Today's TechHelp tutorial from Access Learning Zone addresses the concept of calculating the total of the last orders placed by each customer. I'm Richard Rost, your instructor, and today we're focusing on a query that can help you make future sales predictions by using the most recent order information of regular customers. Instead of relying on last month's total sales, which might include sporadic walk-ins, we're isolating data for those who consistently purchase each month.
One of our platinum members, Grant from O'Lake, Kansas, posed an interesting question: "How do I compile a total using only the most recent order from each customer?" This approach can impressively assist in estimating upcoming sales. I can certainly relate to Grant's needs, as I also analyze my members' activities similarly to forecast my own sales and filter out random transactions.
Let's break down the process involved. It's slightly advanced but doesn't require VBA, although I will share some VBA methods in the members' extended cut, which can sometimes simplify the task.
Firstly, ensure you're familiar with order entry and invoicing systems. I recommend checking my previous videos on building these systems if you're not already. This tutorial also requires an understanding of aggregate queries, so reviewing my DSum-related content is beneficial, too, as it shows how to display totals using DSum efficiently.
We'll construct a query listing each customer's maximum order ID and corresponding order totals. We'll then use DSum to calculate a complete total for the main menu. The completed task can be seen on my website and YouTube channel.
In our scenario with the TechHelp free template, every customer has associated orders that detail their purchases. Our goal is to sum all the most recent orders. Observing the table structure, you'll see that totals aren't stored directly in tables but determined through extended price calculations in order detail queries.
I'll adjust a few orders as examples. After manipulating the data to create scenarios where the date is a point of conflict, we'll proceed to build essential queries. It's crucial to adapt this to your own filtering preferences, like distinguishing between regular customers or specific payment statuses.
Our initial task is to extract a list of customers alongside their maximum order date. This involves creating a query, setting it as an aggregate query, and employing the max function to filter dates effectively. Using a descriptive name for this query will aid in maintaining clarity.
Next, we dive deeper by joining this result with the order summary to identify the corresponding order totals. Sometimes, having multiple identical dates for different orders might complicate things; hence we refine our query further, focusing on maximum order IDs per customer. Secure these detailed results with a second query, which helps resolve discrepancies without overwhelming one single query.
Finally, let's incorporate DSum to aggregate our query results for a synthesized total, showcasing each customer's latest purchase total. This sum can be effortlessly displayed on your main menu. Decomposing tasks into incremental steps is key; resist the urge to overcomplicate individual queries.
An alternative approach involves using a record set loop, looping through customers to obtain and accumulate their maximum order totals. This will be further explained in the extended cut available to members.
To explore additional examples of queries, joins, and functions, consider my Access Expert lessons. The program spans multiple levels and offers members enriching content that bridges non-programming practices with advanced Access functionalities.
In summary, this method aids in understanding and implementing a practical query system for better sales forecasting. For a detailed video guide on all the topics mentioned, visit my website through the link below. Live long and prosper, my friends.
For a complete video tutorial on this topic, please visit https://599cd.com/SumofLastOrders?key=Blogger
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