Hyper-Personalization: In 2024, hyper-personalization will become the norm. As an AI-first company, we are prepared for what’s to come. Advances in AI technology and sophisticated data analysis will allow businesses to create truly personalized experiences across all touchpoints including websites, social media, and email marketing. According to an October 2023 Gartner report, AI technology is poised to be recognized as a primary economic indicator of national power.  By leveraging customer data and predictive analytics, businesses can deliver highly targeted content, offers, and recommendations, creating deeper engagement and higher conversion rates.

Voice Technology: Voice assistants and smart speakers have become an integral part of our daily lives. With tools like Amazon Alexa, Siri, and Google Home, voice technology is predicted to become the primary interface for all digital interactions. It has been predicted by Statista that by 2024 there will be 8.4 billion voice assistants. By optimizing digital content for voice search, businesses can ensure easy accessibility and discovery of their brand through voice-friendly content.

Social-First Approach in E-commerce: Today, consumers increasingly turn to social media platforms not only for entertainment but also as a pivotal space for their shopping endeavors. The advent of TikTok Shop, coupled with its seamless integration with Shopify, has led experts, including those at Inc.com to predict a trend where numerous platforms will embrace similar strategies in 2024. This shift towards social commerce is anticipated to yield substantial profits for both individual content creators and businesses alike.

Data Privacy and Security:  Concerns regarding data privacy and security are paramount. In 2023, a Pew Research Center survey indicated that 67% of those polled said that they understand little to nothing about what brands are doing with their data. With data breaches and the deprecation of third-party cookies becoming increasingly prominent, it is vital for companies to collect first-party data while respecting consumer privacy. Striking a balance between data collection and privacy will be essential in maintaining customer trust.

At NetElixir, our team is dedicated to staying at the forefront of these trends and maintaining an AI-first mindset throughout our work. By embracing these digital marketing trends, businesses can forge meaningful connections, build brand loyalty, and drive growth in 2024 and beyond.

The post Ahead of the Curve: NetElixir’s Insights into the Key Digital Marketing Trends for 2024 appeared first on NetElixir.

Are you ready to face your high-value customers?

Download your copy of FACES 2023 now and read on below for our top learnings from this past year.

How High-Value Customers’ Online Shopping Behavior Has Changed

After diving into the data over the course of 2023, we noticed some interesting trends.

Role of Mobile

The role of mobile is gaining significance YoY, especially in the customer’s initial search. Mobile holds the most significance in the purchase journeys for Beauty and Cosmetics as well as Fashion Accessories high-value customers. The high-value customer for these three categories is a woman between the ages of 25 and 34; by Q4 2023, these shoppers were using their mobiles for more than 77% of first time visits and final purchases.

Customers’ use of a mobile device to search and shop is quickly growing month over month. Across the eleven retail categories we analyzed for FACES 2023, more than half saw an increase in percentage using mobile for a first visit and purchase in Q4 of 2022 compared to Q1. The high-value customers of Office Supplies, Pet Supplies, Sports Apparel, and Tools saw fluctuations throughout the quarter; Food and Gourmet saw a dip in mobile usage in Q4 after steady quarterly growth. Overall, mobile adoption continues to grow, especially during the holiday season so brands should ensure their site is mobile-friendly for browsing and check out.

Day Of The Week and Timing

Throughout 2022, high-value customers shopped throughout the entire week and throughout the day. With an always-on culture and sales quite literally at our fingertips, shoppings are almost constantly browsing, scrolling, and buying. Marketers should keep an eye on their website ativity to know when their customers are most likely to be active online to time promotions accordingly.

The Holiday Season Drives A Sense of Urgency

While this trend comes as no surprise considering the holiday rush, let’s dive into the data.

High-value customers across all categories except B2B, Medical Supplies, and Pet Supplies saw a latency decrease by more than 1 day from Q1 2022 to Q4. Latency decreased most dramatically for Tools’s high-value customer; he took about 3 days and 14 hours less to purchase during the holidays compared to pre-holiday. B2B and Medical Supplies’s high-value customer also visited the website marginally more times during the holiday season compared to the beginning of the year. 

E-Commerce Spending

Across our 2022 dataset, only Sports Apparel saw a steady increase in AOV throughout the year and spent the most during Q4 for the holidays; sportswear marked a good gift this year. 

E-Commerce spending is increasing during the non-holiday times compared to last year’s pre-holiday pandemic times. Except for Gifting and Fashion Accessories, holiday AOV were higher across categories compared to 2021 holiday season. People are getting more comfortable in their spending and budget compared to the prior few years. This can also be, partly, attributed to rising prices: the majority of high-value customers purchased fewer items per order in non-holiday 2022 compared to 2021. 

How Can FACES Help You?

The fundamental flaw in modern digital marketing is settling for attracting only average customers. By understanding the unique shopping habits of your high-value customers, you’ll reframe your marketing efforts to engage friends, not strangers. FACES is a high-value customer benchmark report; use these insights as a directional guide and compare your own high-value customer journey with our industry benchmarks. Identify any overlaps or areas where you can implement tests to learn more about your customers’ preferences. Use these insights to create more responsible and effective campaigns. 

By targeting high-value customers, marketers can increase the impact and reach of each marketing dollar. Leveraging high-value customer insights can improve the quality of signal inputs for machine learning algorithms that campaigns through Google and Meta are now employing almost exclusively.

So say no to average and use FACES to empower your customer acquisition and engagement strategy.

Methodology

We use our proprietary AI-driven digital marketing platform, LXRInsights, to collect and aggregate the customer data to create FACES. LXRInsights relies exclusively on first-party data. We aggregate the daily data for each quarter throughout 2022 to learn how online buying behavior changes during the year and, especially, the holiday season. FACES 2023 marks the fifth edition of the book.

We turned our focus onto high-value customers after noticing that a small percentage of shoppers account for a large portion of e-commerce revenue. High-value customers can generate as much as 3-5X more revenue than the average customer! Their unique shopping experience within your industry reflects their unique value. 

FACES exists as a benchmark to show you our industry’s high-value customer. It encourages you to think about the customer journey of all your customers and how you can better meet your high-value customers’ needs and expectations.

Further Reading

• The Future of Digital Marketing
• Understand Your Customer Lifetime Value
• Repeat Customer Prediction

Updated March 20, 2023 with new data. Originally published February 25, 2022.

The post The FACES Of Your High-Value Customers appeared first on NetElixir.

As marketers, we have to balance consumer privacy expectations with satisfying the needs of the brands we help. That means understanding what privacy means to consumers and what it takes to comply with privacy regulations so we can help brands meet those needs and expectations. But it also means doing what marketers do best – and what both businesses and consumers expect from us: getting the right brands in front of the right potential customers with the right message.

How Can Marketers and Brands Meet Consumers’ Online Privacy Expectations?

While some consumers understand that cookies and other data tracking can make their online shopping experience smoother and more relevant, they now expect to browse and make purchases online with more control over their data and more transparency over how it is used. Apple, Google, Firefox, and other companies are giving consumers more control over what data is shared, and well as how, when, and if it may be used. Marketers and brands would do well to follow their lead with their own data collection – and they need to be transparent with consumers about that data collection. Meeting these consumer expectations is one key step to building trust with potential customers. 

Marketers and businesses have to ensure their data collection practices meet regulations and laws, and we can continue to earn customer goodwill by going above and beyond to find new ways to deliver on protecting consumer privacy. Google is already pointing the way forward with their work on the Privacy Sandbox, which seeks to create a privacy-first web that still allows marketers to reach consumers with targeted marketing. As other industry leaders come up with similar developments, marketers must help businesses take advantage of these advancements.

How Can Brands Reach High-Value Customers While Preserving Consumer Privacy?

Privacy is important to consumers! But even consumers who want control and transparency around their personal data still expect a useful web experience. And businesses continue to want ads to be both relevant and timely to potential high-value customers. Marketers provide a service to both companies and to consumers by helping connect brands with the customers who actually want to buy their products. But with third-party cookies going away, it’s up to marketers to find new ways to make this connection happen.

While some ad tech companies have come up with tracking options that mimic third-party cookies’ ability to identify individual consumers — which may violate the spirit of consumer privacy expectations even if these options don’t run into trouble with regulations — Google has come out to say that once third-party cookies are gone, they won’t create “alternate identifiers” for tracking individuals across the web or for use in their products. Instead, Google is developing ways to create cohorts of individuals with common interests, as well as exploring how to use data stored on consumers’ devices to connect them with relevant content all while protecting privacy and identifying information. Chrome aims to give users more control over their privacy settings and we can expect other industry players to strive for this, too.

Developing strong relationships directly with consumers is one of the best ways to ensure a business can collect the data it needs to successfully market to its customers. Customers are more likely to consent to brands they know and trust accessing and storing their data, so making every effort to build that trust is in a business’s best interest. By taking steps like instituting brand loyalty programs and creating compelling content for web and social media, your business can reach new and existing customers and market to them directly with the products and services that will most interest them.  

How Can NetElixir Help My Business Meet Privacy Needs While Reaching Customers?

NetElixir stays on top of industry updates so your e-commerce business is always ahead of the curve. We’ll make sure your business is complying with privacy regulations and laws while taking advantage of the newest tools and opportunities to meet consumer privacy expectations while serving up digital marketing campaigns that will put your brand in front of the right customers. Whether it’s time to switch over to Google Analytics 4 so you’re ready for third-party deprecation, or you need a web presence refresh to make sure your UX is on point while complying with the latest regulations in your market, NetElixir is here to support you.

Ready to Learn More?
Contact NetElixir to discover how we can help your e-commerce business meet consumer privacy expectations while developing marketing strategies that will effectively reach your best customers in the post-cookie world.

Additional Resources:

• Go Organic: Your SEO Strategy In The Cookieless World
• Christmas in May: Your Holiday Strategy in the Cookieless World
• Navigating Apple’s iOS Privacy Updates

The post How Consumer Privacy Expectations Are Shifting appeared first on NetElixir.

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.

Further Reading

• Our latest e-book: Preparing for a Cookieless World
• NetElixir + Google webinar: The Future of Analytics and Thriving in A Cookieless World
• Preparing for the Cookieless World With Google Analytics 4

Updated June 1, 2022

The post Loyalty Programs: Why Brand Allegiance Is More Important Than Ever appeared first on NetElixir.

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.

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.

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.

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 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.

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.

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.

Further Reading

• Using Data to Guide Your Content
• Insights Into the DTC E-Commerce Explosion
• Managing Your Paid Search Portfolio

References

[1] Why it is important to calculate CLV at an individual level? https://retina.ai/blog/why-calculate-clv-at-the-individual-level/

[2] Peter S. Fader et al., “Counting Your Customers” the Easy Way: An Alternative to the Pareto/NBD Model, 2004, Marketing Science Journal, Volume 2, Issue 2, 275-284

[3] Figure 1: https://antonsruberts.github.io/lifetimes-CLV/

The post Understanding Customer Lifetime Value for E-Commerce appeared first on NetElixir.

These digital-native stores were uniquely positioned during the coronavirus pandemic to weather the near-total shift to online shopping because they didn’t have to change their business models. They were already able to meet customers where they were. The changing e-commerce landscape has opened tremendous opportunities for more and more brands to reach their customers directly and cater to their online shopping behaviors. While the e-commerce boom certainly heightened customers’ interest in DTC brands, the customer experience of these brands is what truly gives them an edge compared to other retailers.

The DTC E-Commerce Explosion

eMarketer forecasts that DTC e-commerce is on the path of exponential growth. Sales from DTC brands currently account for 14% of total retail e-commerce sales and consumer interest in DTC brands is rising, as detailed:

A digital presence is becoming increasingly important for all brands, not just as a way to sell to consumers, but to communicate and engage with them. Direct engagement with customers will play an especially crucial role in the cookieless world. 

More consumer demand — greatly accelerated by the coronavirus pandemic — is driving the DTC growth as customers like the direct engagement and community aspect of interacting with DTC brands.

The DTC Experience

Luckily, there is no singular way to become better online. To adopt more of a digital-first strategy, brands should look to those with a similar product strategy and a strong digital approach, rather than simply looking to copy bigger online brands like Amazon and Walmart. While these bigger brands are seen to dominate the digital commerce landscape, your brand’s digital approach should fit your own strategy and objectives.

The key to a good DTC experience is customer engagement. The DTC model gives brands better control over every moment of a customer’s experience. With better control comes more nuanced insights behind customer motivations and behaviors at each and every touchpoint. Aggregating this data to learn more about customer’s interactions will better inform the overall marketing and business strategy, to be able to create that frictionless experience that will encourage a customer to return again and again.

For customers, DTC is about a direct relationship with the brand their buying from. Customers are looking for a value match from the brands they shop from, both in terms of monetary value and social causes. Because they have so many options available, customers can afford to be choosy about who they buy from.

In regards to the overall DTC journey, Google shared the following consumer stats with NetElixir:

• 57% of consumers say they go out of their way to purchase directly from brands
• 55% of DTC online shoppers are under the age of 45
• 39% of consumers say they are constantly experimenting with different brands, while 61% are loyal to specific brands

So why are shoppers turning to DTC brands more and more? According to Google’s data, it’s about a better value perception: 68% of online shoppers say individual brands offer better quality than third-party retailers and 61% say that individual brands provide a more personalized and authentic experience. 

As DTC brands learn from their customer’s engagements and perceptions, they can improve their site experience and product offerings to appeal more to those customers. 

What Drives Shoppers to a Specific Brand?

To shoppers, quality, experience, and values drive brand love. They turn to DTC brands because DTC offers:

• Consistent quality standards.
• Assurance that brand values are in line with the shopper’s own.
• Superior shopping experience and more informed customer support.
• A better understanding of the environmental footprint of the shopper’s purchases.

Some drawbacks to DTC brands include an increased difficulty comparing prices and products across brands, a smaller selection, and less access to discounts and promotions. However, strong communication between brands and customers, sharing their values and quality, can go a long way in winning over customers. More informed customer support informs the brand directly about what customers are looking for and helps the brand target customers more precisely.

For DTC brands to be successful, they need to be vocal about their values and missions. They need to share original creatives that tell their brand’s story and the social causes they actively support. Because DTC e-commerce is online-first, brands have simple and frictionless shopping experiences. Additionally, DTC brands deliver experiences, not just products. They invite consumers in to be part of the brand’s community and story. Personalized messaging accrued from first-party data helps these brands personalize their customers’ experiences and win new customers. It is a unique experience that relates back to the brand story and value at each and every touchpoint along the customer’s journey so customers know exactly who they are shopping with.

Steps to Elevate Your Brand Experience

Some next steps to help you improve your DTC experience are:

1. Create clear messages so consumers can learn more about your brand story and values.
2. Ensure your brand is discoverable by using non-branded keywords when customers are researching products you sell.
3. Have an easy-to-navigate site through a clean design, clear directions, and a curated selection of products.
4. Begin building your first-party data foundation by collecting insights from site navigation, online sales, and omnichannel interaction.
5. Create opportunities for loyalty and cross-community relationships (which will also give you rich first-party data).
6. Lean on your first-party data by evaluating the more effective strategies for gathering data about your customers.

How NetElixir Can Help

We help brands amplify their values through responsible communication. Our proprietary customer analytics platform, LXRInsights, connects brands with their customers to give deeper insights into their online shopping journey. Request a demo today to start learning about your customers, like what time of day they are more likely to shop and how frequently they make a purchase.

With better insights into your customers, you can craft creatives that engage your target audience and authentically share your brand’s story. Contact NetElixir to see how we can elevate your content strategy to boost your overall brand experience.

Read More

• Using Data to Guide Your Content
• Repeat Customer Prediction of New E-Commerce Customers
• SEO Strategy to Responsibly Engage Your Customers

The post Insights into the DTC E-Commerce Explosion 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

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