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.

#PredictiveAnalytics market will be worth $5.24 billion by 2018 illuminated by new report

Interesting introductory article announcing a predictive analytics report.

The report “Predictive Analytics Market [(Fraud, Risk, Marketing, Operations), Verticals (BFSI, Healthcare, Environment, Government, Retail, Energy, Manufacturing, Transportation, Travel, Telecom, Sports)]: Worldwide Market Forecasts and Analysis (2013 – 2018)”, defines and segments the predictive analytics software market into various sub-segments with in-depth analysis and forecasting of revenues. It also identifies drivers and restraints for this market with insights on trends, opportunities, and challenges.

Global predictive analytics market is driving on the emergence of massive amount of data deluge and innovative technology implementations. Business enterprises focus has changed from traditional Business Intelligence (BI) solutions to predictive analytics, because they have understood the importance of data and its analysis for the future estimation.

Traditional BI solutions are striving to sustain in this highly competitive world. The transformation of BI to predictive analytics gives new opportunities to the big players as well as new startups in this market.

This article highlights the how big data is outpacing traditional BI – both in ability to deliver actionable insights and the technological infrastructure to handle massive amounts of data, much of which is ad hoc and unstructured.

For a complimentary consultations about your analytics and insights needs contact us today.

Source:  Predictive analytics market will be worth $5.24 billion by 2018 illuminated by new report



What Do Marketers Really Want in #DataAndTechnology?

Marketers get data – or at least they get the importance of data. Data answers questions such as:

  • Can you help me understand my customers?
  • Which customers are my best customers and why?
  • How can I find profitable new customers?
  • How can I sell more to existing customers?
  • How can I retain my existing share of each customer?
  • How can I increase the velocity of my sales?
  • How can I integrate my marketing through all available channels?
  • How can I maximize the impact of my marketing budget?

However, data is just data unless you have the marketing technology to “make the data talk.” Marketers are increasingly in charge of marketing technology spend to drive better data outcomes. In fact, technology has become the core of marketing. According to research by IBM, marketing executives are adopting technology in the following areas:

  • 88% Customer Relations

  • 83% Digital Marketing

  • 68% Customer Analytics

  • 49% Mobile Advertising

Read more. . .

Contact us today to discover how Vortisieze analytics can take you to the corner of Marketing and Technology.

Source:  What Do Marketers Really Want in Data and Technology?



#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






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.



Facebook July Leaderboard for CPG Dips and Dressings



Ranking 25 of the most engaging brands on Facebook



July Ranking of 25 Most Engaging Brands on Facebook




#Business Intelligence




Top 3 Roles Required By #CPG Companies for #BigData #Analytics Success

Big data and analytics are at the top of the corporate agenda these days.  While big data and related technologies are relatively new analytics are certainly not new to the CPG category managers and sales analysts on the front line with retailer buyers.  Most CPG companies use some type of analytics to make sense of retailer supplied POS and inventory data.

However, as big data analytics technologies jump front and center ahead of outdated DSR and other data warehouse structured data (read hard to change and easy to break) acceptance by the enterprise, especially those who require the insights in their weekly – if not daily – work with buyers, is essential for success.

Information-management.com published, on Monday, August 10, 2015, a clear and concise explanation of the three essential roles that must be filled by a CPG company to have business analytics success.

  • Suits (consumers of data) – The domain and sector business specialists who have a strong understanding of the organization’s broader business goals and strategy. They use analytics to optimize their business by providing deeper insights and increased efficiencies. They tend to be less focused on the “how” of big data and more focused on the “why.”
  • Math whizzes (producers of data) – The analytics specialists who construct databases, develop analytics scripts / models and design visualizations and dashboards for analytics consumers. They tend to be more focused on crafting the solution using innovative techniques and advanced technology, but generally don’t have as deep of an understanding of the business problem as the suits.
  • Techies (enablers of data) – The architects, who create the infrastructure, configure and implement analytics software, and establish data standards and management procedures. They are focused on enabling the sustainable operation of analytics solutions at an enterprise level, and tend to spend limited time on the specific business analytics solutions.

The article refers to the first role as Suits, invariably meaning upper management, for CPG companies this really means category managers and sales analysts who must gain actionable insight from the analytics.

The other two are more technical in nature – the geeks – typically in the IT departments of major CPG enterprises.  For most ‘suits’ – dealing with IT can be frustrating and painful process.  But it doesn’t have to be that way.

Working with Vortisieze those two roles are fulfilled by our team and technology (big data and cloud computing) and eliminates the need to engage IT at all.  Whew.

To learn more about how Vortisieze can bring you actionable insights better, faster and cheaper (and immensely less painful) contact us today for a complimentary BI consultation.

Even if you are currently with one of the other guys, Vortisieze can show you how to reduce cost of ownership of a BI platform while adding rapid flexibility to add additional streams of (sometimes unplanned) data.


Source:  The Three Types of People You Need for Analytics Program Success






How GoPro Is Using #BigDataAnalytics in The #CloudComputing to Kick Everyone Else’s Butt

We run around talking about how important analytics is and yet there are few really compelling examples of how well it is working. Part of this is because the vast majority of implementations are still in process and haven’t gotten to value yet, part is because they were done wrong and value wasn’t found, and part because firms don’t like sharing with competitors how they are kicking those competitor’s butts.

GoPro, however, is the perfect example of how analytics are being used competitively to out-execute much larger companies like Sony.

So starts the article on TechSpective.net published on August 3, 2015.  This article is a must read on how to use big data and analytics to out maneuver your competitor.  The challenge is adapting the technology to brick and mortar retail.

Interestingly GoPro implemented the same strategy that we at Vortisieze execute every day.  Data in the cloud, strong big data technology and a top-notch analytics engine.  Like GoPro, Vortisieze partners with Cloudera.  To round it out though, Vortisieze maintains its own data cloud and leverages MicroStrategy (et al.) for the analytics engine.

Contact us today for your complimentary BI consultation.


Source: How GoPro is using Amazon, BMC, and Cloudera to kick everyone else’s butt



#BigData and the 2016 Presidential Election #PredictiveAnalytics

What Nate Silver did for President Obama in the 2008 election cycle Deep Root Analytics, a media analytics company formed in response to the 2012 Republican loss in the presidential election, is working to do for several Republican candidates in this election cycle.

Deep Root Analytics partners with data-blending and advanced-analytics company Alteryx to merge voter file information, set-top box data and commercial data to optimize audience targeting and TV ad-space buying.

While this is very interesting to observe ultimately we in the retail space must learn from the lessons that these, and other big data exercises, provide.  As consumers move from one retail channel to another (actually many) channel using all the data available is paramount.  No longer will solely relying on the POS/Inventory data your retailer provides you be enough.

Wondering how to make this all come together for you?  Contact us today for a complimentary consultation.  And enjoy this election as you watch others use big data to move their candidate (brand) forward in the voter’s (consumer) eye.


Source:  How Data and Programmatic TV Will Dominate the 2016 Presidential Campaign





#DataAnalyticsTechnology: Turn insight into action with #PredictiveAnalytics


Good Saturday morning – hope you are enjoying your cup of coffee.  Now enjoy a -not too bad- geeky article on predictive analytics and how to make use of it.

See you again next week at the office.


Source: Turn insight into action with predictive analytics









5 Ways Brand Marketers Can Innovate With #Analytics – #AnalyticsInRetail

Daniel Kehrer heads marketing at MarketShare a company in advanced marketing analytics technology, helping brands improve marketing performance and grow revenue.

In an online Forbes article he writes about 5 Ways Brand Marketers Can Innovate With Analytics.  Below are his thoughts.

  1. Use insights to enrich what you offer: Advanced marketing analytics technology doesn’t just plug you into Big Data. It also connects you to Big Analytical Models (BAM) and Big Visualization to generate insights you can use to innovate customer-facing products and services. It works because you now know a whole lot more about your customers than you did before. For example, with cross-channel attribution technology you gain a clear picture of what influenced a customer – in both the online, offline and non-media worlds – to ultimately make a purchase. That knowledge helps you make better decisions about how to allocate marketing dollars. But it also is valuable intelligence you can use to foster product, service and feature innovation.
  2. Perfect your programmatic: In the headlong rush to let machines do their marketing, companies are channeling huge sums into programmatic ad purchases. But just because machines are handling ad placements at lightning speed doesn’t mean the process is efficient and effective. Marketing innovators are combining the latest attribution technology with media buying engines into a new approach to match their media spending to the analytical insights they are generating.
  3. Eliminate digital and channel silos: Analytics technology brings offline, online, top-down and bottom-up worlds together for the first time. If you think about marketing measurement only in terms of digital, you may be missing a big part of the picture. Customers don’t live a purely digital existence. They are influenced by many things in the offline world, including non-media factors such as the economy, word-of-mouth, or the weather. Innovating in digital today means understanding that you can’t measure impact purely in digital terms.
  4. Be predictive: Most marketers still measure most of what they do by looking in a rear view mirror. But innovation is not backward looking. It has both feet firmly planted in the future. The predictive component of today’s top analytics technology makes it possible to peer into the future more accurately than ever. And – importantly – the same technology lets you quickly simulate possible outcomes before putting money on the line. Innovators are using forward-looking insights to test possibilities and change direction on the fly as new insights become available. The old “campaign” mentality of set-and-forget is gone.
  5. Merge brand measures with direct response: The innovation here is using analytics technology to optimize marketing on both a long- and short-term time horizon at the same time. In the past, brand campaigns were always considered long-term. Their impact was difficult to measure and took place cumulatively over time. Direct response was different, especially in the digital realm. It showed quick results that were easier to quantify. But measuring only one or the other doesn’t provide an accurate picture of what’s really going on. Cross-channel attribution technology changes that and opens new doors to innovation.

By using advanced analytics technology not just for measurement and decision-making, but also for innovation, marketing leaders are driving growth and customer engagement to new levels.


Source:  5 Ways Brand Marketers Can Innovate With Analytics – Forbes