Customer Behavior Archives - NetElixir

Loyalty Programs: Why Brand Allegiance Is More Important Than Ever

NetElixir — Wed, 20 Apr 2022 13:23:24 +0000

Your best customers are your brand’s biggest fans. They don’t just purchase your products or services, they also tell all their friends about how much they love your e-commerce business. They are your cheerleaders and your word-of-mouth marketers. And customers who know and trust your brand are more likely to also trust you with their data. In a world where privacy concerns and regulations mean that third-party cookies are no longer a reliable source for customer insights, your business will require access to zero- and first-party data to drive your decisions around marketing and customer experience. US marketers have already recognized the need to invest in first-party data, and 57% of them plan to incentivize consumers to opt in to the tracking and sharing of personal information. E-Commerce businesses like yours must strengthen the bond with their top customers in order to retain them, and one way to do that is through creating a loyalty program.

Why Should I Consider Offering a Loyalty Program for My E-Commerce Business?

To boost customer retention and customer lifetime value, loyalty programs provide personalized offers and content that encourage ongoing customer engagement with your brand. Your loyalty program is both a method to collect key data to inform your marketing strategy and a distinct channel for direct, targeted communication with your high-value customers. While effective incentives for allowing access to customer data vary by age and generation, free gifts, loyalty rewards, giveaways, and exclusive content are far less effective incentives for customers than discounts and free trial offers. Customers want to experience your brand at low or no cost, and they are willing to provide access to personal data like their email address to do so. A successful loyalty program is mutually beneficial, providing businesses with customer data that will help direct their strategy and presenting customers with valuable incentives that meaningfully impact their connection with your brand. Your loyalty program should be a smooth, curated experience for the customer that guides them through exclusive offers tailored to their interests.

By collecting contact data points like email addresses and cell phone numbers, demographic and location information, and brand app and site browsing history, you can determine what products and services appeal to particular customers and connect with them about incentives. This data influences which offers and messaging you share to reach these customers across channels, from app push notifications and text messages to email outreach and social media ads. You can also use what you learn about your customers to build a strategy for reaching new customers with similar profiles. As third-party data disappears, developing loyalty programs and other means to capture first-party data will have a critical impact on your business’s ability to create effective digital marketing campaigns.

During Google Marketing Live 2022, we learned that shoppers are 77% more likely to chose loyality brands over other businesses. Within the next few months, Google plans to showcase a brand’s loyalty benefits directly on results pages. Existing loyalty members will see what rewards they are eligible to use directly under a product or offer. Performance Max campaigns can use a customer match of existing members to find and target potential new loyality customers.

We foresee that loyalty programs will be especially important for D2C Brands competing against bigger industry players who resell their products. NetElixir’s Trends team has also found that Millenials especially value loyality programs and earning points through their purchases. Relying on key shopper insights and brand differentiators will go a long way to helping a brand succeed.

How Can NetElixir Help?

Two of the biggest barriers to businesses creating loyalty programs are competing priorities and lack of tech support, according to eMarketer. But with the death of third-party cookies, the data that can be collected through a loyalty program is more important to your bottom line than ever before. NetElixir has the experience and the resources to guide you in designing your loyalty program with analytic tools that will produce the customer insights you need to drive sales.

NetElixir works with you to determine what data analytic options will be most useful for your business. Our proprietary technology, LXRInsights, is a powerful tool for tracking, analyzing, and targeting your high-value customers and engaging more like them. LXRInsights allows you to match unique user IDs to specific datasets about products particular customers are buying, including how often those customers are purchasing from your business. Loyalty program customers can maintain their privacy on your website and app while still allowing you access to useful data around their spending habits. LXRInsights also empowers you to easily retarget customers who are likely to buy from you again.

NetElixir can lead the web development process for your business to make your site as customer-friendly as possible, and we can use the data you acquire through your loyalty program to develop customer personas and content that enables you to reach your high-value customers across all channels. We can help you partner with technology vendors to gather and use data in a manner that complies with industry standards around privacy such as adhering to customer consent choices and protecting customer information, while using advanced tracking to capture data on conversions.

Contact NetElixir today to learn more about how we can help your e-commerce business succeed in the cookieless future.

Understanding Customer Lifetime Value for E-Commerce

Ayangleima Laishram, Research Scholar — Thu, 05 Aug 2021 08:20:24 +0000

Understand Your Customer Lifetime Value To Allocate Resources Wisely

Customer Lifetime Value (CLV) is a metric that indicates how much money a customer spends with your brand throughout their lifetime. Knowing the lifetime value of an individual customer helps companies optimally allocate personalized budgets as a part of their retention strategies. In the retina.ai blog, its CTO and Co-Founder, Brad Ito, explains how understanding a customer’s lifetime value at the individual level is beneficial to the business[1], both to maintain a careful budget and lasting customer retention strategy. Among several advantages of CLV applications, understanding and forecasting your customer’s lifetime value will help minimize wasting retention ad spend to highly valued customers who are already engaged on their own.

Customer Lifetime Value is an additional metric analyzed in LXRInsights, NetElixir’s proprietary customer analytics platform, that identifies your future profitable customers. LXRInsights segments your customers into high-value, mid-value, and low-value customers, tracking their purchase journeys from the first point of attribution to final purchase to help you better understand the nuances of their unique journey. Our CLV model built into LXRInsights projects which customers are likely to become high-value customers, who spend 3-5x more with your brand than average customers.

Our objective of calculating CLV at the individual level is to identify the value of each customer and anticipate the customers’ spend; from there, we help design a suitable retention strategy to help turn shoppers into loyal, repeat customers.

Background of Customer Lifetime Value Models

Fundamentally, CLV models are different for contractual (e.g. Subscription) and non-contractual (e.g. Retail) business models. Knowing customer churn is one of the most important factors in calculating CLV, but it is unknown in retail business models. Therefore, the BG/NBD method, a probability-based CLV model, proposed by Peter Fader[2] is used to tackle both the purchase and churn probability of each customer in retail transactional data. Each customer’s purchasing and churn behavior is unique in retail; thus, the model calculates CLV at the individual customer level to use the metric at its full potential.

LXRInsights Customer Lifetime Value model

In LXRInsights, we adopt the BG/NBD model proposed by Peter Fader, calculating CLV for the next twelve months. We specify twelve months because it makes more sense from a business model standpoint to know the value of customers in the near future to better adjust ad strategy so each campaign is impactful. Our predicted twelve months CLV, also called truncated CLV, represents the expected present value of the customers for the next twelve months, based on the customer’s past thirteen months of transactional data. The basic information required to calculate CLV is frequency, recency, age, and average order value (AOV) of the customers.

Diversity of Customer Behavior

The BG/NBD model handles the diversity of customer behavior in purchasing and churn at the customer level. Figure-1[3] below shows the diverse online shopping behavior of five different customers. Each bubble represents one order and the bubble size indicates the amount of money spent. The blue and red customers have regular intervals of purchases but do not purchase frequently. The orange customer made several small purchases in a short period and never returned. The green is a recent customer who hasn’t spent a significant amount until recently.

Figure 1: Diverse behavior of different customers
Image from https://antonsruberts.github.io/lifetimes-CLV/

At the end of Month Four, predicting who will churn and who will be retained and turn into the most loyal customer is challenging. The model predicts the future worth of such customers with different behaviors and includes an estimate of which customers might churn; in this scenario, the orange customer is the most likely to churn. The model also predicts expected AOV on an individual level that would help you to wisely allocate your budgets for any marketing strategy. For example, spending a lesser amount to retain the green customer, and a higher amount to retain the blue, red, and yellow customers with respect to the predicted AOV would be an optimal retention strategy since the green customer is a recent customer who has spent less compared to the rest. Ensuring the whole shopping experience, from initial search to receiving their package, is a pleasant, seamless, and exciting experience that will help customers remember your brand.

Experimental Results

Our CLV model predicts the CLV, expected AOV, and expected number of orders for the next twelve months. Tables 1, 2, 3, and 4 below show some results of the CLV for different customers. The high age indicates an old customer whose first purchase was a long time ago, while the lower age shows a more recent customer.

Referring to table 1, “user_3” has a high age and recency, which indicates that the customer is an old customer and still recently bought from the business. Such customers have a high CLV, as they are loyal customers.

Table 1: CLV data of high-value customers

Referring to table 2 below, we see a group of recent customers who are active and thus are assigned a good CLV by the model.

Table 2: CLV data of recent customers

Table 3 below shows customers with a mix of low and medium CLV. For “user_7” in table 3, there is a large gap between recency and age, indicating that the customer did not return after placing a few orders. Because of this gap, they are assigned a low CLV and predicted as churned by the model.

Table 3: Data of customers with projected low and medium CLV

The customers in table 4 have similar behavior to each other in regards to frequency, age, and AOV, but they are assigned different CLVs. The difference lies in that “user_9” never returns after placing a few orders, but “user_8” returns every so often for similar purchases.

Table 4: CLV data of different behavior

Conclusion

Customer Lifetime Value unveils hidden information about your customers. According to past data, customers who are in the high-value segment now need not necessarily be profitable in the future. A customer in the medium-value segment could have a higher CLV for whom you need to run special campaigns to help them become high-value customers. Realizing the expected value of the customers at the individual level will help business leaders better allocate budgets and strategically retarget the right customers at the right time to encourage them to become loyal customers.

Visit LXRInsights to request a demo of our customer analytics platform to understand your customers’ future profitability better.