Business intelligence is one of those topics that can be so overcomplicated by implementation teams that it never gets installed. But it doesn’t have to be. Microsoft Dynamics GP, along with SQL Server 2005 (or 2008) and Microsoft Excel 2007 give you (in minutes!) the tools to get started.
For instance, with a few simple steps you can use Sales Order Processing history information to learn which of your products are most frequently sold together and, more importantly, learn which additional items you should suggest your customers buy based on what they’re already ordering:
Export your SOP line history to Excel from SmartList.
Format the exported data as a table (on the Home ribbon, choose Format as Table).
Click anywhere in the table and from Table Tools / Analyze, choose Shopping Basket Analysis
Map the columns in your data set to what the mining model needs (really simple: Transaction ID is the order/invoice number; item is the item number and Item Value is the extended price) and click Run.
That’s it. It takes about a minute to complete.
At this point, your workbook will have two new tabs.
Shopping Basket Bundled Item
Shopping Basket Recommendations
This is where business intelligence can add some clear value. Given that someone ordered the item in the Selected Item category, you should ask them about the item in the Recommendation column, because a lot of other people bought those two items together. Typically, you would want to recommend things that aren’t ALWAYS sold together (because that’s sort of obvious), but things that are closer to 85% may have actual value (because people need them together but possibly forget about the second one.)
Other Types of Analysis
In addition to Basket Analysis, the Excel add-in supports several other types of business intelligence analysis.
Analyze Key Influencers
Identifies those properties that most influence a particular outcome. For instance, which properties of a customer are most likely to influence how much money they spend with your company?
Detect Categories Groups records within the table based on key properties. For example, these sales came from middle-class college kids; these sales came from firemen in general.
Fill From Example Predicts missing information based on other information in the set. For example, based on their purchasing history (and the purchasing history of customers like them) this customer is probably 34 years old.
Highlight Exceptions Visually highlights cell values that don’t fit with the general pattern. For example, a customer birth date of Feb 23, 1898 may signify potential credit fraud.
Scenario Analysis – Goal Seek Identifies the values of properties within a dataset to reach a particular goal. For instance, to increase total sales, we need to add new customers between the ages of 30 and 35.
Scenario Analysis – What-If The flip side of goal-seek, this identifies what changes to a particular will cause to other values within the dataset. So, if I increase marketing revenue by $50,000, what effect will that have on net sales?
If Business intelligence is one of those topics that you haven’t approached because you think it is overcomplicated then you need to talk with InterDyn Artis. Our team of consultants can have you up and running in minutes. Contact InterDyn Artis, a Microsoft Gold Certified partner located in Charlotte, NC – serving the Microsoft Dynamics Community for over 20 years.
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