Watch What I Do, Not What I Say

Begin by tracking their Activity Metric, that is, their cadence for fielding inbound leads, making outbound calls and sending prospecting emails.

• Inbound marketing leads: watch closely the Lead Response Time to see who is “on it” and who is letting valuable leads languish.
• Outbound calls and 1:1 emails: track the pace at which these are sent out. Make sure the pace is consistent without the peaks and valleys which may indicate a lack of focus and follow-up.

The Quality Side of the Equation

With lead pursuit and outbound prospecting on track, look to the quality of interactions to identify who needs help prospecting. To gauge this, track sales metrics like Contact Rate. That is, how many activities does it take to generate a single opportunity.

It’s helpful to provide new sales reps with activity quotas. This will help them track against a clear goal and alerts you when a new rep is going sideways. You can then step in to help them get more comfortable with the products they’re selling, fix whatever process issues may be holding them back, or otherwise address the issue.

In conclusion, keep these four sales metrics in mind as you embark on a more data-driven approach for evaluating your new sales reps:

1. Contacts Per Week (calls, emails, inbound leads)
2. Lead Response time (for marketing leads)
3. Contact Rate (# calls to generate an opportunity)
4. Lead-to-Opportunity Conversion % (for marketing leads)

To see some interesting sales metrics in action, take a moment to review this interactive sales dashboard.

The post Sales Metrics That Forecast New Rep Performance appeared first on GoodData.

1. Pacing. This type of analysis helps you measure progress against goals. These goals can be based on historical data, industry benchmarks or user defined. The important thing to remember is that progress isn’t necessarily linear. For example, some organizations close 50 percent of their total bookings on the last day of the quarter. Others, plateau towards the end. You need a way of understanding how you’ve done historically (or how your competitors are doing) and measure your current progress against that. Pacing analysis gives you a big-picture overview of your trajectory and progress against goals.

2. Parts of whole reporting. This will help you understand the moving parts that are helping you reach your goals. If you’re on target for bookings for the quarter, you’ll be able to see which product lines, sales reps, campaigns or other assets are contributing most to that. If, on the other hand, you’re behind, you’ll be able to pinpoint the cause.

3. Scenario analysis. When faced with a big decision, it often makes sense to consider a worst-case scenario, a best-case scenario and the most likely scenario. This is what scenario analysis does for you. It projects possible future outcomes. You’ll get a view of how these outcomes might occur, for example, if your biggest client has a bad quarter, they might cut out your services to save money, or they might rely even more on you in order to focus on their core competencies. Most likely, they’ll have an okay quarter and nothing will change.

4. Cohort analysis. Also known as segmentation, this marketing-analysis technique lets you see how people engage with your content or product over time. Say you roll out different versions of your website, and you want to understand how much your millennial audience engaged with the new site versus the old one. Cohort analysis will tell you that. It’s the way to see different usage patterns and the evolution of usage over time.

5. Correlation. The classic question in this category: what’s the correlation between diapers and beer? The answer is that when a dad goes to the store to pick up diapers, often times he’ll pick up a six-pack. So diaper sales are positively correlated with beer sales. Correlation analysis can help you find these types of unexpected relationships.

Once you have these 5 business analysis techniques in your toolbox, you’ll be able to cover a lot of analytical ground—and have the information you need to make more informed decisions as a result.

Need help crafting your business analysis strategy? Contact Us.

The post 5 Kinds of Business Analysis Techniques Every Business User Should Know appeared first on GoodData.

Enter GoodData for Yammer, which will show you any trend you want, when you want to see it.

In partnership with Kanjoya, the leading platform for emotional engagement, GoodData combines your operational reporting and emotional sentiment in the same dashboard, providing full context on the structured and unstructured data in your Yammer networks.

We’ve built GoodData for Yammer around best practices and KPIs for measuring internal and external Yammer networks, enabling the measuring and scoring of everything from the big-picture health of your network to individual posts:

• Highlight key trends and topics in your network, groups and even individual posts.
• Perform ad-hoc analysis on key data in Yammer networks (internal and external) around users, groups and Yams.
• Ensure you’re making the most out of your ESN by tracking and analyzing internal Yammer usage and adoption.
• Pit historical trends against current data to see the health of your user base and group activity trends. For example, you can track engagement this quarter, versus previous quarters versus all-time to see how usage patterns are changing and to measure the effect of any programs or campaigns.
  
• Receive text and email alerts if your metrics demand immediate attention.
• Gain a complete overview of your social investments by adding additional data sources, including Facebook, Twitter, Google+, Enterprise Social Networks (ESNs), CRM and other internal data sources.

The Wisdom of the Cloud

GoodData for Yammer is actually an application built on top of GoodData’s leading business intelligence (BI) platform. Thanks to our cloud-based architecture, GoodData scales to accommodate Big Data and easily connects into all types of data sources (cloud-based or on-prem).  This openness allows us to integrate with leading technology companies like Kanjoya, for example, which uses their natural language processing (NLP) engine to provide rich sentiment and emotion scoring for all Yams in the GoodData for Yammer application. Moreover, because all data is on the cloud, it is more secure. (To learn why, read this.)

In short, the cloud is enabling the new era of BI. For the first time ever, it is easy to collaborate with other platforms to compile, analyze and score disparate information sources. Users on any device can see and interact this information in an intuitive visual form, gaining insight on exactly where to improve their business. That capability, more than anything before it, will truly enable you to see the ROI of your network.

The post Nail Your Network ROI With GoodData for Yammer ™ appeared first on GoodData.

#1 It’s a new thing.
As a business user, your problems are still the same. What has changed is how you get the solutions to those problems; these answers now come from an unlikely source: your big data. More than 85 percent of organizations have big data efforts in action or planned. Big data gives you access to more data, tools and resources to get answers to your business questions, in a different, better and faster way.

#2 It’s complicated.
If almost 90 percent of data spreadsheets have errors, you’re wasting a lot of time using data that is limited, outdated and may or may not be accurate. Intuitive BI platforms, like GoodData, provide out-of-the-box solutions to do the hard work for you.
Today’s systems are designed to eliminate complexity. They are automated, flexible and crafted to meet your specific business needs—without human intervention and errors.

#3 It’s expensive.
Between marketing automation, CRM and social media platforms, you have a lot of data to keep track of. But you don’t have to break the bank to do it. New big data solutions cost less and can be accessed quickly. Regardless of your role, big data solutions now exist that help you identify problems and react to them instantaneously, saving time and money. The reality is that your business can’t afford to not have these systems in place; data is your most valuable asset.

#4 The more data, the better.
How much data you have matters, but not as much as the type. Using KPIs and tracking metrics, you can know what data to collect and understand how it can be used in making smart, data-driven business decisions that generate ROI.

#5 It’s reserved for the number geeks.
Making sense of your big data no longer requires a data scientist or analyst. Dashboards make it possible for anyone, from the SMB to Average Joe, to get data-driven insights and make data driven decisions at the click of a button.

How well do you know your Big Data?  Now is the time to take charge and translate your volumes of data into business value.

The post 5 Misconceptions of Big Data appeared first on GoodData.

Our vision has always been to enable business people to make smarter decisions so they can turn their data into a source of revenue, profit and competitive advantage. Our mission is to make that insight effortless and real-time for users and their companies, by running and managing GoodData’s powerful infrastructure ourselves.

Our cloud-based platform and apps — mash-ups of reports, analytics, metrics and best practices —  are the result. They prove that big data can be user-friendly. Our apps simultaneously crunch data from the Web, mobile devices, social media and legacy systems, and generate a visually stunning  user interface can easily understand. The result? Better data, better insights and greater  monetization. This year’s CODiE award, announced by SIIA at All About the Cloud conference, offers gratifying proof that we are exceeding our mission.

We’re excited and humbled to receive our CODiE because it means that our years of work and dedication have truly paid off for our users — who have always been our  number one priority.

Once again, we  thank you all.

Contact us to learn more about GoodData.

The post GoodData Wins CODiE Award for Best Monetization Solution appeared first on GoodData.

To eliminate end-of-the-quarter surprises, here are 3 key sales analytics tips to help you deliver “intelligence” across your sales organization.

 Create a visual that allows you to see change  & progress for each stage in the pipeline.
To make an accurate prediction of how the quarter will end, you need to be able to see pipeline movement at a glance. All pipeline opportunities should be thoroughly analyzed to determine actions that may be required. Start by tracking changes in the pipeline over the period – this will show you where your dollars are going and what business was lost, won, reforecast or moved out to another quarter.

A good view into the current position highlights risks in your pipeline in time for you to course correct.

 Identify “Stuck” Deals.
Every organization has an ideal sales pace and velocity. Deals that follow this progression are more likely to close than deals that lose momentum or get stuck in stage. Where is there progress? How are “actuals” trending towards goals? What is the path to reaching goals? By looking closely at actual performance versus the goals, the sales organization thoroughly understands the scope of work that still needs to be completed.

Understanding the ideal velocity for a winning deal can help the sales organization identify which deals are off pace or which deals got off to a good start but got stuck in stage. Focus your efforts on these deals, as they are the ones that can save your quarter.

 Always know your current position.
You can’t manage what you can’t see, and access to the right information at the right time helps you make correct decisions. These decisions are crucial in determining priorities, optimizing sales leads and giving you insight into every sales opportunity. Use your comprehensive sales data to visually show how much business has closed, what deals are expected to close, what deals are at risk and if anything significant happened to effect the quarter. This means showing progress towards quarterly goals across all regions and products, and estimating where the quarter is going to finish.

In other words, what is the overall outlook for the quarter?

Ready to turn your opportunities into wins? Take a look at more “intelligent” sales analytics.

The post 3 Tips to Improve Sales Pipeline Performance appeared first on GoodData.

I couldn’t have made that statement five years ago. What’s changed is a new generation of powerful, best-of-breed cloud products that delivers vertical capabilities — like data analysis, collaboration or customer support — all working together through open APIs. It’s like the cloud version of Lego blocks that can be combined into fascinating new shapes. The result? Accidental innovation, as SaaS partners and customers mix and match apps and functions at will.

Today’s announcement between GoodData and Box shows what I mean. Because of our open APIs, the new GoodBox Bash provides a common data platform that makes it easy for customers to analyze how effectively teams collaborate on Box. Companies can understand, for example, how team members use shared content, see successful habits, and easily measure the value and effectiveness of content. Customers can start using the new GoodBox Bash practically “out of the box.”

Compare that experience with the smoke-and-mirrors press releases of partnerships we’ve come to expect from legacy software vendors — lots of noise signifying nothing.

And that’s my underlying point. When we and other SaaS companies announce joint products or partnerships, it actually means something. This is the beauty of the new “cloud economy.” Imagine being able to combine and immediately deploy cloud-based apps from amazing SaaS companies — like Box, Zendesk for customer service, Zuora for online billing and GoodData for data analysis. I like to think of it as best-of-breed finally putting inbred out of its misery. (Or at least, ending of the misery of enterprises chained to the likes of Oracle, SAP and IBM.)

Just think of the ramifications for IT organizations! After more than 20 years of promise, the tech industry has finally liberated IT to focus on business priorities. That means quickly giving users powerful software that does exactly what they need, with the scale and reach of the cloud. Accent on “quickly” and “powerful” — two attributes that define the new generation of best-of-breed cloud apps and platforms.

I’ll use GoodData as an example. Our GoodData platform handles a mind-numbing array of big-data software built to handle tasks like data processing, data mining, programming, workflow and queries. And while these programs are incredibly powerful, they’re also amazingly complex. At GoodData, we handle all these programs at the back end so that our customers don’t have to. And that means our customers can quickly deploy powerful analytics across all their important data.

We aren’t alone in that philosophy. In fact, I’d say all great best-of-breed SaaS companies share the same mission and goal — which is to make it easy for business to consume technology.

Now, instead of worrying about flipping switches and keeping the lights on, IT folks take on new responsibilities related to the cloud — from governance, integration, capacity planning and security to vetting apps across all the lines of business. And that means the cloud economy satisfies another promise technology vendors have been making for years: Turning IT into a strategic partner that enables business value.

As I said: the cloud wins. Welcome to the new cloud economy.

Read Box’s blog or visit our website for more information on GoodBox.

The post The Cloud Economy: Putting an End to Inbred Software appeared first on GoodData.

To avoid getting caught in the madness of the data sphere, take a look below to find out which marketing metrics deserve attention and which are safe to let go.

When you are feeling bogged down by the metrics and data, take a step back and try to determine which marketing metrics directly relate to your company’s bottom line. You want social media and marketing analytics that are tied to sales or revenue. If the metric does not tell you much about sales and instead simply provides a somewhat meaningless number that makes you feel good about the brand, it is safe to focus your attention elsewhere.

Don’t get caught up in the madness – preview more marketing metrics in action.

The post Marketing Metrics Overload: What Really Matters appeared first on GoodData.

A Sea Change in Trading

Stock trading is about being the first person to get an order on the books. But you don’t just want any order, you want to create a set of moves that maximize your potential while reducing risk.

For a long time, the first trader to jump on the phone and execute the right orders would win. Fifteen years ago, the Securities and Exchange Commission (SEC) allowed computerized stock trading for the first time. A new era of high-frequency trading (HFT) was born.

When traders realized the potential of computerized trading, they built algorithms that could analyze trades in record time and conduct high-volume trades in nanoseconds. Faster than humans, algorithms can adjust their strategies in real-time, testing the market by placing orders en masse, then canceling them if the market doesn’t provide the desired response.

The race to make the first and best trades quickly grew so tight that traders were placing their computers inside the data centers of some markets, reducing the distance that information packets had to travel to nearly nothing. As a result, the most wired traders were executing the best deals before everyone else, from banks to insurance companies to individuals. HFT became the only way to gain a real profit from short-term trades.

The Big Data Revolution

A similar arms race is occurring with big data today. If you ignore it, it’s like trying to conduct a trade on a rotary phone while others have their computers in the data center. Big data is speeding up and adding intelligence to the ways in which we define our progress, our markets and our potential.

Guesswork, experimentation and the hard labor of crunching numbers to make decisions have long been a part of business. Big data is, if not eliminating them, shrinking them significantly. Ignore it, and you’ll find yourself with outdated technology and an empty wallet. The moral of the big data story is turning into the same one as with computerized trading: Adapt or die.

The post How Big Data is Like High-Frequency Trading appeared first on GoodData.

Buffett spends countless hours researching each of his equities. Before making a decision, he reads annual reports, news publications and any other information he can get his hands on. His decisions are informed by research, and based on the strict fundamentals that work best for his style of investing.

The way Buffett operates is not unlike the best use cases for big data. Among other things, his success lies in his ability to make good decisions in accordance with quality benchmarks. This is the key to using big data well, and to business success in general.

Big data users can learn from Warren Buffett. His brain, in effect, is a kind of big data engine. Here are three lessons that Buffett provides for big data users:

1. Know What You’re Looking For

By analyzing large amounts of data from diverse sources, we can gain context and correlations that wouldn’t have otherwise occurred to us. Buffett pulls from actuary reports and bond publications; big data can suck information out of nearly everything, from social media to expense reports.

But it is all for nothing if you don’t know what you’re looking for. You need structure around your queries.

Buffett has a few rules of thumb. He looks for low-volatility stocks with a low price-to-book ratio. They should be profitable and growing, among other considerations.

Similarly, if you ask your data analytics platform the right questions, you’ll know the best answers when you see them. Perhaps you’re looking for a Scotch-drinking customer base that prefers premium product and prioritizes convenience over shopping around. If you know that before you ask your data engine to generate their social media preferences, you’ll get a much better result than if you asked the same question about Scotch drinkers in general.

2. Understand How the Object of Your Research Works

Buffett once said: “Never invest in a business you can’t understand.” Given Buffett’s rational investment style, one can surmise the meaning behind that quote. If you don’t understand the mechanics of your investment, you’re relying too much on emotion and guesswork. Or else the business itself isn’t viable.

Likewise, if you don’t understand how the organization or customer base you’re running a big data query on works, you’re relying too heavily on assumptions. You could ask questions of the data that have nothing to do with the reality of the situation. Asking questions about the rate of email responses in a customer support organization that relies mostly on forums won’t get you very far.

3. Use Solid Benchmarks

Some of Warren Buffett’s more famous quotes illustrate the precision and simplicity of his benchmarks:

• “Rule No. 1: never lose money. Rule No. 2: don’t forget rule No. 1.”

• “Only buy something that you’d be perfectly happy to hold if the market shut down for 10 years.”

• “It’s far better to buy a wonderful company at a fair price than a fair company at a wonderful price.”

These benchmarks demand the best. They don’t compromise. Arrange your own benchmarks to similar standards, ask the right questions, and make data driven decisions that reflect your high standards and style.

Make Data Your Servant

Maybe one of Warren Buffett’s biggest secrets to success is that he made the data work for him. If you aspire to do the same, data will quickly become your best friend.

Want to find the secrets hidden in your data? 

The post Warren Buffett, the Human Big Data Engine appeared first on GoodData.