In today’s hyper-competitive landscape and overwhelming options, brands need to be more selective about attracting and engaging the right customers. The right customer is someone who will return to and recommend your brand again and again. The right customer spends a lot with your brand frequently and is a brand loyalist and evangelist; brands should know this brand loyalist’s interests, online shopping habits, and preferences well to consistently provide them an experience they expect. This right customer is what we call a high-value customer, whose continual engagement leads to a higher lifetime value per customer.

Identifying and understanding the right high-value customers for your brand will help you to sustainably and predictably grow your business.

What Is a High-Value Customer?

A high-value customer spends 3-5X more than average customers and is a crucial brand loyalist. High-value customers make shopping from your brand part of their routine and are more likely to be brand evangelists. They purchase large orders from your brand more frequently.

Customer segmentation is important, as each customer has their own unique habits and behaviors. By exclusively tracking the channels, items, and habits of your high-value customers, you’ll be able to build a profile that is as unique as they are. Brands can better realize the role they can play in a customer’s day-to-day life by understanding the circumstances surrounding the customer’s journey and the choices they make online. With an extensive high-value customer profile, you’ll be able to learn the strategies that can attract and engage more high-value customers, rather than just average customers.

Meaningful customer engagement and retention relies on responsible marketing. By marketing to and for your high-value customers, you can recruit more brand loyalists. You’ll change your marketing strategy to target friends, not strangers. 

Why You Should Market To Your High-Value Customers

The fundamental flaw of modern digital marketing is being comfortable attracting only average customers. But the average customer does not exist; they’re a mathematical impossibility. Say you have three customers purchase from you in one session; one spends $200, the second $20, and the third $75. The AOV is $97. So you create a marketing campaign around the average $97; but because no customer actually spent $97, you’re effectively marketing to a nonexistent target.

If you create a profile, however, around the $200 shopper, then you’re marketing around someone real. You create a journey map around this high-value customer and learn more about their interest, behaviors, and expectations. Now, you create a marketing campaign that meets this customer at the touchpoints they frequent most, with products that address their specific needs at a time they’re most likely to be searching and shopping online. Suddenly, your marketing campaigns are more effective and responsible. Your campaigns are now targeted for customers who are similar to your $200 shopper and thus more likely to purchase from you.

You continually refine your high-value customer profile to engage and nurture brand loyalists to achieve above average growth.

Say No To Average With LXRInsights

LXRInsights is our customer analytics tool that helps you identify and engage your high-value customers. The idea for the tool started with a basic observation that a small percentage of customers drove a higher percentage of revenue share. We noticed that four to ten percent of customers were driving over 40% of revenue. 

From that observation, we cemented a directive: if we can help brands identify those high-value customers early and engage and win them, then each marketing dollar spent would generate 3-5X more ROI. 

At NetElixir, our priority is humanizing every click. By creating personas around your high-value customers, showing their product preference, frequency of purchase, and more key indicators, we can better understand who they are. Customer journeys are crucial to a brand’s marketing strategy. High-value customers have their own unique paths that lead them to your brand. Marketing strategies should take each unique journey into consideration to ensure the right messaging at the right touchpoint. Using LXRInsights, we can track the holistic customer journey, from first website visit to final purchase. With a complete understanding of their journey, you can learn which channels, products, messaging, and more are most effective at converting high-value customers and continually optimize your strategy to win more like them.

LXRInsights relies exclusively on first-party data, as you know your customers better than anyone else. Customers who share their first-party data are more likely to have a deeper connection with a brand and a desire to want to buy from them again, given the right time and products.

Join the world of responsible marketing by understanding your customers’ unique journeys and making every brand dollar go further. Schedule a demo of LXRInsights to learn more about your high-value customers.

Meet your industry’s high-value customer with our latest customer insights report, FACES!

Further Reading

• Understanding Your Customer’s Lifetime Value
• Insights Into the DTC Explosion
• Repeat Customer Prediction of New E-Commerce Customers
• Online Shopping Behavior of Gifting Industry High-Value Customers

The post Who Is Your High-Value Customer? appeared first on NetElixir.

The one-time buyer problem is one of the most widely known challenges for retail e-commerce brands, as it is difficult to identify the potential repeat customers from only one purchase instance. E-commerce companies often acquire new customers in large numbers through intelligent marketing strategies, but it’s imperative to identify which of these customers are likely to repeat purchases.

Our objective of building a repeat buyer prediction model into LXRInsights, NetElixir’s proprietary customer analytics platform, is to anticipate potential repeat customers among the one-time buyers in the last two months’ duration. Our solution is twofold: one, brands can start accruing loyal customers, and two, retaining existing customers costs less than gaining new customers.

Understanding Your Customer’s Buying Behavior

Through LXRInsights, we track customer’s paths-to-purchase from the first point of attraction to purchase to determine what behavior leads them to conversion. The transaction data we collect includes the time and day that the customers made their purchase, the number of items in the cart, the average order value of the purchase, the referrer or the channel that led them to purchase, the device, and more. We interpret customer profiles from the transaction data that unveils the customers’ purchase patterns. Based on this profile, we use a machine learning model to predict whether a first-time buyer will repeat their purchase. 

Request a full demo of LXRInsights for a holistic understanding of how to grow your business responsibly with data-driven insights.

How Your Customer’s Purchase Journey Trains Our Machine Learning Models

LXRInsights needs key features of the customers’ online shopping behavior to train the model accurately. The accuracy of the model depends on the relevance of the features generated. The following list describes some of the features generated for this model:

1. Pageview information such as the total number of pageviews or average daily pageviews made before completing a purchase.
2. Visit action counts to determine the number of website visits per timeframe of the purchase cycle, from first website visit to purchase.
3. The order information, which includes product diversity, number of items in cart, and order value.
4. The top five browsers, top ten first and second referrers, top landing day of the week, top landing hour, and top checkout hour.

For a sense of how we segment this customer data, download our FACES 2021 report, which catalogs the online shopping behavior of high-value customers across ten e-commerce industries, such as food and grocery and online gifting.

Machine Learning Model

Machine learning algorithms come in handy to accomplish our objective of predicting potential repeat customers. By following the ritual of training ML models, we separate the data into two parts —Training Set to train the model and Test Set to test the model’s accuracy.

Pseudo Code of the Model

Below is the algorithm our model uses to predict the likelihood of repeat customers among first-time buyers:

1. Collect data from the past thirteen months.
   
2. Label the customers with repeated purchases as “Repeat Customer” and the customers with single purchases as “Non-Repeat Customer” for a baseline of your brand’s existing customers.
   
3. Chart relevant features of each segment’s path-to-purchase as outlined above.
   
4. Separate the data into two parts as Train and Test data.
   
5. Train the model by using a set of ML algorithms.
   
6. Measure the accuracy of the model by using Test Set data.
   
7. Select the model with the highest accuracy.
   
8. If the accuracy value is high, share the list of anticipated potential repeat customers.
   
9. Provide the features of the predicted customers that could be used to retain and retarget them.

Conclusions

We use the past transaction data to generate the relevant features of the customers and train the model. By understanding your customers’ buying behavior, your brand can better ascertain repeat customers among your current one-time buyers. You can then generate more personalized campaigns to re-engage these potential repeat customers with your brand and possibly convert more loyal customers.

Visit LXRInsights to request a demo of our customer analytics platform to better understand your new and repeat customers’ online shopping behavior.

References

1. Guimei Liu et. al. “Repeat Buyer Prediction for e-commerce” 2016, KDD August 13-17, San Francisco, USA.
2. https://www.custora.com/blog/four-steps-to-solve-one-time-buyer-prob

The post Repeat Customer Prediction of New E-Commerce Customers appeared first on NetElixir.