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

 

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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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Retailers to increase IoT spend fourfold by 2020 to US$2.5bn | #BigData #TechnologyTrendsInRetail

Anticipating the winds of change, major retailers are expected to increase their investment in internet of things (IoT) technology fourfold to US$2.5bn by 2020.

In the commercial space, IoT in retail is one of the clearest examples where connected network technology could have a significant impact on customer interaction in stores.

In particular, retailers have already begun investing in IoT hardware, including Bluetooth beacons and radio frequency ID (RFID) tags that allow them to send information to them in-store and keep a tab on stock and price levels, respectively.
Read more . . .

How will your DSR handle all that new, differently structured data? (Hint – it won’t!)
Contact us today to discover how Vortisieze analytics can bring this, and many more, types of data to your analytics package.

Source: Retailers to increase IoT spend fourfold by 2020 to US$2.5bn

#BigData
#CPG
#CPGMarketing

#TechnologyInRetailIndustry

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New #BusinessIntelligence skills in, BI developers out

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

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

Contact us today for your complimentary BI consultation.

Source: New business intelligence skills in, BI developers out

#BusinessIntelligence
#CategoryManagers
#CPG
#CPGMarketing

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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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5 tips when using #dataanalytics in your #CPGMarketing

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

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

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

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

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

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

 

Source: 5 tips when using data analytics in your marketing

 

#CPG
#DataAnalytics
#CPGMarketing

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

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

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

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

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

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

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

 

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

 

#BigData
#CPG
#CategoryManager

 

 

 

 

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Vortisieze Reduces Stress Related Deaths at #CPG Companies: #CategoryManagers Take Note

Well – ok – that’s a “wee bit of a stretch,” as my Irish grandfather used to say – but what is reported about long hours and early death is not a stretch – and it’s serious.

Two Yahoo articles published today show a direct coorelation between working long hours on the job and increased risk of stress- related early deaths from stroke, heart attacks and suicide.

In Japan, death by over work, or karoshi, is a legally recognized cause of death.

While the demands of CPG category managers and sales managers grow, there is pressure to keep staff levels at predetermined levels, sometimes without regard to the amount of work to be done.

This presents real headaches (and worse) to CPG vendors.  However, one possible solution is outsourcing some of the routine and mundane aspects of gaining insights – building reports and dashboards.

As a category or sales manager for a CPG company you are paid to gain insights from what is happening in your retailer environment.  But do you really need to know the nitty-gritty of building dashboards or reports?

Probably not.  At Vortisieze our founders have over 25 years combined experience in building analytics in the CPG category management arena.  Even if you aren’t ready for big data we can take some of the grunt work off of your desk.  We understand BI – included traditional DSR data warehouses – and especially the analytics engines.  MicroStrategy is our primary expertise but we know other tools as well.

Contact us today to discover how we can lesson your workload so you can focus on what is important – growing your brand.

 

Sources:

The 100 hour work week in Japan

Working longer hours increases stroke risk by up to 33%: study

 

#CPG

#CategoryManagers

#BusinessIntelligence

#CPGMarketing

 

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#CPG #CategoryManagers Take Note:  Smile, You’re on Consumer Camera

We’ve noted recently how beacon technology can track a consumer’s movement throughout the store and broadcast product specific marketing to the consumer using their smart phone.

Additionally, there is new technology that can use the existing security cameras and floor tiles to both watch movement and track traffic location throughout the store.

Of course this depends on the retailer investing in this technology, over which you as a CPG category manager or sales manager have no control.

But something you do control, or at least influence, is how, or if, you can analyze the resulting data once it becomes available.  Older DSR technologies, using database or data warehouse methods, are rigid and hard to change.  Adding new data streams takes months or years to incorporate – if attempted faster can break the data model making is difficult to load or extract data.  Difficult means time consuming.  Not what you need when your buyer’s meeting is 9am Monday and you are waiting on data loading (called ETL) and extraction (reporting) at 10pm Sunday night.

Big data is different – not just the buzz around it – but truly different from a technological point of view.

Big data allows you to add new, even unstructured data (think your latest spreadsheet creation), for analytics insights in hours or days.  So when this new consumer tracking data is made available to you analyzing and acting on it means you can take steps toward better promotions and product launches.

Big data technology is demonstrably faster in loading (literally minutes versus hours) and extraction and reporting.

Which would you rather do on Sunday night – sit at the office until midnight just waiting on your first peak at last week’s data – or home with your family watching Sunday Night Football?

Vortisieze is the first big data analytics package designed exclusively for the CPG community.

Contact us today and makes us prove just how fast Vortisieze can put you ahead of your competition.

 

 

Source:  Smile, You’re on Consumer Camera

 

#AnalyticsInRetail

#CPG

#CPGMarketing