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.

Leveling the Playing Field: #MobileLocationAnalytics | #RetailCustomerAnalytics

This is an interesting article on how Mobile Location Analytics (aka Beacon Technology) is helping brick-and-mortar retailers compete effectively with online retailers by capturing customer behavior near and in the store.

You can read the article below, however, the question for you is how will you incorporate this new data source once it is provided to you by the retailer?

Will your rigid, difficult to modify, DSR incorporate this data stream in a timely manner – or the usual months or years that data model changes sometimes take in a data warehouse environment.

Contact us today to discover how Vortisieze analytics can rapidly adapt to new, sometimes ad hoc (think your latest spreadsheet creation), data sources.

Source: Leveling the Playing Field: Mobile Location Analytics

#MobileLocationAnalytics
#RetailCustomerAnalytics
#CPG
#CPGMarketing

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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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?

#DataAndMarketing
#BigData
#CPG
#CPGMarketing

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

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

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

He further states in his introduction:

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

Big-Data-Pyramid

  • Data as a Service (DaaS)

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

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

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

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

data-as-a-service

  • Information as a Service (IaaS)

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

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

information-as-a-service

  • Answers as a Service (AaaS)

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

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

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

answers-as-a-service

 

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

Contact us today to discover how.

 

About the article author:

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

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

 

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

 

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5 tips when using #dataanalytics in your #CPGMarketing

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

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

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

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

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

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

 

Source: 5 tips when using data analytics in your marketing

 

#CPG
#DataAnalytics
#CPGMarketing

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

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

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

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

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

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

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

 

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

 

#BigData
#CPG
#CategoryManager

 

 

 

 

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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