U.S. Farm Data 2018-05-30T21:08:47+00:00

U.S. Farm Data Analytics

A guide for strategy, marketing and sales professionals to start integrating U.S. farm data into their everyday work. 

U.S. Farm Data Analytics

A guide for strategy, marketing and sales professionals to start integrating U.S. farm data into their everyday work. 

Table of Contents

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Introduction: Business Questions that Data Can Answer

Questions Data Can Answer - U.S. Farm Data

For agribusiness professionals, it’s a simple question: why data?

Big Data has taken over the world by storm. Nearly everyone – from business execs to marketers to salespeople – is talking about the use of data and how it allows businesses to expand their markets and make more intelligent decisions.

But why is that a big deal for the ag industry?

Let’s look at some of the best kinds of questions that your data can answer.

How Am I Doing?

Periodic data checks can help you gauge your success in any area where you’re collecting U.S. farm data. Monitoring feedback from ad campaigns, for example, can show you which ads are performing well and which farmers they’re resonating with.

Data can help you before you launch a new campaign or product, as well. A/B or yes/no testing can give you the insight that you need to see how different options perform among certain demographic segments.

Who Are My Customers?

You can learn a lot about your existing farmers and new markets by analyzing data on farmers within your territory. Data will tell you not only who your customers are (or might be), but should also provide insight on what your farmers are growing, spending and earning.

Data analysis can be most useful when applied to which farmers are part of your ideal audience, and which are not. Knowing which market segments to focus on saves you time, money and energy.

What Do They Need?

In addition to telling you who your clients are, data analysis can also help you identify what they need. Are your current farmers in need of revamped solutions to protect their financial investments? Would a new fencing product bring in a certain type of farmer that lives in a heavily wooded area?

While pointed surveys can address some of these questions and should provide answers, they’re likely to be limited in scope and won’t be helpful beyond a certain point. Data sets with information about land perimeters, Gross Farm Income and crop data allow you to diversify your data questioning and pivot when necessary.

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Introduction: Business Questions that Data Can Answer

Questions Data Can Answer - U.S. Farm Data

For agribusiness professionals, it’s a simple question: why data?

Big Data has taken over the world by storm. Nearly everyone – from business execs to marketers to salespeople – is talking about the use of data and how it allows businesses to expand their markets and make more intelligent decisions.

But why is that a big deal for the ag industry?

Let’s look at some of the best kinds of questions that your data can answer.

How Am I Doing?

Periodic data checks can help you gauge your success in any area where you’re collecting U.S. farm data. Monitoring feedback from ad campaigns, for example, can show you which ads are performing well and which farmers they’re resonating with.

Data can help you before you launch a new campaign or product, as well. A/B or yes/no testing can give you the insight that you need to see how different options perform among certain demographic segments.

Who Are My Customers?

You can learn a lot about your existing farmers and new markets by analyzing data on farmers within your territory. Data will tell you not only who your customers are (or might be), but should also provide insight on what your farmers are growing, spending and earning.

Data analysis can be most useful when applied to which farmers are part of your ideal audience, and which are not. Knowing which market segments to focus on saves you time, money and energy.

What Do They Need?

In addition to telling you who your clients are, data analysis can also help you identify what they need. Are your current farmers in need of revamped solutions to protect their financial investments? Would a new fencing product bring in a certain type of farmer that lives in a heavily wooded area?

While pointed surveys can address some of these questions and should provide answers, they’re likely to be limited in scope and won’t be helpful beyond a certain point. Data sets with information about land perimeters, Gross Farm Income and crop data allow you to diversify your data questioning and pivot when necessary.

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Choosing a Focused Research Question for Your Analysis

Choosing a Focused Research Question - U.S. Farm Data

The start to any data analytics project is to develop a research question. This provides the North Star for your efforts and helps you stay on track. A research question is the very core point of your research endeavors.

A good research question will drive your research to arrive at the answers you want to find. It’ll also open new doors that allow you to explore related concepts and possibilities to strengthen your overall findings and related goal setting.

However, finding the right research question is harder than you might think. It’s easy to get weighed down in the vast world of research and end up mining through more data than you’ll ever need.

Here are some tips for narrowing and focusing your research question.

Determine Your Goals

The products or services that your company offers will necessarily drive your research process. Since there are nearly endless data points to collect on farms and farming practices, you could waste a lot of time chasing data that’s not really relevant to your business.

For example, if you’re selling fertilizer, you probably won’t need much or any information on dairy farming practices within your sales territories.

Use your business strengths, goals and objectives to help narrow your research question so you’re only looking for the U.S. farm data you need.

Narrow Your Audience

When you’ve established the data you feel are most relevant to business goals, you need to consider the audience that you want to reach. Even if your specialty leads you to a single category – let’s say crop yields – there’s no way to make use of all of the information you could possibly gather on the topic.

Ask some leading questions, like:

  • Who are my farmers?
  • What are their crop yields from 2017? 2016?
  • Are there any significant outlying factors (market prices, weather phenomenon) that contribute to these numbers?

And so forth. Likely, you can guide your research question using facts and figures that you’ve already gleaned from farmers in your territory.

Recognize a Problem

For the most part, research questions and their related data searches stem from a central problem. Maybe the problem you recognize is that you’re not getting as many sales from a territory as you believe you should.

But maybe the problem is deeper. Perhaps you’re interested to know the effect of a drought on nutrient bio-availability in the soil in your territory. Or maybe you’re curious to know how precision ag products might improve the yield of each square acre of corn farmed on any given piece of land.

Find out what your problem is, and ask questions that can help you solve it.

Discern Relationships

As you plot the connections between your company’s specialty, your audience and the problems that they face, you’ll start to put together the basis for your research question. The relationships between your audience and their problems or the problems and possible solutions your company offers should drive the bulk of the research you conduct.

You will want to place some boundaries around how far you chase relationships, since focusing too closely in on any aspect will almost certainly lead to more questions to chase and end up unravelling the purpose of your initial research question.

With the right checks in place, you can develop strong research questions that’ll help you solve problems and analyze data in an effective way to meet your sales and growth goals.

Choosing a Focused Research Question for Your Analysis

Choosing a Focused Research Question - U.S. Farm Data

The start to any data analytics project is to develop a research question. This provides the North Star for your efforts and helps you stay on track. A research question is the very core point of your research endeavors.

A good research question will drive your research to arrive at the answers you want to find. It’ll also open new doors that allow you to explore related concepts and possibilities to strengthen your overall findings and related goal setting.

However, finding the right research question is harder than you might think. It’s easy to get weighed down in the vast world of research and end up mining through more data than you’ll ever need.

Here are some tips for narrowing and focusing your research question.

Determine Your Goals

The products or services that your company offers will necessarily drive your research process. Since there are nearly endless data points to collect on farms and farming practices, you could waste a lot of time chasing data that’s not really relevant to your business.

For example, if you’re selling fertilizer, you probably won’t need much or any information on dairy farming practices within your sales territories.

Use your business strengths, goals and objectives to help narrow your research question so you’re only looking for the U.S. farm data you need.

Narrow Your Audience

When you’ve established the data you feel are most relevant to business goals, you need to consider the audience that you want to reach. Even if your specialty leads you to a single category – let’s say crop yields – there’s no way to make use of all of the information you could possibly gather on the topic.

Ask some leading questions, like:

  • Who are my farmers?
  • What are their crop yields from 2017? 2016?
  • Are there any significant outlying factors (market prices, weather phenomenon) that contribute to these numbers?

And so forth. Likely, you can guide your research question using facts and figures that you’ve already gleaned from farmers in your territory.

Recognize a Problem

For the most part, research questions and their related data searches stem from a central problem. Maybe the problem you recognize is that you’re not getting as many sales from a territory as you believe you should.

But maybe the problem is deeper. Perhaps you’re interested to know the effect of a drought on nutrient bio-availability in the soil in your territory. Or maybe you’re curious to know how precision ag products might improve the yield of each square acre of corn farmed on any given piece of land.

Find out what your problem is, and ask questions that can help you solve it.

Discern Relationships

As you plot the connections between your company’s specialty, your audience and the problems that they face, you’ll start to put together the basis for your research question. The relationships between your audience and their problems or the problems and possible solutions your company offers should drive the bulk of the research you conduct.

You will want to place some boundaries around how far you chase relationships, since focusing too closely in on any aspect will almost certainly lead to more questions to chase and end up unravelling the purpose of your initial research question.

With the right checks in place, you can develop strong research questions that’ll help you solve problems and analyze data in an effective way to meet your sales and growth goals.

Why You Shouldn’t Source Through Surveys

Weakness of Surveys - U.S. Farm Data

The most important asset you have is information. You want to know about farmers’ behaviors, interests, needs and more. So gathering this data should be a top priority.

We’ve seen that many in the ag industry gather data through surveys. The thought here is that by getting data directly from the farmers themselves, you’ll have the most reliable information at your fingertips. Right?

Wrong.

While surveys seem like an easy way to DIY your data needs, survey data can be unreliable. Here are some of the weaknesses that come from U.S. farm data collected through surveys.

Survey Design Can Cause Error

Let’s say you’re at a trade show and you want to figure out how many farmers are growing soy. Because lots of farmers are at the show, this sounds like a great chance to get some real data for your marketing and sales efforts.

So you set up a survey outside an event and let the farmers fill it out. Once you see the results you’ll notice – all of the respondents are dedicating 50 percent or more of their acreage to soy.

You may be tempted to think that, based on this data, soy farming is becoming a new, sweeping trend across U.S. farming.

But looking at the data closely, you notice that only 10 farmers out of over 100 at the trade show filled out the forms. Based on the low number of respondents, it’s going to be impossible to draw conclusions.

That’s because when you do a survey it’s hard to get a representative sample – a group of respondents that’s large enough and representative enough of farmers across the board.

Looking a bit closer, you could also notice that you set up the survey outside a panel discussion on soybean farming. So the people who passed by your survey were already those interested in soy, so obviously they’d all be dedicating a large portion of their operation to soy farming.

Not only does this mean that your sample is non-representative, but it’s also a sign of selection bias – only the people interested in soy filled out the survey.

There are many ways that surveys can give incorrect results. Sometimes, the survey design words questions poorly, confusing the respondent into giving an incorrect answer. Sometimes, the organization administering the survey may influence who does and doesn’t respond.

A great example of this is the U.S. Census. Because it’s a government survey, people often under-report on their income, for fear that it would cause them to have to pay more in taxes later. While this may or may not be true, the fact that the government is administering a survey impacts the results.

Surveys are helpful for driving engagement and for gathering qualitative data to help guide your agribusinesses’ efforts. They’re excellent for feedback on products and to help understand what your audience wants to see you do in the future.

But when it comes to market research and analyzing trends in the industry, survey data is weak and will often yield inaccurate results.

Not All Respondents Are Created Equal

Of course, there’s more to surveys than who’s creating and administering it.

Surveys depend on respondents giving correct answers – and finding out who’s given “bad data” can be challenging.

Some people may simply wish to speed through your carefully-crafted survey. Marking that they grow 500 acres of soybeans could be the fastest path to the end, regardless of whether it’s true or not.

If you’re running an incentivized survey, they may be driven more by gaining the gift card and not by providing accurate responses. Motivating people who are tangentially related to your business is hard to do under the best of circumstances, and downright impossible in less-than-ideal conditions.

Plus, surveys can cause problems when it comes to garnering contact information. Transposing digits in a phone number or even giving an incorrect email address are fairly commonplace errors that can render your carefully-collected data nearly useless.

Facts Are More Useful Anyway

While surveys can be powerful when done correctly, it’s much faster, simpler, and more accurate to work with facts. With segmented marketing data, you don’t even have to give up on getting highly targeted information about your audience.

Segments like Gross Farm Income, Planted Acres for a variety of crops, and Total Farm Acres, can give you real, actionable insight into your target audience. Sources like the USDA and others give you U.S. farm data that gives you a more accurate picture into your market – more so than any survey could do.

Sure, surveys used to be considered an effective way to gather information about customers and prospects. Today’s marketers, however, have incredibly powerful tools just a click away – so why not take advantage?

Ditch the old-school survey and jump into the digital age with easy, effective fact-based U.S. farm data.

Why You Shouldn’t Source Through Surveys

Weakness of Surveys - U.S. Farm Data

The most important asset you have is information. You want to know about farmers’ behaviors, interests, needs and more. So gathering this data should be a top priority.

We’ve seen that many in the ag industry gather data through surveys. The thought here is that by getting data directly from the farmers themselves, you’ll have the most reliable information at your fingertips. Right?

Wrong.

While surveys seem like an easy way to DIY your data needs, survey data can be unreliable. Here are some of the weaknesses that come from U.S. farm data collected through surveys.

Survey Design Can Cause Error

Let’s say you’re at a trade show and you want to figure out how many farmers are growing soy. Because lots of farmers are at the show, this sounds like a great chance to get some real data for your marketing and sales efforts.

So you set up a survey outside an event and let the farmers fill it out. Once you see the results you’ll notice – all of the respondents are dedicating 50 percent or more of their acreage to soy.

You may be tempted to think that, based on this data, soy farming is becoming a new, sweeping trend across U.S. farming.

But looking at the data closely, you notice that only 10 farmers out of over 100 at the trade show filled out the forms. Based on the low number of respondents, it’s going to be impossible to draw conclusions.

That’s because when you do a survey it’s hard to get a representative sample – a group of respondents that’s large enough and representative enough of farmers across the board.

Looking a bit closer, you could also notice that you set up the survey outside a panel discussion on soybean farming. So the people who passed by your survey were already those interested in soy, so obviously they’d all be dedicating a large portion of their operation to soy farming.

Not only does this mean that your sample is non-representative, but it’s also a sign of selection bias – only the people interested in soy filled out the survey.

There are many ways that surveys can give incorrect results. Sometimes, the survey design words questions poorly, confusing the respondent into giving an incorrect answer. Sometimes, the organization administering the survey may influence who does and doesn’t respond.

A great example of this is the U.S. Census. Because it’s a government survey, people often under-report on their income, for fear that it would cause them to have to pay more in taxes later. While this may or may not be true, the fact that the government is administering a survey impacts the results.

Surveys are helpful for driving engagement and for gathering qualitative data to help guide your agribusinesses’ efforts. They’re excellent for feedback on products and to help understand what your audience wants to see you do in the future.

But when it comes to market research and analyzing trends in the industry, survey data is weak and will often yield inaccurate results.

Not All Respondents Are Created Equal

Of course, there’s more to surveys than who’s creating and administering it.

Surveys depend on respondents giving correct answers – and finding out who’s given “bad data” can be challenging.

Some people may simply wish to speed through your carefully-crafted survey. Marking that they grow 500 acres of soybeans could be the fastest path to the end, regardless of whether it’s true or not.

If you’re running an incentivized survey, they may be driven more by gaining the gift card and not by providing accurate responses. Motivating people who are tangentially related to your business is hard to do under the best of circumstances, and downright impossible in less-than-ideal conditions.

Plus, surveys can cause problems when it comes to garnering contact information. Transposing digits in a phone number or even giving an incorrect email address are fairly commonplace errors that can render your carefully-collected data nearly useless.

Facts Are More Useful Anyway

While surveys can be powerful when done correctly, it’s much faster, simpler, and more accurate to work with facts. With segmented marketing data, you don’t even have to give up on getting highly targeted information about your audience.

Segments like Gross Farm Income, Planted Acres for a variety of crops, and Total Farm Acres, can give you real, actionable insight into your target audience. Sources like the USDA and others give you U.S. farm data that gives you a more accurate picture into your market – more so than any survey could do.

Sure, surveys used to be considered an effective way to gather information about customers and prospects. Today’s marketers, however, have incredibly powerful tools just a click away – so why not take advantage?

Ditch the old-school survey and jump into the digital age with easy, effective fact-based U.S. farm data.

Challenges of Self-Sourced Data

U.S. Farm Data - Challenges of Self-Sourced Data

Beyond surveys, any form of self-sourced data is going to provide a challenge for your business. Here are some of the roadblocks you may run into with self-sourced U.S. farm data.

Data Collection Is Time Consuming

Gathering your own data is a time consuming process if you approach it the right way. There’s no quick way to gather the data points you want without taking the time to research and develop the data collection methods you need for gathering the information you want.

Often, this requires intense research and months of collecting raw data points. Then there’s the struggle of categorizing and storing them, as well as designing a database where your team can access the data.

It can be years before you ever have the amount of data you need to make a positive impact on your sales and marketing strategies. You probably don’t have this extra time to waste.

Your Data May Not Be Reliable

Getting data and getting reliable data are not necessarily the same thing. You can collect data on virtually anything, but the data can be skewed by bias or incomplete data sets.

The worst part? You may not even understand the extent of your data’s unreliability until you’re deep into a project and can’t make sense of the feedback you’re getting.

Your Data is Likely Incomplete

It’s very hard to gather complete data sets. Even getting someone to fill out an entire survey can be a struggle.

There will be a certain cross section of those that you survey who will simply disregard or partially finish a survey. Unless you’re surveying tens of thousands of farmers and can afford to throw out incomplete surveys without harming your desired outcome, you’re either going to be dealing with incomplete data sets or an incredibly small response pool.

The Scope of Your Data May Be Too Narrow

Developing data survey questions is an incredibly complex process that requires a strong knowledge of data collection and statistical interpretation. Ideally, a well-crafted survey has questions that have been through multiple rounds of testing and verification for their statistical accuracy.

Additionally, you’re almost always going to be working from a bias, which means that you may develop questions or response types that elicit a certain kind of response while minimizing the likelihood of another. There’s nothing wrong with bias – it gives you an edge in your own business – but bias has no place in data collection and can render entire data sets useless.

It May Be Hard to Store and Access Your Data

Storing data requires a large amount of server space and a professional team to manage those servers. You can’t simply load surveys onto an office computer’s hard drive and call it a day.

Many companies that conduct their own data collection run into challenges in storing and accessing their data. This results in lost time and lost money as they chase their tails to implement workable solutions.

There Are Hidden Costs

Some firms are attracted to self-sourced data because it seems like a money saving venture at the outset. However, hidden costs always creep up during the data collection process and almost always negate any assumed budget savings.

Survey distribution and collection are expensive, and so is secure data storage. Not to mention that lost time – which may end up being years before everything is said and done – will eat into your profits.

While self-sourced data collection might be tempting, as you can see, it’s a lot of work for potentially very little return. Instead of relying on your own data, consider using well-curated data sets that can give you unparalleled insights into your farmers’ activities and help you plan sales strategies that let you capture your market and grow your influence.

Challenges of Self-Sourced Data

U.S. Farm Data - Challenges of Self-Sourced Data

Beyond surveys, any form of self-sourced data is going to provide a challenge for your business. Here are some of the roadblocks you may run into with self-sourced U.S. farm data.

Data Collection Is Time Consuming

Gathering your own data is a time consuming process if you approach it the right way. There’s no quick way to gather the data points you want without taking the time to research and develop the data collection methods you need for gathering the information you want.

Often, this requires intense research and months of collecting raw data points. Then there’s the struggle of categorizing and storing them, as well as designing a database where your team can access the data.

It can be years before you ever have the amount of data you need to make a positive impact on your sales and marketing strategies. You probably don’t have this extra time to waste.

Your Data May Not Be Reliable

Getting data and getting reliable data are not necessarily the same thing. You can collect data on virtually anything, but the data can be skewed by bias or incomplete data sets.

The worst part? You may not even understand the extent of your data’s unreliability until you’re deep into a project and can’t make sense of the feedback you’re getting.

Your Data is Likely Incomplete

It’s very hard to gather complete data sets. Even getting someone to fill out an entire survey can be a struggle.

There will be a certain cross section of those that you survey who will simply disregard or partially finish a survey. Unless you’re surveying tens of thousands of farmers and can afford to throw out incomplete surveys without harming your desired outcome, you’re either going to be dealing with incomplete data sets or an incredibly small response pool.

The Scope of Your Data May Be Too Narrow

Developing data survey questions is an incredibly complex process that requires a strong knowledge of data collection and statistical interpretation. Ideally, a well-crafted survey has questions that have been through multiple rounds of testing and verification for their statistical accuracy.

Additionally, you’re almost always going to be working from a bias, which means that you may develop questions or response types that elicit a certain kind of response while minimizing the likelihood of another. There’s nothing wrong with bias – it gives you an edge in your own business – but bias has no place in data collection and can render entire data sets useless.

It May Be Hard to Store and Access Your Data

Storing data requires a large amount of server space and a professional team to manage those servers. You can’t simply load surveys onto an office computer’s hard drive and call it a day.

Many companies that conduct their own data collection run into challenges in storing and accessing their data. This results in lost time and lost money as they chase their tails to implement workable solutions.

There Are Hidden Costs

Some firms are attracted to self-sourced data because it seems like a money saving venture at the outset. However, hidden costs always creep up during the data collection process and almost always negate any assumed budget savings.

Survey distribution and collection are expensive, and so is secure data storage. Not to mention that lost time – which may end up being years before everything is said and done – will eat into your profits.

While self-sourced data collection might be tempting, as you can see, it’s a lot of work for potentially very little return. Instead of relying on your own data, consider using well-curated data sets that can give you unparalleled insights into your farmers’ activities and help you plan sales strategies that let you capture your market and grow your influence.

Expand Your Data Through Append

On the ACT standardized test, an incomplete or incorrect answer is better than none at all. When it comes to data, the same principle applies: incomplete data is better than no data at all.

That’s because the power of append can help you get the most out of your data.

We all have those records in our CRMs that only have a name and maybe a city and state. We want to fill out those records with addresses, emails, purchasing behaviors, and more. But finding that missing information takes hours upon hours of tedious research – and often it’s hard to guarantee your research has turned up accurate information.

Fortunately, data append services make filling out incomplete records easy and effective, giving you reliable data pulled from a variety of sources.

If you’re looking to get the most out of your data, here are some ways that data appending can help you.

1. Find elusive contact info

We live in a world where people are harder and harder to contact. People are changing their email addresses regularly. While street addresses are a matter of public record, identifying the correct address based on a name can be unreliable (especially if the person you’re looking for is named John Smith). And a lot of addresses have incorrect phone numbers listed with them – if at all.

And these are just the traditional ways to contact people. In the digital age, people are increasingly becoming mobile-only with their phones. Social media is a growing way of communication – especially when it comes to tools like Facebook Messenger and LinkedIn InMail.

This contact info is elusive. Data appending can help you find that info so you can get in touch with potential customers.

Whether you’re emailing or calling directly, or using that data to build targeted audiences for digital ads, use data appending to find out the best ways to contact your prospects and customers.

2. Add niche-specific info

As markets become increasingly focused and niche, the kind of data you need to be successful is going to be harder to find. But with the right data appending service, you can add niche-specific data right alongside your general information on growers.

For example, if you’re a tire company looking to find farmers whose tires are nearing the end of their life, you could append automotive data to know when they purchased their vehicles, and estimate when the best time to reach out to them with a special offer or deal will be.

Data appending makes it possible to have that information quickly, so you’re spending your time building strategy, marketing and selling – not doing endless hours of research.

3. Identify related records

Your database could be filled with thousands of grower records, but you’d have no idea whether any of these farmers know each other. You could have the contact info for entire communities of related growers and not even know it.

Sometimes, you can infer which growers are related to whom, based on location and whether they have the same last name, but you can’t infer how they’re related to each other. But with data appending, you can find out.

See the common relationships among the farmers so you can build a holistic, strategic plan for your marketing and sales efforts.

4. Access up-to-date records

The world is moving faster every day, so the data you have in one moment is going to be outdated the next. Even in the slow-paced world of agriculture, data still has a shelf life, and you can be relying on years-old data to make business decisions.

Data appending helps you keep all your records up to date.

Whether you’re looking for crop info, grower behavior, or a reliable phone and email address, use data appending to always have the most reliable data at the time you need it.

5. Access historic data

Even though data becomes less reliable over time, that doesn’t mean old data is irrelevant. In fact, historic data has incredible value, because it helps you put your current data into historic perspective.

Data tells you very little when it’s taken out of the context of the larger trend. Thus, having historic U.S. farm data going back as far as possible will help you best understand how your data stacks against previous years’ results.

Expand Your Data Through Append

On the ACT standardized test, an incomplete or incorrect answer is better than none at all. When it comes to data, the same principle applies: incomplete data is better than no data at all.

That’s because the power of append can help you get the most out of your data.

We all have those records in our CRMs that only have a name and maybe a city and state. We want to fill out those records with addresses, emails, purchasing behaviors, and more. But finding that missing information takes hours upon hours of tedious research – and often it’s hard to guarantee your research has turned up accurate information.

Fortunately, data append services make filling out incomplete records easy and effective, giving you reliable data pulled from a variety of sources.

If you’re looking to get the most out of your data, here are some ways that data appending can help you.

1. Find elusive contact info

We live in a world where people are harder and harder to contact. People are changing their email addresses regularly. While street addresses are a matter of public record, identifying the correct address based on a name can be unreliable (especially if the person you’re looking for is named John Smith). And a lot of addresses have incorrect phone numbers listed with them – if at all.

And these are just the traditional ways to contact people. In the digital age, people are increasingly becoming mobile-only with their phones. Social media is a growing way of communication – especially when it comes to tools like Facebook Messenger and LinkedIn InMail.

This contact info is elusive. Data appending can help you find that info so you can get in touch with potential customers.

Whether you’re emailing or calling directly, or using that data to build targeted audiences for digital ads, use data appending to find out the best ways to contact your prospects and customers.

2. Add niche-specific info

As markets become increasingly focused and niche, the kind of data you need to be successful is going to be harder to find. But with the right data appending service, you can add niche-specific data right alongside your general information on growers.

For example, if you’re a tire company looking to find farmers whose tires are nearing the end of their life, you could append automotive data to know when they purchased their vehicles, and estimate when the best time to reach out to them with a special offer or deal will be.

Data appending makes it possible to have that information quickly, so you’re spending your time building strategy, marketing and selling – not doing endless hours of research.

3. Identify related records

Your database could be filled with thousands of grower records, but you’d have no idea whether any of these farmers know each other. You could have the contact info for entire communities of related growers and not even know it.

Sometimes, you can infer which growers are related to whom, based on location and whether they have the same last name, but you can’t infer how they’re related to each other. But with data appending, you can find out.

See the common relationships among the farmers so you can build a holistic, strategic plan for your marketing and sales efforts.

4. Access up-to-date records

The world is moving faster every day, so the data you have in one moment is going to be outdated the next. Even in the slow-paced world of agriculture, data still has a shelf life, and you can be relying on years-old data to make business decisions.

Data appending helps you keep all your records up to date.

Whether you’re looking for crop info, grower behavior, or a reliable phone and email address, use data appending to always have the most reliable data at the time you need it.

5. Access historic data

Even though data becomes less reliable over time, that doesn’t mean old data is irrelevant. In fact, historic data has incredible value, because it helps you put your current data into historic perspective.

Data tells you very little when it’s taken out of the context of the larger trend. Thus, having historic U.S. farm data going back as far as possible will help you best understand how your data stacks against previous years’ results.

Signs You Need to Invest in Data Verification

When you’re making marketing decisions based on data, it’s essential that your data is accurate and reliable. Otherwise, you risk failed connections, lost sales and missed revenue.

If you’re having problems with making connections in the field, you may be dealing with unverified U.S. farm data. Here are some of the signs that you need to invest in data verification.

  1. You’re having trouble getting your message in front of farmers.Whether you’re working with a direct mail campaign that’s falling flat, dealing with bounced emails or trying to schedule face-to-face meetings, the farmers you’re trying to reach simply aren’t there to listen.
  2. You’ve noticed a disconnect between what you’re offering to farmers and what they’re actually interested in buying.You may be able to discern this disconnect through declining closing rates and frustrated agents failing to meet their monthly sales goals.
  3. You’re not entirely sure where your farmers’ data is coming from.You’ve got data and are sending the emails or letters, but nothing is happening. Maybe your data is from an outdated list, or was gathered at a conference where surveyed farmers were distracted or otherwise disinterested.
  4. You suspect one (or more) of your data sources is disreputable.You doubt some of the data you’ve been using is completely reliable. Maybe it was collected in a haphazard way. Perhaps you know that certain pieces of information are over- or underrepresented. If you have any doubts about your data, you’re unknowingly undermining the success of your marketing strategies.
  5. You’ve gone two years or more without a significant update to your data.You’ve been relying on the same lists for years and unsurprisingly, you’re missing connections with farmers in your territory. Information is constantly changing, and data points, like email addresses are unlikely to stay static year over year. If your data is outdated, then your marketing strategies risk meeting the same fate.
  6. You’re relying on various storage methods rather than a Customer Relationship Management (CRM) system.You instruct new salespeople to pull email addresses for a certain list from an Excel sheet. But for addresses, they need to access a PDF on a shared company drive. Storing data across multiple platforms means you’re probably dealing with various sources and potentially incorrect data.
  7. Your marketing, sales and customer service teams are out of sync. Your customer information is siloed by department and your teams are having a hard time making essential connections. Data should never be confined to a single department that “needs it more”. Data should be fluid across all of your departments so that your team members can solve problems and make the connections they need to propel your company to success.

Signs You Need to Invest in Data Verification

When you’re making marketing decisions based on data, it’s essential that your data is accurate and reliable. Otherwise, you risk failed connections, lost sales and missed revenue.

If you’re having problems with making connections in the field, you may be dealing with unverified U.S. farm data. Here are some of the signs that you need to invest in data verification.

  1. You’re having trouble getting your message in front of farmers.Whether you’re working with a direct mail campaign that’s falling flat, dealing with bounced emails or trying to schedule face-to-face meetings, the farmers you’re trying to reach simply aren’t there to listen.
  2. You’ve noticed a disconnect between what you’re offering to farmers and what they’re actually interested in buying.You may be able to discern this disconnect through declining closing rates and frustrated agents failing to meet their monthly sales goals.
  3. You’re not entirely sure where your farmers’ data is coming from.You’ve got data and are sending the emails or letters, but nothing is happening. Maybe your data is from an outdated list, or was gathered at a conference where surveyed farmers were distracted or otherwise disinterested.
  4. You suspect one (or more) of your data sources is disreputable.You doubt some of the data you’ve been using is completely reliable. Maybe it was collected in a haphazard way. Perhaps you know that certain pieces of information are over- or underrepresented. If you have any doubts about your data, you’re unknowingly undermining the success of your marketing strategies.
  5. You’ve gone two years or more without a significant update to your data.You’ve been relying on the same lists for years and unsurprisingly, you’re missing connections with farmers in your territory. Information is constantly changing, and data points, like email addresses are unlikely to stay static year over year. If your data is outdated, then your marketing strategies risk meeting the same fate.
  6. You’re relying on various storage methods rather than a Customer Relationship Management (CRM) system.You instruct new salespeople to pull email addresses for a certain list from an Excel sheet. But for addresses, they need to access a PDF on a shared company drive. Storing data across multiple platforms means you’re probably dealing with various sources and potentially incorrect data.
  7. Your marketing, sales and customer service teams are out of sync. Your customer information is siloed by department and your teams are having a hard time making essential connections. Data should never be confined to a single department that “needs it more”. Data should be fluid across all of your departments so that your team members can solve problems and make the connections they need to propel your company to success.

Take Advantage of GIS

U.S. Farm Data - GIS

You know how important it is to understand your potential customers. But that doesn’t just mean understanding how they do business. Perhaps in no other industry more than agriculture, you need to know where your customer operates, since farmers are so intimately connected to the land.

Geographic information systems, or GIS, are software tools for collecting, interpreting, and manipulating geographic data. This kind of U.S. farm data is used to correlate other information like demographics or production with a specific location on the Earth.

With the ability to sort and analyze geographic data, you can begin to draw a clear picture of your customer’s business—often quite literally.

Analyzing Geographic Trends

The first step to utilizing GIS information in your marketing efforts is to analyze and identify trends connected to location. By correlating sales and other data with specific locations, you can determine what other customers in the same area may need.

It is especially important to identify geographically linked trends in the ag industry. Different areas have different weather, soil conditions, growing seasons, and so on. Farmers use GIS themselves to analyze historical growth patterns, allowing them to determine what crops to plant and where they will grow best.

Developing Market Segments

By analyzing trends and identifying what potential customers in an area might need, you can build more accurate location-based market segments. GIS tools let you distinguish between customers based on a wide range of data points:

  • Farm size in acres,
  • Field-by-field growing history,
  • Location, defined by county or even ZIP code,
  • Relation to other nearby farms, and so on.

With properly defined segments, you can target messages that will appeal most to a given group. At the geographic level, you may construct material that references local events and conditions to connect with prospective customers nearly as individuals.

Take Advantage of GIS

U.S. Farm Data - GIS

You know how important it is to understand your potential customers. But that doesn’t just mean understanding how they do business. Perhaps in no other industry more than agriculture, you need to know where your customer operates, since farmers are so intimately connected to the land.

Geographic information systems, or GIS, are software tools for collecting, interpreting, and manipulating geographic data. This kind of U.S. farm data is used to correlate other information like demographics or production with a specific location on the Earth.

With the ability to sort and analyze geographic data, you can begin to draw a clear picture of your customer’s business—often quite literally.

Analyzing Geographic Trends

The first step to utilizing GIS information in your marketing efforts is to analyze and identify trends connected to location. By correlating sales and other data with specific locations, you can determine what other customers in the same area may need.

It is especially important to identify geographically linked trends in the ag industry. Different areas have different weather, soil conditions, growing seasons, and so on. Farmers use GIS themselves to analyze historical growth patterns, allowing them to determine what crops to plant and where they will grow best.

Developing Market Segments

By analyzing trends and identifying what potential customers in an area might need, you can build more accurate location-based market segments. GIS tools let you distinguish between customers based on a wide range of data points:

  • Farm size in acres,
  • Field-by-field growing history,
  • Location, defined by county or even ZIP code,
  • Relation to other nearby farms, and so on.

With properly defined segments, you can target messages that will appeal most to a given group. At the geographic level, you may construct material that references local events and conditions to connect with prospective customers nearly as individuals.

I’ve Got All This Data. Now What?

U.S. Farm Data - Data, Now What

Good data is only as good as what you do with it. Now that you’ve collected the data you need, it’s time to get busy.

Data analysis is the process through which you take bits of data and turn them into pieces of useful information. Then you can use that information to make better connections with farmers in your territory.

Data analysis can be as simple or complicated a process as you want it to be. Here are some practical ways that data analysis is used to make the most of some common farm data types:

Contact Information

You can’t expect to get very far with your prospects if you don’t know their names and relevant contact information. Regardless of which marketing techniques you employ, you need to know where to reach the farmers with whom you’re trying to connect.

Contact information cycles quickly, with people updating their addresses and email addresses often. Not only do you need contact information, but you need regularly updated contact information to stay on top of prospects and future leads.

Whether you want to build an email list or direct mail list, sending your sales info to the places where farmers want to be reached increases your likelihood of running a successful campaign.

Common Land Units and Farmland Data

Data on where farmers are farming is essential to understand the farm sizes and management needs of farmers in your territory. Common Land Units (CLU) are designated land areas that show land size based on physical boundaries, like fences, roads and waterways.

CLU information also breaks down land units by common owner, so you know how much land any individual farmer owns or manages. Define land areas by owner so you can make meaningful connections at every farm you visit. Or use CLU information to put a name and face to farms you drive by and are curious about.

Crop History Data

In addition to knowing what land your farmers own, you need to know what they’re doing with that land. Crop history data provides insight to the crops grown on the land in your territory. It also shows how land may be underutilized, and where your products or services might be a natural fit.

Use crop history data to ascertain whether farms are specialized or whether farmers are employing crop rotation techniques. With a better picture of how land is utilized, you can make more targeted pitches and determine which products are going to best suit the needs of farmers in your territory.

Related Grower Data

Farms don’t function in insolation. Farmers have connections in their local communities, as well as throughout the country and world as members of various farming organizations.

Chances are, the farmers you work with have connections who would be interested to learn more about the products and services your company offers. Use related grower data to interpret the local farming network and see how the farmers you work with function in tandem with other farmers in the community.

I’ve Got All This Data. Now What?

U.S. Farm Data - Data, Now What

Good data is only as good as what you do with it. Now that you’ve collected the data you need, it’s time to get busy.

Data analysis is the process through which you take bits of data and turn them into pieces of useful information. Then you can use that information to make better connections with farmers in your territory.

Data analysis can be as simple or complicated a process as you want it to be. Here are some practical ways that data analysis is used to make the most of some common farm data types:

Contact Information

You can’t expect to get very far with your prospects if you don’t know their names and relevant contact information. Regardless of which marketing techniques you employ, you need to know where to reach the farmers with whom you’re trying to connect.

Contact information cycles quickly, with people updating their addresses and email addresses often. Not only do you need contact information, but you need regularly updated contact information to stay on top of prospects and future leads.

Whether you want to build an email list or direct mail list, sending your sales info to the places where farmers want to be reached increases your likelihood of running a successful campaign.

Common Land Units and Farmland Data

Data on where farmers are farming is essential to understand the farm sizes and management needs of farmers in your territory. Common Land Units (CLU) are designated land areas that show land size based on physical boundaries, like fences, roads and waterways.

CLU information also breaks down land units by common owner, so you know how much land any individual farmer owns or manages. Define land areas by owner so you can make meaningful connections at every farm you visit. Or use CLU information to put a name and face to farms you drive by and are curious about.

Crop History Data

In addition to knowing what land your farmers own, you need to know what they’re doing with that land. Crop history data provides insight to the crops grown on the land in your territory. It also shows how land may be underutilized, and where your products or services might be a natural fit.

Use crop history data to ascertain whether farms are specialized or whether farmers are employing crop rotation techniques. With a better picture of how land is utilized, you can make more targeted pitches and determine which products are going to best suit the needs of farmers in your territory.

Related Grower Data

Farms don’t function in insolation. Farmers have connections in their local communities, as well as throughout the country and world as members of various farming organizations.

Chances are, the farmers you work with have connections who would be interested to learn more about the products and services your company offers. Use related grower data to interpret the local farming network and see how the farmers you work with function in tandem with other farmers in the community.

Why Data Visualization Matters

Having farm data is one thing. Understanding what it’s telling you is something entirely different. Farm data visualization bridges the gap between the two.

You actually know exactly what data visualization is; you just may not call it that. Anytime you see a pie chart, a bar graph or a trendline, you’re seeing data that’s being presented visually to help you better understand what it means.

If you want to make the most of your farm data, you need to start visualizing it. Here are five benefits you’ll get from farm data visualization.

Trends are going to be more obvious

Any piece of data, whether it’s the number of opens in an email campaign or the amount of a particular crop harvested in a given year, is just a snapshot. It doesn’t do you much good until it’s put into a proper context. It needs to be seen as part of a larger context.

Think about it. If 300 farmers open your most recent email campaign, how do you know if that’s good or not? Is it higher or lower than previous campaigns? Do you know if your email campaigns are trending up or down?

The same questions can apply to any piece of data you’re looking at.

Data visualization helps make trends more obvious. Depending on the type of graph you’re using, you should be able identify the trend, whether it be up or down, within seconds.

Now tell me, could you do that looking at a spreadsheet? Probably not.

It’s easier to tell the data’s story

Open Excel and look at the first spreadsheet that you see. Now tell me whether you honestly think that spreadsheet looks compelling, personal and human.

The answer’s probably a hard no. But as someone whose line of work is telling stories to farmers that resonate with them, you need to take impersonal data and turn it into something human.

The good news for you: behind every number is a person, a farmer who thinks of his land, crops and equipment not as a number, but as something that’s an intimate part of his day-to-day life. So there’s humanity and story behind the numbers, you just need to find it.

Farm data visualization helps with this. Watch crop output change year over year through a bar graph. If you see multiple years of less than desirable output, it may mean that the farmer’s ready to look for some new ideas – and maybe he’ll pay for yours.

If you’re prospecting a farmer who harvests multiple crops, use a pie chart to visualize which crops take up the biggest piece of the pie, so to speak. That’s where your farmer’s going to be most interested in improving and investing in.

For farmers specifically, seeing the data as it’s tied to the land the farmers operate can help you see the area where the farmers are. This puts each farmer in an appropriate context and lets you see the full picture.

It helps you better understand segmentation

As marketers, we (hopefully) deal in big numbers: hundreds of leads, thousands of clicks, millions of impressions (that would be great, wouldn’t it?). Sometimes, it’s easy for us to forget what those numbers actually mean.

Here’s a real-life example: the average conversion rate from website to lead hovers around 2%. That means that the number of visitors has to be in the tens of thousands in order to generate just a few hundred leads.

Obviously, the number of leads is incredibly small compared to visitors – and everything else down in the funnel is going to be even smaller, based on standard conversion rates. But you may not realize how large the gap is just by looking at the raw numbers. The human mind can only wrap its head around so much.

But by visualizing your marketing funnel, you see how big the gap is between visitors and leads. This helps you see just how many people in one category you need to engage to drive the numbers in another one.

It makes comparing data sets easier

Let’s say you’re in the seed business and trying to decide whether to prospect a group of corn farmers versus soy farmers. Which group would be worth your time the most?

Take a look at the data. If you have good crop data that’s tied to geospatial maps of the farmland, you should be able to see how much land is taken up by corn and how much is taken up by soy. If you have several years of data going back, you’ll be able to see which crop is growing the fastest.

Visualizing the two datasets and comparing them will make it clear where the trends are headed, and the decision will become pretty clear which one should be your priority.

It takes lines of text and turns them into something intuitive

Let’s face it – lines of text and numbers are boring, and most people have neither the time nor the interest in trying to make sense of them. And that’s ok – because visualizing data makes it very easy to take numbers and turn them into something intuitive:

  • We all know about stacking items. That’s what a bar graph does.
  • We know about “pieces of the pie,” like a pie chart shows.
  • We know about following points A to B to Z, like a line graph will show you.

If you specifically want to see how crop data is tied to the acres farmers own, a map shows you that – again, it’s something intuitive.

By taking your raw data and turning it into something that you – and the higher ups – can understand and actually use day-to-day.

Why Data Visualization Matters

Having farm data is one thing. Understanding what it’s telling you is something entirely different. Farm data visualization bridges the gap between the two.

You actually know exactly what data visualization is; you just may not call it that. Anytime you see a pie chart, a bar graph or a trendline, you’re seeing data that’s being presented visually to help you better understand what it means.

If you want to make the most of your farm data, you need to start visualizing it. Here are five benefits you’ll get from farm data visualization.

Trends are going to be more obvious

Any piece of data, whether it’s the number of opens in an email campaign or the amount of a particular crop harvested in a given year, is just a snapshot. It doesn’t do you much good until it’s put into a proper context. It needs to be seen as part of a larger context.

Think about it. If 300 farmers open your most recent email campaign, how do you know if that’s good or not? Is it higher or lower than previous campaigns? Do you know if your email campaigns are trending up or down?

The same questions can apply to any piece of data you’re looking at.

Data visualization helps make trends more obvious. Depending on the type of graph you’re using, you should be able identify the trend, whether it be up or down, within seconds.

Now tell me, could you do that looking at a spreadsheet? Probably not.

It’s easier to tell the data’s story

Open Excel and look at the first spreadsheet that you see. Now tell me whether you honestly think that spreadsheet looks compelling, personal and human.

The answer’s probably a hard no. But as someone whose line of work is telling stories to farmers that resonate with them, you need to take impersonal data and turn it into something human.

The good news for you: behind every number is a person, a farmer who thinks of his land, crops and equipment not as a number, but as something that’s an intimate part of his day-to-day life. So there’s humanity and story behind the numbers, you just need to find it.

Farm data visualization helps with this. Watch crop output change year over year through a bar graph. If you see multiple years of less than desirable output, it may mean that the farmer’s ready to look for some new ideas – and maybe he’ll pay for yours.

If you’re prospecting a farmer who harvests multiple crops, use a pie chart to visualize which crops take up the biggest piece of the pie, so to speak. That’s where your farmer’s going to be most interested in improving and investing in.

For farmers specifically, seeing the data as it’s tied to the land the farmers operate can help you see the area where the farmers are. This puts each farmer in an appropriate context and lets you see the full picture.

It helps you better understand segmentation

As marketers, we (hopefully) deal in big numbers: hundreds of leads, thousands of clicks, millions of impressions (that would be great, wouldn’t it?). Sometimes, it’s easy for us to forget what those numbers actually mean.

Here’s a real-life example: the average conversion rate from website to lead hovers around 2%. That means that the number of visitors has to be in the tens of thousands in order to generate just a few hundred leads.

Obviously, the number of leads is incredibly small compared to visitors – and everything else down in the funnel is going to be even smaller, based on standard conversion rates. But you may not realize how large the gap is just by looking at the raw numbers. The human mind can only wrap its head around so much.

But by visualizing your marketing funnel, you see how big the gap is between visitors and leads. This helps you see just how many people in one category you need to engage to drive the numbers in another one.

It makes comparing data sets easier

Let’s say you’re in the seed business and trying to decide whether to prospect a group of corn farmers versus soy farmers. Which group would be worth your time the most?

Take a look at the data. If you have good crop data that’s tied to geospatial maps of the farmland, you should be able to see how much land is taken up by corn and how much is taken up by soy. If you have several years of data going back, you’ll be able to see which crop is growing the fastest.

Visualizing the two datasets and comparing them will make it clear where the trends are headed, and the decision will become pretty clear which one should be your priority.

It takes lines of text and turns them into something intuitive

Let’s face it – lines of text and numbers are boring, and most people have neither the time nor the interest in trying to make sense of them. And that’s ok – because visualizing data makes it very easy to take numbers and turn them into something intuitive:

  • We all know about stacking items. That’s what a bar graph does.
  • We know about “pieces of the pie,” like a pie chart shows.
  • We know about following points A to B to Z, like a line graph will show you.

If you specifically want to see how crop data is tied to the acres farmers own, a map shows you that – again, it’s something intuitive.

By taking your raw data and turning it into something that you – and the higher ups – can understand and actually use day-to-day.

Choosing the Right Graph to Visualize Your Data

If your data visualization strategy involves sifting through spreadsheets, you’re probably missing the big picture. Instead, choose the right graph to visualize your data and make the big picture clearer.

But how do you decide which way to visualize your data? After all, data points arranged in a pie chart tell a different story than ones arranged in a line graph. Not all data fits well into every visual format, and just because you can turn something into a graph doesn’t mean it’s going to be relevant.

To understand which graph is going to be most helpful for representing your data visually, consider the most popular: pie, bar and line, and which one will work best for different visualization needs and make the most sense of your data.

Determine What You Need to Know

Your data can tell you a lot of things, but only you know what information you need. When you know what you need, you can get a better feel for which graph will best represent the information you’re looking for.

For example, if you want to see whether dairy farm production is trending up or down, a line graph will probably be the best way to represent this data. On the other hand, if you want to see which dairy farmers are feeding their cattle supplements A, B or C to boost milk production, a pie chart may be more informative.

Let Your Chart Tell the Story

Before we get too far into determining which chart is best for which situations, let’s review some of the more common charts you’re likely to come across. Though you probably see them every day in reports, online or in the news, you may not be familiar with their proper names.

  • Pie Chart. A pie chart is, as it sounds, a circular chart that looks like a pie cut into pieces. Pie charts display components as parts or percentages of a whole. Each piece is colored and labeled to represent different players and figures, and the visual representation makes it very easy for you to point out which parts are influencing the big picture.
  • Bar Graph. A bar graph is represented on a grid, and is a simple visual way to determine min and max values. While a bar graph can represent other data – like the distribution of ages of farmers in your territory – it’s generally most useful for quickly analyzing highs and lows among a data distribution.
  • Line Graph. A line graph connects scattered data points into a cohesive whole and allows you to quickly determine trends. Line graphs connect various data points distributed on a graph to show trends and patterns among data sets.

Build a Chart

When you’re confident in where your data analysis is taking you, ask some simple questions to narrow your scope for visualization. The following questions should guide your thought process:

  • Do I need to compare values? If so, use a bar graph to represent your data.
  • Do I want to see the composition of something? An example of this may be determining which brand of tablet computer your farmers use to keep track of crop data. If this is the type of data you want to see, use a pie chart to represent your data.
  • Do I want to see how data is distributed? If so, use a line graph to represent your data.
  • Do I want to spot trends in my data? If so, use a line graph.
  • Do I want to spot relationships between different data sets? If so, use a line graph to represent this data.

Of course, there are many ways to represent your data visually, and this post only scratches the surface. But with this information, you should be better prepared to interpret and analyze your data visually.

When you can accurately visualize your data, you can see where you’ve been and plot where you’re going. There’s no one way to analyze and visualize data, but when you have the right tools at hand, you can make your data come alive.

Choosing the Right Graph to Visualize Your Data

If your data visualization strategy involves sifting through spreadsheets, you’re probably missing the big picture. Instead, choose the right graph to visualize your data and make the big picture clearer.

But how do you decide which way to visualize your data? After all, data points arranged in a pie chart tell a different story than ones arranged in a line graph. Not all data fits well into every visual format, and just because you can turn something into a graph doesn’t mean it’s going to be relevant.

To understand which graph is going to be most helpful for representing your data visually, consider the most popular: pie, bar and line, and which one will work best for different visualization needs and make the most sense of your data.

Determine What You Need to Know

Your data can tell you a lot of things, but only you know what information you need. When you know what you need, you can get a better feel for which graph will best represent the information you’re looking for.

For example, if you want to see whether dairy farm production is trending up or down, a line graph will probably be the best way to represent this data. On the other hand, if you want to see which dairy farmers are feeding their cattle supplements A, B or C to boost milk production, a pie chart may be more informative.

Let Your Chart Tell the Story

Before we get too far into determining which chart is best for which situations, let’s review some of the more common charts you’re likely to come across. Though you probably see them every day in reports, online or in the news, you may not be familiar with their proper names.

  • Pie Chart. A pie chart is, as it sounds, a circular chart that looks like a pie cut into pieces. Pie charts display components as parts or percentages of a whole. Each piece is colored and labeled to represent different players and figures, and the visual representation makes it very easy for you to point out which parts are influencing the big picture.
  • Bar Graph. A bar graph is represented on a grid, and is a simple visual way to determine min and max values. While a bar graph can represent other data – like the distribution of ages of farmers in your territory – it’s generally most useful for quickly analyzing highs and lows among a data distribution.
  • Line Graph. A line graph connects scattered data points into a cohesive whole and allows you to quickly determine trends. Line graphs connect various data points distributed on a graph to show trends and patterns among data sets.

Build a Chart

When you’re confident in where your data analysis is taking you, ask some simple questions to narrow your scope for visualization. The following questions should guide your thought process:

  • Do I need to compare values? If so, use a bar graph to represent your data.
  • Do I want to see the composition of something? An example of this may be determining which brand of tablet computer your farmers use to keep track of crop data. If this is the type of data you want to see, use a pie chart to represent your data.
  • Do I want to see how data is distributed? If so, use a line graph to represent your data.
  • Do I want to spot trends in my data? If so, use a line graph.
  • Do I want to spot relationships between different data sets? If so, use a line graph to represent this data.

Of course, there are many ways to represent your data visually, and this post only scratches the surface. But with this information, you should be better prepared to interpret and analyze your data visually.

When you can accurately visualize your data, you can see where you’ve been and plot where you’re going. There’s no one way to analyze and visualize data, but when you have the right tools at hand, you can make your data come alive.

What to Look for in Data Management Software

FarmFocus - Farm Market iD - Sales Prospecting

So you’ve decided to start using agri-data to drive your strategy, marketing and sales efforts. That means you’re probably looking for agri-data management software.

Having good software not only helps you organize, store and secure your data, but it also makes sure you get the most mileage out of that data.

Whether you’re using stacked file folders filled with paper records or Excel spreadsheets stored across multiple devices, old data management practices are hindering your ability to find, contact and sell to new and current customers.

You may be using a customer relationship management (CRM) system to store data. And that’s a great start. But there’s so much more potential to your data that you just can’t realize without the right software.

Here are five things to look for in agri-data management software to help you get the most out of it.

1. Reliable data

So technically, this first point has less to do with the software you’re using than the data you’re using it. But it’s important to talk about the data before we start to talk about the software.

The reason it’s so important is that your data management software isn’t going to do you any good without reliable data. Don’t expect to spend dollars on data management software and expect it to work miracles on your bad or incomplete data.

There’s a rule in data science: bad data won’t give you an error message, just bad results. If you input bad data, you’ll get bad results and bad insights. And you may not be able to tell that you’re insights are bad until you’ve already invested thousands of dollars in a marketing strategy.

Now if you got a bad score on that guide, don’t worry. Some data management software – like Farm Market iD’s FarmFocus and FieldVision – will come with reliable data as a part of the application. So if you need new U.S. farm data and new software, consider Farm Market iD’s data-powered software to get the best of both worlds.

Now – on to what you need to look for in agri-data management software.

2. Drill down, filter & search capabilities

There are millions of farmers and hundreds of millions of farms in the U.S. – too much for any one person to sort through.

To put that in perspective – if you spent just one minute reading each of U.S. farm data records, and did that 24/7, it would take you 57 years to go through all of the data. To risk stating the obvious, you don’t have that kind of time on your hands.

So if you want to actually make use of U.S. farm data, your agri-data management software has to have drill down, search or filter capabilities to query the data and find the data you need in the moment.

Whether you’re drilling down by state and county, filtering by crop type or searching by current geographic location, your software should be able to find the data you need when you need it.

3. Data visualization

We’ve talked before about the benefits of data visualization – and how it helps you make sense of what your data is telling you. Because this is such an important part of data management, your software needs to help with data visualization.

This could take place in a variety of ways. Your software can plot out farm and grower locations on a map. It could create a graph showing crop yield year over year. Or it could call up a pie chart with a grower’s crop mix for a given year.

Having data visualization abilities within your software will help you start making sense of your data as soon as you upload it – and reduce the amount of work on your part.

4. Exportable Excel files & CRM integration

No matter what data management software you use, it won’t be able to do everything you want it to – and that’s ok! One of the recent trends in software-as-a-service (SaaS) is that few applications do everything, but the few things they do, they do exceptionally well.

So you’ll want to be able to export data from your data management software so you can access it elsewhere – either in an Excel file or upload into another application. Whether you’re uploading to your CRM, marketing automation software, social media marketing platform, etc., having this ability will help you better turn your data into actionable marketing and sales strategies.

And if your software integrates with your CRM directly, that’s even better.

5. Ability to leave notes and comments

Unless you’re a one-man shop, you’re not going to be using the software alone. Odds are, you’re going to have various team members using it as well.

Often, the team members accessing the data are going to spread across various departments. Business intelligence, marketing, sales and customer service team members are going to need to use the data for various reasons.

Having the ability to make notes and comments within the software will let each team member record their own insights, ideas and interactions with growers – and let you store those notes alongside the data.

Inter-team communication is important for integrated strategy, marketing & sales – and having the ability to share notes is instrumental in making that possible.

What to Look for in Data Management Software

FarmFocus - Farm Market iD - Sales Prospecting

So you’ve decided to start using agri-data to drive your strategy, marketing and sales efforts. That means you’re probably looking for agri-data management software.

Having good software not only helps you organize, store and secure your data, but it also makes sure you get the most mileage out of that data.

Whether you’re using stacked file folders filled with paper records or Excel spreadsheets stored across multiple devices, old data management practices are hindering your ability to find, contact and sell to new and current customers.

You may be using a customer relationship management (CRM) system to store data. And that’s a great start. But there’s so much more potential to your data that you just can’t realize without the right software.

Here are five things to look for in agri-data management software to help you get the most out of it.

1. Reliable data

So technically, this first point has less to do with the software you’re using than the data you’re using it. But it’s important to talk about the data before we start to talk about the software.

The reason it’s so important is that your data management software isn’t going to do you any good without reliable data. Don’t expect to spend dollars on data management software and expect it to work miracles on your bad or incomplete data.

There’s a rule in data science: bad data won’t give you an error message, just bad results. If you input bad data, you’ll get bad results and bad insights. And you may not be able to tell that you’re insights are bad until you’ve already invested thousands of dollars in a marketing strategy.

Now if you got a bad score on that guide, don’t worry. Some data management software – like Farm Market iD’s FarmFocus and FieldVision – will come with reliable data as a part of the application. So if you need new U.S. farm data and new software, consider Farm Market iD’s data-powered software to get the best of both worlds.

Now – on to what you need to look for in agri-data management software.

2. Drill down, filter & search capabilities

There are millions of farmers and hundreds of millions of farms in the U.S. – too much for any one person to sort through.

To put that in perspective – if you spent just one minute reading each of U.S. farm data records, and did that 24/7, it would take you 57 years to go through all of the data. To risk stating the obvious, you don’t have that kind of time on your hands.

So if you want to actually make use of U.S. farm data, your agri-data management software has to have drill down, search or filter capabilities to query the data and find the data you need in the moment.

Whether you’re drilling down by state and county, filtering by crop type or searching by current geographic location, your software should be able to find the data you need when you need it.

3. Data visualization

We’ve talked before about the benefits of data visualization – and how it helps you make sense of what your data is telling you. Because this is such an important part of data management, your software needs to help with data visualization.

This could take place in a variety of ways. Your software can plot out farm and grower locations on a map. It could create a graph showing crop yield year over year. Or it could call up a pie chart with a grower’s crop mix for a given year.

Having data visualization abilities within your software will help you start making sense of your data as soon as you upload it – and reduce the amount of work on your part.

4. Exportable Excel files & CRM integration

No matter what data management software you use, it won’t be able to do everything you want it to – and that’s ok! One of the recent trends in software-as-a-service (SaaS) is that few applications do everything, but the few things they do, they do exceptionally well.

So you’ll want to be able to export data from your data management software so you can access it elsewhere – either in an Excel file or upload into another application. Whether you’re uploading to your CRM, marketing automation software, social media marketing platform, etc., having this ability will help you better turn your data into actionable marketing and sales strategies.

And if your software integrates with your CRM directly, that’s even better.

5. Ability to leave notes and comments

Unless you’re a one-man shop, you’re not going to be using the software alone. Odds are, you’re going to have various team members using it as well.

Often, the team members accessing the data are going to spread across various departments. Business intelligence, marketing, sales and customer service team members are going to need to use the data for various reasons.

Having the ability to make notes and comments within the software will let each team member record their own insights, ideas and interactions with growers – and let you store those notes alongside the data.

Inter-team communication is important for integrated strategy, marketing & sales – and having the ability to share notes is instrumental in making that possible.

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