Revenue, if I had a dollar for every time someone told me that this was their most important metric, I’d be a millionaire. “Let’s increase revenue, and decrease costs”, doesn’t seem very helpful. That’s because it isn’t. We know that Revenue = conversion rate x sessions x average order value, so we can use this formula to lead us to metrics that we can use. Once we have all the building blocks of revenue tracked we can now find the factors impacting revenue with more granularity and accordingly can take action on those metrics.

Let’s break down this formula into its parts and look at them individually along with some other metrics that will help understand revenue more holistically.

Conversion Rate

The conversion rate is a ratio that indicates how many transactions you make per 100 visitors. It’s a great indicator of marketing efforts and the traffic those efforts attract. If your conversion rate is slipping, you should start by spending time diagnose potential reasons as to why. Here are some, amongst many, potential problems that affect conversion rate;

Technical Problems

  • Site speed
  • SEO
  • Mobile compatibility
  • Payment problems

UX Issues

  • Distracted objects
  • Low-resolution product photo
  • Cart problems

Product Issues

  • Price
  • Inventory
  • Product variations

Marketing Problems

  • Wrong audience
  • Unrelated landing pages

As you can see from the bulleted list above there are a host of areas that link to conversion rate performance. Merely tracking conversion rate as a sole metric is too general, it’s important to track all metrics that impact conversion rate. Accordingly, when you have a conversion rate drop you can identify the affecting metrics and take action.

Average Order Value

Average Order Value (AOV) is a metric that helps you understand your customers purchasing habits. The better you know your customers’ purchasing habits, the better you can deploy a more effective marketing campaign. For example, if you were to run a marketing campaign for items on your website where your customers’ AOV is 120 USD, but advertise an item priced at 200 USD, you may not get as much return on that campaign.

AOV sheds light on which products should be kept in inventory and the overall sales tactic of those items. Just like with campaigns you don’t want to be keeping items in inventory that are much higher than your AOV, these items could be difficult to sell if you don’t have a strong upsell plan in place. Accordingly, you can use AOV to help you focus on your cross-selling and upselling efforts.

Bounce Rate

Bounce rate measures the rate at which a visitor comes to your site and leaves with no interactions. Businesses want to monitor this metric because it’s a good indication of their websites “stickiness”. Stickiness refers to the overall appeal of your website to the user, this could be (amongst many) the relevance of information provided, a clear website UX/UI, page load time, incoherent terminology. If your bounce rate is going up you immediately need to start diagnosing potential issues, but as shown in the previous example there can be a wide array of problems spanning multiple departments, where would one start?

Page / Sessions

Pages / Session is key in determining the overall “flow” of your site, are people having trouble navigating through your website? Is your interface slow and laggy? This metric works closely with your conversion sales funnel, the closer pages/session is to the number 1 the less likely it is your customers are going along your conversion path. Ideally, this ratio should be closest to the number of pages you need for a conversion. Navigating through your website with ease is key for SEO, page/session is a good indicator of how easily users can navigate through your site once they have landed.

Tracking pages/session can help tackle issues such as how to categorize products so that the customer reaches the desired product more quickly. All site improvements on the user experience or user interface front will encourage the user to stay on your site and increase the odds of a user conversion.

Revenue is a general goal that all businesses look at, by following the metrics above you can look at sales driving metrics with more granularity which leads to actionability. Breakdown revenue into conversion rate, traffic, and AOV and now you can focus on those metrics individually and make a decision on how to improve them. But as you drill down deeper into the equation you realize that you need to track more and more metrics which can be overwhelming. This creates a need to automate the process to alert you to specific issues relating to conversion rate drops or spikes in bounce rates but more importantly providing the root cause for those issues so the user can take action by sharing the anomaly with its root cause to the necessary people to fix the problem.

Business Intelligence – Then and Now

Howard Dresner, President of Dresner Advisory is partially known for readapting the term “Business Intelligence” to “describe making better business decisions through searching, gathering, and analyzing the accumulated data saved by an organization.” He adapted that umbrella term in 1989; it can easily be said that data along with business intelligence has come a long way since then.

In the 1980’s we saw the growth of data warehouses, this was the first sign of the breadth in which data was headed. As technology progressed (Moore’s law) and hard drives consequentially got cheaper, so did data storage. The architecture of data warehouses was developed to help transform data coming from operational systems into decision-making support systems. For example, enterprises were now able to store sales data along with the respective time through a data warehouse, which enabled them to plot and analyze data in order to make decisions.


The chart above published by Mckinsey&Co shows the level of change in multiple industries’ core business practices brought on by data and analytics for the past three years. Published Jan. 2018 1

Following the onslaught of data warehouses, the 1990s were the era of data mining. Businesses were now able to house much more data, and technology allowed this to be done faster and more efficiently. Data mining focused on discovering patterns in large data sets, and this enabled companies to do predictive analysis through historical patterns. Businesses now began focusing on pattern recognition in data in order to make more educated forecasting decisions.

By 2005, the data collected on average was so large that business intelligence tools simply could not cope with the volume – requiring the invention of new data analysis technologies such as Hadoop. Accordingly, the term “big data” was coined by Roger Magoulas in 2005 to describe data of this size.

Data-Driven Initiatives – What do businesses want from their data?

According to a data analytics survey by IDG, the top three challenges organizations hope to solve through data-driven initiatives are: finding correlations across disparate data sources (60%), predicting customer behaviour (47%), and predict product or service sales (42%)2. Businesses are looking to make data-driven investments in order to bring further insight into their operations and segments, helping them predict trends. Businesses can take a few avenues in order to reach these goals.

Manpower

The analysis of big data today is reaching a point of inefficiency from a personnel standpoint. You can always hire more analysts but the marginal return of every analyst you add to your team will decrease with time and will open you up to a host of personnel related issues. Entry-level data analysts have a yearly national average salary of $40K3, after a certain point, scalability is no longer viable through manpower. The insight deduced from the data will not justify the cost.

Software

Pioneers in the technology industry such as Netflix and Twitter have decided to tackle the big data analysis issue by developing their own AI-powered Anomaly Detection and Automated Analytics platform. The investment made by these tech giants shows us a few things. Firstly, if Netflix and Twitter are both willing to invest the time and money it requires to develop an automated analytics tool, then clearly they see value in this tool. Secondly, Netflix and Twitter have $111B and $21B dollar valuations, making the in-house investment for anomaly detection plausible and fundable.  Thirdly, they are best practice creators, their investment into automated analytics shows where the industry is headed in terms of data management. But is automated analytics just a game for giants?

Automated Analytics

Data analysis is forcibly moving toward automated analysis. Automated analysis is done through machine learning; the software constantly scales through data to find insights that are important to your business. A business that aims to reach their data-driven initiative goals (mentioned above) will find that automated analytics through Software as a Service (SaaS) providers is the quickest route to take. Automated analysis tools through SaaS providers are quickly scalable and can generate insights more quickly at a fraction of the cost. As shown above, we saw that the in-house coding of these tools may be too costly for SMBs.

Looking back at the growth of data and accordingly the expenditure on data-driven initiatives it is fair to say big data is here to stay!

What Millimetric Offers

Millimetric is a SaaS automated analytics provider that offers you what the tech giants are

developing in-house and what you can’t scale with manpower. We provide you with:

  • Anomaly detection email alerts
  • Root cause analysis for technical issues
  • 24H Anomaly monitoring
  • Anomaly Reports and analysis tools
  • Anomaly Share & Collaborate

Don’t take our word for it, though, give it a shot and let us know what you think!

1 https://www.mckinsey.it/idee/fueling-growth-through-data-monetization
2 https://cdn2.hubspot.net/hubfs/1624046/IDGE_Data_Analysis_2016_final.pdf
3 https://www.ziprecruiter.com/Salaries/Entry-Level-Data-Analyst-Salary

When it comes to business analytics it is crucial to keep your metrics in clear sight. The metrics that we keep an eye on are specific to the relative departments, or if you are a business owner your metrics might differ from operational sector to sector. We see that in today’s world data is king, and accordingly what we do with that data and how we analyze it could be the difference between a company that goes boom versus bust.

Where data is today?

Businesses today are striving to become more data-driven in hopes to get a clear picture of what is going on in their business, their sector and the overall economy. Over the last 15 or so years, we have seen businesses (especially online businesses) segway into using some form of dimension and metric-based data analysis software. As data compiling got more and more specific and holistic the data load for analysis has grown tremendously. Today we see that using dashboards are coming to a glass ceiling.  Not only has it gotten harder to keep track of all the necessary metrics, but it has also gotten even more difficult to derive meaning from the current metrics we have.

When you need to (and can) track so many metrics it is that much more crucial to hypothesise about the relationship between said metrics. This is where the analysis of the data comes into play, however, this is highly prone to human error. Not only are humans limited in their ability to be aware of all metrics, but their analysis of said relationships may also be flawed, due to improper conclusions or lack of data. This is where a missed metric can be costly for your business. We are going to dive deeper into this notion with an example.

Let’s look at an e-commerce business that produces clothing and sells it on their website. Some of the metrics it would be driven by are revenue, conversion rate, and shopping cart abandonment rate. When looking at these generalized metrics it is important to understand the thousands of factors that impact these e-commerce metrics. For example, if on any given day your shopping cart abandonment rate increased dramatically, and if it is due to a metric that you have not been following it could take you a very long time to find the source of the issue. Here you can see how any overlooked metric can be costing your business money.

Where to go from here?

The solution to this is anomaly detection. Millimetric can analyze and scale through data at a rate that cannot be done through manpower. Most importantly, Millimetric will make sure no key metric goes overlooked and accordingly uncover relationships you never knew existed. With this added insight Millimetric is able to find the root cause of issues so that you can not only see the affecting metric, you can go to the source of that metrics issue. Accordingly, you can take the necessary steps to fix the issue.

Anomaly detection is the only logical path to help businesses steer their ship through the sea of data. It is important to have a system that alerts you when you need to be alerted, data needs to be constantly scaled and analyzed for insights. The need to do this poses a problem with execution. The data load is simply too much to be analyzed at such great detail that anomaly detection tools such as Millimetric are a must.