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The Truth Finds A Way

One trend that continues to gain momentum in the BI world is self service business intelligence, and it has IT groups concerned that the whole focus of a BI team is to champion a single version of the truth. MicroStrategy just released it’s desktop application for free, and version 10.6 is now available. If you have not taken a look at it, it is worth spending some time on. Tableau has built virtually it’s entire business model on self service BI. Any department that can’t get enough resources from the BI team can go and build their own dashboards now. Qlik is the same way. Now Microsoft’s Power BI has stepped into the ring with a growing offering. Alteryx, Sisense, Birst, Zoho – all of these are pouring resources into self service BI. IT groups are running scared, and maybe rightly so. Self service BI doesn’t have to have any training, any experience, any skill sets, any data governance, any single vision of what the single version of the truth is supposed to look like. Power to the people. Democratizing data like never before. Gartner is even saying that this is going to be the death of BI.

ms-excel

Or is it? People have always gone outside of the BI or the IT teams to build reports and analysis. It’s called Excel, or Access, and its been around for, oh I don’t know, a few decades? I’ve seen entire departments run from Excel and Access applications. You can’t stop them from using these. I’ve seen desktop computers that used to belong to an enterprising employee 10 years ago, that built an Access application that became mission critical to a department, and the department has seen the employees turnover two or three times in that period. Nobody remembers who the original developers were, or even what it was running, but each newly appointed department head got the instructions to make sure that desktop computer remained powered on under their desk and hooked up to the network. God help them is this computer dies or something. And these scenarios are a nightmare for IT groups that get handed to them to support – but you cannot stop it. Much like that Jeff Goldblum line from Jurassic Park – Life finds a way.

 

jp93-eggs1These new tools just give the enterprising users a new means to create things that BI teams or IT groups are going to have to support. They are given tasks to run the business, and then the BI group doesn’t have the time or means to provide them the reports or analysis they need to meet those new milestones that leadership keeps placing on them. And, nobody likes a whiner, so they invent what they need outside of the process. Business is happy. Users are happy. IT is blissfully unaware. All they know is that they stopped emailing them asking for a status on their request and didn’t even miss the emails. Is this really such a bad thing? I mean, if the BI team built every single thing the business thought it needs to run then they would collapse under their own weight. Rather than cringing at these outlaw scenarios, if you look at them as a proof of concept exercise, and let the POCs that live life past a year or some determined amount of time that proves the reasoning and the needs were real, then really everyone who does these are helping out the BI team.

 

Rather than seeing these activities as competition or amorphous growth that cannot be supported, BI teams should looking to guiding these rogues in a way that helps keep some sanity to possibly taking over the project when it has matured. Choosing one self service BI tool and embracing it, training on it, training others on it, would be a much better alternative than leaving it up to the department and needing expertise on 5 or 6 different tools de jour. For one, it enables you to hire or train a resource for the BI team to assist users in developing their own projects and for taking over projects that have grown to big to be a department only project. Secondly, it provides some consistency to the rogue POCs so that the company looks like it knows what it is doing. Third, taking the lead to facilitate this movement means BI and IT can guide the company down the path to some degree, rather than being handed who knows what to support. Don’t fight the tidal wave. Grab it, embrace it, lead it. Sticking your head in the sand is never a good strategy.

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

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

The formula is:

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

where

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

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

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

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

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

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

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

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

Business Intelligence:

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

Analytics:

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

Big Data:

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

Data Mining:

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

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

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

Source: http://blog.apterainc.com/

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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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

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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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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/

 

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