eCommerce Software Development to Boost Retention, Loyalty, and Personalization

At first, many businesses want to measure success by the number of clients. It feels like a good proof for the founder's vision because the product satisfies a need. But if you think ahead, returning customers, or regulars, will make more money for eCommerce than a myriad of new ones. The Pareto principle works here, too: 20% of your audience provides 80% of the profit.

7 min read
All articles
By Denis Baranov
Head of Retail & Distribution Practice
eCommerce Software Development to Boost Retention, Loyalty, and Personalization

Once a product or service becomes a valuable solution to a problem, consumers develop a sense of dependency. Later, they are likely to share their experience with the product to their friends and colleagues, who may fall in the same target audience. This way, the business gets new users for free, improves the relationship with existing solvent customers – all that keeps your place in the market.

To quantify loyal customers, marketers use Retention Rate. To make it easy, DataArt develops eCommerce dashboards software, so the retailer can easily collect all data in one place (for example, merge Google Analytics, Mixpanel, Amplitude, Warehouse Management Systems, and so on). Analytics software shows what percentage of users re-entered the site or launched the application after some time (a day, week, or month) after the first visit or installation.

Smart Loyalty Programs with a Custom Approach and Machine Learning

This method has gone through an evolutionary path of tens of years and can be considered, perhaps, the most effective way to attract and retain customers. The meaning of loyalty programs is simple - to give the client the benefit of regular access to the service. However, they have several forms of implementation, and today, they can no longer aggressively appeal to consumer greed. To remain effective, loyalty programs must be easy to use and understand for clients.

Usually, the best way is to use machine learning for Smart Loyalty program. Every second, loyalty programs process tens of thousands of transactions in stores scattered across the country while managing millions of user accounts. Identifying fraudulent activities when processing data at such a volume and such a speed is a task that is extremely difficult, and sometimes impossible, using traditional methods. Therefore, companies are increasingly looking towards solutions based on artificial intelligence, in particular machine learning, as a tool to protect against potential financial and reputational losses from fraud with data from loyalty programs.

Machine Learning can help retailers understand:

  • ratio of scoring transactions to transactions used by the majority of users
  • data on the status of accrual of bonus points
  • frequency of transactions and use of bonus points
  • account expiration dates
  • distribution between purchases and points accrual
  • correlation between registration and interactions.

Engagement and Gamification KPIs

On top of increasing loyalty and sales, games are entertaining for customers - especially millennials who appreciate everything that is fun and fancy.

For example, Mastercard has offered great entertainment to customers from Denmark. After each payment with the system card, the Tap, Spin & Win application allows you to spin the virtual wheel of fortune and win a fun perk, like cinema tickets or a gift certificate.

Premium Experience Driven by Analytics and Purchase Levels

Owners of this status receive privileges and, according to estimates, spend twice as much time on the service as usual.

For example, Amazon customers have the option to subscribe to Amazon Prime for one year and get two-day shipping and free access to streaming video and music; the conditions are even better with Prime Now - shipping takes two hours, plus videos, music, and Kindle books.

Thrive Market, an American low-cost store, has built its entire business model on premium accounts. Select users get access to discounts on already-cheap products, and the fact that they paid upfront encourages them to order more to enjoy the benefits.

AliExpress visitors can join a special club, whose members receive points for purchases and even get a special status. At each step, users meet news opportunities - from new discounts to resolving conflicts and refunding the money in days.

Smart Retargeting and Personalization Approach

Surveys show that shoppers spend more if the product is customizable. It requires significant data collection behind this since companies have to find patterns in the user experience (what products they use, what content they are attracted to, do they use a phone or an app, how they interact, etc.)

Data processing also helps you speed up the second sale if you discern the general behavioral pattern for each customer who purchased for the first time. For example, if most returns occur after 30 days, no need to lure customers with discounts in this period - they will probably return to you without a reminder. But if they do not come back, then start introducing special offers into the game. A progressive increase in discounts can help 10% for 31 days, 30% for 45 days, and 50% for 60 days.

Reactivation Techniques and Acquisition

Customers who have not been with you for a long time, left, or are planning on it, might need a little reminder. Using the correct tone, reach out to them through a call, an email, or a text message. But first, it would be better to find out the reason for the refusal.

An excellent example of a reactivation campaign is the Instacart food delivery service. The correspondence begins with an invitation to the company to download the app and try free shipping for five days using the popular artificial scarcity trick. Starting with the second letter, Instacart describes the benefits of using the service. In the sixth letter, when the five-day limit is exhausted, Instacart extends the free delivery period by adding another seven days. And finally, in the ninth letter Instacart, in addition to free shipping, promises a $5 discount on the first order.

Machine Learning Recommendation System

Algorithms take into account each user's preferences and actions and use them as the basis for predicting what users might like. Various models can be used to create recommender systems, such as collaborative filtering, custom collaborative filtering, expert model, and hybrid model.

Netflix from 2006 to 2009 used the Cinematch recommendation system that worked on the Pragmatic Chaos hybrid model, created by the BellKor team as part of the Netflix Prize. The result increased the accuracy of relevant recommendations by 10.06%, which seems insignificant only at first glance.

What did the recommendation system give Netflix? The audience began to watch more. This led to a decrease in churn, an increase in LTV, a service retention rate, and, accordingly, its revenue.

Virtual and Augmented Reality

Virtual reality took its first significant step when Facebook bought Oculus VR in 2014. In 2016, eBay and Australian retailer Myer were the first in the world to open the doors of a virtual store. All the consumer needs is a phone with any of the two operating systems, VR glasses, and the eBay Virtual Reality Department Store app. The purchase is formalized with a long look at the product.

IKEA has released the IKEA Place app. With its help, you can arrange furniture from the store's catalog around the apartment and assess whether the purchase is worth it. The app is available on both mobile platforms, but the number of supported devices is limited.

Convenient and Versatile Search

More and more large retailers, especially in the fashion and DIY sectors, are adding photo search. Its is convenient when you see a cool item that you would like to add to your interior or wardrobe, but you do not know how to describe it in words.

One of the first to add image search to the AliExpress app. It helps to cut off unnecessary goods somehow and navigate in the variety of offers.

Asos added image search to the app back in 2017. It determines the style of clothing in the picture and matches similar wardrobe items.

Voice Search and Virtual Assistants

According to recent statistics 51% of people use voice commands to find products, 36% to add to a list, 30% to track parcels, 22% to make a purchase, 17% to reordering.

In 2019, Walmart announced Siri integration. The voice assistant allows you to add items to your cart over a week gradually and then place an order for pick-up or delivery when the user is ready.

Walmart uses an order history for voice ordering. For example, if a user says “add orange juice to cart,” the Walmart app will check their purchase history to see what kind of orange juice they usually buy and add that particular item to their cart.

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