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Northwest Arkansas Weekend Happenings – August 29-30, 2015

Enjoy your Saturday cup-o-joe and then get out with the family this weekend.

The weather forecast looks like mostly sunny skies with the high temperatures in the mid-80s.

Here are a few offerings – enjoy your weekend and we’ll see you at the office next week.

 

#NorthwestArkansas

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Drawing the Wrong Conclusion: Why #BigData Alone Is An Inadequate Source Of #CustomerIntelligence | #CPG

Naturally, anytime an article’s headline is about Big Data I take notice.  And especially so when it claims to tell my why Big Data is inadequate.

So after digesting this information, please allow my a couple of minutes to discuss why the author totally missed the mark on, and the point of, big data.

He makes three main points.  As he puts it, “Here are three reasons why a lot of investments in big data fail to deliver ROI.

  • Most companies don’t know how to use big data for strategic decisions.

Companies need to learn how to manage information, analyze it in ways that advance their understanding of its customers, and then act intelligently in response to new insights.

“Companies don’t magically develop those competencies just because they’ve invested in high-end analytics tools. They first need to learn how to use the data already embedded in their core operating systems, much the way people must master arithmetic before they tackle algebra,” information science academics Jeanne W. Ross and Anne Quaadgras from MIT and Cynthia M. Beath from the University of Texas at Austin wrote in the Harvard Business Review.

One reason that companies are unable to benefit fully from their investments in big data is that “management practices haven’t caught up with their technology platforms,” according to Ross and Quaadgras. For example, companies that have installed digital platforms, such as enterprise resource planning (ERP) systems and customer relationship management (CRM) systems over the past 10 to 15 years, haven’t yet taken full advantage of the information they make available. A cultural change is needed within companies so that “all decision makers have performance data at their fingertips every day,” Ross and Quaadgras write.

As an example of a company that uses data effectively, Ross, Quaadgras and Beath cite 7-11 Japan, which provided its employees with daily sales reports and supplemental information such as weather forecasts, what sold on the last day the weather was similar, what sold the previous day, what sold on the last the same date a year prior, and what was selling in other stores. Importantly, clerks were connected to suppliers “to encourage the development of items that would suit local customers’ tastes.”

The 7-11 Japan story was not about big data or investments in data, but about a lot of little data. “It’s about betting your business success on the ability of good people to use good data to make good decisions,” the authors wrote. “Empowering employees in this way, and arming them with the data they need, helps them make better operating decisions on a daily basis. It can also lead to a constant stream of innovation.”

Big data, as it’s described today, is not the answer to all questions—and it’s no replacement for the on-the-ground decision-making of real people interacting with real customers.

 

First, no one in the “big data” arena, that I have read has suggested that any technology develops competencies for anyone – only people can develop competencies.  But this reminds us of a common theme this week – a CPG category or sales manager must have a marketing strategy, which then drives questions, that dictates the data to be mined.

Second, he writes, “The 7-11 Japan story was not about big data or investments in data, but about a lot of little data.”  Hello?!  Here’s where he doesn’t understand big data – big data is a LOT of little data.  The ability to pull in unstructured data with the existing structured data and rapidly blend the data (i.e. data blending) to draw new insights.  That is the purpose of the technology and why it exceeds the capabilities of rigid DSRs built with old data warehouse style databases.

Finally (on this point), he writes, “—and it’s no replacement for the on-the-ground decision-making of real people interacting with real customers.”  He is correct – no technology is sufficient to replace real people – that characteristic is not unique to “big data.”  People, whether they are consumers in your retailer’s store or the buyer you deal with at the retailer home office, do business with people – not technology.  We use technology to facilitate fast, and sometimes global, interactions.  He criticizes this technology for sharing the common characteristic of all technologies – what’s the point?

 

  • Big data doesn’t provide a complete picture.

Another telling example of the danger of relying on big data alone comes from the world of social media analytics. Seen widely as a holy grail for companies seeking insight on their customers, social media analytics falls short on several fronts. Consider this: 85 percent of social media updates come from so-called “enthusiasts”, but only 29 percent of a typical company’s audience are enthusiasts.

The vast majority of social media users are in fact relatively quiet. Companies can’t hear them, although they’re listening to you. That means that social media analytics can mislead companies about what matters to customers as a whole, when in fact what they’re seeing is only a very thin slice of their audience.

Colin Strong, a leading consumer researcher in the U.K. emphasizes this point in his 2015 book Humanizing Big Data. “Since Twitter users make up only about 10 per cent of the U.S. population,” he notes, “some demographic or social groups won’t be represented. The result? More data … does not necessarily mean more insight as it does not necessarily reflect real life.”

People who spend a lot of time online are typically younger, better educated and more affluent than the overall population—again, offering many companies a limited view of existing and potential customers.

Note:  He makes this argument by coming to a false conclusion about big data from the flaws he sees in social media data.  Social media data != big data.  Social media data is a subset of big data.  One thread in the fabric creating a complete customer tapestry.  Not understanding the definition of something will inevitably result in false conclusions.

Premise: Ducks are birds.
Premise: Ducks swim in the water.
Premise: Chickens are birds.
False Conclusion: Chickens swim in the water.

  • It lacks the “why.”

Big data can reveal much about what’s going on, when it happens and where it happens. But we haven’t really arrived at the day when big data can reliably tell us why customers behave in a certain way.

See my final point on issue #1.  No technology does our thinking for us.  What technology delivers, especially big data technology, is the data (information) much faster – allowing CPG marketers to analyze that data (again with BI technology), so to develop actionable insights much sooner (a human function).

As computing advances and analytical tools progress, we may get to that point. But for the foreseeable future, big data is only one tool in the marketer’s toolbox. Market research that involves more direct human-to-human interactions with consumers will still be vital. Big data will only take us so far, and at some point a human perspective needs to join the effort.

For marketing departments to derive value from big data, they have to get better at leveraging social science, data analytics and consumer insights. Understanding the nuances of customer behavior—the motivations, or the “why” behind behaviors—gives us true insights. And those cannot come from a centralized and isolated big data department.

 

  • Conclusion

Big data surely has a role to play in gaining insights into the behavior of these empowered customers. That’s why enterprises are pouring billions of dollars into the big data industry.

But big data doesn’t have all the answers—at least not yet, and perhaps never. Companies need to respond quickly to identify changes in customer behavior and take action to address their concerns.

In short, the emergence of big data doesn’t change the fact that people matter. A human touch is still integral in business today. Big data can offer some answers but continual human-to-human connections are also needed to fully understand rapidly evolving marketplace.

My conclusion:

While in agreement with his statement, “the emergence of big data doesn’t change the fact that people matter. A human touch is still integral in business today.”  That has always been, and will forever be, true – regardless of the technology used to gain insight into the consumer’s behavior.  People do business with people.  Relationships always matter.

The author lacks an understanding of “big data,” doesn’t know the definition of “big data,” and criticizes the characteristics of “big data” that all technologies share.

This article reaches the wrong conclusions for all the wrong reasons.

 

If you are interested in finding out how big data can really give you the tools you need to outpace your competition please contact us today.

 

 

Source: Why Big Data Alone Is An Inadequate Source Of Customer Intelligence

 

 

 

#BigData

#BigDataAnalytics

#CategoryManagers

#CPG

#CPGMarketing

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Exploring #BigData Business Models & The Winning Value Propositions Behind Them | #CPGMarketing

This article, by Justin Lokitz – a thought leader in big data, provides an excellent overview of three distinct business models using big data.  Any one of which, or a hybrid, can be incorporated in a CPG category manager’s analytics process.  The article is worth a separate read but the main points are summarized, and applied to CPG, below:

It goes without saying, innovative, sustainable Big Data Business Models are as pervasive and sought after as they are elusive (i.e.  “data is the new oil”).  For every startup that designs and implements what amounts to a devilishly simple and effective big data business model (see any social network), perhaps changing the entire landscape with it, there are literally hundreds (if not thousands) of larger, more mature companies looking for ways to monetize their own big data in the hope that they can capture new revenue streams (and compete effectively in the future).  Of course some of the larger, mature companies have done quite well in this regard.  Apple (40 years old) and Amazon (20 years old), for instance, have vastly different business models.  Yet, both companies have built solid business models around big data; both use big data to present to consumers products and services that might be relevant to them.  Similarly, Netflix and Pandora, 18 and 15 years old respectively, designed brand new big data business models around understanding and creating value for customers in ways that seemed like magic at the time.  So, what’s behind these business models?  And, are there other business models that might help other (mature) companies create, deliver, and capture value using big data at the core?  The answer (to both questions) is simple: it’s all in the value proposition.

He further states in his introduction:

“Fall in Love with the Problem, Not the Solution.” As simple as this quote is it speaks volumes when considering how mature companies tend to think about utilizing their own big data stores to create new business models.  That is to say most mature companies first ask, “What big data do we have today?” followed by, “how might we sell this data?” Looking back on my favorite aforementioned quote, you can probably see the discrepancy here: most mature companies believe there is some mythical marketplace where they can simultaneously sell their big data whilst not pissing off their customers.  These assumptions are more often than not wrong.  Moreover, while there are LOTS of “problems” to fall in love with when it comes to big data business models, in order to provide some focus, this post highlights three categories of big data business models based on their value propositions and customers (e.g.  DaaS, IaaS, and AaaS respectively).

Big-Data-Pyramid

  • Data as a Service (DaaS)

DaaS hinges on a value proposition for supplying large amounts of processed data with the idea that the customer’s job-to-be-done is to find answers or develop solutions for their customers.

For CPG companies partnering with large retailers as a trusted supplier – this usually begins with the POS/Inventory data supplied at the vendor level or, where appropriate, the category level.

The granularity of data can be daily or weekly and provide historical data – usually 104 weeks.  While the author speaks in general terms about marketing data to monetize it (in fact the entire article has an eye toward this), CPG companies cannot sell retailer supplied data.  This does not mean that you, as a CPG category or sales manager cannot monetize the data.  For you – monetization occurs when you use analytics to gain insights to share with your buyer(s).  The goal of this activity, of course, is to flank your competitors within the category, increasing your brand’s market share within the retailer ecosystem.

Part of the Vortisieze service offering is providing a fast, clean, single source of the truth, aggregated data and the analytics tools to empower you to produce your own insights.

data-as-a-service

  • Information as a Service (IaaS)

IaaS focuses on providing insights based on the analysis of processed data.  In this case the customer’s job-to-be-done is more about coming up with their own conclusions or even “selling” an idea based on certain information.  Additionally, IaaS customers don’t want to or do not have the resources to process and analyze data.  Rather they are willing to exchange value for analysis from trusted parties.  Unlike the DaaS business model, which is about aggregation and dissemination of lots of processed data for customers to create their own value propositions from, the IaaS business model is all about turning data into information for customers who need something – and are willing to pay for something – more tailored.

Because we have category manager DNA in our company’s DNA, Vortisieze can meet this need by providing ready-to-use analytics, dashboards such as Business at a Glance (BaaG) for example.

information-as-a-service

  • Answers as a Service (AaaS)

AaaS is focused on providing higher-level answers to specific questions rather than simply the information that can be used to come up with an answer.  CPG companies who implement the AaaS business model do so in gain answers to answer specific questions.

This business model, as you might guess, is the top of the pyramid when it comes big data.  The key with this business model is that given the CPG company’s ability to create real, trusted value in the answers it provides to buyers, buyers take note and value the insightful answers provided.

When a category manager partners with Vortisieze by asking very specific questions needing answers (remember strategy drives questions), we can provide answers to your most important questions.

answers-as-a-service

 

Vortisieze technology, unlike rigid DSRs or yesteryear, provides pliable, and rapid, solutions to meet your analytics needs.

Contact us today to discover how.

 

About the article author:

Justin Lokitz is Strategy Designer & Managing Director at Business Models Inc.  San Francisco

Source:  Exploring Big Data Business Models & The Winning Value Propositions Behind Them

 

#BigData
#BigDataAnalytics
#CategoryManagers
#CPG
#CPGMarketing
#DataasaService
#InformationasaService
#AnswersasaService

 

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5 tips when using #dataanalytics in your #CPGMarketing

Data analytics can deliver great ROI and personalization abilities for marketers, and data-driven solutions can result in highly accurate insights into customer behavior, but only if you know where to start.

This ties back into our conversation yesterday about having a clear strategy that allows you to then ask the right questions.

Indeed, having the ability to collect and analyze data easily and then turn it into actionable insights that feed back into the business – fast – is crucial in a world where there is so much information available on consumer activity, their likes and dislikes.

Here are the 5 tips the author of this article lays out for data analytics in marketing.

  1. Ensure your data is clean
    There’s no use analyzing data if it is of poor quality. You wouldn’t expect great performance from a badly maintained car, so don’t neglect your data either. Your data is your most important business asset, so audit it and make an effort to improve its quality before you start trying to analyze it.
  2. Know what data you have and make sure you can access it all
    To get a full picture of what’s going on, you will need to be able to access data from various systems. Chances are that you have CRM, HR and ERP systems full of information as well as web-based tools full of data. Whatever your setup, make sure your data is centralized for all to access. Ensure people aren’t storing important data in siloed spreadsheets on their own devices.
  3. Have a clear goal in mind
    Figure out first what you are trying to achieve with your analytics before you embark on your analytic journey. Too often companies start analyzing data without having a clear goal in mind and they end up trying to find out everything in one go. So, take a step back and define the goals that you want to meet when running analytics projects.
  4. Use the right tool for the right job
    The term big data is thrown around by many, and there are tools for just about every way of making sense of it. Once you know what your goal is, make sure you use the right technology to meet your objectives. For some analytics, you could use open source technology, for others you might need a fast analytic database. Do not try to shoehorn your analytic workloads into technology that just wasn’t designed to cope with them.
  5. Stay focused
    There is a lot of data that you can do a lot of things with. Don’t try to do it all at once; keep your focus on what you are trying to find out and don’t get side-tracked by anything else that might come up. It’s a common occurrence that companies end up frustrated with analytics because they have lost sight of what they were trying to achieve in the first place. So, focus is absolutely key.

Top advice – contact us today to discover how Vortisieze can help you develop your strategy and deliver fast, reliable actionable analytical insights.

 

Source: 5 tips when using data analytics in your marketing

 

#CPG
#DataAnalytics
#CPGMarketing

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#BigData: Too Many Answers, Not Enough Questions – What Questions Are #CPG #CategoryManagers Asking?

This article on Forbes.com today nicely points out one potential issue with Big Data – data on its own is meaningless.    The author starts off with a useful parable that illustrates the point.

One of my favorite examples of why so many big data projects fail comes from a book that was written decades before “big data” was even conceived. In Douglas Adams’ The Hitchhiker’s Guide to the Galaxy, a race of creatures build a supercomputer to calculate the meaning of “life, the universe, and everything.” After hundreds of years of processing, the computer announces that the answer is “42.” When the beings protest, the computer calmly suggests that now they have the answer, they need to know what the actual question is — a task that requires a much bigger and more sophisticated computer.

Data is only useful when it answers questions that drive or support your brand strategy at the retailer level.  Knowing your strategy is key – once that is well understood coming up with the right questions is straightforward.  As Yogi Berra once said, as only Yogi could, “If you don’t know where you are going, you’ll end up someplace else.

So, what questions do you as a CPG Category Manager, or Sales Manager, need to ask to drive your brand strategy?

Reply below with your pressing questions that aren’t, currently, being answered by your DSR and analytics package.

Remember – you can always contact us to discuss ways that Vortisieze will get you to the answers you need.

 

Source: Big Data: Too Many Answers, Not Enough Questions

 

#BigData
#CPG
#CategoryManager

 

 

 

 

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#NorthwestArkansas End-of-Summer Fun – #ArkansasSwimmingHoles

With school having started within the last two weeks for most NWA students and families – this weekend may be the last chance to grab that last bit of summer fun.

If you like swimming then this article below is for you – take some time with the kids, pile into the SUV and head to a scenic swimming hole.

When I was a kid my dad took us to the “ole swimming hole” near where he grew up. Some of my best childhood memories are from those Saturday trips.  Later, I was able to make memories by taking my children to the same spot.

Check out the link below and discover some late summer fun.

See you at the office next week.
Source:  Adventure Arkansas: Swimming Holes

#NorthwestArkansas
#NaturalDam
#ArkansasSwimmingHoles

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Vortisieze Reduces Stress Related Deaths at #CPG Companies: #CategoryManagers Take Note

Well – ok – that’s a “wee bit of a stretch,” as my Irish grandfather used to say – but what is reported about long hours and early death is not a stretch – and it’s serious.

Two Yahoo articles published today show a direct coorelation between working long hours on the job and increased risk of stress- related early deaths from stroke, heart attacks and suicide.

In Japan, death by over work, or karoshi, is a legally recognized cause of death.

While the demands of CPG category managers and sales managers grow, there is pressure to keep staff levels at predetermined levels, sometimes without regard to the amount of work to be done.

This presents real headaches (and worse) to CPG vendors.  However, one possible solution is outsourcing some of the routine and mundane aspects of gaining insights – building reports and dashboards.

As a category or sales manager for a CPG company you are paid to gain insights from what is happening in your retailer environment.  But do you really need to know the nitty-gritty of building dashboards or reports?

Probably not.  At Vortisieze our founders have over 25 years combined experience in building analytics in the CPG category management arena.  Even if you aren’t ready for big data we can take some of the grunt work off of your desk.  We understand BI – included traditional DSR data warehouses – and especially the analytics engines.  MicroStrategy is our primary expertise but we know other tools as well.

Contact us today to discover how we can lesson your workload so you can focus on what is important – growing your brand.

 

Sources:

The 100 hour work week in Japan

Working longer hours increases stroke risk by up to 33%: study

 

#CPG

#CategoryManagers

#BusinessIntelligence

#CPGMarketing

 

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#CPG #CategoryManagers Take Note:  Smile, You’re on Consumer Camera

We’ve noted recently how beacon technology can track a consumer’s movement throughout the store and broadcast product specific marketing to the consumer using their smart phone.

Additionally, there is new technology that can use the existing security cameras and floor tiles to both watch movement and track traffic location throughout the store.

Of course this depends on the retailer investing in this technology, over which you as a CPG category manager or sales manager have no control.

But something you do control, or at least influence, is how, or if, you can analyze the resulting data once it becomes available.  Older DSR technologies, using database or data warehouse methods, are rigid and hard to change.  Adding new data streams takes months or years to incorporate – if attempted faster can break the data model making is difficult to load or extract data.  Difficult means time consuming.  Not what you need when your buyer’s meeting is 9am Monday and you are waiting on data loading (called ETL) and extraction (reporting) at 10pm Sunday night.

Big data is different – not just the buzz around it – but truly different from a technological point of view.

Big data allows you to add new, even unstructured data (think your latest spreadsheet creation), for analytics insights in hours or days.  So when this new consumer tracking data is made available to you analyzing and acting on it means you can take steps toward better promotions and product launches.

Big data technology is demonstrably faster in loading (literally minutes versus hours) and extraction and reporting.

Which would you rather do on Sunday night – sit at the office until midnight just waiting on your first peak at last week’s data – or home with your family watching Sunday Night Football?

Vortisieze is the first big data analytics package designed exclusively for the CPG community.

Contact us today and makes us prove just how fast Vortisieze can put you ahead of your competition.

 

 

Source:  Smile, You’re on Consumer Camera

 

#AnalyticsInRetail

#CPG

#CPGMarketing

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ZuumSocial Releases Facebook Leaderboard – Most Engaged #CPG Brands on FB

Last month Zuum announced it will begin monthly ranking of the 25 most engaging brands on Facebook.  The rankings are limited to the top brands operating in the U.S.  This is how they summarized their criteria for selection:

The goal of this is to capture the Facebook content and community traits of the top brands operating in the US. So we’ve put several constraints on which brands are selected. Brands must be operating in the US, we’ll use the US page unless there’s only a global page, brands must be consumer products vs B2B, and we’re excluding technology and entertainment brands, as our goal is to better understand how typical consumer products work on social media, and both of those categories tend to skew towards extremely high engagement due to the product type.

For July and August Monster Energy Drinks captured the top.  This month Zuum has released the rankings for a specific category CPG Dips and Dressings.  Here is the lead-in to their rankings:

CPG is a very broad business category, with sub-categories like soft drinks having brands with some of the larger social media fan counts anywhere. In our July ranking of the 25 most engaging brands on Facebook, the three top brands are CPG.

Of course, the CPG umbrella includes many smaller brands, with more niche or regional appeal. One subcategory in particular is dips and dressings. Below is the Facebook Leaderboard for some of the top dips and spreads brands in the US for the month of July.

You can see that the fan counts, while not what you’ll see from Pepsi or Coke, are still substantial. Posting volume is a little light in this category, with even the top brand, Sabra, only posting about 1 time per day.

Once the novelty of these new rankings are gone what value does this have to a CPG category manager or sales analyst?  “Well – Nancy . . .” – if your analytics cannot lay this new, unstructured data alongside your retailer supplied POS and inventory data – there is no value outside of water cooler talk.

This is one of the major limitations of rigid, outdated, DSRs built using data warehouse technology.  Rigid in the sense that to add new data feeds takes months – sometimes years.  The loading of the data – including current feeds – is long and cumbersome.  From a technological point-of-view these simply will break under the weight of increasing amount and type of data.

If you want to use these new – and exciting – data points (social media, weather, and et. al.) to draw insights and correlations to sales – then the only technology that can pull this off – today – is Big Data.

That’s what the buzz is about.  Will your brand be buzzing going forward?

We are Vortisieze.

Contact us today for a complimentary consultation on your BI strategy.

 

Sources:

Facebook July Leaderboard for CPG Dips and Dressings

http://blogs.imediaconnection.com/blog/2015/08/17/facebook-july-leaderboard-for-cpg-dips-and-dressings/

 

Ranking 25 of the most engaging brands on Facebook

http://zuumsocial.com/ranking-25-of-the-most-engaging-brands-on-facebook/

 

July Ranking of 25 Most Engaging Brands on Facebook

http://zuumsocial.com/july-ranking-of-25-most-engaging-brands-on-facebook/

 

#CPG

#Business Intelligence

#BigData

#AnalyticsInRetail

NYC Shoppers (Still) Want Walmart

This very interesting article on Forbes.com yesterday reminds those of us who live and work in the shadow of the Walmart Home Office that there are areas where there is no Walmart store.

New York City is one such area – no Walmart exists within the five boroughs.  The mayor and most of the city council oppose Walmart entering NYC.

As the article points out – “25 percent of respondents go to the suburbs to shop Walmart stores. And it is obvious that many New Yorkers go to the burbs to shop other big box stores, discounters and outlet malls.”

The arguments against Walmart remain the same – and each one is Luddite in nature.

  • Walmart pays low wages. There is no denying that Walmart pay and benefits are modest by comparison, especially at the part-time and entry levels.  However, for people who just want jobs Walmart is the perfect place to start and learn retail.  And, Walmart hires from the communities around its stores.  Since the retailer appeals to middle to low income shoppers this means that those who need opportunity the most can find it – with the chance to move up the ladder in position and pay.  Additionally, those opposed to Walmart based on pay fail to look at the comparison of revenue per associate with other “high paying” companies – Walmart is at the low end of that scale.  And the margin of profit is much lower for Walmart, compared to other Fortune 500 companies, so there is a smaller pie to slice.  However, lower margins means lower prices – great for the consumers.
  • Walmart is anti-union. Walmart has worked actively against unionization of its workforce throughout its history.  Advocates of unions fail to demonstrate the benefit to their workforce.  By artificially raising the cost of labor unions in private industry have reduced available jobs so fewer people are working.  When the best hand-out is a hand-up through job opportunities how is limiting the number of jobs available helping the unemployed find skill training through work?
  • Walmart is hurting small, local businesses. On April 1, 1975 Walmart store #85 opened in my hometown.  Before then, only small, family-owned stores were available for items like hardware and clothing.  My mother drove my sister and I over 30 miles to the state capitol to shop at the large retail chains (primarily JCPenny for school clothes since her first job was as a clerk at JCP).  The local department store simply charged prices that were too high for my budget-minded parents.  Yes – after Walmart opened those businesses lost revenue and, over time, most eventually went out business.  What the critics fail to acknowledge is that Walmart has always offered lower prices for the budget minded.  As to the jobs lost through the shutdown of small, local business – Walmart more than made up for that by hiring more people than what was lost – and the part-timer had greater flexibility in the hours worked – the full-timer has opportunity for advancement that were not available through family-owned stores.  When was the last time a store owner promoted a talented, ambitious person over his son?

I have never understood the resistance to a retailer like Walmart, which achieved its success through competing for the hearts, minds and wallets of the consumer.  And it must still compete today or go the way of Sears.  The consumer is always the winner and should always foremost in the minds of the political elite.

Should these elitists have their way and force Walmart to change its model in any or all the points above then the inevitable result will be higher prices to the consumer.  Essentially – a hidden tax to protect constituent groups these politicians depend on for power.

Why is this important to category managers and sales analysts?  When the retailer you partner with is arbitrarily blocked from entering certain “political zones” you have fewer outlets through which to efficiently distribute your product.

The bottom line is – your brand loses – the retailer loses – and most importantly – the consumer loses through higher prices and fewer choices.

And that’s just my opinion.

 

Source:  NYC Shoppers (Still) Want Walmart

#Retailing

#Walmart

#CategoryManagement