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Get A Data Scientist to Work On Your Problem For Free

According to the Consumer Goods Technology and RIS 2016 Analytics Study, 33% of companies lag behind their competitors in analytics skills and people, and 31% lag behind in analytics tools.
2016-05-17 10_27_33-2016 Retail and Consumer Goods Analytics Study.pdf - Adobe Reader

In an effort to help, we are seeking CG companies that have a real data science need or question that they would like to see solved or automated, and we would like to help for free. Whats the catch? Well, we have a short survey we want to collect data on and use to analyze the industry, specifically which industries are having the most problems, but we also want to examine the real world problems out there and see if we can help solve them. We are not collecting any personally identifiable information to sell off to anyone – we just want to chew on some data science problems with you and see if there things we can do to help. With 77% of company respondents to the Consumer Goods Technology and RIS 2016 Analytics Study stating they either planned to or did not have any investment in data science, here is a way to possibly test drive a data science team at no cost to you.

Just fill out the 18 question survey below and if you have a real question and think we can get the data to solve it – then you are in! We are not limiting the winning questions so more than one question could be selected.

2016-05-17 10_41_56-Retail Questions Needing Answers

CPG Retail Data Question Survey

If you have any questions about the survey or want to find out what we do, feel free to contact us here.

 

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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.

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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!

What is Cluster Analysis? And Why Use It?

2015-09-14 15_01_17-K-Means Clustering - Store. MicroStrategy 9

Why would you want to use cluster analysis on your retail sales data? Well, cluster analysis helps you identify non-independence in your data. Here is an example to help illustrate the point. Lets say we want to ask loads of teachers from many different schools what they think of their principal. If you ask two different teachers from two different schools, you will get two completely different answers that will be independent. But, if you ask two teachers from the same school, the answers will not be completely independent and could be very similar – but not EXACTLY the same.  Now if your job was to take the raw data and try to predict which school each teacher came from based on their answer – then you have an application of clustering.

2015-09-15 09_53_10-K-Means Clustering - Store - Original. MicroStrategy 9

The same thing can be applied to Walmart store performance for a supplier. You have some data points for a store like how long that store has been open, how many competitors it has located in its vicinity, what was your products sales performance for that store, some demographics for that area like unemployment and population, possibly even some historic weather data. Now you use a clustering algorithm to group your stores that are most closely related. This could be the first step in identifying under performing stores and why. It could give you a viable store list for a product test based on more than sales performance. It might help you further identify your product identity and who your actual customers are using enough demographic data. You might not find anything you didn’t already know. The important thing is that you are diving into your data to truly understand it on a level you never have before, and uncovering one of these nuggets could be millions of dollars difference to your company.

Once you’ve built your base analysis, and in our case we built our report that you see above, turned it into an in-memory cube, and then built a MicroStrategy dashboard on top of it – we can then explore slicing and dicing our data along the different data points to help identify if any of the metrics in our analysis are a key contributor to a cluster alignment. This way we can determine what factor affects sales the most. Could it be store age? or store square footage? or unemployment? Ethnic breakdown? What of these are driving markdowns?

The great thing about using this analysis as a MicroStrategy dashboard is that it is pretty easy to tweak to look for your top performing stores, and refreshing the data source is very easy. In fact, this report could be automated each week and emailed to you. There might even be an application to look for cluster changes and have something like that generate an alert so you only need to be bothered if anything changes.

Contact us today to discover how Vortisieze analytics can help you explore your own data science.

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Retailers to increase IoT spend fourfold by 2020 to US$2.5bn | #BigData #TechnologyTrendsInRetail

Anticipating the winds of change, major retailers are expected to increase their investment in internet of things (IoT) technology fourfold to US$2.5bn by 2020.

In the commercial space, IoT in retail is one of the clearest examples where connected network technology could have a significant impact on customer interaction in stores.

In particular, retailers have already begun investing in IoT hardware, including Bluetooth beacons and radio frequency ID (RFID) tags that allow them to send information to them in-store and keep a tab on stock and price levels, respectively.
Read more . . .

How will your DSR handle all that new, differently structured data? (Hint – it won’t!)
Contact us today to discover how Vortisieze analytics can bring this, and many more, types of data to your analytics package.

Source: Retailers to increase IoT spend fourfold by 2020 to US$2.5bn

#BigData
#CPG
#CPGMarketing

#TechnologyInRetailIndustry

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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

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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