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.

How #PredictiveAnalytics is Changing the Retail Industry | #CPG Take Note

This article is so important we are reprinting it in its entirety. As always, the link to the source is below.
Please contact us to see how predictive analytics can give you the competitive advantage over your brand’s competitors.

Ideally, a retailer’s customer data reflects the company’s success in reaching and nurturing its customers. Retailers built reports summarizing customer behavior using metrics such as conversion rate, average order value, recency of purchase and total amount spent in recent transactions. These measurements provided general insight into the behavioral tendencies of customers.

However, reports summarizing average behavior don’t provide the useful insights needed to determine how individual customers are likely to behave because general behavior tendencies are simply too broad. In order for retailers to create a meaningful dialogue with customers that honors the shopper’s preferred level and mode of engagement, it takes more than summarized reports, which is why customer intelligence and predictive analytics provide the opportunity to significantly change the retail marketing industry.

Customer intelligence is the practice of determining and delivering data-driven insights into past and predicted future customer behavior. To be effective, customer intelligence must combine raw transactional and behavioral data to generate derived measures. The process can best be described using the saying, “It’s not the data that is collected, it’s the data that is created.” Put into a predictive modeler’s perspective, the team not only collects a large amount of data, but also contextualizes that data by building derived attributes that provide additional insight into customer intent.

But how do data scientists and predictive modelers determine which derived attributes are relevant? Usually data scientists lack the deep domain expertise needed to clarify and prioritize their efforts. Therefore, a collaboration with domain experts is essential. This collaboration is like a three-legged stool. Each leg is critical to the stool remaining stable and fulfilling its intended purpose. When it comes to generating customer intelligence, the three legs of the stool are retail experts, data geeks and coders, and predictive modelers or data scientists.

Retail experts have domain expertise and can best frame the problem customer intelligence is aiming to solve. They suggest derived attributes that will provide value to both the brand and the company’s marketing campaign. Data geeks are needed to program these ideas and store them in a suitable database, which can often lead to greatly increased data storage requirements for the retailer. However, if the data can only be used to create solutions or make key marketing decisions if it’s properly stored and accessed. Inaccessible data means useless data and a wasted opportunity.

Predictive modelers and data scientists are then needed to use the stored data to build models that achieve those business objectives originally set by the retail expert. Predictive models find relationships between historic data and subsequent outcomes so that near-term and long-term customer behavior can be predicted. This leg of the stool aims to answer problems such as the likelihood of when a shopper will make their next purchase and what the value of that purchase will be. Sometimes, these relationships are so complex that only machine learning techniques will find them.

In a real world example, consider a retailer that would like to appropriately message high-valued, loyal shoppers who appear to be disengaging from the brand. A predictive model built from stored data could identify which shoppers are likely to purchase again with seven days, allowing the retailer to let them be the loyal customers they truly are. The predictive model can also show if certain shoppers are unlikely to purchase within seven days but have a high average order value. For these shoppers, the retailer could provide an incentive to bring the shoppers back to the brand. In either case, predicting what shoppers are likely to do is critical to understanding how best to complete the dialogue with them.

Moving forward, retailers will need to big data augment marketing decisions using insights gained from customer intelligence and predictive analytics. Each retailer’s data team must bring in elements from all aspects of the business, including retail experts, data geeks and predictive modelers. These key elements will set retailers up for success as we move forward into the era of big data.

Source: How Predictive Analytics is Changing the Retail Industry

#AnalyticsInRetail
#CategoryManagers
#CPG
#CPGMarketing
#NorthwestArkansas
#PredictiveAnalytics
#PredictiveAnalyticsRetail
#RetailingInNorthwestArkansas

 

#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

#PredictiveAnalytics
#DataAndMarketing
#BigData
#CPG
#CPGMarketing

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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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New #BusinessIntelligence skills in, BI developers out

Consultant Lee Feinberg says organizations need to build up their data storytelling and visualization capabilities — and details a competition for finding people with the right business intelligence skills.

You’re investing a lot of money in business intelligence tools and applications to keep up with your organization’s changing business needs — perhaps hundreds of thousands of dollars, if not more. But are you investing enough in the people who you expect to use those tools — or in the right kind of people to begin with? Probably not.
Read more . . .

Contact us today for your complimentary BI consultation.

Source: New business intelligence skills in, BI developers out

#BusinessIntelligence
#CategoryManagers
#CPG
#CPGMarketing

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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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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