1. 9 Holes of Sales Analytics Best Practices
2. Sales Analytics for the Board of Directors
3. What is Your Data Actually Telling You?
4. SaaS BI - Analytic Aspirin in a Difficult Economy?
5. What’s in Store for Business Intelligence in 2008
6. Guest Blogger Alert - The LucidEra Opportunity
7. We Couldn’t Get the Answers
8. Everything is a Platform
9. What’s in Store for Business Intelligence in 2009
10. Transforming Marketing’s Traditional 4 P’s into SaaS’s 5 A’s
11. Take the SaaS BI Survey
12. Sales 2.0 Data Points and Anecdotes
13. Using Analytics to Avoid Wasting Time
14. The Call for Basic Numeracy
15. What is an Analytic Application?
16. What is Cloud Computing?
17. IDC Predictions for 2009
18. Ashe on SaaS
19. A Visionary Who Thinks Clouds will Cloud His Vision
20. The Power of Pie Charts

Thanks for reading!

Darren

“Employing a methodology that is primarily learning by example and “thinking with our eyes,” this manual features graphs and practical analytical techniques that can be applied to a broad range of data analysis tools—including the most commonly used Microsoft Excel. This approach is particularly valuable to those who need to make sense of quantitative business data by discerning meaningful patterns, trends, relationships, and exceptions that reveal business performance, potential problems and opportunities, and hints about the future. It provides practical skills that are useful to managers at all levels and to those interested in keeping a keen eye on their business.”

If you often don’t know what your data is actually telling you, love charts, or are simply looking for some dashboard visualization tips and tricks, be sure to check it out.

• What’s the difference between software-as-a-service (SaaS) and cloud computing?
• Why do business intelligence vendors and practitioners seem to focus so much on implementation rather than functions such as data visualization and interpretation, which is presumably what they really want to do?
• How does SaaS BI solve this issue?
• What role does IT play in a SaaS implementation?
• What role does the business side play? What role does the business side play?
• How has the role of software buyer changed with SaaS?
• What is LucidEra’s approach to SaaS and BI?

Be sure to check out the Q&A here. And because you can’t add comments on the TWDI site, please share your feedback / comments below or on Digg.

• 9 Tips to Boost Sales Now (Our favorite - #2: “You Get What You Measure.”)
• The New Rules of Sales Enablement (Some great sales resources from Reachforce and Kadient.)
• Why B2B Salespeople Say they Win Software Sales? (Marketing messaging is last! Ouch!)
• Salesforce.com: Punchlist for VPs of Sales After Rollout (Author of the new Salesforce book offers up some tips.)
• Feeling the Flow (Some classic sales lines from the trenches…I mean Garth.)

To receive the complete LucidEra newsletter (which also highlights our most popular blog post), feel free to register here.

• Increase revenue
• Decrease pipeline risk
• Improve forecast accuracy in this difficult economic climate.

We hope you can attend. Please register here.

• Cutter Consortium: Corporate Adoption of SaaS BI (feel free to mention your friends at LucidEra!)

By the way, I’ve also received the findings of this BeyeResearch report on SaaS business intelligence. We did not sponsor this research so LucidEra is not featured unfortunately, but the report does contains some very interesting survey data on why SaaS is being considered for business intelligence, where it’s being used, and the primary challenges and requirements.

• EcSell Institute Sales Management Survey (LucidEra partner focused on best-practice sharing for sales managers)
• Sales Management 2.0 Poll (What new features will be most useful to you on this great site for sales managers? So far it looks like a Sales Wiki is the most popular choice.)

Check out this 5-star review for Pipeline Insight:

As always, thanks to our joint Salesforce.com / LucidEra customers for taking the time to post their feedback on the AppExchange. We appreciate the continued support!

• Sales Intelligence Leads to Pipeline Impact
• Business Intelligence that Leads to Improved Sales Rep Performance

But anyone who has implemented CRM knows that one of the biggest challenges to adoption (and ultimately business value) is data quality. It’s GIGO, right? (Garbage In, Garbage Out.” Along these lines, we used to hear this a lot: “We’d love to invest in sales analytics, but our data is bad.” Okay, we still hear it, but now, thanks to the approach we take with our Pipeline Healthcheck for Salesforce.com, we can actually quantify this and make some specific sales process improvement recommendations. Here are a few examples of sales process analysis that can lead to increased revenues and forecast accuracy.:

Stage Conversion Analysis

By analyzing historical stage conversion ratios, we discovered that an enterprise sales team that was focused on selling bigger ticket items, was having trouble with “sticker shock” at the Proposal stage. By attempting to accelerate sales cycles, reps had been skipping the Trial stage and, as a result, not enough value was being established early in the process. With the goal of helping reps avoid wasting time on unqualified opportunities, this led to a discussion about either introducing price earlier in the sales cycle or to develop new ways ways to sell value / ROI up front.

Win Rate Analysis by Stage

By analyzing win rate by sales stage, the Pipeline Healthcheck determined that 37% of the deals that made it to Stage 4 became customers, which was exactly the sames percentage as  Stage 5. This meant that Stage 5 was adding no value to the process and could be eliminated. It was doing nothing to effectively move the customer much closer to buying. This helped to dramatically improce sales cycle times.

Data Quality Analysis

By looking at the current pipeline by 30 day increments, the Pipeline Healthcheck identified that 20% of the Salesforce.com customer’s open pipeline had expected close dates in the past. We recommended that an automated monthly report of all deals with expected close dates in the past be esablished in order to get reps to clean up their deals on a regular basis.

Stealth Pipeline Analysis

One organization we worked with had an unusually rapid sales cycles in Europe, which implied that reps weren’t entering deals until they were already won. By analyzing pipeline performance by region, the Pipeline Healthcheck identified that 35% of Europe’s deals were actually stealth. This led to a CRM adoption initiative in Europe involving the introduction of carrot and stick measures to accelerate usage.

I hope you found this series of sales pipeline, people, and process analytic examples to be useful. If you did, here are a few additional resources you may also like:

• 9 Holes of Sales Analytics Best Practices
• Sales Analytics for the Board of Directors
• What is Your Sales Data Actually Telling You?

“If there was an Oscar for best picture (of a pipeline…..) LucidEra would win hands down!”

“Data warehouses are extremely valuable, but can also cost a lot in dollars, resources and time to implement. We’re getting this value at a fraction of the cost in a SaaS model.”

It’s extremely satisfying to hear that our analytic applications are having this level of customer impact. You can check out all of the Pipeline Insight AppExchange reviews here.

• 41% of sales reps are failing to make quota
• Companies on average are hitting 86% of plan
• 15% of reps are turned over
• 70% of reps are taking 7 months or more to ramp up
• 86% of companies have higher sales targets in 2009

Yesterday I wrote about a few examples of immediate pipeline impact from LucidEra Pipeline Healthcheck. In light the numbers above (which I suspect have only gotten worse over the past few months for most sales organizations), here are some examples of sales analysis that can lead to improved sales productivity, new revenue opportunities, and increased forecast accuracy.

Sales Rep Tenure Analysis

You typically expect to see better metrics for more seasoned reps, right? After analyzing a number of key performance metrics of sales reps by tenure, the LucidEra Pipeline Healthcheck discovered that newer reps actually had larger deal sizes. It turned out that a year ago, the company had switched their sales model to Solution Selling, whereby services were included with new product sales. Further analysis showed that newer reps were selling services with 50% of the deals. However, the most tenured reps had fallen back to their old product-centric selling approach, and sold services with only 20% of the deals. This led to a Solution Selling training initiative for the seasoned reps designed to help them increase their deal size by including services more often.

Average Days to Close (Won) Analysis

The Pipeline Healthcheck creates a sales rep report card, which includes looking at days to win versus days to lose. We identified that the days to lose a deal (actually, the days until a sales rep recognized that a deal has been lost!) was 2.5 times longer than days to win. Further analysis of win rates showed how win rates decline rapidly with age.  The recommendation was to go back and re-qualify all deals when they reach 175 days to avoid wasting excessive amounts of time. This process had a major impact on sales rep productivity.

Sales Rep Ramp Analysis

The LucidEra sales rep scorecard identified that reps didn’t hit peak performance until nine months, as opposed to the typical assumption of six months. The impact was that when planning for sales growth in a region, the sales organization needed to hire nine months in advance of their goals in order to deliver on the sales plan.

Tomorrow I’ll finish of this series of Pipeline Healthcheck examples by focusing on sales process analysis that can impact your performance. In the meantime, you may also find these posts to be useful (if not somewhat entertaining):

• Using Analytics to Avoid Wasting Time
• SaaS BI - Analytic Aspirin in a Difficult Economy?