7 reasons ecommerce companies should combine product and marketing analytics

No one will argue that marketing is unimportant. But in a competitive space like ecommerce, I will argue that focusing only on marketing performance isn’t enough.

Ecommerce sales will cross the $6 trillion mark this year. Customers have more options than ever, and competition is fierce.

As a solutions engineer manager, I lead a team at Mixpanel that helps customers build analytics solutions tailored to their product and business goals. We’ve worked with dozens of retailers and ecommerce companies, and we know what works—and what doesn’t. 

I see too many ecommerce companies focusing all of their time, efforts, and budget on marketing and marketing analytics. But once users are on their website, they neglect their users’ in-product experience. 

Ecommerce companies that want to acquire and retain customers need to go beyond marketing. They need to provide a personalized and optimized user experience, from the user’s first interaction with the brand all the way to the moment the order arrives on their doorstep. To do that, they need to combine marketing analytics with product analytics to capture and analyze the full user journey.  

Not convinced? Here are seven things retail and ecommerce companies can unlock when they combine product and marketing analytics.

Ecommerce and offline retail are linked: Most brick-and-mortar retailers also sell their products online, creating a hybrid customer experience. Customers want a smooth omnichannel user journey whether they are browsing online, in-store, or both. 

Attribution is a challenge in the retail and ecommerce spaces because customers often have so many different touchpoints. Ecommerce and retail companies need tools that can analyze the omnichannel customer journey, and marketing analytics alone can only get you so far. 

To make attribution possible, I always recommend that retailers and ecommerce businesses start a loyalty program, if they don’t already have one. A loyalty program encourages customers to use a unified profile across online and offline channels—and it drives long-term repeat purchases. Do note that a loyalty program also has to be good. Giving customers a 1% rebate from a loyalty program isn’t going to just work. A good loyalty program will consist of achievable milestones, and your members will be proud of keeping that status.

Retention rates are falling across industries, and ecommerce is no exception. Our 2024 Ecommerce Benchmarks Report found that ecommerce week one retention fell from 39% in 2022 to just 22% in 2023. Acquiring a new customer is more expensive than retaining an existing one, which makes improving retention even more crucial for long-term growth.

Creating a positive user experience is one of the keys to better retention. If your customers have a bad experience, they won’t come back. But without insights into your users’ on-site experience, you won’t be able to spot problems and optimize that experience.

Here’s what successfully using product analytics to improve user experience can look like: 

A grocery store delivery app guarantees order delivery within 20 minutes of purchase. That’s the store’s unique selling point (USP), and customers find it very attractive. Users flock to the platform. 

But over time, the grocery app realizes that a lot of users are churning. They dig into the metrics and realize that many of those users received their delivery in 30 or even 40 minutes—the platform isn’t delivering on its 20-minute promise. 

When the app’s product team digs deeper into their product analytics, they spot the problem. Whenever there are 10+ items in the cart or the cart is too heavy, items tend to fall outside of the delivery threshold. 

Why? Because delivery drivers have to load and carry all of those items, which slows them down and causes delays. 

By adding a 10-item limit to the cart, the company increases on-time deliveries, which in turn improves retention. 

When companies rely exclusively on marketing engagement numbers, they run the risk of falling for vanity metrics (metrics that sound impressive but don’t tell you anything about your product performance). Knowing how many times a user visits the website or how many items they add to a cart might look good on paper, but it doesn’t tell you much about whether users are getting value. 

To get valuable insights, ecommerce companies need to formulate an analytics strategy and build a framework to track the metrics that genuinely have an impact on their business. 

For example, marketing metrics like sessions and visits captured don’t tell us much because visitors could be sitting on the homepage or checking the FAQs. But if we look at how many times a user has a session and browses for an item, that can give us more information.

When companies combine marketing and product analytics in one platform, they are working from one data source across the entire user journey, and they don’t have to worry about discrepancies across data sources. They can more easily get insights about user behavior across the entire ecommerce journey, from initial engagement all the way to purchase.

One mistake I see too many ecommerce companies make when they build their analysis framework is to tie their North Star metric to revenue and purchases completed. 

That might seem logical on the surface. The more purchases completed, the more revenue, and the more success for the company. 

But what if the product never gets delivered to the customer? 

The order goes missing, it gets delayed, or it’s delivered to the wrong address. Revenue might stay high for a while, but retention will suffer. 

Instead of focusing on purchase completion (which is when the company gets value), ecommerce companies should build their analytics strategy around delivering value for their customers. And customers aren’t getting value until they receive their item. 

In my time as a solutions engineering manager for Mixpanel, I’ve had plenty of users tell me that they want a tool that will give them insights without having to do the analysis. 

I understand the appeal, but using a tool that gives you insights without the analysis is useless. It’s less important to see how users are behaving than it is to understand why they’re behaving that way. 

If a tool automatically identifies a group of users more likely to purchase, that’s great. But if you don’t know why, you can’t nudge other users to replicate that behavior. And if you can’t use it, what’s the point of having it? 

Let’s say you have a group of users who completed a first purchase. You know they like your product, but you don’t know what they did before converting. 

With product analytics, you can track their behavior up to completing a purchase. You might notice that most users search at least four times or add 10 items to their cart before pulling the trigger. That information can help you nudge a user once those actions have been completed—i.e., when they are most likely ready to purchase. 

These are the kinds of insights that you need product analytics for. Business intelligence tools can sometimes give you these answers, but they take a lot more time and effort (and often help from someone on your data team) to get to.

Ecommerce businesses also need real-time analysis. If a clothing retailer has a 24-hour flash sale and conversion drops unexpectedly, the company needs to be able to jump right in and find the source of the problem. Waiting days for an answer from your data team isn’t an option.

Marketing best practices exist for a reason—in the absence of data, they’re the average behaviors that historically have delivered results across a wide spectrum of business cases.

But what if instead of doing what’s best for your industry in general, you could do what’s best for you and your users specifically? 

Let’s use an abandoned cart email as an example. 

Industry best practices will say to send an abandoned cart email after an hour, a day, and a week. 

But that’s not personalized to your business, and it can even have negative consequences: 33% of global consumers leave a favored brand due to irrelevant offers and promotions, according to CapitalOne. 

Let’s look at the grocery delivery example again. 

If a user adds a single tomato to their cart, they aren’t likely to check out any time soon. Who orders a single tomato? They’re most likely filling their cart with groceries over a few days. 

But if you follow industry best practices and send them several abandoned cart emails to prompt them to complete their purchase of that one tomato, your user will get frustrated. They might unsubscribe from future marketing campaigns, and the only way you’ll be able to reach them in the future is through paid media—which is a lot more expensive. 

With product analytics in place, you can analyze when a user is most likely to check out. You might see that they are most likely to make a purchase after seven days or when they have five items in their cart. So that’s when the abandoned cart email is triggered. 

The result is your customers complete the purchase and they don’t opt out of your marketing campaign because you are reaching them at the right time. When you combine product and marketing analytics, you can throw out the playbook and do what works best for you and your users.

One of the most important metrics for ecommerce retailers is average order value (AOV). Even a small percentage increase can make a difference to the bottom line. 

The good news is that product analytics can help companies increase AOV with relatively little effort.  

This is a strategy I recommend for all of our ecommerce customers who want to move the needle on AOV:

Figure out your median cart value. With product analytics, finding the median cart value takes just a few clicks. (Note: I use median instead of average cart value because it controls better for unusual outliers that might throw the numbers off.) 

Once you know your median cart value, offer free shipping for orders starting at 10% above that number. It’s a small enough increase that customers are likely to purchase just a little bit more to get that attractive free shipping offer. This pushes up your median cart value, and your AOV increases along with it. 

This process sounds simple, and with the right product analytics in place, it can be. But finding your median cart value without product analytics is tricky and time-consuming. You can ask data analysts to use BI tools to figure it out, but we’ve already gone over how that can add a lot of wait time to your project, and you won’t have access to real-time, up-to-date information whenever you need it.  

When you compare two marketing campaigns, you can easily see which one performs best by comparing conversion rates.

But when you dig a little bit deeper, the answer gets more complicated. 

Let’s say you have two campaigns: one with a 5% off voucher code and one for 20% off. 

Twenty percent is a very attractive discount. Chances are that the offer will see a higher conversion rate. You might conclude that is the higher-performing campaign. 

But when you look at user behavior, you realize that customers using the 20% voucher are taking 14 days to convert. Users with the 5% voucher are converting in five days. 

If a user who normally purchases every five days becomes a user who purchases every 14 days, the 20% voucher might be getting positive results in the short term, but it’s negatively affecting your purchase cycle in the long term. Unless conversion rates on the 20% voucher are high enough to be worth it, your campaign is doing more harm than good. 

Over time, that can affect order volume and profit margins. What looks like a successful marketing campaign can hinder your company’s long-term success. 

As I mentioned at the top, ecommerce is a competitive industry. Companies that don’t invest in their user experience will quickly get left behind. Product analytics is the key to accessing real-time insights that can have a strong impact on a company’s bottom line. Instead of just counting conversions, it allows you to track user behavior before and after conversions. 

But in my opinion, combining marketing and product analytics is the ultimate sweet spot: All of your data is pulled from the same sources, so you eliminate discrepancies or duplicates, and your processes are more streamlined. With the right analytics, ecommerce companies can power their growth and stay ahead of the competition.