4 easy tips to increase ecommerce customer conversion

First published on April 26, 2022

 

7 minute read

Felicia Kuan

Growth

TLDR

Why aren’t prospects doing what we want? Figure out how we can use ML to show viewers what they’re searching for and get them to buy immediately.

Glossary

  • Intro

  • 1. Automatic compilation of prospect data for personalization

  • 2. Overseeing marketplaces by prioritizing data

  • 3. Predictive lead scoring that hone in on the best prospects

  • 4. Dynamic pricing based on user interest

  • Conclusion

Intro

Conversion varies for every e-commerce business, depending on their business model, but the goal is always to get as many prospects to do a desired action. 

In this piece, we’ll be exploring common issues that hinder users from converting into paying customers:

  • In what ways can we personalize recommendations for items users are most interested in?

  • What can we do to keep the interest of viewers with short attention spans?

  • How can we predict how much/little a user is willing to pay for a product?

1. Automatic personalized, product recommendations

When the online shopping experience is completely digital, a robust 

product recommendation

system is like a helpful store employee at a traditional brick and mortar store. It’s a fast, non-intrusive way of showing high-intent prospective customers the most relevant products.

This machine learning (ML) recommender system considers:

  • Who the prospective buyer is

  • Understand their purchasing intent 

  • What would push them to make the purchase

For example, when a user enters the term “desk chair” into the search, the algorithm would try to gather the user’s personal information and previous purchasing history. Thus, when the search results are returned, since the user is a developer, the user is shown a list of home office chairs that best match their query and most clicked-on chairs by other developers. This helps users 

find exactly what they want

, even if the search term they queried didn’t completely capture the product. 

Data analysts at Accenture found that prospects are 

91%

more likely to purchase from companies that remember their preferences and offer relevant recommendations. And, at Amazon, 

35%

of their revenue comes from recommended products, indicating that users found these automatically generated recommendations useful and took action because of them.  

Thus, these tools help increase products sold and help companies gain a better understanding to make predictions on what the customer will need next. Most importantly, product recommendations give customers the impression that this business can help them find what they need. The data gathered from observing customers and the recommended products they purchased, give marketers insight into the profiles of their customers. This can help them tailor future marketing campaigns that will appeal to them.

2. Overseeing marketplaces by prioritizing data

Some e-commerce businesses are C2C (consumer to consumer) or B2C (business to consumer) marketplaces like eBay, Etsy, AirBnb, and Booking.com. With so many listings, how do these businesses prioritize the products being displayed?  

These marketplaces typically use a 

ranking algorithm

to display products in relevance to the user’s search. When ranking products, the following elements are often considered:

  • Seller quality: responsiveness, items sold, high user reviews

  • Listing quality: Percentage of fields filled out by seller, user feedback, contains image

  • Item availability and freshness

These elements (which measures the “quality” of a product) are used to calculate a score that influences whether the product is listed at the top of a user’s search results, which improves the chances of the product being sold. With this, sellers are incentivized to provide all the data and responsibly fulfill orders to ensure they’re ranked at the top and continue to maximize their revenue. Subsequently, buyers are able to find satisfactory products quickly (driving 

conversion rates

), and sellers who prioritize customer satisfaction are rewarded with higher revenue and click-through rates (drives seller retention rates). It’s a win-win situation for everyone!

3. Predictive lead scoring that hone in on the best prospects

For marketing and sales teams, one of the challenges of finding qualified leads is sifting through thousands of qualified leads and compiling data to gain a full understanding of the ideal buyer.

Predictive lead scoring is the perfect solution to this pain point. To put it simply, 

predictive scoring

is a machine learning application that analyzes your prospect’s data and ranks your prospects by importance based on their likelihood of becoming customers. It will account for data on the level of interest, order or usage history, account size and industry. Then, it compares what information your customers have in common, in addition to comparing the leads your business 

didn’t

close to generate your list of high-intent prospects. 

The benefit of using machine learning to research prospects is its 

unbiased lead scoring

skills. It can analyze all aspects of prospective data– not just the fields we think are important— to pin-point any trends or indications that might indicate a desire or need for your product! And because it’s machine learning, the predictive ability gets more accurate over time, which lets you invest time in the leads that will 

actually

become customers. 

4. Dynamic pricing based on user interest

As a business, imagine a user is trying to choose between a similar product you and your competitor are selling. Perhaps you’d like to identify and offer these users a discount immediately to ensure they purchase from you instead of your competitor. With ML, we’d be able to identify users who have been returning to the same page many times within a week and not buying and automatically offer them a small discount to make them feel like it’s a better deal. 

Thus, depending on your own input, you can set different parameters for identifying high-intent prospects that your machine learning model can use to make exclusive offers to users, for example:

  • High-intent

    : Enable a range of discount offers (i.e. 5 - 30% off) and use ML to pick an optimized discount that ensures the user purchases the item and maximizes revenue

  • Low- intent

    : Ask users who have spent more than 2 minutes browsing the website to spin to receive random discounts and require them to sign up to the newsletter to claim the coupon (a re-engagement opportunity for new users)

According to research by Skai, consumers are 

70%

more likely to make purchases from a retargeting attempt. Therefore, it's definitely useful to build a relationship with prospects by finding ways to maintain the prospect’s interest and personalize their shopping experience. 

Conclusion

We predict the e-commerce landscape will continue to grow. As more and more people conduct business online through mobile and social in the coming years, it’s crucial to stay on top of the rapid changes and understand the optimal ways to reach your customers– because they’re definitely out there, Google searching for you.

We have some other pieces that relate to improving ecommerce businesses, so please check them out if they seem useful to you:

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