Blog New functionalities in SalesClic for Salesforce and SalesClic standalone

We will be upgrading SalesClic next Thursday (Sept. 29th) from 18:00 to 19:00 CET. The solution

won’t be available during that period.

The upgrade will add both to our Salesforce module and standalone solution.

Salesforce module

• A reality check for closing probabilities : as in SalesClic standalone, the headings of pipeline stages will include the historical (real) closing probability of the correspondingopportunities.
• A « population pyramid » for the pipeline : the sidebar of the SalesClic pipeline will show a small graph summarizing the pipeline (amount per stage). This is one of the last steps towards the implementation of the “ideal pipeline” forecasting methodology in SalesClic for Salesforce.

SalesClic standalone

Three significant developments here.

• The “population pyramid” described above.
• Improved measurement of sales efficiency, with the calculation of historical, “real”conversion rates from one pipeline stage to another. This information will appear in the headings of pipeline stages.
• Easier inputs in the opportunity card, with the ability to change and save each section of the card independently.

Don’t hesitate to contact us for feedback and support.

We will be upgrading SalesClic next Tuesday (Aug. 23rd) from 18:00 to 19:30 CET. The solution won’t be available during that period.

This is an interesting upgrade, both for our Salesforce module and SalesClic standalone.

 
Salesforce module

Users with the appropriate Salesforce rights will now find under the SalesClic tab the individual sales pipelines of their colleagues and the “consolidated” pipeline of their team, in addition to their own individual pipeline.

This is especially relevant to sales managers with no sales activity of their own.

 
SalesClic standalone

Four significant developments here.

• We are very happy to launch the “SalesClic weekly summary” – a summary of your sales pipelines you will receive every Monday in your mailbox. In keeping with the SalesClic spirit, this summary will focus on the business implications of your pipelines, not on their administrative activity. You can set your weekly summary up in Settings > Profile.
• In your pipelines, the headings of pipeline stages will now include the historical (real) closing probability of the corresponding opportunities. This is obviously a big analytical and forecasting plus. (Note that historical closing probabilities will soon come to our Salesforce module too.)
• We are adding autocompletion to the “Opportunity”, “Company”, “Product” and “Reference” fields of the opportunity card.
• You will now be able to drag-and-drop opportunities on the iPhone.

 
Enjoy, and don’t hesitate to contact us for feedback and support.

Hello,

We are pleased to announce the release of SalesClic’s English version. To switch from French to English, go to :

Réglages → Profil → Langue

…then chose « English » in the select list and click « Valider ». Done !

We particularly recommend the definitions of our 50+ statistics, which we had good fun translating.

Feel free to suggest potential improvements to the English text through our online support.

Best,

Thomas

In my last post I introduced Apache POI for generating Microsoft Office files, as well as RJB, a Ruby to Java bridge. Today I would like to explain how to use these tools to generate Excel files.

Apache POI handles both the old Excel file format (from Excel 97 to 2007) and the new OOXML format. This article only deals with the first one.

For a full overview of Apache POI’s features you can browse the offical website and the related Java documentation.

[...]

In this post, we’ll be discussing a proposal for a measure of the forecasting error that has some very nice properties: the saMAPE (for symmetric in absolute error MAPE).

Everything starts with the MAPE (Mean Absolute Percentage Error): as forecasting 1000 and making 1100 on the one hand, and forecasting 100 and making 200 on the other hand feel very differently, one may want to study the relative error and not the absolute difference between forecasted and actual values. To capture this difference in a statistical analysis, a common error measure is the MAPE. However this measure has two notable drawbacks:

• There is no upper bound.
• As you cannot divide by zero, you just cannot have an actual outcome of zero.

Even though this is perfectly fine in many cases, the zero problem can prove serious. [...]