Math, to the owners of many eCommerce stores, is a cause of stressful memories of calculus in high school. However, in online retailing, mathematics does not simply concern itself with the abstract equations, but it is the engine that drives growth.
Mathematical analysis involves using logical and quantitative methods to solve problems and analyze data. When applied in business, it implies going past the so-called gut feelings and using the objective numbers to base decisions. When applied correctly, mathematical analysis can transform a struggling online store into a conversion machine.
Through a process of reducing the complex customer behaviour into measurable data, businesses have the ability to project trends, optimise pricing and laser-cut user experiences. If you are ready to stop guessing and start growing, understanding the relationship between mathematical analysis and eCommerce success is your first step.
Understanding eCommerce Conversions
We should first of all know what we are attempting to improve before we delve into the intricate algorithms. ECommerce conversion is simply a situation where a visitor to your website is able to accomplish an intended task which in most cases is a purchase. It may also mean, though, registering to a newsletter, placing an object into a cart or downloading a whitepaper.
The number of people who do it is a percentage that is considered the conversion rate. When 1,000 individuals visit your shop and 20 individuals purchase an item then your conversion rate is 2. Whereas this may seem easy, the variables that drive this figure are enormous. These are the User Experience (UX), site speed, product photography, quality of copy, pricing strategy, and trust signals.
Historically, these aspects were altered by the marketer based on best practice or hunch. Today, the most successful brands use mathematical analysis to determine exactly which levers to pull. When you can examine the records of each time a person clicks and scrolls, you can determine the bottlenecks in your sales funnel which the naked eye would not expose.
Key Areas Where Mathematical Analysis Can Help
Mathematical analysis can optimize nearly every facet of an online business. Nevertheless, there are four specific areas that are most profitable to invest in when being approached in a quantitative manner.
A/B Testing Optimization
The mainstay of Conversion Rate Optimization (CRO) is A/B testing. It is about displaying two variants of a webpage to various groups of your audience and observing which one is more successful. Though the idea is easy, the implementation takes some strict statistical analysis to work out.
Most companies are guilty of halting a test prematurely or misinterpreting the findings because of the absence of statistical significance. Mathematical analysis ensures that the difference in performance is not just a fluke. Through confidence intervals and P-values, you are bound to know that version B is indeed performing better than version A. This will help you not to make permanent changes based on misleading data and make sure that every update will help to boost conversions.
Customer Segmentation
Not distinguishing between customers is an effective method of reducing the rate of conversion. Your customers are bargain-hunters, impulse buyers, brand champions and thorough researchers. Mathematical analysis helps you identify these groups through clustering algorithms.
Clustering is a machine learning algorithm which clusters data points and groups them together. This could be in the form of grouping customers, who make purchases at a certain rate, the average order value and surfing history in the case of an eCommerce store. After you have these different segments, you are able to customize your marketing messages. You can mail a discount code to the price sensitive segment and a new entry display to your big ticket fashionistas. Such customization would go a long way in enhancing the probability of making a purchase.
Price Optimization
Pricing is mostly considered to be an emotional move by business owners, yet should be a mathematical one. Price it too high and lose volume, price it too low and leave money on the table. Mathematical analysis helps find the “Goldilocks” zone–the optimal price point where profit is maximized.
This is done by the use of regression analysis which is a model of the relationship between price (the independent variable) and demand (the dependent variable). Using the past sales history, you can ascertain the price elasticity of demand of your products. More sophisticated optimization models are also able to make dynamic adjustments based on competitor pricing, time of day, and inventory, among others. This keeps you competitive and you maximize your margins and this directly affects your bottom line.
Inventory Management
Being out of stock is a conversion killer. In case a customer intends to purchase but is unable to do so, it is a missed sale which may be gone forever. On the other hand, an excess in the stock level has a binding effect on the cash flow. Mathematical analysis solves this dilemma through time series analysis.
Time series analysis is the analysis of data that is accumulated over time, e.g., the sales made per day during the past three years, to determine trends, cycles, and seasonality. Projecting these trends into the future allows you to make a very precise prediction of demand. This can be used to manage inventory on a just-in-time basis to make sure that you have just enough inventory to meet the demand and not spend too much on storage.
Tools and Techniques for the Non-Mathematician
You do not need a PhD in statistics to start using mathematical analysis. The tools that are on the market are numerous and can fill the gap between the complex data and actionable insights.
Essential Tools
As a beginner, it is marvelous how powerful Microsoft Excel or Google sheets are. They are able to deal with simple regression, correlation and forecasting. Further on, Python and R have their libraries, created specifically to perform data analysis, such as Pandas and SciPy.
Browser extensions can be priceless to the people who require simple solutions to certain equations or statistical issues. An example is with an AI problem solver, you can check a calculation or decipher the process of a statistical formula in a few seconds without doing it manually or leaving your computer. These tools can make high level math more accessible.
Fundamental Techniques
Regression Analysis: It is applied to comprehend the relationship between variables. As an illustration, does spending more money on ads actually result in the increase in sales, or is there another variable at work?
Hypothesis Testing: A/B testing is based on it. It will enable you to know whether an outcome is statistically significant or it is just noise.
Descriptive Statistics: The fundamentals- mean, median, mode, and standard deviation. They provide you with a picture of your current performance and assist in detecting the outliers in your data.
Introduction to Data-Driven Decisions
Implementing mathematical analysis in your business operations can feel overwhelming, but it is best approached as a step-by-step process.
Step 1: Data Collection
Nothing can be analysed and not followed. Make sure that you have strong tracking installed through GoogleAnalytics or other such sites. Make sure that you are not only keeping track of page views, but certain events such as Add to Cart, Initiate Checkout and Purchase.
Step 2: Identify a Question
Don’t just “do math.” Begin by having a business problem. An example might be: Why does mobile have high traffic and low conversions? or “What is the best discount to give to recapture an abandoned cart?
Step 3: Run the Analysis
Since the relevant data are to be analyzed using the tools listed above. In case you are doing pricing, then perform a regression analysis of price and sales volume. Look at the statistical difference between landing pages, suppose you are considering UX. A math solver might also come in handy should you have a hard time on a particular calculation to be sure that your numbers are correct.
Step 4: Implement and Monitor
In your analysis, change something. Next, keep on tracking data. Mathematical analysis is not a one-time fix; it is a cycle of continuous improvement.
Excellent resources are available to those who desire to delve deeper. Courses such as Data science for business are offered on course platforms such as Coursera and edX, and books such as lean analytics are a great guideline to what metrics really count.
The reason why Math Matters to your bottom line
The eCommerce environment is more competitive than ever. Trusting intuition is no longer enough to develop. Mathematical analysis offers a competitive advantage, allowing you to see patterns that your competitors miss.
Whether it is determining whether your A/B tests will hold valid or not, or whether you can divide your customers into a profitable category, math is the map to the increased revenue. It is the best way to maximize your prices and optimize your inventory to ensure that you are not left empty-handed.
By adopting such quantitative techniques you transform into being a store owner who hopes to make sales in to an engineer of sales. It would be advisable to begin new with the tools you have and allow the numbers to lead you to a brighter and better future.
What Is EcomBalance?
EcomBalance is a monthly bookkeeping service specialized for eCommerce companies selling on Amazon, Shopify, eBay, Etsy, WooCommerce, & other eCommerce channels.
We take monthly bookkeeping off your plate and deliver you your financial statements by the 15th or 20th of each month.
You’ll have your Profit and Loss Statement, Balance Sheet, and Cash Flow Statement ready for analysis each month so you and your business partners can make better business decisions.
Interested in learning more? Schedule a call with our CEO, Nathan Hirsch.
And here’s some free resources:
- Monthly Finance Meeting Agenda
- 9 Steps to Master Your Ecommerce Bookkeeping Checklist
- The Ultimate Guide on Finding an Ecommerce Virtual Bookkeeping Service
- What Is a Profit and Loss Statement?
- How to Read & Interpret a Cash Flow Statement
- How to Read a Balance Sheet & Truly Understand It
Thanks to Zenecom for collaborating on this post!
