7 Sales Metrics to Optimize for Your E-commerce

It’s essential to not only analyze customer data individually but also to keep an eye on sales metrics. Below you’ll find a few indicators that are worth combining to find out more about your customers’ shopping habits.

Total transaction value in specific time and channel

Components:

• Total transaction Value
• Time Range
• Channels (online e-commerce/offline — POS/mobile)
• Source of contacts (customers from Social Media, RTB campaign, Newsletter, previous customers of your competitors, etc.)

You can analyze transaction data during specific time periods — a day, week, month or a custom time range. This allows you to quickly compare the value of sales at certain times.

You can also add different channels to the analysis in order to check their effectiveness at a given time. Based on the conclusions drawn from the data, you can decide on which days/months to promote specific channels.

Average value of purchases per customer in a specific time

Components:

• Overall value of purchase
• Number of customers
• Time range (e.g. month)

You can use metrics that can be viewed immediately after uploading data to the system, such as the average value of purchases per customer. Thanks to comparing the overall value of purchase and the number of customers who bought in a particular time period, you get information about the average purchase value among your customers and how much they spend (profit per client).

Analysis of the last purchase

Components:

• Value of the last purchase
• Time range

Analyze the date of the most recent payment and segment customers by time of last purchase. Special targeted campaigns can then be prepared to activate customers who haven’t bought anything in a certain minimum amount of time.

Analysis of the most frequent buyers

Components:

• Number of transactions per client
• Time range
• Other indicators (behavioral or demographic data)

Use the funnel and data stored in the system to search for customers who are the most loyal and who show the highest purchasing potential. You can analyze this group of users and correlate it with other indicators like demographic or behavioral data.

Track RFM for everyone

Collectively analyze the elements the RFM indicator:

• the last transaction (recency)
• frequency of purchases (frequency)
• and their size (monetary)

This lets you extract groups of, for example, the newest and most active clients and direct personalized messages to them based on transactional data. It will also allow you to divide your base into three segments; those who buy MOST (VIP customers), MOST FREQUENTLY (loyal), LAST (new customers). Use this information to refine your buyer persona buyer. Knowing who your loyal, new and VIP customers are, you can check which channels they use most — whether they prefer online or offline, which days of the week they buy the most, at what time of the day etc.

Identifying your VIP customers

Components:

• Amount of money spent in your shop
• Time range
• Other data about customers (behavioral data & demographic data)

Pay attention to average frequency and value of purchases especially customers who have spent the biggest amount of money. Analyze this group by taking into account demographic and behavioral data. Check out their shopping habits and how they react to campaigns, their age and location. This will help you prepare more targeted campaigns and get them know better.

Analysis of the most loyal customers

Components:

• Number of transactions per client
• Time range
• Other data about customers (behavioral data & demographic data)

Identify those who most often make purchases in your store and are the most loyal. Supplement this segment with demographic indicators like age, location etc. to get to know their characteristic features. Pay particular attention to their behavioral data, what channels they buy in, what activities precede a purchase, whether they respond to marketing messages, etc. This will all help you to prepare more effective campaigns and messages addressed to this group.

Testing shopping cart versions

The response to a particular message may be different depending on many circumstances. Optimizing creative work, taking into account their various versions, sources and places where they are displayed, but also the responses of various customer segments. It’s a good practice to test each message based on different clients and their positions in various purchase stages.

Send test versions of the campaign to any number of selected users in order to evaluate the sales hypothesis. Thanks to the possibility of exploring the entire shopping path and dividing it into steps, you can trigger specific messages to the right customer segments with a personalized creation.

You can start tests from the cart side by, for example, changing the location of information about free delivery, additional promotions etc. It’s easy to check which version is more often abandoned. From there, it’s a small step to reducing your abandoned cart rate.

Testing forms

There is a general belief that the fewer fields to fill, the better, and that shorter forms mean higher conversion rates. However, what if circumstances force you to use more fields than you’d like? The solution is to use A/B tests to check different layouts of content. This way, you can assess which layout is more convincing for users and less time-consuming.

Analyzing a campaign’s creative elements

Evaluating the effectiveness of a campaign is not just about analyzing conversions. You can build a lot more extensive analysis and metrics, analyzed in real time. It will allow you to better manage communication channels and evaluate their effectiveness based on advanced analysis. Here are some examples of more tests you can run:

• You can send a test version of the campaign to any number of selected users in order to evaluate the sales potential of the creation or eliminate errors. This allows you to evaluate the layout and design in consultation with others before the final shipment.
• Send test versions of the campaign to a small group of users in order to evaluate the sales hypothesis. Thanks to the possibility of exploring the entire shopping path and dividing it into steps, we can trigger specific messages to the right customer segments with a personalized design.
• Thanks to A/B tests and the ability to define the appropriate steps of the client, you have the opportunity to conduct tests in any communication channel. For example, you can test the most optimal shipping time for an abandoned cart based on specific customer segments.
• You can also test cross-channel attribution.

When analyzing data, take into account not only simple metrics taken from analytical tools. Always try to combine these indicators with each other, which will allow you to analyze and extract more of the necessary information. Also, take note of two important elements:

• Always analyze changes in metrics over time, using so-called histograms. This makes it easier to capture changes and anticipate trends
• It is worth analyzing the metrics both as a whole (total number of transactions/value of purchases etc.) but also for specific clients (average value of the basket/average number of transactions per customer). This will allow you to gain a more complete picture of the situation. For example, an increase in the number of transactions doesn’t always indicate success. It may turn out that there is not a huge increase in the number of customers, because only a few of the existing ones are responsible for the spike in sales.

Currently, the simultaneous analysis of many indicators is easy, thanks to advanced analytical tools. On top of that, these tools can provide real-time updates to keep you informed of any changes that will help you to adjust your strategy.

When collecting a large amount of customer data, it is also worth investing in tools used to predict future events. This will make it easier to keep up with changing trends, anticipate changes and respond faster to them.

Originally published at Synerise: 7 Sales Metrics to Optimize for Your E-commerce on August 24, 2018.