they think or feel about the product?” Based solely on the name, a consumer might expect Avon’s Skin So Soft to soften their skin and Heineken’s Dos Equis to be a Mexican-style beer.

There are six core criteria you can use to evaluate potential brand elements:

1. Meaningful – Does the brand element suggest something about the type of person who might purchase the products? Is it suggestive of both the products and the category they belong to? Good examples are Nestle’s Lean Cuisine, FHP’s O’Cedar, and GSK’s Aquafresh.
2. Adaptable – Can the brand element be updated as
   time goes by? The Aunt Jemima logo has been modified at least once a decade since the brand was introduced in 1893.
3. Memorable – Can the brand element be easily
   recognized and recalled by consumers? Does the brand element reinforce itself both when the product is purchased and later when it’s actually used? Generally, the simpler the brand element the better it is (think about P&G’s “billion dollar brands” with single-syllable names like Tide and Crest).
4. Protectible– Can the brand element be trademarked or otherwise legally protected? Can you keep it from becoming a generic name for an entire section of a category? (Kitty Litter and Kleenex are examples of brand elements that have become generic.)
5. Transferable – Can you use the brand element when you
   introduce new products to the category? Revlon’s Heavenly Metal brand of lipstick could be used to introduce similar shades of eye shadows.
6. Likable – Does the brand element appeal to one of the
   five senses (especially visually or verbally)? Softsheen Carson’s Dark and Lovely brand is a good example.

Clearly grocery and cosmetic shopping have become different ballgames ever since the latter part of 2008. Consumers are more strategic in their shopping due to worries about their pocketbooks. But to dig a little deeper past the obvious recession era trends, I thought it would be interesting to pull out a few of the more prominent points brought up in the report:

• Nearly two thirds of all coupons offered in 2010 were for grocery items – up 8% from the prior year.
• The remaining one third were for health and beauty care products.
• The average coupon face value distributed for HBC products was $1.94 – up 6.6 percent from the prior year.
• Consumers now have a week and a half less time to use coupons, compared to the prior year, due to an overall shortening of offer expiration dates in both the grocery and HBC segments. The average expiration is 10.1 weeks.

“In 2010, consumers saved $3.7 billion by using CPG coupons,” said NCH Vice President of Marketing Charlie Brown. “With so much money at stake, brand manufacturers and fast-moving consumer goods retailers must ensure their expenditures are protected while continuing to collaborate for the most effective use of coupon promotions to meet strong consumer demand.” However, with so much money at stake, the proliferation of coupon offers and coupon offers being capitalized upon can be a blessing or a curse for your organization. In one sense, such greatly expanded coupon use adds an inordinate amount of new data and segmentations to be analyzed. But it is only inordinate if your tools are not robust enough. When bearing an adequate data analysis platform, a report like this is only music to analysts’ ears. Coupons are initially segmented to an extent by their very nature.

            Similar to many lifestyle adjustments brought on by the last few years, coupon-influenced consuming does not seem to be going anywhere soon:

• Among those consumers who reported using more coupons than the prior year, the largest share in 2009, 37.4 percent, explained their reason for doing was so to stretch a limited grocery budget out of necessity.
• Once consumers adopt these frugal habits, they quickly discover that they like the feeling of saving money. Acceptance of these new habits can be seen in the largest share of response for increased coupon usage in the 2010 survey — 29.3 percent of consumers stated they are using more coupons for the enjoyment of saving, an increase of 11.7 share points over those stating that reason the prior year.
• Nearly three quarters of those who made such shopping changes expect to continue their frugal habits in the future, even as the economy improves.

Coupon distribution and usage is yet another indicator of the concept people have been constantly redefining as a sort of “new normal” for some time now. And just like the increase of raw data production every year, the proliferation of this kind of data will require a conscientious effort on the part of every member of your organization for maximum value extraction, and for profit.

Well here’s a new one: according to Google chairman Eric Schmidt, quoted in a recent CGT Magazine article, the company now creates 5 exabytes of data every three days. To trump the iPod-football stadium illustrations of yesteryear, 5 exabytes is the amount of information created from the beginning of time through 1978. This figure is obviously no less ridiculous. But the title of the article which offered this stat, “Are Consumers Drowning in Data?” is as telling as it is commonplace in 2011. The answer was and still continues to be: yes.

As data continues to increase in its rate of production, the technology to capture and utilize that data has too evolve as well, just like our superlative articulations of just how big the data is becoming. In the analytical sphere, keeping your data profitable means constantly adapting your solution to the behemoth. And these kinds of timely adaptations must be prime considerations in your chosen data management solution. Next year there will be an even more over the top analogy to describe 2012’s data proliferation. Will your organization be ready to not bat an eye when that analogy hits headlines?

• Scheduling automated tasks to streamline POS data imports and updates
• Restating all products in a  DataAlchemy data source to accurately reflect data provider restatements
• Seamlessly importing large Retail Link datasets from Access Databases
• Importing all worksheets in an Excel file during a data source creation or update
• Utilizing the Excel add-in enhancements, including support for tabular data format and the addition of    a data range summary button
• Displaying Attributes and Segmentations only once for nested reports in Tables and Workbooks
• Quickly sharing formatting between regular charts and workbook charts

We have not only listened to customer feedback but also incorporated many of those suggestions into this release.  Users will see “Merged Item” enhancements, Data Import Wizard enhancements  and many improvements to the software that make it even more intuitive.  Join us for this June 14th live webinar for a sneak peek into DataAlchemy 6.3.

Date: Tuesday, June 14th

Time:  2:00 PM EDT

Sign Up: https://www1.gotomeeting.com/register/532871176

The minor will be offered for the first time this coming fall. Assistant Professor of Retail and Consumer Science, Rod Runyan, says of the new minor, “Our goal is to be the leading university for this field. When someone-whether it’s a student, another university, or a company looking for employees-thinks of retail technology, we want them to think of UT Knoxville.” Commenting on the changes in the field of retail data analysis Runyan also stated that in the past, students wanting to become buyers could start as an assistant and work their way up. Now most companies have replaced assistant buyers with planning and allocation specialists, positions that require knowledge not only of merchandise but also of the software systems that help plan space usage and product allocation. The new minor is UT-Knoxville’s answer to the changing face of the industry. And, as you might expect, the industry is already catching on. “We’re already seeing the positive reaction of retailers,” Runyan said. “For instance, executives from several large national retailers came to campus to recruit for the first time this past academic year.”

One such barrier is the fact that India has not fully opened its retail market to foreign companies. Last year, after India’s Department of Industrial Policy and Promotion (DIPP) was non-committal on foreign investments in retail, the DIPP invited opinions from large retailers and small shop owners about foreign direct investment in multi-brand retail. And that discussion still continues. Walmart, however, never idle, has opened the previously mentioned wholesale stores (called “Bharti Walmart Best Price Modern Wholesale Stores”), and the partnership has lofty plans in the works. Those plans involve opening twenty new stores in the next two years, thereby becoming the top wholesaler in the country. Raj Jain, chief executive of Bharti Walmart, said recently, “In the next one or two years we do anticipate we will be in a market leadership position.”

This expansion plan is definitely of characteristic Walmart proportions. At a rate of investing approximately $7.5 million per new store, Bharti Walmart will invest $75 million by the end of the year if it opens its slated ten stores. This plan would also bring the total investment in India to about $195 million by the end of 2012.

Despite its reputation as a massive mechanism that rolls over all competition and impediments, it stands to be recognized that as a foreign country Walmart must adapt its strategies in certain ways to meet the needs of the Indian market. For instance, in an effort to make deeper inroads, the company is finding new ways to build up its brand. The Bharti Walmart partnership is in talks with the Punjab government on how to maintain and modernize slaughterhouses in the state. The initiative will help the company to source hygienic meat for its ‘Best Price Modern Wholesale stores’. It is the exact kind of project that may soften the opinions and subsequent rulings of the DIPP. And of course, if any company will find a way to overcome barriers to entry, Walmart will be at the front of the pack doing it.

To find out more about our company and the DataAlchemy solution, and to hear about a specific feature we’ve implemented to better support our customers, attend our previously mentioned webinar, “Shine Light on your Data with the Data Import Wizard”.

Date/Time: Wednesday 2/2/11, 2:00-3:00 PM EST

Registration: https://www1.gotomeeting.com/register/392270160

Registration: https://www1.gotomeeting.com/register/392270160

Overview: Whether you are familiar with the Data Import Wizard or new to the DataAlchemy solution, this 45 minute webinar is worth your time. The webinar will highlight the use of the Data Import Wizard that is available within the DataAlchemy tool, demonstrating how non-standard data sets can be quickly transformed into easily interpreted DataAlchemy reports. Learn how to import segmentations for simplified data analysis and profit from the depths of your data with the use of advanced and ad hoc reporting.  This webinar will then further demonstrate how more intricate and customizable features within DataAlchemy can be easily implemented with the use of the Data Import Wizard. Observe how the Wizard provides a simple method for importing valuable but hard-to-format data from Access, Excel, Text and/or CSV files. 

Who should attend:  Category managers and analysts who are looking for a reporting and analysis tool to leverage data while simplifying the analysis process.   Both viewers new to DataAlchemy and current DataAlchemy users looking to explore the intricate features within the Data Import Wizard will benefit from this webinar.

Presenter: Glenn Geho- Chief Operating Officer

In October 2010 Glenn began his sixth year with alqemyiQ.  Prior to his role as Chief Operating Officer, Glenn served as Vice President of Consulting & Product Development at alqemyiQ.  He has been using DataAlchemy for over 10 years and has 20+ years of consumer goods industry experience, including various sales, category management and  knowledge management positions with GlaxoSmithKline Consumer Healthcare and Acosta Sales & Marketing.

• 56% improvement in on-shelf availability
• 45% better management reporting
• 44% better sensing of product category change
• 41% better demand forecast accuracy
• 41% lower inventory and safety stock levels
• 40% faster demand insights to drive new product development
• 40% improvement of promotion design, forecasting and execution
• 30% improvement of order to cash efficiency – including the reduction of deductions and more accurate billing for promotions

The benefits of realizing just one of these statistics for your organization are numerous and obvious. The merits of a tool that helps your organization touch positively on each of theses stats cannot be overstated. Better on-shelf availability, management reporting, forecast accuracy, etc., are valuable metrics that make lives easier across the organization – for the CIO, analysts, IT departments. Data analysis blogs and consumer goods conferences have been abuzz with talk of DSRs for some time now, but now we see a report of results after DSR implementation. Just like any solution used to wring efficiencies from the enterprise and boost profits, your DSR must perform. A mediocre tool is no less mediocre just because it is new. It must be able to take in the large and constantly increasing amounts of data flowing in, and it must distribute that data quickly and accurately enough to be useful to analysts. But if your organization integrates a strong DSR solution – and does so soon – it will inevitably begin producing results like the preceding bullets at the end of each quarter.

A recent article posted online from Bloomberg Businessweek shows Wal-Mart employing similar tactics in regard to the transportation of their suppliers’ products. The retailer has distribution centers located densely around the country, and these centers are constantly taking in truckloads of products from Wal-Mart’s many suppliers. But now Wal-Mart is looking to take over that transportation from the supplier to the distribution centers, instead of only being responsible for the movement of goods from distribution centers to their stores. The company believes that they can perform the transportation process more punctually and at a lower cost than the suppliers themselves or any outside transporter. “It allows our suppliers to focus on what they do best, manufacturing products for us,” says Kelly Abney, Wal-Mart’s vice president of corporate transportation, “with lower costs usually comes increased sales.” It isn’t only the punctuality and the presumed focus of suppliers on “what they do best” that is lowering Wal-Mart’s transportation bottom line, either. “A Wal-Mart truck coming from a distribution center in Bentonville to stores hundreds of miles away may pick up goods from manufacturers on the way home,” thus taking care of the money-wasting empty container problem that has existed in their goods transportation system to date.

It is another way Wal-Mart is cutting costs by reaching back into their supply chain. The company believes there is a cost disconnect in the transportation of goods; they believe they are often paying too much for services that could be more timely. Thus by taking control of even more of the processes that make their supply chain function, they have the ability to cut their own costs, and, in the fashion that has made Wal-Mart the largest retailer in the world, pass those lowered costs along to the consumer. It is a view of Wal-Mart’s relationship to their supply chain that has been responsible for so many increased revenue figures over the years.

MarketWatch’s article says, “a ‘tipping point’ has been reached on the expanding use of the DSR, specifically as a method and set of tools they [CPG companies] say can improve competitive advantage.” Immediately following this statement, the article quotes a Consumer Products Industry Practices Expert as saying, “For some time, companies have been focused on the daunting task of harmonizing data and choosing the right path by developing their own systems in addition to working with technology vendors. And rightly so, many have been discouraged in their journey. But our research shows an encouraging trend as leading companies are finally moving towards optimizing the DSR with standardized technologies.” “Rightly…discouraged,” is a good way of putting it. With the amount of data that has been materializing even in recent years, companies looking to develop their own systems are having an increasingly difficult time, and that path does not look to be changing its direction. Marketing leaders are choosing to optimize their data management solutions with DSR technology. The industry leaders are all making moves towards the implementation of this kind of software. In fact, the article closes with the following statement: “Overall survey findings suggest that to become demand-driven, companies must first capture downstream data and utilize a Demand Signal Repository to drive actionable insights and respond with a profitable demand-shaping response.” Thus CPG companies that are able to implement the DSR well will not only be able to extract the most value from the demand data coming into their enterprises, but will be able to extract it so profitably that they begin to shape the very demand that produces the incoming data in the first place.

Here is another well written blog entry from the Retail & CPG Blogger for Infosys on the potential of the DSR: http://www.infosysblogs.com/retail-cpg/2010/04/digital_signal_repository_will.html#more

• The digital information created by every man, woman and child on Earth “Tweeting” continuously for 100 years
• 75 billion fully-loaded 16 GB Apple iPads, which would fill the entire area of Wembley Stadium to the brim 41 times, the Mont Blanc Tunnel 84 times, CERN’s Large Hadron Collider tunnel 151 times, Beijing National Stadium 15.5 times or the Taipei 101 Tower 23 times
• A full-length episode of FOX TV’s “24″ running continuously for 125 million years
• 707 trillion copies of the more than 2,000-page U.S. Patient Protection and Affordable Care Act signed into Law in March 2010. Stacked end to end, the documents would stretch from Earth to Pluto and back 16 times or cover every inch of the United States in paper three-feet deep 

That’s right. The data that organizations will be looking at having to store in the coming decade (termed “The Digital Universe Decade”) is already being discussed in terms of how many times it could reach Pluto and back if transcribed. The writers go on to quote a study performed by EMC Corp., which states that while files, images, records and other data “containers” will grow by a factor of 67 in the coming decade, the number of IT professionals will only grow by a factor of about 1.4. This is a potentially foreboding fact for many companies, in the CPG industry and many others. But it does not have to be an intimidating figure; rather it should be another justification of the need for an efficient, manageable data technology platform. SourceMedia does well to point out the fact that, “Individuals generate more than 70 percent of the digital universe, but enterprises have responsibility for the storage, protection and management of 80 percent of the digital universe.” Not only is that storage, protection and management the responsibility of business enterprises, for many it is their livelihood. If a company’s data management technology platform is mediocre or falling behind the company’s needs at this point, how data its capture and management look in the coming year when there is enough to fill 41 soccer stadiums with fully loaded 16GB Apple iPads? The time is now for companies to ask themselves, “Are we prepared to store all this data?” The answer is crucial to both survival and profit.

• Organization and policies, principles and standards
• Processes and practices
• Governance metrics
• Technology
• Data architecture

Referring to the first bullet, the author recommends appointing a chief data officer, or CDO, with an accompanying data council to enact and enforce the policies of data management within the company – what management will look like, how the enterprise will go about it, etc. While this is not a misguided concept, a full-fledged data council may not be necessary for smaller companies if they have the right data management software and internal infrastructure (here referring to a small scale model of a “data council” from insightful members of the company).

With regard to processes and practices, Griffin writes that good data management practices begin on the business side of the enterprise, with “data collection, transformation and dissemination processes used by the business functions.” The author writes that these practices on the business side will necessarily become “guiding principles for developing a set of synthesized, standardized processes that can facilitate the delivery of accurate data across the enterprise.” After all, although the constantly growing and evolving state of global data can be distracting, this is the final goal: to deliver accurate data across the enterprise so that it can be used to grow business.  

Metrics to track the effectiveness of data governance across the enterprise are often glossed over in companies’ data management solutions, but they are entirely crucial. Efficient data management is costly to implement, and supervisors of the effort must be able to account for how the data is used and whether or not the return is consistently justifying the investment.

Griffin refers to the need for the right technology, by which she means is that it is necessary for a company to have policies and metrics in place to govern how data is captured and determine if its implementation is profitable and continues to drive efficiencies in the data management system. The author claims that the company-specific attributes of this technology will, to an extent, make themselves clear to each company out of necessity over the course of observing the efficiencies and inefficiencies of the data management system implemented. Once it does form out of the companies needs and certain aspects of the data management technology become standard across the enterprise, Griffin writes that these technological tools will form a kind of data architecture. The fleshed out data architecture to which she refers is basically a data management software solution. One point she fails mention is that there are already tools, such as alqemyiQ’s Enterprise Suite, that are designed to act as this architecture from initial installation, instead of companies having to forge a trial and error architecture on their own.

Ms. Griffin makes an interesting comment to begin concluding her article. “Data governance is not rocket science,” she writes, “It is more like a journey of discovery down a road that you’ve traveled before but didn’t really notice how you got where you are.”  This statement makes sense with regard to the ever-changing and expanding world of capturing and wringing efficiencies from data. However, when an organization finds the right tools and technology, the process of data management becomes far easier and much more profitable.

USA Today also notes that Juniper Research, based in the U.K., recently forecasted that more than 1-in-10 mobile subscribers in developed regions around the world will use mobile coupons by 2014, generating nearly $6 billion in redemption value. In addition, the consumer redemption rate is greater with mobile couponing at 5% to 20% , compared with about 1% for print coupons. Neil Strother, an ABI Research analyst, was quoted in the article, commenting on “how this area is ripe for growth.” He suggests that not everyone will clip a coupon, but most everyone is looking for a bargain and this form of discounting is easily accessible. These percentages show that with the increase in mobile couponing, the need to analyze mobile couponing data in real time will be as equally important for CPG companies to invest in.

            But Ericson warns that just as the data management field and its economy are making leaps forward, it can all quickly unravel. Every new data service is a new source to be compared to other data, which requires a great deal of governance. Data could be entirely useful and valuable within the four walls of a company, but then can lose some of all of its value when leaving those four walls. This is because the data generating company cannot ensure quality of data by an enterprise standard. So even though he writes of many information management challenges being conquered (or at least understood), there is still a need for many aspects of the industry to evolve – standards for classifying data being a major part of that. Ericson closes with the interesting point that although many of these challenges of the present or recent past have been technological or infrastructural, the new challenges will be ones of assessment and value propositions. “And while standards of dependability continue to mature,” he writes, “the market will set the rules and we’ll judge the interim value of data in part by what we are willing to pay for it.” But of the future he concludes, “There is every reason to expect we will be talking more about the value of information and less about the details of infrastructure that support it.” There will be new tools that emerge that help companies measure the value of this information. Software such as alqemyiQ Insight can help measure the return on investment of a company’s information investment. Too many companies spend enormous amounts of money on data, and yet, even as far up as the CIO, cannot confidently answer one question: who used that data yesterday? CEOs and other senior executives need and will want to know how useful data capture efforts are, and tools like alqemyiQ Insight aim to offer answers to that question. The data management industry continues to sprint forward. Here we see an expert speaking of the industry’s near future, and he reiterates that the companies with systems in place that understand the changes to data are the ones that will win.