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Get A Data Scientist to Work On Your Problem For Free

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

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

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

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

CPG Retail Data Question Survey

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

 

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NWA BI Meetup – Thursday, April 21

Hey NWA BI Data Ninjas!

We’ve got a place, a date and time for our next meetup. Thursday, April 21st at 5:30 PM – at the Mars offices thanks to Chris.

We have not locked in an agenda yet, so if you would like to present a project you are working on or have something you would like to see let me know.

Also, you may have noticed that we have changed the user group name. 75% of the survey respondents preferred “NWA Business Intelligence User Group” as opposed to the previous “Arkansas Area MicroStrategy User Group”. I’m sure we will still talk quite a bit about MicroStrategy, but this opens us up to other tools more easily.

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My Analytics Budget is $0, So Now What?

The reality is, that some companies just don’t have a big enough presence in Walmart to warrant any kind of real IT spend. It was a huge deal when they brought on their first full time analyst that has some experience, and is located in Bentonville so that they can meet with their buyers whenever they need to. You get your company issued laptop, with a fresh install of Microsoft Office, a retaillink ID, and you are off and running, right?

FundingPlanPRONo Problem – you are an excel wizard I bet. But as you are constantly maintaining horrific formula pages and trying not to break everything when your boss is requesting new feature engineering in their 20 page workbook, have you ever wondered, there has to be a better way?

You get what real data integration brings. Process automation that can refresh data without inventing the dashboard each and every single week. Feature engineering without such massive headaches. Adding new data sets to a model in a database, not a new tab. Oh the things you could do if you could get that time back.

Something that came about in MicroStrategy 10 was a new feature called Data Wrangling. This allowed an analyst to combine corporate data from the data warehouse with adhoc data and gave you come cleansing and harmonizing functionality. You’ve seen this need before. You get a data set where some states are all spelled out, while others have the two letter code. You see two rows for Texas (Texas and TX), and you have to scrub this in excel before it is usable. Or your data warehouse team does this in their ETL. Well, now you can do this directly in MicroStrategy with data you import yourself. You only need the $600 copy of MicroStrategy Desktop. Build a recipe and save it, and now this is semi-automated when you need to refresh your data. All you need to do is kick it off, and all of your cleansing tasks have been saved and are rerun.

I’ve always thought this was cool, ever since they showed this off at MicroStrategy world last year. But back to your budget of $0. Even $600 is going to be tough to get permission for. Well, I took a tour through a brand new product today that looks like it can do many of the same data wrangling techniques as MicroStrategy Desktop, but the cost is FREE. $0. Nada. It’s called Talend Data Preparation. It has a free version and a subscription version if you would like to work in a big environment and integrate with their other ETL platforms, but the free version looks like it is perfect for the lone rangers out there munging spreadsheets and cleaning data each week just so they can get to their analysis.

Check it out here: https://www.talend.com/products/data-preparation

You still need to kick it off when it’s time to refresh your data, but it lets you build recipes and save them, automating much of your work to refresh your data. It looks like it can connect to HTTP, local, and HDFS data sets.

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Is there ever a perfect storm for suppliers?

walmart-snow

In the article below, RetailWire talks with Weather Expert Paul Walsh on customer behavior, and what retailers are doing to balance things out.

“What almost always happens when you get a big event like this is you really see three phases from a retailer perspective,” Walsh told Retail Dive. “Actually, it’s really from a consumer perspective, and the retailers’ job is to be ready for their customers’ needs.”

Retailers don’t lose or gain sales as weather events hit, unless their supply chain is not prepared for the push ahead of the event. What this signals is that retailers are getting out ahead of these events using weather data, and suppliers are being increasingly asked to help anticipate supply chain strains ahead of the curve as well.

“The weather ate my homework” is no longer going to be a viable excuse in the coming months and years. There will be an expectation that a supplier network will need to be out ahead of this. It won’t matter if you are a top tier supplier maximizing your category, a mid-tier supplier scratching and clawing for their business every day, or a small supplier just trying to keep up. The forecasting models are becoming more and more accurate. Not having your products attributed to weather sensitivity will be a hindrance.

oos-walmart-snowstormIt’s also important to consider “just weather data” may not be enough. Feature engineering both the weather data to tailor it to your products and your products attributes may be required to extract enough insights as to what weather patterns and events are effecting your sales. Make sure you have people on board that can do this with your data, and it may take some experimentation, since no two product assortments are the same. Having the data at the same level as your geography dimension also helps take some of the burden off of your analytics platform. Its a lot of data, and most of it comes at the weather station level.

Have I mentioned yet that we can do this for you? We can provide weather data at the zip code level, and we have experts on staff that can help with your feature engineering, as well as your reporting and data mining.

You can read the whole article here

Please contact us today to see how we can help you with your BI weather challenges.

28+ Category Management Articles To Improve Your Work in 2016

From the Category Management Learning Forum…

28 BLOG POSTS TO IMPROVE YOUR WORK IN 2016.
1. CatMan Overview-Back To The Foundations:http://blog.cmkg.org/blog/category-management-overview-back-to-the-foundations
2. Why be Retailer Centric in your CatMan Approach?:http://blog.cmkg.org/blog/retailer-strategy_why-be-retailer-centric-in-category-management
3. Category Definition: 3 Considerations For Success:http://blog.cmkg.org/blog/category-definition-3-considerations-for-success
4. Why All Categories Are NOT Created Equal:http://blog.cmkg.org/blog/category-roles_are-not-created-equal
5. Category Plan Success-Driven By Solid Foundations:http://blog.cmkg.org/blog/category-plan-success-category-management-foundations
6. 3 Tips For Effective Data Insights: http://blog.cmkg.org/blog/category-management-data-insights
7. 5 Ways to Improve Data Insights with a Category Assessment:http://blog.cmkg.org/blog/analysis-and-data-insights-category-assessment
8. 3 Reasons to Develop Your Tactical Analytic Skills:http://blog.cmkg.org/blog/category-management-category-tactics
9. 3 Ways to Address CatMan Data Gaps:http://blog.cmkg.org/blog/category-management-data-gaps
10. 3 Steps to Create Killer Implementation and Category Plans:http://blog.cmkg.org/blog/implementation-and-category-plans-for-retail
11. 4 Ways to Give Your CatMan Presentations a Makeover:http://blog.cmkg.org/blog/4-ways-to-give-your-category-management-presentations-a-makeover
12. 3 Tips: Turn Your Excel Skills From Good to Great!:http://blog.cmkg.org/blog/category-management-excel-skills
13. Remember CONSUMER When Analyzing Sales Results: Consumer Panel Data: http://blog.cmkg.org/blog/consumer-panel-data
14. 5 Ways to Analyze Your Retail POS Data:http://blog.cmkg.org/blog/5-ways-to-analyze-your-retail-scanned-sales-data
15. 4 Steps to Uncover Strategic Business Insights/Opportunities:http://blog.cmkg.org/blog/4-steps-to-strategic-business-insights
16. 8 Ways to Move to Advanced CatMan Analytics:http://blog.cmkg.org/blog/8-ways-to-move-to-more-advanced-category-management-analytics
17. 4 Ways to Inject Strategy Into Your Assortment Analysis:http://blog.cmkg.org/blog/4-ways-to-inject-strategy-into-your-efficient-assortment-analysis
18. Key Considerations for Strategic Pricing Analysis:http://blog.cmkg.org/blog/key-considerations-for-strategic-pricing-analysis
19. Get Back to the Basics to Improve Promotion Effectiveness:http://blog.cmkg.org/blog/get-back-to-the-basics-to-improve-promotion-effectiveness
20. Why CatMan is the Answer to Collaboration, Shopper-Centric, and Big Data.: http://blog.cmkg.org/blog/category-management-collaboration-shopper-centric-big-data
21. Fact-Based Presentations-5 Secrets!: http://blog.cmkg.org/blog/fact-based-presentations-5-secrets
22. How Strategic Is Your Organization in Space Management?:http://blog.cmkg.org/blog/strategic-space-management-approaches
23. 4 Best Practices for Advanced Shelving: http://blog.cmkg.org/blog/4-best-practices-for-strategic-space-management-advanced-shelving
24. Drive Shopper Satisfaction: Retail Store Clusters in 3 Steps:http://blog.cmkg.org/blog/retail-store-clusters
25. Achieve Shopper Satisfaction in 3 Steps:http://blog.cmkg.org/blog/achieve-shopper-satisfaction-in-3-steps
26. How do Category Managers Affect a Retailer Income Statement?:http://blog.cmkg.org/blog/how-do-category-managers-affect-retail-income-statement
27. Product Supply Chain Is More Than Just Moving Product:http://blog.cmkg.org/blog/product-supply-chain-is-more-than-just-moving-product
28. Strategic Selling Skills: How to Develop Them & Why You Need Them: http://blog.cmkg.org/blog/strategic-selling-skills
29. Improve Collaboration and JBP Results in 3 Steps:http://blog.cmkg.org/blog/improve-collaboration-and-joint-business-planning-results-in-3-steps

 

The original article can be viewed here

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Mobile Development vs MicroStrategy Mobile

Mobile-Application-Development

I had a potential customer ask me yesterday if we could build mobile apps. Apparently they had just spent a lot of money on an app that would allow them to collect data in the field, but it did not integrate with any of their shipment data or POS sales data to complete the picture for their field personnel. This company also did not have the resources to build any kind of custom app from scratch themselves. So, if you are thinking about taking this feat on, let me break it down for you in a few high level steps.

There are many scenarios that you can walk down, but I am going to walk down two specifically: Custom app vs. MicroStrategy mobile app.

Scenario 1 – Building a custom app from scratch.

First off, you are going to need a good overview on iOS development. Try here for starters. When you go down this road, you are going to need a Mac to do your development on. You will also need a developer account with Apple to be able to publish the app. If you want to be backwards compatible, you may need more than one Mac to test on, as the XCode environment is tied to the OS (from what I can tell). You also will need an iPad or two, or three for testing. If you want to support iPhones, you will need some of those. What about the version of iOS? We are currently on 9.x. Do you want to support 8.x as well? While there are testers for some of this in xCode, if you want to make sure your app works across all of these environments I think it is a good idea to develop a test plan on actual HW so that your app isn’t flakey.

On your app side, you are going to be writing a lot of Objective C code to run the app, but you are also going to need a service in the background to dish out data and be the backend for the app. I doubt you would want the app to connect directly to your database. This service should also handle secure logins, passwords, user management, resetting a user password – all of the plumbing that will enable a user to mange the app, their account, and themselves. It also needs to grab data from the data warehouse and package it back to the app. You might need to compress it to make the app more faster.

Now, once you’ve climbed through all of that, you get to manage change management coming from user feature requests, from corporate, from bugs. You get to roll out new versions, craft a test plan to make sure it all remains backwards compatible with older versions of iOS, across all apple devices. To keep up, you may have to juggle a roadmap with multiple versions in play at various lifecycle stages – or in other words, you may be performing user acceptance testing on version 2.5 while you are working on publishing version 2.4 to the app store, as well as scoping changes to version 3.0 to be released next quarter.

I would not say any of this is rocket science, but it can grow to be quite an undertaking if you want to do it right. Wait – where is your Andriod app at? Corporate CFO has an android and wants his version for his phone. Where do you start for that? Now, remember that app that you thrashed in the comments because it was so buggy last week? Feeling even the least bit sorry for that company if this is one or two people trying to keep up with all of this?

Scenario 2 – MicroStrategy Mobile

Now that your head is spinning from trying to develop and support a custom app, there is a bright side to all of this – MicroStrategy Mobile. There are lots of other platforms, and this article could go on for days, but we have direct experience in MicroStrategy Mobile so we will give a glimpse of this one to compare and contrast.

First off – you will need a MicroStrategy environment. This of course is not free – you will need to get an enterprise license for this and each user will need a license. Second – you will need to develop your data objects. This also is not for the faint of heart. Most companies do all of this because they want slick reports, dashboards, and gorgeous data visualizations., regardless of mobile or not. This is pretty much MicroStrategy’s bread and butter. It handles all of the service back end, scheduling, report automation, security, throttling, and presentation. You just need to get your data into a data warehouse. There are lots of strategies for BI – but, if you go down the MicroStrategy route, then you inherit a Mobile strategy second to none.

All of the reports you built for your monday morning dashboard can translate directly into a mobile app with just a small amount of effort. There is no source code you need to master. MicroStrategy can handle much of the iOS compatibility and hardware testing. It’s almost like a buy one, get one free. You get enterprise class reporting along with enterprise class mobile.

MicroStrategy also has transaction services, which allows you to input data on the iPad. Need to capture store shelf quantity, or survey questions? No problem. It can capture data alongside all of your enterprise data warehouse metrics for a complete, 360 degree dashboard. It can show images, take pictures, capture data, report data, drill into your data, visualize your data in graphs and charts. You can build an entire customer service app – just in MicroStrategy – with your company icon and logo.

Summary

Now, if you just needed a mobile app, is this the easier route? Depends on how you look at it. There is probably equal amount of effort getting both scenarios up to speed. I won’t lie and say that MicroStrategy is easy. The payoff comes downstream when you need to support your app. If someone requests changes to your app, you can make a change to your MicroStrategy dashboard inside of MicroStrategy – without needing to recompile, test, and publish your app to the Apple app store. This change, depending on the significance, could literally take you 2 minutes to log in and change something minor. Want to roll out a version of this app for a new customer? Copy, paste, and change the logo – again, maybe a 10 minute change. Because of the object oriented development nature of MicroStrategy, each dashboard will inherit all of the building blocks in the foundation you build. So if you formatted a date wrong, you go change the date attribute. All of your reports, dashboards, and mobile apps then inherit the change – no need to touch them.

Hours or days – not weeks or months. No objective C code to maintain. No API service backend to maintain.

80% of what you build in MicroStrategy is reusable. This is not the case with Tableau, Qlikview, SSRS, Crystal Reports, or custom ASP.NET portals. This is why we lead with a MicroStrategy solution. If we build a customer a neat dashboard to be consumed in a web browser, and the CFO determines they want it on their iPad, we just have to copy, paste, and then do a little resizing so it fits nicely and viola – instant mobile app. Maybe less than a day’s work. If you are building a custom app from scratch – where is your git repository hosted at again?

If your organization could benefit from a BI platform to deliver reporting,dashboards, and data discovery – and also needs a mobile app strategy – then this seems like a no brainer to me. Even if you think it might be useful down the road, then having a combined strategy for BI and mobile makes sense. If you go down the road of separate BI and mobile, then you are eventually going to have to join them up, and it will be twice the support at that point. Twice the cost and twice the fun.

Please contact us today to see how we can help you with your mobile app and BI challenges.

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Is A Data Tidal Wave Heading Your Way? #CPGBI #BigData

Consumer Goods Technology came out with a whitepaper today on Bridging the Data Divide. In this whitepaper is a quote from Gordon Wade, senior vice president of category management best practices at the Category Management Association (CMA):

“Every category manager, whether at a retailer or a manufacturer, has more data than anyone could possibly review, much less analyze and understand,”

The paper also goes on to discuss some very neat things people are doing with mobile and shopping data to simulate store sales and people movements through aisle changes and shopper personalization. This might be the beginning of the tidal wave that is coming. My question to you is – what is your data strategy? Are you feeling like you are already drowning in data? The goal is not to drown retailers or suppliers, but to find ways to integrate data that will keep your business floating in the short and long term future.

data-explosion2

It is clear that the ability to combine new data sources in innovative, cohesive ways will be integral to grocery and CPG success. We can help with that. Store sales and inventory, weather, demographics, store traits, social media, supply chain – we combine all of this into powerful, user driven analytics in a drag-n-drop, build your own reporting and dashboard environment. We are also willing to take in your specific data sources to give you a complete picture of your business, and apply statistical models for predictive metrics to your data for even more insights.

Please contact us today to see how we can help you out of your data deluge.

Source: CGT

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

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

The formula is:

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

where

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

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

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

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

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

What is Cluster Analysis? And Why Use It?

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

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

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

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

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

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

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

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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