Commerce Intelligence Dashboards Deep Dive

Transcript

Thank you everyone for joining us today and welcome to MBI Dashboard’s deep dive webinar. I am Sonika Viramnini, a product analyst and I am your host for today’s webinar. I am joined by my coworker, Webstila. She’s a product analyst and she’s our presenter for the session. I’m very excited to kick off the webinar and I’m really hoping that you all would get some great information out of it. Before we jump in, I just wanted to make a note to all of you that throughout the webinar, if you face any technical issues with audio or the screen share or even if you have questions related to functionality of MBI or the content that’s being presented, please feel free to put them in the Q&A pod. We will try to answer them right away through chat or during our Q&A segment that is towards the end of the webinar. All right, so we will be playing a recording of the content today and Webstila, I’ll let you get started on that. Thank you and thanks again to everybody for joining us today. We’ll spend most of our time today doing a live walkthrough of the MBI dashboards, but first we’ll briefly go over our goals for today and the idea behind the dashboards. We’ll follow that with a walkthrough and wrap up with a Q&A portion at the end. When our product team hears from merchants, there are some needs that come up pretty regularly. So we’ve set up MBI to include a way to start resolving those needs from the first day you log in. If you’re trying to get a picture of how your store’s recent revenue, compares with your overall trend or say, assess the purchasing behavior of a group of your customers who came in via a particular acquisition campaign versus other customers or tell other very common business stories. Today we’ll go through how your MBI account helps you out. The initial reports MBI provides are distributed onto four dashboards for ease of navigation and each dashboard tells a story about a critical piece of your business. As we walk through these, we’ll discuss the value and story you can find in each one. We’ll start off with the executive summary dashboard. Say an executive at your company comes to the analyst team asking for a quick view of the most recent high-level performance measures for your store. In that case, they can come to this dashboard and see some at-a-glance KPIs without getting into the details that the other three dashboards contain for analysts. The first business metric folks usually think of, of course, is revenue. So here right up top, you can see your store’s revenue for the past six months to view any trends over time. Here for our demo store, we see that our revenue has really been climbing for the last month or two especially, which is great. Maybe we rolled out some new store features and they’re working well to help our customers find the products they want and we can see that success reflected here. The bars on this report show daily revenue numbers, so if there is weekly or a monthly periodic behavior, you can also spot that, like if Saturdays are a high sales day for your store. The trend line on this bar and line chart shows a seven-day rolling average to smooth out some of that day-to-day volatility, giving you a slightly higher level trend. Each point on this line shows the average revenue for the preceding seven days, so the first point shows the average of days one through seven, the second point shows the average of days two through eight, and so forth. On the left, there are a few reports that show a closer look at the current month’s revenue performance, so this whole top section is about your store’s revenue story. These scalers cover how your store has been performing revenue wise in the current month. Our demo store has brought in 3.4 million dollars. We can also see that so far this month, our store has gained about 50% more revenue than during the same period last month, so if today were the 15th of the month, this would tell us that compared to the first 15 days of last month, our store has made one and a half times as much revenue so far this month. We’d like to know a bit about where this revenue is coming from, so we also have a look at which group of customers is bringing in the most revenue for us so far this month, new or existing customers. New customers are the customers that have placed their first order this month. We can see that in our demo store, our customers who have been with us longer are actually generating more revenue for us, so it looks like currently we might do well to focus on benefits for our loyal customers over trying to bring in lots of new buyers. The way your store brings in revenue is, of course, when people place orders, so this next section addresses some key questions around that topic. We know for the current month, our revenue is continuing to trend a bit upward. We see this on both chart-based reports up here. Does this result from more orders in our store or from people spending more each time they order? The average order value for our store this month, which we see here on a daily basis, is pretty flat, whereas these scalars to the right, looking at order volume, tell us that the total orders so far this month, 2.7 thousand, is a more than 50% increase over this time last month. So we seem to be doing particularly well right now at getting our customers to come place more orders in our store. And then, say your exec team is looking for a quick update on your customer base this month. You can immediately open up this view of how many registrations you’ve had, how many of those registered customers have placed an order, and also what that conversion rate looks like in the context of a longer time frame. Some of our merchants don’t require registration for customers to place an order, and if this describes your store, then you’ll see a slightly different customer section here. In this version of the executive summary dashboard, this third section focuses more on comparing the activity by your new versus existing customers, showing which groups you’re really getting value from.

Now let’s say, for example, you want to know more details on your customers registration and conversion behavior, or what your customers buying patterns look like. For that kind of information, we can come to the customers dashboard. Here you can dig in a bit more on your new customer conversion process. In the past 30 days, our demo store has seen 1,168 customers register for a new account, and 798 registered customers placing their first order. This represents about a 68% conversion rate. That sounds pretty good, but we also want to see how that rate stacks up against our stores conversion rate trend over time. Keep in mind, again, this is all demo data, so it may not always look like a real store’s results. Here in this conversion rate report, we see that actually our conversion rate has been going down recently, so if we want to attract those new customers, we may need to change something up. On the other hand, we look at the report to the right, and notice our customers who have placed multiple orders are actually placing more orders over time as a group. So maybe this downward trend in new customer conversions is because we’ve decided to focus more strongly on repeat customers in our store. Similar to the executive summary dashboard, if your store allows guest checkouts rather than requiring registration, you’ll see something a little different at the top of this customer’s dashboard. In this version, again, the focus is more on comparing your new versus existing customers rather than looking at those registration numbers that won’t represent your entire customer base. These scalers show the total number of orders placed by your new customers and your existing customers in the past 30 days, and the stacked column chart at the right then puts that activity level in context compared to customer orders since the start of last year. You also get a look at the total number of unique customers who have made purchases on your store in the past 30 days. Whether you’re focusing on acquiring new customers or not, once your customers have made their first purchase, you can start looking at retaining them for additional subsequent purchases. Many merchants do tell us it’s cheaper to retain existing customers than to acquire new ones, so the next section of this customer’s dashboard helps identify which customers to target for a reactivation campaign and when. If you’ve seen our Getting Started with MDI webinar, you heard us discuss this approach to customer retention there as well. You can see how your store is doing at retaining customers for a next purchase with the subsequent order probability reports, which calculate how likely customers are to place another order after the latest one they’ve placed. If we first check out the report that breaks this likelihood out by number of orders placed, we see that our customers who have placed one order have about a 38% chance of eventually placing a second order, whereas customers who have placed two orders have a nearly 60% chance of coming back to place a third. This drop-off for one-time customers is the steepest drop-off that we see in this report. The overall rate at which our customers place another order in their lifetime is about 66%. Keep in mind, each report on MDI’s pre-built dashboards comes with a description filled in that helps you understand how to interpret your data, so you can always refer back to that. You can also compare the likelihood that customers will place a subsequent order to the percent of all your customers who have placed more than one order in your store. In our demo store, overall 66% of orders our customers have placed are followed by another order from that customer, but only 38% of our customer base has placed more than one order in their lifetime. So we know a good chunk of that 66% is coming from customers with a higher number of orders, rather than, for example, a large number of one-time customers coming back to make a second purchase. This matches with what we see in the broken-out subsequent order probability report. To determine when to target your customers for reactivation, you can check the typical time between when they place orders. Overall, the median time our demo store customers go between orders is about 35 days. So if we keep track of when a customer last purchased something from us, we may want to reach out to them a bit more than a month later to make sure they know what’s new in our store. In particular, we may want to target those one-time customers we saw in the subsequent order probability report for a reactivation campaign to decrease that drop-off and also bring them into a customer segment where they may be more likely to continue making purchases. To see when to target that particular segment of customers specifically, we look at this breakout of time between orders. This shows us that how long our customers go between purchases depends on how many orders they placed in their lifecycle. The more orders they placed, the quicker they come back again. For those one-time customers, the optimal time frame to retarget them is on average about 220 days compared to a median of about 170 days, which works out to a range of three to six months. We can check how many customers overall have placed one purchase in their lifetime with this tabular report here. In our demo store, this is definitely a large enough group to make it worth our while to try to bring some of them back. So now we can find which customers are in this group, one-time customers who last purchased three to six months ago. These are the folks who are coming up to a point when they’re most likely to make another purchase. So we can export this list and use it in a reactivation campaign, for example, by importing that list to our CRM system. Another useful perspective to have on your customers is how different groups of them perform revenue-wise at different points in their lifecycle. Many stores find that a large proportion of their revenue comes from a small proportion of their customer base. These two reports at the bottom of the customers dashboard help you increase your revenue by identifying your high-value groups of customers over time. Both reports segment your customers by the month they placed their first order and then look at the value those customers have brought in over their lifetime thus far. These reports are a little more complex but can really give you a lot of value once you understand how to read them. The bubble chart lets you spot groups of your customers who have brought in a lot of value in their lifetime and see if that’s related to their early purchasing behavior. I’m going to click and drag to zoom in since our demo data is a bit clustered up. There’s one bubble here per month. For example, here’s December of 2018 which tells us in this hover that on average each customer whose first purchase was in that December spent just over $1,300 in the first 30 days starting with that first purchase shown on the horizontal axis and then has spent a total of about $6,700 in their entire lifetime thus far shown on the vertical axis. The hover information also tells us that our store had 228 unique customers making their first purchase in that December. So we know what number those dollar amounts are averaged over. The size of the bubble here shows how that number of unique customers compares to other months. A fairly small number versus say this bubble down here which is January of 2021. April 2018 is one of the earliest months on here so we generally expect those customers to have a higher lifetime value but it’s still a standout among 2018 months not to mention other years so maybe we had a special that month that brought in some very loyal customers. In comparison the cohort report on the left shows your customers cumulative lifetime value over time by month rather than just the total to date. Let’s hide some of these lines to see what’s going on a little bit better. And again I’m going to zoom in. If we start by looking at the oldest cohort in this report April 2020 what this tells us is in the first month these customers placed a purchase in our store ie April 2020 they spent on average about $1,300. Then in our second month which in this case would be May the total cumulative lifetime value of that group same group of customers increased on average to $1,376. So to find out how much on average they spent in their second month subtract month one from month two to get about $72 they spent in the second month. We continue out month by month tracking this cohort of customers to see how their behavior trends over time. If we now add a couple more lines back into this report to compare with we can see how that April cohorts behavior stacks up against other cohorts ie groups of customers defined by which month they first made a purchase on our store. For August we can see that this group of customers started with a slightly higher average value in their first month than the April group did and has continued to be above that April cohort overall in terms of average lifetime value per customer for the corresponding months in their lifetime. Their second month cumulative average lifetime value is about $1429 so if we subtract the first month’s value we see that this cohorts second month September they spent an average of $73 almost exactly what the April cohort spent in their second month of May. If we look at the most recent cohort that’s represented on this report from November of 2020 we see they only have four full months of data since they joined more recently. This cohort started a snitch lower than that initial April cohort stayed on a pretty parallel track for their first few months but then recently saw an uptick in the increased rate of average lifetime value in their fourth month of activity on our store so they spent more in their fourth month February than they did in the previous couple of months. So now they’ve surpassed that April cohort for average lifetime value as of four months in the April cohorts total lifetime value is higher of course because they’ve been around longer but if the November cohort continues on this new trajectory there’ll be a more valuable group of customers than those who first purchased in April and they may even outperform this August cohort that had a slightly higher value. For questions on your store surrounding the order fulfillment process and tracking orders that have been placed we have the orders dashboard. Right away we start by seeing the number of complete orders placed in your store in the last 30 days. The bar chart below then shows how that 30-day number compares with your store’s monthly order volume since the start of last year. So you can tell if this month is roughly in line with your overall trend. Keep in mind that the current month isn’t over yet so the final bar doesn’t represent a full month’s worth of data unless you’re right at the end of the month when you check it so it may look a little lower than you expect in some cases. So we know the number of orders placed in our store but how much revenue do those orders actually bring in for us? The revenue for that same past 30 days is right here next to orders and below it again a report with the trend since the start of last year. Here revenue is broken out by payment method your customers used. Seeing a distribution of payment methods customers are using on your store lets you make sure you’re sufficiently accommodating the most popular ones during the checkout process. This stacked area visualization shows total revenue as you track along the very top of the area with layers showing the breakdown through time by payment method. When you add the values for each payment method for a given month the total is the total revenue for that month. The average order value reports in this third column combine the orders and revenue data so you don’t have to do that math yourself. The report comparing new versus existing new versus existing customer AOB since the start of last year is also showing us that for our demo store these two sets of customers tend to spend similar amounts per order to each other and are also very consistent through time on how much they spend per order. The next row in this dashboard highlights the order fulfillment process. Making sure your store isn’t experiencing fulfillment bottlenecks helps keeps your customers happy so these reports help you identify where orders aren’t being completed in a timely manner. We generally expect the percent of incomplete orders which is orders with statuses such as processing or pending to be higher in more recent days but in our demo store here we’re still seeing some spikes and how many orders are you’re still processing from a few weeks ago. We can use this table sorted by date to check what specific orders comprise those spikes. Let’s look at February 18th the first day on this left-hand report. So here we get a list of individual order numbers that are currently in, in this case, the processing status and then other incomplete statuses as well. So if we want to we can look into these orders further if we think they need more investigation. Here’s another report we discussed some in that getting started webinar that I mentioned earlier. The heat map is a relatively new visualization in MVI. This one shows your stores number of orders in the past week broken out by the day of the week and then additionally by hour of the day on a 24 hour clock. Heat maps are useful for seeing changes in aggregate behavior such as here times during a week when you might need to add resources such as inventory or staffing to support increased sales volume. The darker tone of color shows a larger order volume so you may want to be so you may want to pay attention if there are blocks of dark color on your heat map. The revenue breakout table shows the components that go into MVI’s default revenue metric for a closer understanding of reports using that metric. This report is broken out per day so you can see how each piece of that revenue metric varies through time. To understand the revenue breakout add the value in each column along a row and that gets you to the grand total value at the far right. Finally we mentioned a couple times in the customers dashboard how to see the impact on your customers of things like running a special deal in your store. When you offer your customers coupon codes these bottom two charts in the orders dashboard show how much of an impact those coupons have on several key store metrics and how much usage they get in your store. For example this table report lists how many orders in the past 30 days used a given coupon code and what percent of all your orders that represents. So here you can see how successful and popular your particular your different coupons are with your customers. When you want to look at your stores performance on a product level rather than an overall order level come to the products dashboard. For example you may be interested to determine which products are the most popular with your customers and those your customers may be less satisfied with and are refunding at higher rates. Since we’re looking at a product level rather than the order level here we see included the GMV metric rather than revenue. This scaler tells us in our demo store we’ve sold 4.8 million dollars worth of products in the past 30 days. However of that amount we’ve had 12% refunded. Looking at refunds as well as sales makes sure that we don’t over inflate how well we think our store is doing. As we’ve seen in other places on these dashboards you can also take a look at how your stores GMV for the past 30 days stacks up against the longer-term trend since the start of last year. Now how many products did our store sell to make up that 4.8 million GMV? In the past 30 days we’ve sold about 105,000 individual products from our store and had 15% of that quantity refunded back. The refunded percentage is different for GMV than for quantity reflecting that we have a range of prices for the products we have sold. So in this case the products that were refunded were a bit on the lower end of the price range since they make up a smaller percentage of the refunded GMV than of the refunded quantity. Especially if your store has a wide range of product prices combining the GMV and quantity views of your performance gives you a much more comprehensive understanding of how your products are selling and if your customers are sending them back. You can directly compare each individual products sales quantity and refund rate for the past seven days in this scatterplot report to pinpoint any of your products that are doing especially well or poorly right now. Each point represents a given product with the number ordered shown horizontally and the refund rate shown vertically. So products toward the top of this report with a higher refund rate may be causing your customers some issues and you might want to ask those customers for feedback or look at your reviews or so on. Products that are currently performing especially well with high numbers of sales and low refund rates would show up toward this bottom right corner. The product performance details table gives a more detailed breakdown by individual product of sales and refund numbers in the past 30 days. And then at the bottom we have a set of top tens. When you want to know exactly which products are selling best with your customers or getting refunded the most, take a look at these four top 10 reports. The way our demo store is set up, each size and color of a product is listed separately. So you can see in the GMV top 10 report there are a lot of these Lando Jim jackets of varying styles showing that they’re a fairly popular purchase or maybe just kind of pricey. To see which of those is the case we can compare with a quantity top 10 report below where we only find one Lando Jim jacket as one of our most frequently purchased products. So it’s likely that the price of these jackets is what’s landing them in the top 10 on the GMV report. When we take refunds into account as well we see that many of the refunded products comprising the most GMV refunded are also Lando Jim jackets. So we may want to consider if the pricing of these jackets is where it should be or whether our customers are finding issues with the jackets when they purchase them. Remember we’re looking at demo data here. So most likely your store will have a different list of, for example, products that are most often purchased versus products that are most often refunded. Each of these charts is independent of the others. So the top refunded products by quantity doesn’t necessarily reflect the number of refunds of all the top purchased products by quantity. They’re totally separate data sets. As a final note you’re not restricted to just these four dashboards when your MVI account is set up. They’re a jumping-off point. You can build your own stories and analyses to best serve your specific business and you can edit any of the reports and dashboards in your account to be most useful to you and to stay current as your business grows and evolves. As a quick overview of how to do so you can add a new dashboard from the dashboard drop-down. Let’s just call this my dashboard. Here we have a few options for where to organize it into a group. So now you have a blank dashboard where you can collect any existing report listed in this drop-down to address your business needs in exactly the way that makes most sense for you. For example, maybe it would be informative for analyzing your store to think of orders by new versus existing customers as more closely associated with orders than with customers and compare it with some of the other orders metrics. That report is currently on the customers dashboard but you can also add it to your new dashboard by searching for it and then clicking its name. Then you could also add say the AOV by new versus existing customers report.

And now you can look at them side by side. If you want to make changes to the reports on your new dashboard but not affect the versions on the existing dashboards you can use the save as option in your reports drop-down settings to create a copy which you can then give a new name and then make adjustments to. Perhaps you want to add revenue to this dashboard about new versus existing customers. We saw in the executive summary dashboard a report on revenue split out this way. But it’s for the current month to date rather than the past 30 days so it’s not quite comfortable with these other two reports.

You could save a copy that you edit to instead cover this same 30-day time period and then you’d have a nice matching set. You can also create new reports in the report builder.

Check out our getting started with MVI webinar for a look at how the report building process works.

Some of our other webinars go into more depth on topics like data management and getting ready for the holiday season and we have recordings of those on the Magento resources library. We’ll send out a link to that library as well as a recording of this session in a follow-up email. And now I’ll hand it back over for our Q&A portion. Thank you, Wepsloe. That was a great presentation and the content was very informative. Before we dive into Q&A session as Wepsloe mentioned I wanted to reiterate that you’ll be able to find the recordings of our MVI webinars on Magento resources library. For your reference we’ll be including the link for it in our follow-up email for this webinar. Now if you notice we have displayed few questions on the screen related to this webinar. We request you to take a minute and give us your feedback on the session. With that we will open up our Q&A session. So we have quite a good number of questions coming in.

We’ll just take them in the order we have received. So the first question we have is why am I not seeing the default dashboards and the reports in my account? Yeah, so that’s a good question. We actually rolled out these dashboards somewhat recently during the last year. Your admin account, sorry your account admin should have access to these dashboards that you saw in today’s webinar. Note that they do need to share these dashboards with you for you to be able to see them if you’re not that admin. If you are the admin and you’re still not seeing the default dashboards then go ahead and reach out to support and they can help you out by taking a look at what is going on in your account. Thank you Absalom. So the next question we have is is there a way to dynamically filter the data in our reports? Yeah, so MBI has a couple of options for dynamic filtering at the dashboard level and this is helpful so that when you’re looking at a dashboard you can adjust all the reports on that dashboard simultaneously to see the same subset of data throughout so it filters all the metrics that are involved in that dashboard’s reports all at the same time so the data you see all matches.

There’s a little bit more detail on this in that getting started with MBI webinar but a quick summary is that the dashboards have two filters depending on how your store is set up. Every dashboard has a date filter or a time range filter is another word for it and that can be used to change what range of dates that the dashboard reports cover. So for example you want to change it to see all of your data for the last quarter or for the last year or just for the last 30 days any of those are options. And then if you have if your Magento account is set up to have multiple stores and your MBI account is connected to those multiple commerce stores then you should also see the store filter on your dashboard so that lets you filter for each individual store and only see one store’s data at a time. It also has an option to see all of your stores aggregated up together in one place. Thank you Absalom and the next question we have is do these dashboards show real-time data? Yeah so that’s a that’s a good question. So MBI does not show real-time data in general. So the way that MBI’s data setup works is that it makes a copy of the data from your live database from your production database. It makes a copy so that when you run all these reports or load them all it doesn’t impact your performance on your store’s front end. But that does mean that the replication process takes a little bit of time and it also the update cycle includes the calculations that are required for your metrics and things like that. So basically in MBI your data reload gets your data rather gets updated based on the data update cycle that you’ve set for your account and you have some options on how that gets configured. You are able to choose to have the update happen you know once a day.

There are some options for like pick a specific time that you want your updates to start each day and you can also choose to have a new update automatically kick off within a couple hours after the previous update finishes. If you need help getting your account configured again feel free to submit a support request.

Perfect thank you Vibhsla for the clear explanation. So we have another question on filtering. So this is about asking what is the best practice for filtering out bad or useless data from your reports? Yeah so MBI has got a couple of options. In each report you have the ability to filter the metrics that are involved in that report and that lets you include or exclude exactly the data that you want to show on that particular report. If you have if you know that you want to include or exclude only a specific subset of data for a particular metric in all cases MBI also includes the ability to create filter sets which is a similar functionality but then you can add a filter sorry but then when you apply a filter set to a metric then everywhere that that metric gets used that filter set is also applied to it. So basically if for example your orders count metric you set up the filter set for that to exclude orders that you know your team your test orders that your team has created just to check if your store is working correctly. You can define that filter set in that way and then everywhere that that order metric is used in your reports and your dashboards those test orders will be excluded so you don’t have to go through and exclude them individually every time.

Filter sets are also really convenient because that means if you need to make a change then you can make a change in one place and it has an impact across the board wherever that metric is used. Since they’re powerful like that they are they are also you need to be a little bit careful when you do that and make sure you’re aware of the impacts but they’re they’re really helpful in that way. If you want a little more information we do cover those also in that getting started webinar which is kind of a broad introduction to the core features of the MBI software.

So I would really recommend going to the resources library and checking that out.

Thank you, Babslav. So the next question is can I change the cohort report to show number of orders instead of lifetime add? Yes definitely. You can do this pretty easily just go into the edit option on that reports drop down if you want to change the report itself. So under the edit then you will have the choice to change the metric that the report is built on. You also will be able to you also have control in a cohort report over some other options like what data is used to segment the customers. So in the example one that we saw on the default dashboard the customers are segmented by their first order month and there’s a few other options that you could segment them by instead. You can also change what range of dates are represented and that one I believe we shot we saw customers who had from four to 11 months of data or four to twelve perhaps and so you can adjust how many cohorts you see, how long, how much data that they must have, all of that. If you want more information on cohort reports then that actually is not covered in that getting started webinar it’s a little bit more advanced. So the MBI user guide has a step-by-step walkthrough to create or edit a cohort report.

All right so the next question is what if we would want to have an executive overview dashboard show data about products instead of customers? Can we edit it? Yep you can edit any of your dashboards any way that you want. If you’re the owner of that dashboard then you also have the option of creating a copy of it and then you can make changes on that copy if you want to keep both the original version with the customers related reports and also your new version with your orders related reports.

Those options are all under the settings drop down for the dashboard. All right so the next question is if someone leaves our organization how can we get access to their dashboards? Sure so first of all the person who left that user’s email login still needs to be active in order to follow these steps. If they’ve been deactivated then you just re-invite them through that same email that they were using before to re-invite them to your MBI account. So once they’re active then you can contact support and provide that email of the person who left and also the email of the person that you want to transfer their dashboards to and then what the names are of any of the dashboards that you want transferred. If all of them should be transferred you can support that too and then they’ll take care of that process for you and let you know when it’s done or if they need any additional information.

Thank you Absalom. So the last question we have here is how long does MBI retain data after an account is deactivated? Okay so I actually need to go double check on this answer but what I believe is the case is that once the account’s deactivated in general data is screened for 30 days and we have an automatic process that cleans it up. We also I think we may have updated our policies to be appropriate to GDPR and other privacy requirements so I know for sure that we can delete the data more quickly if we get a request for it for example through support. So your account is now being reset.

Thank you Babslav for the clear explanation and all the questions we have got. So for now let’s call it a session but before we end I want to let you all know that we have an email specifically intended to answer questions of webinar attendees for the next seven days. For your reference we’ll be including the email address in our follow-up email as well. So if you have any question after this session please feel free to send it there. We will respond back to you with possible answers. Once again thank you so much everyone for joining us today. We hope you got some really great information out of the session. Have a nice rest of the day.