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How eCommerce Product Teams Can Use Data to Influence Their Next Decision

J.D. Crabtree
November 5, 2020
eCommerce

The sales team at your company likely already knows the benefits of big data. They can use it to optimize prices, determine which products to recommend to customers in order to get the best chance at a sale, fix problems in the sales funnel, and much more. Tracking and optimizing KPIs (key performance indicators) is a vital part of modern digital sales. Used correctly, it can also be a vital part of your product team's decision-making as well.

In this post, we'll take a look at how data can be used by a product team that is deciding on the next product to offer. To do so, we'll examine two major sets of metrics that a sales team tracks and show how they relate to the types of decisions made by a product department as well. 

Growing the relationship and learning about the customers

Sales people are interested in learning more about customers so that they can grow the relationship and get more sales. All of this data can be used by a product team as well. In some instances, the data can carry over directly. Other times, it will simply help give you clues about current market trends that can be used to empower your product decisions. Regardless, eCommerce and the Direct-to-Consumer business model allow for, and rely on, closer relationships with consumers. Product teams can use metrics to improve those relationships. Let's take a look at some of those metrics.

Track average order value 

If you know the average order values, then you have an idea of the price range that your customers like to purchase in. Creating or offering a product that falls within the range specified by the data will put your product right in the sweet spot of what customers like to spend. Where a sales team is just interested in getting the average order value up, a product team will want to take a closer look at the data. By looking at order values as a distribution, rather than an average, you'll see clear ranges and probably multiple price points that are popular among your customers. This extra data will help your team make more informed decisions, and will help your marketing team close more sales from visitors to your site.

Calculate purchase recommendations

This one translates fairly directly from the way that a sales team uses it. Today's machine learning algorithms can use big data and the purchase history of a customer to predict the types of products they would like to purchase. Sales teams can use this data to recommend products that your company currently offers to the customer in the hopes that they will buy it. Product teams can use this data like built-in market research. You'll have a good idea of the types of products that large segments of your customer base enjoy. By introducing products that are similar in nature to those predictions, not only will you be bringing in a product that is likely to sell, but a proven eCommerce sales tactic that will fit nicely into your company's existing upsell strategy.  

Identifying why customer churn

While the last example was directly related to picking products, this one is a little more abstract and its utility will depend greatly on the types of product or service that you are offering. 

There are many reasons that a customer may churn. In eCommerce, product teams play just as vital of a role in reducing churn as the sales and marketing teams. If you find that customers are churning because the price is too high, or because they don't feel the product or service has enough features, that is information you can use to help you decide which products to bring to market to keep customers around longer.

Identifying shopper motivations

Some shoppers don't mind spending top dollar on an item. These are the people who will buy something as soon as it comes out. They may even pay a premium if the item is frequently out of stock. Other shoppers are more value oriented and will wait until an item goes on sale before making a purchase. Knowing what motivates shoppers to make a purchase will allow you to decide between top of the line or more budget friendly products and services. If applicable, it can also help decide how to break down the product tiers on an eCommerce site.

Tracking and optimizing product launches

When a new product launches, the sales team is looking to the future. They are tracking a variety of key performance metrics that they can optimize in order to increase sales and ensure the success of the product. Like the customer information, this sales information can also be of great use to a product team. By looking at how past products of a similar nature performed after the sales team worked their magic and optimized the numbers, you can get an idea of how a future product will perform. We'll look at some of the important metrics now.

Track conversion rates

A product category that converts really well is a prime candidate to introduce additional goods from. Of course, there are many variables at play that determine how well a product sells, so you'll want to keep the products used for comparison as close as possible to the products you are considering. Even still, this metric alone isn't going to give a complete picture of how a product will perform. By weighing it with the other metrics that are tracked during a product launch, you'll be able to get a more complete picture of how the new product may fare on the market. 

Return on ad spend

A key metric to track in eCommerce is customer acquisition cost vs the average lifetime value of a customer. Therefore, the amount of money your company must spend to get conversions is also important for product teams at an eCommerce business to take into account. Here, it's important to remember that you are looking at similar products, so a ROI might not be horrible if only one of the similar products performed poorly and there were several others that performed well. If an entire category of products takes a lot of money to move, then it might be best to skip that type of product entirely and move on to the next candidate.

Returns per 1000 orders

If a product is being returned a lot, it might be a clue to the marketing and sales teams to stop pushing it. It might even be a clue for management to pull the product, assuming the returns are due to problems with the product, and they can't be fixed. It can be the opposite clue to a product team, however. One product suffering from quality issues and being returned a lot is a perfect opportunity to replace it with a higher quality product that will make customers happier.  This is especially important in eCommerce businesses that rely partly or totally on platforms like Amazon, where poor customer experience can result in account suspension.

New customers over time

Selling to existing customers is great, because it gets your average customer value up. But as long as your sales staff is able to get those numbers higher, adding new customers is the best way to see sustained growth. A hot item in a particular category may bring in a ton of new customers where its competitors wouldn't, but if an entire category of products is bringing in new customers then you'll likely benefit by bringing in more products of that type and letting your marketing department use them to add even more new customers. Conversely, product types that are no longer bringing in many new customers might be worth skipping. 

Conclusion

Your company, and likely many of the companies you partner with, collect a lot of data every day. This data empowers a level of business intelligence that would have seemed like science fiction not too long ago. Many companies already know how to use this data across most aspects of their eCommerce operation to improve sales, optimize shipping operations, reduce labor costs, and drive decisions about how the company operates. Product teams are generally not thought of when it comes to putting big data to use, but the reality is that every aspect of your business can benefit from this resource that you are already collecting as part of your day-to-day operations.

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