This years ATG Insight Live conference is taking place online. This is ATG first virtual conference so it should be pretty exciting. There are a number of both business and technical tracks so there’s something for everybody. I strongly recommend attending for anyone doing work with ATG.
The conference begins October 21st. For conference agendas and registration use the link below.
ATG took top honors in the Q1 2009 Forrester Wave™ B2C eCommerce Platforms. The report is available from the ATG web site but you can read the report directly by clicking here.
ATG and IBM both lead the pack this year in both strategy, offering and market presence. ATG landed perfect scores in a number of important categories such as architecture and scalability. But ATG scored a perfect score in the following cetgories a feat none of the other participating vendors could match. Congratulations ATG!
I recently posted an entry entitled Build A Better Dashboard in which I described how to build a product dashboard using ATG Customer Intelligence. In this follow up to that post I’ve built a multi-channel sales dashboard using Report Studio.
Sales Dashboard (click to enlarge)
You can find a screenshot of this dashboard to your right. To show the detail just click on the image to enlarge it. I’ve even included the source code and instructions for how to deploy it below if you wanted to take a closer look at it in your own environment.
The dashboard includes key performance indicators for important business processes. In the upper left hand corner are the most important metrics as this is where the eye generally starts looking for content on a dashboard. You’ll find metrics on sales, traffic and returns and sparkline chart indicating how these metrics are trending.
To the left of this area are key metrics broken out by sales channel. It compares the key performance indicators across channels. In this case how they are performing on the web channel versus how they are performing in the contact center channel.
Finally at the bottom we have some more interesting content indicating the top ten selling products by net sales as well as top revenue producing search terms and what products were purchased using those terms.
The great things about this dashboard is that it brings together a lot of data from multiple sources into a single place where it can be reviewed daily. Add baslines and targets for these metrics and the dashboard because a great tool for performance management. It’s an excellant example of how you can use ATG Customer Intelligence to bring data from multiple products in the ATG Commerce Suite including Commerce Search and ATG Commerce Service Center.
The following step-by-step instructions can be used to install the report in your environment.
Click here to download the report definition (Note: In order to workaround some limitations in wordpress I’ve created the definition in a word document.)
ATG Customer Intelligence offers a multitude of great features for business reporting and analytics but the real prize isn’t with all the tools it ships with but with the information that lives in your ATG data warehouse. There are so many things that you could be doing with that data. Here’s just a few ideas to get you thinking this year.
Build a Better Dashboard
Unless you’re like me you can’t really expect that everyone will look at 20 different reports on a daily or weekly basis to get an understanding of how their business is doing. So put together a dashboard that combines all the pertinent information you want to know about your business on a daily/weekly basis. ATG Customer Intelligence contains all the tools you need to build such a dashboard. See the following post for more details about building a better dashboard.
Performance Management
The data in the ATG data warehouse contains a lot of information about whats happening on your eCommerce site. But how do you really know if your merchandising or marketing strategy is reaching their goals. That’s where performance management comes in. Performance management allows you to measure the business outcomes against stated goals. All you need to do is supply the targets and then use ATG Customer Intelligence to track your progress against these goals.
Market Basket Analysis
I’ve posted about this before so I won’t go into too much detail about it here. Market basket analysis allows you to get a better understanding of what products sell well together. It allows you to understand that the “Camel Pea Coat” sells extremely well with the “Wool Hat” and “Wool Mittens”. This technique is a great way to understand what items might cross-sell together. And it’s all back by concrete data and not just intuition.
Mine for Gold
If you’re a real data nut then you might try your hand at applying some data mining techniques to mine information from the data in the data warehouse. The market basket analysis technique mentioned above is actually one such technique that can be used to mine associations rules between products but many other techniques exist that allow you discover other types of information. For instance knowledge discovery techniques exist that will allow you segment your customer base. You could then turn around an use this information to build better customer segments and help improve your personalization efforts.
Analyze This!
Query studio is a tool that ships with ATG Customer Intelligence that allows to perform ad-hoc queries against data in the ATG data warehouse. This tool is great for those times that you want to explore a little. You might uncover something interesting during your explorations. I know I’ve found some really interesting facts using this tool such as gift boxes tend to sell more during the holidays. That’s probably not surprising but they were purchased by males 10x more then females on your typical clothing retailer site. This is the type of stuff that you probably don’t have a report built to tell you this and just shows you how exploring a little can get you to better know your customer.
There’s so much you can do with the data in the ATG Data Warehouse. One of the things I recently tried exploring was building a better dashboard. ATG Customer Intelligence ships with a number of interesting dashboards but I was recently inspired by the work of Stephen Few and others on building better a better dashboard. So challenged myself to see if I could build something similar using Report Studio.
It turns out that you can and it really isn’t that difficult. The following Product Dashboard was developed in just a few hours using Report Studio and uses many of the guidelines described by Stephen Few such as placing your most important metrics in the upper left, the use of spark lines for showing trends and making the data (vs non-data) prominent and clear.
Product Dashboard (click to enlarge)
The place where I spent the most of my time was in the layout. That may be because I’m really bad when it comes to laying out visual components. I settled on a grid layout using the cells of a table to layout the major components.
I used a few tricks to produce the spark lines charts. I started with a simple line chart. Next I modified the size of the chart to a fixed height and width of 35×80 pixels. I removed the legend and hide the Y1 and ordinal axis. I also removed the grid lines and axis labels. If you do all that you can pretty much simulate a spark line chart using report studio.
I did some of the same things for both the pie and horizontal bar charts. Report studio gives you a lot of fine control over the visual aspects of your charts. You just need to fool around a little with the various properties to get the look you want.
Besides all the whiz-bang stuff described above this dashboard also demonstrates some really interesting things you can do with the data in the ATG data warehouse. For instance under the Channel Sales section I’ve broken out sales metrics by channel. In the Top Search Terms section I’ve included data about top search terms used to purchase the selected product. There’s also a section entitle Returns which shows information about returns and refunds. Lastly the Top Co-Buys section demonstrates how you can discover cross-sell relationships using the data in the warehouse.
If you’ve got an interesting dashboard idea write to me and let me know.
I just came across this awesome blog about ATG. It’s called ATG Solutions Strategy and it contains a wealth of knowledge about implementing ATG solutions from a business perspective.
Some folks at spark::red have put together a ATG developer community wiki.. They’ve got forums, technical articles, FAQs and other content. They’ve even got some open source code posted.
If you’re interested in checking it out the link is below.
For anyone doing development in J2EE and ATG I wrote an extremely useful servlet and filter base class developers might be interested in. These classes were used as base classes for many of the framework components that were used as part of ATG Portals but they can be used in any standard J2EE application.
I think the javadoc explains them nicely. Here’s the javadoc for the GenericFilterService which implements the javax.servlet.Filter interface. Similarly there’s a class called the GenericServletService which implements the javax.servlet.Servlet interface. See the ATG documentation for more information on these classes.
Another ATG Insight has once again come to a close. ATG put on another great show this year from the developer bird-of-a-feather to Tuesday’s game night. Besides all the great events and presentations the highlight of my experience is always connecting with other developers and customer about how they were using ATG.
ATG is planning on putting all the slide decks online sometime over the next week. So keep an eye out for them. BTW If you couldn’t make the event you might want to talk a look at the slides when they’re made available.
See you all again in 2009!
Market basket analysis is a technique that discovers relationships between pairs of products purchased together. The technique can be used to uncover interesting cross-sells and related products. In this post I’ll show you how simple market basket analysis can be and how to use the data in the ATG data warehouse to do your own market basket analysis.
The idea behind market basket analysis is simple. Simply examine your orders for products have been purchased together. For example using market basket analysis you might uncover the fact that customers tend to buy hot dogs and buns together. Using this information you might organize the store so that hot dogs and buns are next to each other. In an e-commerce environment you might create a cross-sell rule to offer the shopper buns whenever they place hot dogs in their shopping cart.
There are a couple of measures we use when doing market basket analysis and they are described here. The first measure is the frequency. The frequency is defined as the number of times two products were purchased together. If hot dogs and buns were found together in 820 baskets this would be its frequency.
Frequency by itself doesn’t tell the whole story. For instance if I told you hot dogs and buns were purchased 820 times together you wouldn’t know if that was relevant or not. Therefore we introduce two other measures called support and confidence to help with the analysis.
If you divide the frequency by the total number of orders you get the percentage of order containing the pair. This is called the support. Another way to thinking about support is as the probability of the pair being purchased. Now if 820 hot dogs and buns were purchased together and your store took 1000 orders the support for this would be calculated as (820 / 1000) = 82.0% .
We can extend this even further by defining a calculation called confidence. Confidence compares the number of times the pair was purchased to the number of times one of the items in the pair was purchased. In probability terms this is referred to as the conditional probability of the pair. So going back to our hot dogs example if hot dogs were purchases 900 times and out of those 900 purchases 820 contained buns we would have a confidence of (820 / 900) = 91.1%.
Now that we’ve defined frequency, support and confidence we can talk a little about what a market basket analysis report might look like. The report would have the user select the product they are interested in performing the analysis on (ie hot dogs). Then it would list all the products that were purchased together with the selected products ranked by it frequency. It might look something like the following
Market Basket Analysis: Hot Dogs
| Product | Frequency | Support | Confidence | |
|---|---|---|---|---|
| Buns | 820 | 82.0% | 91.1% | |
| Ketchup | 800 | 80.0% | 23.2% | |
| Mustard | 750 | 75.0% | 34.5% | |
| Jello | 321 | 32.1% | 45.2% |
As a merchandiser I tend to look for pairs with a high confidence. A confidence of 100% means that the products are always purchased together. The higher the confidence means that there is probably a strong relationship between the products. When looking at reports like this you’ll find that some of the obvious pairs have the highest confidence (like hot dogs and buns!) but as you keep looking down the list you should discover some of the more interesting pairings you might not have thought of.
After examining the confidence I next look at the support. A low support means that the pair isn’t purchased a lot. This doesn’t mean it would make a bad cross sell just that they aren’t ordered together very frequently. Use your own judgment. Obviously if you see something with both a high confidence and high support you’ve found something interesting.
You can create a report to perform market basket analysis with ATG Customer Intelligence. See the attached xml for the report definition. To install this report just copy the report definition xml to your clipboard. Next start Report Studio with a blank report. Finally use the Tools->Open Report from Clipboard option and save the report to your environment.
download sample report : market-basket-analysis
