Monthly Archives: January 2016

Start Strong in 2016: A Round-Up of Email Essentials

Below are five of our evergreen tips for email, along with the assets necessary to put them into action. Give them a look, and put your best foot forward in 2016.

1. Show your subject lines love.

This may seem a straightforward enough suggestion, but it’s one that bears repeating, for the vital part subject lines play in the success of an email. Subject lines are there to entice, to encourage their reader to proceed further, and they deserve your utmost care and attention accordingly.

2. Tailor your content to the individual.

Ours is a noisier world than most, and if marketers have any hope of getting themselves across to the prospects and customers they court, it’s incumbent on them to personalize their offers. They should think long and hard about their customers’ unique needs and expectations, and make every effort to draw on the data they’ve gathered on their buyers’ habits and preferences – extending surveys to those who’ve completed surveys previously, videos to those who share videos widely, and so on and so forth.

Learn to speak your buyers’ language with either one of these handy how-tos: 10 Ways to Nurture the Buyer’s Journey and Turn Your Website into a Lead Generation Machine.

3. Make mobile a priority.

The modern buyer accesses email across a wide array of devices – their mobile phones, their tablets, their desktops – and it’s crucial that you accommodate this multi-channel reality. After all: as of 2014, 53 percent of all opens occur on a mobile phone or tablet, in a 48 percent increase between quarters (Experian).

4. Segment your subscriber lists for greater variety and engagement.

As you set about personalizing your messaging, it’s crucial you make an equal effort to segment your lists – group like prospects with like, and leverage the behavioral data you’ve gathered (demographic and firmographic details) in your outreach, for a more tailored, intimate approach.

For a start, you’ll want to identify parameters for your ideal buyers: their job titles, education levels, departments, that kind of thing. From there, you might try tracing their online footprints, for a clearer picture of where they’ve been and where they’re headed – the pages they’ve visited, the webinars they’ve attended, the emails they’ve responded to.

Get more Best Practices in Segmentation today, and find out how to streamline your parameters further with Frictionless Forms and Better Landing Pages.

5. Be realistic about your goals and objectives.

As with any marketing program you oversee, your emails should keep to a concrete plan – one that accounts for your budget for the quarter, your overarching business objectives, your company’s bottom line. See to it in the new year that this plan more or less aligns with past efforts: consistent in its KPIs, realistic in its goals. It should be a strategy that won’t drastically change from month to month and that you easily can replicate as you forge ahead.

…and, measure what matters.

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Act-On Marketing Blog

New data preparation tools open up more info to analysts

A growing number of business analysts are sharpening their skills at writing ad hoc queries and analytical algorithms…

to uncover useful information in corporate data stores — and help their organizations become more data-driven in making business decisions. Yet as these workers become more sophisticated in their use of analytics tools, many find that conventional data warehouse architectures impede their ability to analyze the data they want to look at.

There are three core reasons for that — all things that potentially can be addressed by an emerging class of self-service data preparation tools designed to enable business analysts, data scientists and other end users to bypass the data warehouse and carry out key pieces of the data integration and preparation process themselves.

First, the traditional data warehouse typically is a repository for data sets that have been extracted from internal transaction processing or operational systems for use in reporting on business performance. This limits the scope and types of analyses that can be performed against the data.

Second, the extracted data sets are integrated and standardized, using a monolithic set of business rules, to align with a predefined data model designed for dimensional slicing and dicing. Doing so filters out information that may be relevant to particular analytics applications. And third, the IT group is usually responsible for developing the rules and processes for transforming the data going into a data warehouse — an approach that similarly may not meet the information needs of the analysts who are ultimately expected to use that data.

Obviously, conventional data warehousing processes can work for companies, but the data landscape is rapidly changing. Organizations increasingly are looking to blend their transaction data with information coming from a variety of other sources, including website clickstream and activity logs, sensors on manufacturing equipment and other devices, customer emails, social networks and streaming data feeds from customers, data aggregators, and third-party information services providers.

New data types, new data platforms

Exploiting these often external data sources can boost efforts to generate actionable intelligence that, when paired with changes in business processes, provides the means to make a company truly data-driven. In many cases, though, the added data is better suited to being processed and stored in a big data platform — a Hadoop cluster, NoSQL database or Spark system, for example — than in a data warehouse. Or it may be accessible through an external Web portal.

In addition, business analysts, as well as data scientists and other analytics professionals, often want to access different combinations of the available data — sometimes in its raw form.

For example, the marketing team at a consumer products maker may want to analyze a mix of customer profile records, news feeds and social media data to look for patterns that can help in planning an online marketing campaign. Meanwhile, the customer experience team may want to monitor social media feeds and product reviews from various websites to identify potential product issues, so it can take action to placate dissatisfied customers. And so on for other departments. Because each has different requirements and goals, it’s virtually impossible for a homogenized data warehouse to enable all of their analytics objectives to be met.

Empowering analysts to work with the data that best meets their individual needs can be a more fruitful approach. It has implications for the various aspects of data integration, including data discovery, ingestion, profiling, validation and quality. But the new self-service data preparation tools developed by a variety of vendors offer a potential helping hand.

Logical separation on data preparation

The technologies create a sensible segregation of duties between analytics users, and IT and data management teams. Business analysts and data scientists can use the data preparation tools to find relevant data in different systems, pull it together, profile and cleanse the data for consistency, and define the business rules that govern their use of the information. With the data prep software at their disposal, they’re able to get more comprehensive and customized views of the data they’re interested in than they typically could from a data warehouse.

Ideally, the analysts also become more accountable for how the data is used. That means they should be tasked with understanding and adhering to high-level governance policies on data usage and collaborating with others to ensure that data, and how it’s interpreted, remains consistent across the enterprise.

Because data sets are being captured and maintained in their original formats, the IT department is freed from having to implement integration and transformation rules dictating what data is available for analysis. Instead, IT’s responsibility transitions into managing the overall infrastructure supporting data discovery, integration and analysis, and providing control mechanisms to monitor for inconsistencies in data definitions and noncompliance with defined governance directives on using business data.

Data warehouses likely aren’t going away in most organizations that have deployed them. And self-service data preparation software is a relatively new and still-maturing technology, sold primarily by startup vendors. But the blossoming of these data preparation tools points the way to increased analytical flexibility and effectiveness in companies that are looking to get more out of their data.

About the author:
David Loshin is president of Knowledge Integrity Inc., a consulting and development services company. Email him at

Email us at and follow us on Twitter: @sDataManagement.

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SearchBusinessAnalytics: BI, CPM and analytics news, tips and resources


Not my usual fare here but I liked it:

João Pereira de Souza, a retired bricklayer from Rio de Janeiro, shares a heartwarming bond with a Magellanic penguin native to South America’s Patagonian region. For the past five years, the bird seems to have altered its natural migratory pattern just to be able to visit de Souza several times a year.

The unlikely friendship began in 2011, when de Souza found the bird, nicknamed Jinling, soaked in oil on the beach near his house. He brought the penguin home, cleaned him up, and offered him a meal of cool sardines and a shady spot to rest. Since then, Jinling has never stayed away from de Souza for too long.

Even though the kindhearted man tried to get the penguin reacquainted with the open sea after he got better, the bird just kept coming back. He even took him out in a boat, far from land and turned him loose in the ocean, but by the time he got back home, Jinling was already waiting for him.

Although Magellanic penguins migrate thousands of miles each year between breeding colonies in Patagonia and feeding grounds further north, Jinling doesn’t stay away from Rio for more than four months at a time. He always waddles back to de Souza’s little chanty by the sea, sometimes spending as long as eight months to a year with the old man. And he’s possessive too – he apparently can’t stand other animals getting anywhere close to his human.

The local fishermen are bewildered by this unusual behavior. “The funniest thing is that the penguin might stay here for a week, then it walks down to the beach and leaves,” said Mario Castro, a fisherman. “It spends 10, 12, 15 days and comes back to the same house. They’re supposed to join together, find some path to the south, but he doesn’t.”

When they are not swimming in the ocean together, the unlikely duo hang out with the locals from João’s village, where Jinling is known as the village mascot.

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Visual Awesomeness Unlocked – Box-and-Whisker Plots

By Amir Netz, Technical Fellow and Mey Meenakshisundaram, Product Manager

Numbers tell the story. But when you have diverse data points and sources, telling the story with just one aggregation to represent the whole range of numbers might often not tell the fully story.

Showing averages over time or across some series of data often allows us to answer questions like: How long did the app take to load in the mobile device? To answer this question, most commonly, we would find all data points for the day and then compute the average.  While the average is often a useful metric, by itself is a lossy compression algorithm. What if sizable number of customers are experiencing a slow load time even though the average is within the limits of our expectation?  Imagine that we had a dataset that showed on average it took 300ms to load the app.   Now we may be happy with that metric, but what happens if every now and then it takes 6000ms to load?  The 300ms average number hides that alarmingly bad experience for sizable customer base. This is also where other metrics come into play, like the median, 95 percentiles that can give us a better understanding of the data.

Half a century ago, one mathematician thought out-of-the-box, to solve this problem and came up with the box plot. In his words, the greatest value of a picture is when it forces us to notice what we never expected to see and box plot does it perfectly.

The box whisker plot allows us to see a number of different things in the data series more deeply.  We can see outliers, clusters of data points, different volume of data points between series; all things that summary statistics can hide.   A box whisker plot uses simple glyphs that summarize a quantitative distribution with: the smallest and largest values, lower quantile, median, upper quantile. This summary approach allows the viewer to easily recognize differences between distributions and see beyond a standard mean value plots.

This week we have two submissions to the gallery about Box and Whisker – one from Brad Sarsfield and another from Jan Pieter Posthuma. Thanks to both them for producing this very important visual and publishing it to the gallery.

In Brad’s chart, every data point is plotted as a circle on the axis; this lets us visualize the distribution of the data points, the top and bottom 5% as ‘outliers’ and color them red and mark the ‘whiskers’ at those points, the 95th quantile and the 5th quantile. You can also adjust these quantile values to meet your needs. In this chart, you have to explicitly say ‘Do not summarize’ in the Values bucket to view each series and data point.

The one from Jan Pieter allows category to make the box colorful. It has a second ‘Samples” category to provide different sample results of one experiment group. The values are aggressed at this second group. But If you want to treat each data point separately, then you can have a column which has unique value for each row and put this in the Sample bucket

Here is the video from Brad

 5141.01 Visual Awesomeness Unlocked – Box and Whisker Plots

Make sure to mark the aggregation as ‘Don’t summarize’ in the Value bucket for each series.

38087.02 Visual Awesomeness Unlocked – Box and Whisker Plots

In the formatting section, you can also specify the percentile for each of the Quantile.

3872.03 Visual Awesomeness Unlocked – Box and Whisker Plots

Here is the one from Jan.

2110.04 Visual Awesomeness Unlocked – Box and Whisker Plots

To use, simply download Box and Whisker chart from the visuals gallery and import it to your Power BI report and use it.

Here are the links to Brad’s and Jan’s Box plots.

You can also download the pbix file with sample file attached to this post.

As usual, we can’t wait to hear your thoughts and your ideas for improvements.


Perfect Benchmarking-Tool! Thanks a lot !!!

Awesome, thanks for this great viz!

Yet to try this but it looks like it will fill a big gap in allowing data to be viewed in the context of what is normal!  Good work!

Thank you for working on this, it’s a great improvement icon smile Visual Awesomeness Unlocked – Box and Whisker Plots

Thanks. Great visual.

There is a similar style of tool in Technical Analysis for share trading. It’s called a candlestick chart. One other feature of the candlestick chart is that the box is coloured in if the share closes lower than it opens on that day, it is left uncoloured if it closes higher.

A really cool feature might be to colour the box if the mean is lower than the previous time period,leave it uncoloured if it is higher. This way you also get a quick visual indicator of where the mean is moving.

This visualization is FLAWED. I’ve been using Box and Whisker plots for a long time and never seen anything like this. Excel 2016 B&W plot does it correctly (and is compatible with R’s standard box and whisker plot).

How Excel, R, and other packages calculate limits:

1) Q1 is ALWAYS the 25th percentile

2) Q2 is ALWAYS the median, or the 50th percentile

3) Q3 is ALWAYS the 75th percentile

4) The bottom of the lower whisker is ALWAYS Q2-1.5IQR

5) The top of the whisker is ALWAYS Q3+1.5IQR

6) Values outside these limits are outliers

7) There are various methods of calculating the median – which method is used?

When you don’t follow these calculations, the user won’t know what they are looking at, and cannot relate the visualization to any similar visualization done in another application. And why build a visualization so contrary to Excel, which many Power BI folks will be using? Also, B&W plots are equally valid when shown horizontally (a fact that the Excel team readily acknowledges).

I implore you to redo this chart to conform to Excel’s box and whisker plot, with the addition of a horizontal orientation option.

@Colin  – These visualizations for provided by community for the community. In the Visual gallery ,for each of these visuals , there is ‘Contact Author’ link and you can provide any feedback/issues to them using that mechanism.   So please use the ‘Contact Author’ link in the gallery to reach out to the authors directly.

BTW , Brad’s visual allow you to specify the percentile for each quartiles. If you check the Wikipedia article…/Box_plot , it clearly says that  top & bottom of the box are always the first and third quartiles but the ends of the whiskers can represent several possible alternative values

Mey, thanks – I’ll contact the author.

“BTW , Brad’s visual allow you to specify the percentile for each quartiles”

And that’s a problem. When is a quartile something other than a quartile? (25, 50, 75)?

“If you check the Wikipedia article…/Box_plot , it clearly says that  top & bottom of the box are always the first and third quartiles but the ends of the whiskers can represent several possible alternative values”

True, but if you’re building a box plot in Power BI, why would you go out of your way to do something different from Excel?

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Prevent the Dynamics CRM Email Router from Processing Old Emails

Recently I had an issue while setting up the Microsoft Dynamics CRM Email Router, where I was connecting to an existing email mailbox and all the emails were getting pulled through to CRM dating as far back as 2008. This created a lot of irrelevant data and made things difficult for users.

Initial research suggested that setting the Message Expiration field in the Email router configuration could be the answer. “Threshold time after which a message in the mail box is considered old to be neglected in processing.” Thanks to this blog explaining the Microsoft.Crm.Tools.EmailAgent.xml file.

While this did not turn out to be the case the solution did in fact turn out to be in editing this file. The MessageProcessingThresholdDays tag in this file is the key. In the configuration file this tag is initially set to -1, indicating to the Email Router that it should process all the emails it finds in the mailbox.

So the steps to fixing the issue of the CRM Email Router processing old and irrelevant email data are as follows:

  1. Stop the E-mail Router service if you haven’t already.
  2. Go to the Service folder of the CRM Email Router – C:\Program Files\Microsoft CRM Email\Service
  3. Take a backup copy of the Microsoft.Crm.Tools.EmailAgent.SystemState.Xml and Microsoft.Crm.Tools.EmailAgent.xml XML files.
  4. Edit the EmailAgent file, add this tag <MessageProcessingThresholdDays>X</MessageProcessingThresholdDays> to the <ProviderOverrides> tag, where X is the number of days you want to go back. Save.
  5. Edit the SystemState file, clear everything from between the <State> tags. Save.
  6. Restart the Email Router.

NOTE: it is important to place the MessageProcessingThresholdDays tag in the ProviderOverrides as if you simply edit the existing tags the Email Router will overwrite them when is restarts.

1653bd16 22fa 47d2 b625 af84aa5f0074 012416 2144 PreventtheD1 Prevent the Dynamics CRM Email Router from Processing Old Emails

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Magnetism Solutions Dynamics CRM Blog

Expert Interview with Rick Chavie About Managing Big Data for Supply Chain Managment

If you want to learn more about the challenges of managing supply and demand chains, then EnterWorks CEO Rick Chavie is your man.

Rick served as as SVP, Global Solution Management with hybris and SAP’s Customer Engagement and Commerce group, where he brought together digital and physical commerce and CRM assets for seamless customer experiences. And Rick not only has industry experience from leadership roles at retailers such as The Home Depot and C&A, but also technology experience from his role as the global marketing leader for NCR’s retail and hospitality business, and management consulting expertise from his partner roles at Deloitte and Accenture, where he served clients across retail, branded consumer and wholesale verticals.

We recently caught up with the Harvard MBA and Fulbright Scholar to get his insight on how supply chain managers should be using Big Data. Here’s what he had to say:

What do supply chain managers need to know about how big data can improve their work?

Among others, there are two major ways that big data can expand the reach of supply chain managers beyond traditional sources of information:

  1. Internet of Things: By adding new information sources ranging from meters on gas lines to monitors on machine usage, there is the opportunity to transform replenishment and fulfillment cycles from a human-driven cycle to automated, real-time means to execute just-in-time fulfillment. Supply chain managers are used to relying on indirect means and forecasts to predict when to initiate a replenishment order, in particular for items that need maintenance periodically. Now, with direct connections to the masses of big data made available by their IoT collection devices, it can transform the replenishment process.
  2. Demand-driven replenishment: Similarly, it has long been a challenge to directly observe downstream demand pull cycles, with most companies relying on push mechanisms that are increasingly based on sophisticated forecasting mechanisms. However, as large data feeds are increasingly made accessible through B2B2C platforms for managing master data and product data, the same is true of accessing customer transactions in real-time data stores that shorten the cycle of informing replenishers what is happening on the sales or dealer floor. In the past, the supply chain manager may have known that a store has a certain amount of sales and inventory at the end of a day; now, they can know these things by customer, by location within store and by time of day. This visibility, when combined with the much higher “resolution” of insights available through ecommerce transactions can truly enable omnichannel excellence in supply chain execution.

Have supply chain managers learned yet to take full advantage of big data?

Few companies have mapped their systems to take full advantage of such data. The first order of business is taking on the challenge of having base data stores for product, logistics, location and other supply chain information related to the brand. Once the right taxonomies and filters are incorporated in such base data that is shared across the supply chain, it can then be enriched with other big data elements that are ever more granular in nature. But first, the initial data model must be established with proper data governance and with the capability of having a robust dynamic data modeling capability that can be refreshed continually as new data feeds and channels come on line.

What are some of the obstacles that supply chain managers must overcome when leveraging big data to improve their processes?

Beyond having the right data modeling capability and team members, the other challenge is external in the form of supply chain partners and demand enablement constituents. Across the lifecycle of a product, there is also the lifecycle of related data and content streams that must meet required standards for the right attributes, product specifications and descriptions, images and other customer-facing information such as videos and “how-to” product knowledge that can accompany the product to the marketplace. Such information is a collaborative effort across the manufacturer brand, the wholesale distributor and the retail/commerce outlet, in the form of B2B2C structure of connecting business to business to customer for both product supply chain, as it converges with the content value chain to inform both supply side and demand side needs.

What are some of the best tools available for supply chain managers to work with big data?

Certainly the base case is that supply chain managers need a product lifecycle management (PLM) system that feeds and informs a Product Information Management (PIM) system under the umbrella of Master Data Management (MDM) platform that extends in related domains such as locations, brands, customer, etc. to put the product flows into the right context for executing against demand patterns. Then, from the unstructured data side where people look to have other big data sources complement such typically internal information, there needs to be collaboration tools such as Portals for exchanging information, syndication capabilities for distributing information to a broad set of downstream retailers/commerce entities, and a platform for managing such external data such as using Hadoop or Cassandra to make major external data stores accessible for use by supply chain managers.

How can big data help with demand management, supplier management, customer management and other aspects of managing a supply chain?

A key aspect of big data is enabling the kind of fulfillment personalization and personalized logistics that is becoming pervasive in all types of commerce transactions. Whether it is fulfilling a drop ship Direct-to-Customer (D2C) or putting inventory directly to shelf through direct store delivery (DSD) mechanisms, demand management and fulfillment informed by big data and real-time foresting capabilities is more robust than ever in responding to demand efficiently. The level of customer service – in the form of in-stock and “ship complete” compliance correspondingly is enhanced. Whether in a B2B setting or B2C, supply chain managers can have an increasing influence not only on minimizing cost of supply chain and inventory levels, but also can affect and lift sales through more timely responses to demand.

What are some of the reasons that supply chain managers should consider taking on big data?

Such managers need to tackle big data – whether for a region, a category or a major customer segment – to avoid falling behind the wave of innovation that is occurring. In addition, the upside is clear: It will be more profitable to do so!

Connect with EnterWorks on LinkedIn, Twitter and Facebook.

 Expert Interview with Rick Chavie About Managing Big Data for Supply Chain Managment

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Syncsort blog

Choo Choo Bitches

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 Choo Choo Bitches

About Krisgo

I’m a mom, that has worn many different hats in this life; from scout leader, camp craft teacher, parents group president, colorguard coach, member of the community band, stay-at-home-mom to full time worker, I’ve done it all– almost! I still love learning new things, especially creating and cooking. Most of all I love to laugh! Thanks for visiting – come back soon icon smile Choo Choo Bitches

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Deep Fried Bits

FCC Chief Proposes End of Set-Top Box Rule

Federal Communications Commission Chairman Tom Wheeler on Wednesday announced that he’d shared with colleagues a long sought-after proposal to loosen the set-top box’s grip on home entertainment. The proposal seeks to spur competition and consumer choice in an arena dominated by large cable and satellite television providers.

fcc tom wheeler FCC Chief Proposes End of Set Top Box Rule

Wheeler’s proposal would provide a mechanism for creating new ways to access video content, whether through a competing device, or through an app or other software that would function as a device, thereby unlocking wider access to programming for consumers. A vote is expected on Feb. 16.

Cable Box Replacements

The proposal calls for multichannel video programming distributors — that is, cable and satellite TV service providers — to share certain information with developers of new devices. That information would include services available to the consumer, as well as information about what devices were allowed do with content, such as recording shows. They also would be required to provide access to the actual content.

On the other hand, it acknowledges the importance of protecting copyrights, preventing theft and honoring contract terms.

Ninety-nine percent of cable customers are locked into their set-top boxes because cable and satellite providers have locked up the market, according to Wheeler.

Consumers pay an average of US$ 231 a year in rental fees for set-top boxes, which totals about $ 20 billion per year for all cable and satellite customers, his proposal notes.

The consumer cost of set-top boxes has risen 185 percent since 1994, to $ 7.43 per month, while the cost of computers, televisions and mobile phones has dropped about 90 percent since then, it says.

New Competition

Wheeler’s proposal has drawn praise as a long-overdue move to spur competition among the big cable and satellite providers, and give consumers the opportunity to make their own choices about where they get their content.

With the current system, “you’re stuck in a consumer-be-damned relationship from the get-go. Any time the cable company wants to increase your fees, you have no leverage,” independent analyst Craig Settles pointed out.

“If you look at the electronics of it all, you realize there isn’t a whole lot of high tech in that tech box to justify the rental fees over several years, or even several months,” he told the E-Commerce Times.

The proposal is an interesting one, coming during the presidential election cycle, observed Charles King, principal analyst at Pund-IT.

The $ 20 billion in set-top box rental fees are “essentially bogus charges,” he told the E-Commerce Times.

The plan will face vigorous opposition, King predicted, as “coming on top of steadily declining subscribers, cable companies must feel like their practice of charging a bundle for ungainly, consumer unfriendly bundles looks to be in serious jeopardy.”

However, it’s likely the writing is already on the wall.

The FCC proposal will help to unleash competition that could save consumers billions of dollars and create opportunities for independent, minority and other voices to get their programming in front of viewers, noted Public Knowledge.

“The video marketplace has been slow to respond to the changes in business models and technology that have swept through other media,” noted John Bergmayer a senior staff attorney at Public Knowledge. “In part this is because many incumbents control content, distribution and the devices people can access programming on.”

However, the Future of Television Coalition a newly formed organization with backing from AT&T, Dish Network, the Motion Picture Association of America, and other industry firms — blasted the proposal, saying it would render licensing agreements and other protections for the industry useless.

Consumers already have plenty of choices for accessing content on a broad range of devices, the group argued, and Wheeler’s proposal amounts to a solution in search of a problem. end enn FCC Chief Proposes End of Set Top Box Rule

David Jones is a freelance writer based in Essex County, New Jersey. He has written for Reuters, Bloomberg, Crain’s New York Business and The New York Times.

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CRM Buyer

Survey Module – A Unique approach towards FMCG Sales

A man’s feet must be planted in his country, but his eyes should survey the world.”

George Santayana
Spanish Born American Philosopher

“According to a recently-released Accenture Strategy studycustomers in the FMCG retail sector were the most prone to switching (most likely due to the ease of doing so), with 30% reporting having switched providers due to poor service & product availability issues.”

This data shows that a survey has provided actual picture of business downfall in FMCG retail sector. The company faces lot many other problems in this particular sector which cannot be monitored such as :

  • Competitive pressure
  • Untapped market
  • Demoralized sales team.
  • Lack of new sustainable market strategy for Product.
  • Products subject to seasonal restrictions.

To overcome these points, it is required that companies shall know the pulse of the market and to do so they require the information which can be collected by organizing “A Survey”.

What is a Survey? How it will be organized?

Who is Surveyor ? How a survey team can be created?

Surveyor can be a skilled person with specialization who can plan, collect and submit the information gathered. The data collected normally revolve around organizations or people buy, need, do or think and the reasons why.

Survey team can comprise of ideally experienced sales persons who can gather data or now a-days MBA freshers or students doing projects can also be recruited to carry out a survey process.

 Survey Module – A Unique approach towards FMCG Sales

Why do we do Survey? Or what is the need of Survey?

  • Through this process, Companies can gather information of their field sales staff, whether they are doing their work properly or not.
  • How Effective and salable are their products?
  • Are there any challenges faced by retailers in selling the products?
  • Is there any quality issue in our product?
  • Popularity of product among a range of products and so on…

Survey Target Area & Target Questions

Why do we need to define a target area and questions?

  • To ensure the right information is gathered.
  • Geographically results and performances vary.
  • Precise target questions helps in furnishing proper data.
  • Target Questions shall be dynamic as well accurate.
  • To identify and select questions for a survey will always be on need basis.

What do we gather from a Survey?

  • Companies can gather information of their field sales staff, whether they are doing their work properly or not.
  • How Effective and salable are their products?
  • Are there any challenges faced by retailers in selling the products?
  • Is there any quality issue in the product?
  • Popularity of product among a range of products and so on…

What is the need of Survey?
  • Add times retailer doesn’t want to share his grievances with the TSI.
  • Sometimes field sales staff books only orders but doesn’t address any issues.
  • Ratings of a product or retailer are not possible on feed back of a TSI.
  • Sometimes after repetitive visits retailer doesn’t gives any order and we don’t know the reason.
  • There is no possibility of mutual understanding among retailer and surveyor so the chances of accuracy of data gathered is high.

Automated Survey Module – a different Approach

A software based Survey module can help to automate the traditional manual survey process. It can add dynamism to the process & its outcome. We can gather information about a particular product, Retailer, stockist and Headquarter etc.
Questions sets can be automated. It can also support the complaint redress system. It can come in handy as a mobile app. And Mobile field force reporting software

 Survey Module – A Unique approach towards FMCG Sales

Benefits of Automated Survey Software:

  • It will be dynamic and no retailer or distributor will ever be left out.
  • Retailers can be categorized and so the questions can.
  • Geographically it can be segregated.
  • A survey program can be generated periodically.


There are manual conventional techniques and practices which are incredible like survey itself, but when it is automated and feature enabled like survey software, the results will be outstanding and the Survey module can be a revolution in Fmcg software for salescategory. And we provides the best software for your business like-
Business Intelligence, Sugarcrm custom apps, Sugarcrm developer,Talend developerso if you want to increase your business so this software help you can increase your business.

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Advanz101 System – Business Intelligence

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About Krisgo

I’m a mom, that has worn many different hats in this life; from scout leader, camp craft teacher, parents group president, colorguard coach, member of the community band, stay-at-home-mom to full time worker, I’ve done it all– almost! I still love learning new things, especially creating and cooking. Most of all I love to laugh! Thanks for visiting – come back soon icon smile Guess Who?!

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