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Trends and Outliers

TIBCO Spotfire's Business Intelligence Blog

Category Archives: Sales Analytics

03/03
2014

Boosting Sales with Data Analysis

In a market where buyers have multiple channel options for interacting with companies and even more options for researching and getting recommendations for products and services, sales teams are under more pressure than ever to deliver results.

shutterstock 101170021 300x225 Boosting Sales with Data AnalysisWhile sales organizations may have eschewed advanced technology in favor of spreadsheets, the most effective organizations are providing their sales teams with predictive analytics to gain the insight needed to boost revenue.

That’s according to a blog post from Mark Smith, CEO and chief research officer at Ventana Research, which has found that more than half (59%) of organizations still use spreadsheets to manage sales forecasts.

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12/30
2013

Data Analysis To Focus on the Right Sales Metrics

Many sales managers have a laser-like focus on tracking their goals and progress against these goals, but to be most effective they should be tracking different metrics.

shutterstock 117313246 300x199 Data Analysis To Focus on the Right Sales MetricsThat’s according to a blog post from Scott Edinger, founder of Edinger Consulting Group, who notes that sales professionals and their managers too frequently focus only on numbers when accessing performance.

In his post, Edinger points to a recent study by the Sales Education Foundation and Vantage Point Performance that identifies 306 different metrics that sales leaders are using to manage their businesses.

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05/21
2013

Data Analysis to Better Manage the Sales Pipeline

sales pipeline Data Analysis to Better Manage the Sales PipelineThe recent Spotfire on-demand webcast, “What’s Hiding in Your Sales Data?” covers the many challenges faced by sales organizations trying to turn hot leads into new customers.

The webcast also discusses how Spotfire is used to seamlessly access Salesforce.com data and enhance traditional reporting capabilities.

One of the most common reasons given for higher than anticipated losses in any quarter is that deals in the pipeline fail to close as expected.

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04/19
2012

Are You Asking the Right Questions with Predictive Analytics?

asking right questions Are You Asking the Right Questions with Predictive Analytics?Predictive analytics can help companies forecast business results with stunning accuracy – for example, how a particular group of customers might react to a targeted offer or what the potential business impact might be as a result of a marketing program.

But do data scientists always ask the right questions to get at the heart of what business leaders are attempting to accomplish? For instance, when is the right time to make an offer to a customer in order to generate the most favorable results?

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01/31
2012

CRM Analytics Go Mobile

Roambi1 150x150 CRM Analytics Go MobileBusinesses that want to remain competitive must build stronger relationships with customers.  This means CRM vendors will need to build systems that interact more with customers, rather than systems that are developed primarily for organizations, according to this article in Selling Point. Providers of CRM systems will have to continue developing and releasing CRM application modules that are bundled with or work on a large variety of handheld and/or wireless devices.

And CRM vendors will have to offer analytics through mobile devices so companies can have instant access to various kinds of analytics that were pretty much limited to being on computers in their offices.

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12/12
2011

The Top Challenges with Sentiment Analysis

Sentiment Analysis 300x199 The Top Challenges with Sentiment AnalysisDid you know that there are more than 200 tools and platforms that can help you track and assess how many times your business or brand has been mentioned in social media channels?

And many of these listening platforms do more than just the basic monitoring. In fact, they now offer integrated approaches to get the right information to the right parts of your business: product development; customer support; public outreach; lead generation; market research; and campaign measurement.

It’s a big responsibility and commitment to listen to your customers. That’s why businesses are making it a priority to enhance their social monitoring efforts and sentiment analytics to pick up and decipher what their customers are saying about them.

Analytics strategist, Seth Grimes (@SethGrimes) says sentiment analysis lets marketers et al., “get at root causes, at explanations of behaviors that are captured in transaction and tracking records.” Sentiment analysis lets you better target your marketing, detect opportunities and threats faster, protect the reputation of your brand, and most importantly, turn a profit.

Still, it’s important for data scientists to use caution when accepting customer statements at face value since context has such a great bearing on meaning. Analyzing natural language is difficult enough. Sarcasm or other forms of derisive language are extremely problematic for technologies to interpret.

For instance, let’s say Karen learns from a Facebook friend that an electronics company has just started charging customers a support fee for a popular product that had historically been free. Karen posts the following response on Facebook: “Oh, that’s just great.”

Taken literally, or by narrowing the analysis to positive or negative words that are made about the electronics company in social media, Karen’s statement would be interpreted to mean that she’s pleased by the change in the support policy. But more than likely, she’s simply being sarcastic.

In many cases, analytics teams are evaluating larger samples of customer statements to help spot potential product issues or indicators that could signal customer churn.

They also do this to help identify possible trends within different customer segments. It’s not cost effective nor efficient for data scientists to analyze the sentiments of individual customers, with the possible exception of companies that market a limit number of high-end products such as luxury shoes.

This also brings up the issue of scale. One of the biggest issues with analyzing tweets made on Twitter, based on research of the service, is that only about 60% of the people who use the service are actually tweeting. Forty percent of Twitter users don’t tweet or haven’t tweeted in 30 days. That means that more than one-third of the people on Twitter are simply observers.

Taking this a step further, let’s say an automotive maker uses Twitter to analyze comments that are made about its competitors. Taken on its own, such an analysis will capture the opinions of a subset of Twitter users. But it’s not necessarily a fair representation of the universe of Twitter users.

Sentiment analysis tools continue to evolve and will continue to improve over time. In the end, organizations that augment sentiment analysis with analysts who are able to interpret context in comments and take comprehensive approaches to sentiment analysis are those that are likely to benefit most.

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09/22
2010

Mobile Business Intelligence – Real-Time Data is Key

mobile analytics Mobile Business Intelligence   Real Time Data is KeyThe mobile business intelligence app is growing in popularity as one of the many types of  tools helping mobile workers do their jobs.  But it’s not the reports they’re using – it’s real-time data, says one report from Ann Hall in her article Do BI Vendors Need to Think Niche for Mobile Apps?

There’s also a very interesting question thread over at LinkedIn on this topic. The topic is centered on what mobile applications are good for business.

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Filed under: Mobile BI, Sales Analytics

04/08
2010

Participate in Aberdeen Group Research on Sales Forecasting and Analytics

j04094902 150x150 Participate in Aberdeen Group Research on Sales Forecasting and AnalyticsAberdeen Group has just launched a survey on sales forecasting and analytics and they need your opinions!  Are you using a business intelligence tool or analytics tool to forecast and analyze your sales efforts?  Then you should share your experience in Aberdeen’s “Sales Forecasting: Analytics to the Rescue!”

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02/02
2010
01/08
2010

How To Sell Less And Earn More – Analytics Naturally…

bs00874  How To Sell Less And Earn More   Analytics Naturally... Tapping the business intelligence of your own data can tell you where, when, how and how much of sales is really profitable.  And long-term analytics in utility companies, for example, are showing the power of shifting buyers to lower-cost but higher-income options.

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