Cluster positioning tool

It allows you to approach each group with a better understanding of who they are, so you can use this data to create more effective campaigns.

Cluster analysis is a research tool used to help you segment your target market. In regards to market research, a cluster analysis is typically used as a stand-alone tool to get insight into the market segmentation. It can help marketers define distinct niches in their customer base, so they can develop targeted marketing programs for optimal results. When used in tandem, segmentation and cluster analysis are effective tools at unearthing insight into your target market and customer base that allow you to unearth key differentiators.

These key differentiators can allow you to tailor marketing campaigns, strategies, and other high-level decisions to effectively cater to each segment and optimize resources while driving your bottom line.

These two tools also lay the foundation for further research and data, particularly for when conducting a SWOT analysis. Unearthing your market segments via a cluster analysis allows you to determine the strengths, weaknesses, opportunities, and threats of each customer group.

You have analytics regarding your daily site traffic the data of past customers. The segmentation and cluster analysis has allowed you to define three distinct groups of customers. Now, when you create social media for your products, you can effectively target these different groups in order to optimize your ad spend. Want to learn more about the Fuel Cycle platform? A grey cell is placed in the column of the person they also match in the brown cluster.

Clearly, these clusters are somehow related to each other. The goal, of course, is to identify and associate these clusters with ancestors, or more specifically, ancestral couples, pushing back in time, as we identify the common ancestors of individuals in the cluster.

For example, the largest orange cluster represents my paternal grandparents. The smaller clusters that have shared members with the large orange cluster represent ancestors in that lineage. Identifying the MRCA, or most recent common ancestor with our matches in any cluster tells us where those common segments of DNA originated. Remember, colored clusters are people, and you can match different people on different, sometimes multiple, chromosomes.

Click on any cluster in your report, above, to see the chromosome painting for that cluster. The brightly colored painted segments show the triangulated segment locations on each chromosome. You can easily see the 8 different segment clusters in cluster 1. Interestingly, three separate groups or chromosome clusters occur on chromosome 5. They are just coincidentally both 5 in this case. You can mouse over the segment to view additional information, but I prefer the next tool because I can easily see how the DNA of the people who are included in this segment overlap with each other.

This view shows the individual chromosome clusters, or groups, contained entirely within the orange cluster 1. Please note that you can adjust the column widths side to side by positioning the cursor at the edge of the column header and dragging. Fortunately, I recognize one of these matches, Deb, and I know exactly how she and I are related, and which ancestor we share — my great-grandparents. Because these segments are triangulated, I know immediately that every one of these people share that segment with Deb and me because they inherited that segment of DNA from some common ancestor shared by me and Deb both.

To be very clear, these people may not share our exact same ancestor. They may share an ancestor upstream from Deb and my common ancestor. Regardless, these people, Deb, and I all share a segment I can assign at this point to my great-grandparents because it either came from them for everyone, or from an upstream ancestor who contributed it to one of my great-grandparents, who contributed it to me and Deb both.

I can see that the common triangulated segment between these two people and me occurs on chromosome 3. This segment on chromosome 3 is entirely contained in green cluster 2, meaning no members of other clusters triangulate on this segment with me and these two people. Remember that the two people who triangulate in green cluster 2 also match people in orange cluster 1? However, the people from orange cluster 1 are NOT shown as members of green cluster 2.

This could mean that although the two people in the green cluster 2 match a couple of people in the orange cluster, they did not match the others, or they did not triangulate. This can be because of the minimum segment overlap threshold that is imposed. So although there is a link between the people in the clusters, it is NOT sufficient for the green people to be included in the orange cluster and since the two matches triangulate on another segment, they become a separate green cluster.

In essence, trust the tool if people are NOT included in multiple clusters. At this point, I often run one-to-one matches, or other matching tools, to see exactly how people match me and each other. You can see that Mark, one of the members of red cluster 3 shares two triangulated segments, one on chromosome 4, and one on chromosome Scrolling down, I can view additional information about the cluster members and the two segments that are held within red cluster 3.

Cluster 3 has two members, Mark and Glen. Mark and Glen, along with Val who is a member of orange cluster 1 triangulate on chromosome Remember, I said that chromosome 10 would be important in a minute when we were discussing orange cluster 1.

Now you know why. However, Mark, who is a red cluster 3 member also triangulates with Iona and me on a segment of chromosome 4. This segment also appears in AutoSegment brown cluster 4 on chromosome 4. Now, the great news is that I know my earliest known ancestors with Iona, which means that I can assign this segment to my paternal great-great-grandparents.

If I can identify a common ancestor with some of these other people, I may be able to push segments back further in time to an earlier ancestral couple. For each cluster, all members are listed. You can click to view and can hopefully identify an ancestor or at least a surname.

For example, I can tell my matches in cluster 1 that I know this line descends from Lazarus Estes and Elizabeth Vannoy , their birth and death dates and location, and encourage my match to view my tree which I have uploaded to GEDmatch. If you happen to have a lot of matches with trees, you can create a tag group and run the AutoTree analysis on this tag group to identify common ancestors automatically.

I wrote about AutoTree, here. EJ utilizes a list of pileup regions, based on the Li et al paper. You may match other people on these fairly small segments because humans, generally, are more similar in these regions. Many of those segments are too small to be considered a match by themselves, although if you happen to match on an adjacent segment, the pileup region could extend your match to appear to be more significant than it is.

This entire However, if the segment overlap with the pileup region is 3. In the example above, if the AutoSegment threshold was 7 or 8 cM, the entire segment would be retained. If the matching threshold was 9 or greater, the segment would not have been included because of the threshold. Of course, eight regions in the pileup chart are large enough to match without any additional adjacent segments if the match threshold is 7 cM and the overlap is exact.

If the match threshold is 10 cM, only two pileup regions will possibly match by themselves. However, because those two regions are so large, we are more likely to see multiple matches in those regions.

Having a match in a pileup region does NOT invalidate that match. I have many matches in pileup regions that are perfectly valid, often extending beyond that region and attributable to an identified common ancestor. You may also have pileup regions, in the regions shown in the chart and elsewhere, because of other genealogical reasons, including:.

In my case, I have proportionally more Acadian matches than I have other matches, especially given that my Dutch and some of my German lines have few matches because they are recent immigrants with few descendants in the US. This dichotomy makes the proportional difference even more evident and glaring. I want to stress here that pileup regions are not necessarily bad. In fact, they may provide huge clues to why you match a particular group of people.

Both groups are endogamous and experience pedigree collapse. An unreliable match might be signaled by people who match on that segment but descend from different unrelated common ancestors to me.

However, this may not be the right selection for everyone. Just remember, you can run the report as many times as your want, so nothing ventured, nothing gained. Regardless of whether you select the remove pileup segments option or not, the report contents are very interesting. I want to be very clear here. If that person still matches and triangulates on another segment over your selected AutoSegment threshold, those segments will still show. I was curious about which of my chromosomes have the most matches.

According to the Pileup Report, my chromosome with the highest number of people matching is chromosome 5. The Y vertical axis shows the number of people that match on that segment, and the X axis across the bottom shows the match location on the chromosome. Sure enough, when I view my DNAPainter results, that first pileup region from about location are Brethren matches from my maternal grandfather and the one from about are Acadian matches from my maternal grandmother.

This too makes sense. Please note that chromosome 5 has no general pileup regions annotated in the Li table, so no segments would have been removed.

Based on the chromosome table from the Li paper, chromosome 15 has nearly back-to-back pileup regions from about with almost 20 cM of DNA combined. The only way to tell how many segment matches were removed in this region is to run the report and NOT select the remove pileup segments option. I did that as a basis for comparison. You can see that about three segments were removed and apparently one of those segments extended further than the other two.

If I want to see who those segments belong to, I can just view my chromosome 15 results in the AutoSegment-segment-clusters tab in the spreadsheet view which is arranged neatly in chromosome order. The demo data should have enough on-hand inventory of these items. Make sure that you have enough inventory to complete the transactions.

The new sales order is opened. On the Sales order lines FastTab, add a line that has the following settings:. On the Sales order lines FastTab, add a second line that has the following settings:. On the Action Pane, on the Warehouse tab, select Release to warehouse.

When the release is completed, you receive informational messages that show the wave and load IDs that were created. Two work IDs should have been created, each of which has two pick lines. Follow these steps to find the work IDs and license plate assignments. In the Overview grid, search the Order number column for the two sales orders that you just created. For each sales order, make a note of the corresponding work ID. Select the row for each sales order to show related information in the Lines grid.

Make a note of the location that each item will be picked from. On the Action Pane, select Dimensions to open the Dimension display dialog box. Make sure that the License plate , Warehouse , and Item number check boxes are selected, and then select OK. Because the cluster profile set the number of positions to 2, the system automatically directs you to the first consolidate pick: two pallets PL of item L At any time during the following steps, you can select the Details tab to view additional information about the task, such as the picking location.

This confirms the item number, which is required for this menu item you configured this earlier by selecting Work confirmation setup from the Mobile device menu item page when you created this menu item. Enter the license plate number that is associated with the item in the location that is being picked. And to help to undertake this grouping clustering process, we use cluster analysis to review and create market segments.

This graph shows this customer database information mapped onto a scatter-plot graph. But if we look closely at the plot points — for the purpose of identifying clusters market segments , there is a suggestion of three possible inherent market segments — this is done using a rough visual basis as shown in the next chart — which the same as above, except for the addition of a top-level segmentation approach using the extra large circles.

You should be able to see that there are three clusters segments of consumers suggested by the data as presented. The black circle top-right appears to be loyal customers, with a high level of customer satisfaction.

The blue circle bottom-left appears to be less loyal customers, with a lower level of CSAT. This relationship is probably obvious and to be expected — and our existing marketing programs to existing customers are probably built around this CSAT-loyalty correlation.