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NWA BI Meetup – Thursday, April 21

Hey NWA BI Data Ninjas!

We’ve got a place, a date and time for our next meetup. Thursday, April 21st at 5:30 PM – at the Mars offices thanks to Chris.

We have not locked in an agenda yet, so if you would like to present a project you are working on or have something you would like to see let me know.

Also, you may have noticed that we have changed the user group name. 75% of the survey respondents preferred “NWA Business Intelligence User Group” as opposed to the previous “Arkansas Area MicroStrategy User Group”. I’m sure we will still talk quite a bit about MicroStrategy, but this opens us up to other tools more easily.

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My Analytics Budget is $0, So Now What?

The reality is, that some companies just don’t have a big enough presence in Walmart to warrant any kind of real IT spend. It was a huge deal when they brought on their first full time analyst that has some experience, and is located in Bentonville so that they can meet with their buyers whenever they need to. You get your company issued laptop, with a fresh install of Microsoft Office, a retaillink ID, and you are off and running, right?

FundingPlanPRONo Problem – you are an excel wizard I bet. But as you are constantly maintaining horrific formula pages and trying not to break everything when your boss is requesting new feature engineering in their 20 page workbook, have you ever wondered, there has to be a better way?

You get what real data integration brings. Process automation that can refresh data without inventing the dashboard each and every single week. Feature engineering without such massive headaches. Adding new data sets to a model in a database, not a new tab. Oh the things you could do if you could get that time back.

Something that came about in MicroStrategy 10 was a new feature called Data Wrangling. This allowed an analyst to combine corporate data from the data warehouse with adhoc data and gave you come cleansing and harmonizing functionality. You’ve seen this need before. You get a data set where some states are all spelled out, while others have the two letter code. You see two rows for Texas (Texas and TX), and you have to scrub this in excel before it is usable. Or your data warehouse team does this in their ETL. Well, now you can do this directly in MicroStrategy with data you import yourself. You only need the $600 copy of MicroStrategy Desktop. Build a recipe and save it, and now this is semi-automated when you need to refresh your data. All you need to do is kick it off, and all of your cleansing tasks have been saved and are rerun.

I’ve always thought this was cool, ever since they showed this off at MicroStrategy world last year. But back to your budget of $0. Even $600 is going to be tough to get permission for. Well, I took a tour through a brand new product today that looks like it can do many of the same data wrangling techniques as MicroStrategy Desktop, but the cost is FREE. $0. Nada. It’s called Talend Data Preparation. It has a free version and a subscription version if you would like to work in a big environment and integrate with their other ETL platforms, but the free version looks like it is perfect for the lone rangers out there munging spreadsheets and cleaning data each week just so they can get to their analysis.

Check it out here: https://www.talend.com/products/data-preparation

You still need to kick it off when it’s time to refresh your data, but it lets you build recipes and save them, automating much of your work to refresh your data. It looks like it can connect to HTTP, local, and HDFS data sets.

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Is there ever a perfect storm for suppliers?

walmart-snow

In the article below, RetailWire talks with Weather Expert Paul Walsh on customer behavior, and what retailers are doing to balance things out.

“What almost always happens when you get a big event like this is you really see three phases from a retailer perspective,” Walsh told Retail Dive. “Actually, it’s really from a consumer perspective, and the retailers’ job is to be ready for their customers’ needs.”

Retailers don’t lose or gain sales as weather events hit, unless their supply chain is not prepared for the push ahead of the event. What this signals is that retailers are getting out ahead of these events using weather data, and suppliers are being increasingly asked to help anticipate supply chain strains ahead of the curve as well.

“The weather ate my homework” is no longer going to be a viable excuse in the coming months and years. There will be an expectation that a supplier network will need to be out ahead of this. It won’t matter if you are a top tier supplier maximizing your category, a mid-tier supplier scratching and clawing for their business every day, or a small supplier just trying to keep up. The forecasting models are becoming more and more accurate. Not having your products attributed to weather sensitivity will be a hindrance.

oos-walmart-snowstormIt’s also important to consider “just weather data” may not be enough. Feature engineering both the weather data to tailor it to your products and your products attributes may be required to extract enough insights as to what weather patterns and events are effecting your sales. Make sure you have people on board that can do this with your data, and it may take some experimentation, since no two product assortments are the same. Having the data at the same level as your geography dimension also helps take some of the burden off of your analytics platform. Its a lot of data, and most of it comes at the weather station level.

Have I mentioned yet that we can do this for you? We can provide weather data at the zip code level, and we have experts on staff that can help with your feature engineering, as well as your reporting and data mining.

You can read the whole article here

Please contact us today to see how we can help you with your BI weather challenges.

28+ Category Management Articles To Improve Your Work in 2016

From the Category Management Learning Forum…

28 BLOG POSTS TO IMPROVE YOUR WORK IN 2016.
1. CatMan Overview-Back To The Foundations:http://blog.cmkg.org/blog/category-management-overview-back-to-the-foundations
2. Why be Retailer Centric in your CatMan Approach?:http://blog.cmkg.org/blog/retailer-strategy_why-be-retailer-centric-in-category-management
3. Category Definition: 3 Considerations For Success:http://blog.cmkg.org/blog/category-definition-3-considerations-for-success
4. Why All Categories Are NOT Created Equal:http://blog.cmkg.org/blog/category-roles_are-not-created-equal
5. Category Plan Success-Driven By Solid Foundations:http://blog.cmkg.org/blog/category-plan-success-category-management-foundations
6. 3 Tips For Effective Data Insights: http://blog.cmkg.org/blog/category-management-data-insights
7. 5 Ways to Improve Data Insights with a Category Assessment:http://blog.cmkg.org/blog/analysis-and-data-insights-category-assessment
8. 3 Reasons to Develop Your Tactical Analytic Skills:http://blog.cmkg.org/blog/category-management-category-tactics
9. 3 Ways to Address CatMan Data Gaps:http://blog.cmkg.org/blog/category-management-data-gaps
10. 3 Steps to Create Killer Implementation and Category Plans:http://blog.cmkg.org/blog/implementation-and-category-plans-for-retail
11. 4 Ways to Give Your CatMan Presentations a Makeover:http://blog.cmkg.org/blog/4-ways-to-give-your-category-management-presentations-a-makeover
12. 3 Tips: Turn Your Excel Skills From Good to Great!:http://blog.cmkg.org/blog/category-management-excel-skills
13. Remember CONSUMER When Analyzing Sales Results: Consumer Panel Data: http://blog.cmkg.org/blog/consumer-panel-data
14. 5 Ways to Analyze Your Retail POS Data:http://blog.cmkg.org/blog/5-ways-to-analyze-your-retail-scanned-sales-data
15. 4 Steps to Uncover Strategic Business Insights/Opportunities:http://blog.cmkg.org/blog/4-steps-to-strategic-business-insights
16. 8 Ways to Move to Advanced CatMan Analytics:http://blog.cmkg.org/blog/8-ways-to-move-to-more-advanced-category-management-analytics
17. 4 Ways to Inject Strategy Into Your Assortment Analysis:http://blog.cmkg.org/blog/4-ways-to-inject-strategy-into-your-efficient-assortment-analysis
18. Key Considerations for Strategic Pricing Analysis:http://blog.cmkg.org/blog/key-considerations-for-strategic-pricing-analysis
19. Get Back to the Basics to Improve Promotion Effectiveness:http://blog.cmkg.org/blog/get-back-to-the-basics-to-improve-promotion-effectiveness
20. Why CatMan is the Answer to Collaboration, Shopper-Centric, and Big Data.: http://blog.cmkg.org/blog/category-management-collaboration-shopper-centric-big-data
21. Fact-Based Presentations-5 Secrets!: http://blog.cmkg.org/blog/fact-based-presentations-5-secrets
22. How Strategic Is Your Organization in Space Management?:http://blog.cmkg.org/blog/strategic-space-management-approaches
23. 4 Best Practices for Advanced Shelving: http://blog.cmkg.org/blog/4-best-practices-for-strategic-space-management-advanced-shelving
24. Drive Shopper Satisfaction: Retail Store Clusters in 3 Steps:http://blog.cmkg.org/blog/retail-store-clusters
25. Achieve Shopper Satisfaction in 3 Steps:http://blog.cmkg.org/blog/achieve-shopper-satisfaction-in-3-steps
26. How do Category Managers Affect a Retailer Income Statement?:http://blog.cmkg.org/blog/how-do-category-managers-affect-retail-income-statement
27. Product Supply Chain Is More Than Just Moving Product:http://blog.cmkg.org/blog/product-supply-chain-is-more-than-just-moving-product
28. Strategic Selling Skills: How to Develop Them & Why You Need Them: http://blog.cmkg.org/blog/strategic-selling-skills
29. Improve Collaboration and JBP Results in 3 Steps:http://blog.cmkg.org/blog/improve-collaboration-and-joint-business-planning-results-in-3-steps

 

The original article can be viewed here

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Is A Data Tidal Wave Heading Your Way? #CPGBI #BigData

Consumer Goods Technology came out with a whitepaper today on Bridging the Data Divide. In this whitepaper is a quote from Gordon Wade, senior vice president of category management best practices at the Category Management Association (CMA):

“Every category manager, whether at a retailer or a manufacturer, has more data than anyone could possibly review, much less analyze and understand,”

The paper also goes on to discuss some very neat things people are doing with mobile and shopping data to simulate store sales and people movements through aisle changes and shopper personalization. This might be the beginning of the tidal wave that is coming. My question to you is – what is your data strategy? Are you feeling like you are already drowning in data? The goal is not to drown retailers or suppliers, but to find ways to integrate data that will keep your business floating in the short and long term future.

data-explosion2

It is clear that the ability to combine new data sources in innovative, cohesive ways will be integral to grocery and CPG success. We can help with that. Store sales and inventory, weather, demographics, store traits, social media, supply chain – we combine all of this into powerful, user driven analytics in a drag-n-drop, build your own reporting and dashboard environment. We are also willing to take in your specific data sources to give you a complete picture of your business, and apply statistical models for predictive metrics to your data for even more insights.

Please contact us today to see how we can help you out of your data deluge.

Source: CGT

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The Crystal Ball – Exploring a Walmart POS Seasonal Forecast #CPGBI

One of the most common uses of machine learning in analytics is to forecast time based data. It’s the quintessential sales question – what will my sales look like next month, or next quarter, or next year even – the proverbial crystal ball, if only it were that simple. Something that we were fairly quickly put together using MicroStrategy’s visual insights and R-Integration is an “Ordinary Least Squares” regression algorithm to fit the best curve that captures the general trend and seasonal variability of Walmart POS data to predict future sales.

The formula is:

Y = bTrend*Trend + Σ (bSeason_i*Seasoni) + bIntercept

where

  • Y is a numeric metric (called the Dependent Variable)
  • Trend is a numeric metric that’s an arithmetic sequence of monotonically increasing values
  • Seasoni is a binary indicator metric derived from Season, a numeric or string metric that represents each season. Binary indicators have a value of 1 for the i-th season and are 0 for all other seasons. For n seasons, there are n-1 XSeason_i variables
  • bTrend, bSeason_i, and bIntercept are coefficients determined by the regression algorithm.

2015-09-28 09_32_23-WM POS Seasonal Monthly Forecast. MicroStrategy 9

As sales drop in for the coming months, we should be able to gauge the accuracy of our prediction for the rest of the year. If this hold true, we could use it for some of our business decisions going forward. We could also look at just the latest complete months, so we would not see that monthly drop in month 201402. We could also look at this weekly by switching out just a couple of metrics.

Something else we could do is create a variance against actual POS sales, and if the variance exceeds some number, like 10% difference plus or minus, we could create an alert and send out warning emails to key people in our business so that they can plan for unanticipated high sales, or research a drop in sales.

Please contact us to see how we can help you leverage regression analysis with your data to help predict your future!

Business Intelligence vs Analytics vs Big Data vs Data Mining #CPGBI

The business intelligence landscape is rapidly changing, and there is a lot of confusion on what the difference between BI, Analytics, Big Data, and Data Mining is. Whats more, you turn your head for just a minute and then there are whole new classes of terms that you’ve never even heard of before.

In the below article from Dennis Junk at Aptera’s blog, he breaks it down into four main categories to help you understand. As a CPG company supporting Walmart, I believe it is important to have a strategy for all of these concepts – especially in the wake of their new supplier terms and squeezing they are employing. If you don’t, we can help you with that, as we are a full service Business Intelligence company that can deliver Analytics from Big Data sources and use the R statistical package to mine the data for meaningful insights.

Business Intelligence:

This is the broadest category and encompasses the other three terms here (at least as they’re used in a business IT context). BI is data-driven decision-making. It includes the generation, aggregation, analysis, and visualization of data to inform and facilitate business management and strategizing. All the other terms refer to some aspect of how information is gathered or crunched, while BI goes beyond the data to include what business leaders actually do with the insights they glean from it. BI therefore is not strictly technological; it involves the processes and procedures that support data collection, sharing, and reporting, all in the service of making better decisions. One of the trends in recent years has been away from systems that rely on IT staff to provide reports and graphs for decision-makers toward what’s called self-service BI—tools that allow business users to generate their own reports and visualizations to share with colleagues and help everyone choose what course to take.

Analytics:

This is all the ways you can break down the data, assess trends over time, and compare one sector or measurement to another. It can also include the various ways the data is visualized to make the trends and relationships intuitive at a glance. If BI is about making decisions, analytics is about asking questions: How did sales for the new model compare to sales for the old one last month? How did one salesperson do compared to another? Are certain products selling better in certain locations? You can even ask questions about the future with systems that perform Predictive Analytics. Some companies treat analytics and BI as synonymous—or simply rely on one to the exclusion of the other. But analytics is generally the data crunching, question-answering phase leading up to the decision-making phase in the overall Business Intelligence process.

Big Data:

This is the technology that stores and processes data from sources both internal and external to your company. Big Data usually refers to the immense volumes of data available online and in the cloud, which requires ever more computing power to gather and analyze. Because the sources are so diverse, the data is often completely raw and unstructured. Since you’ll probably be using this data for purposes it wasn’t originally intended to serve, you’ll have to clean it up a bit before you can garner any useful insights from it. The systems you put in place internally to track KPIs are obviously the main source you turn to when you need to answer a question about your business, but Big Data makes available almost limitless amounts of information you can sift through for insights related to your industry, your business, your prospective customers. Big Data is the library you visit when the information to answer your questions isn’t readily at hand. And like a real library it allows you to look for answers to questions you didn’t even know you had.

Data Mining:

Finding answers you didn’t know you were looking for beforehand is what Data Mining is all about. With so much information available, you can never be sure you’re not overlooking some key fact pointing the way to better performance. Data Mining is the practice of sifting through all the evidence in search of previously unrecognized patterns. Some companies are even hiring Data Scientists, experts in statistics and computer science who know all the tricks for finding the signals hidden in the noise. Data Mining probably fits within the category of analytics, but most analytics is based on data from systems set up to track known KPIs—so it’s usually more measuring than mining.

Not everyone will agree on these terms, as Dennis points out in his article, but it’s a good start. As a core strategy I believe your BI should encompass all three: easy to use analytics that allows your users to ask their own questions, big data to capture MORE than just sales data, and data mining so that you can leverage all of your data for the best insights possible.

Please contact us to see how we can help you create a strategy in all of these areas that might unlock a competitive advantage you didn’t know existed!

Source: http://blog.apterainc.com/

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Here Are the #CPG Brands Men and Women Trust Most | What Can #CPGMarketers Learn?

Interesting read.

Results are based on a survey of nearly 89,000 U.S. consumers age 15 and older in June and July of this year. Each respondent rated 40 randomly selected brands, and each brand received about 1,000 ratings. (A few months ago, Harris Poll released research about non-CPG products and services. In that study, top brands included Subway—before the revelations about pitchman Jared Fogle—and Target, despite the company’s high-profile data breach in 2013.)
Overlaps on the CPG lists underscore a key societal insight: Some responsibilities and activities, and the products associated with them, are no longer viewed as mainly the domain of one gender or another.
“Family backyard barbecues [Reynolds] and family bumps and scrapes [Band-Aid, Neosporin] are becoming gender-neutral domains,” de Vere said. “If I were a brand manager or advertising executive, I would be really intrigued to understand whether or not men and women see different benefits in some of these overlapping brands.”
Marketers should keep in mind that “brand choice for both men and women is emotional and rational,” he said, and strive to understand consumer motivation on both levels as they construct campaigns.

Top-10 Most Trusted CPG Brands for Men

  1. Band-Aid Adhesive Bandages
  2. Heinz Ketchup
  3. Neosporin Antiseptic
  4. Reynolds Aluminum Foil
  5. Duracell Batteries
  6. Ticonderoga Pencils
  7. Glenlivet Single Malt Scotch Whisky
  8. Energizer Batteries
  9. Ghirardelli Chocolate
  10. Scotch Tape

Top-10 Most Trusted CPG Brands for Women

  1. Ziploc Food Storage Bags
  2. Band-Aid Adhesive Bandages
  3. Reynolds Aluminum Foil
  4. Neosporin Antiseptic
  5. Dawn Dish Soap
  6. Kleenex Facial Tissues
  7. Sharpie Markers
  8. Q-Tips
  9. Clorox Bleach
  10. Tide Laundry Detergent

What can CPG marketers learn from this?  This data could be useful overlaid with other data in a big data analytics tool.

Contact us today for a complimentary consultation.

 

Source:  Here Are the CPG Brands Men and Women Trust Most

#BigData
#BigDataAnalytics
#CategoryManagers
#CPG
#CPGMarketing

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

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#Walmart: The Big Data Skills Crisis And Recruiting #BusinessAnalytics Talent

As the amount of digital information generated by businesses and organizations continues to grow exponentially, a challenge –or as some have put it, a crisis–has developed.

There just aren’t enough people with the required skills to analyze and interpret this information–transforming it from raw numerical (or other) data into actionable insights – the ultimate aim of any Big Data-driven initiative.

One survey recently carried out by researchers at Gartner IT +0.93% found that more than half of the business leaders they queried felt their ability to carry out analytics was restricted by the difficulty in finding the right talent.

Overcoming this problem is a challenge that all companies will have to face, and market leaders–aware that they have more to lose than many by falling behind in the race to keep up with technology–have come up with some innovative solutions.

Walmart decided to apply one of the fundamental weapons in the Big Data arsenal–crowdsourcing–to the problem, with positive results.

Last year, they turned to crowdsourced analytics competition platform Kaggle. At Kaggle, an army of “armchair data scientists” apply their skills to analytical problems submitted by companies, with the designer of the best solution being rewarded – sometimes financially, in this case with a job.

To continue reading click the source link below.

 

Source: Walmart: The Big Data Skills Crisis And Recruiting Analytics Talent

 

 

#DataForAnalytics

#DataAndAnalytics

#BigData