How eCommerce companies can build better data-driven marketing strategies
eCommerce, fueled by increasing technological development and globalization, has seen steady growth over the past decade. COVID-19 further boosted this trend—even the most ardent brick-and-mortar advocates were suddenly forced to buy goods online. Direct-to-consumer (DTC) eCommerce is a major part of online retail today, and it is driven heavily by how well companies can engage with their customers for repeat business. eCommerce companies are constantly looking for better messaging, different ways to attract consumers, and techniques that will drive repeat buyers and increase the lifetime value of each shopper. There is fierce competition among eCommerce vendors which makes capturing the customer’s attention and dollars more difficult. eCommerce companies understand the value of data in making marketing decisions and are seeking to be more data-driven. Data-driven marketing means discovering insights based on in-depth analysis informed by data to get a better understanding of customers’ motivations, preferences, and behaviors. This method helps companies to optimize marketing channel performance and enhance their user experience. eCommerce companies are starting to turn more towards data science, machine learning, and artificial intelligence algorithms to help them with in-depth analysis and to make more accurate predictions. In order to build the most accurate machine learning and predictive analytical models, they need to incorporate external data. When marketers are able to build predictive models that have been trained with internal data and relevant external data, they are also able to build more successful marketing strategies. The right data enhances marketing tools, marketing campaigns, and customer experiences. Being data-driven means looking beyond the four walls of the company in order to obtain the data that will help build a more comprehensive picture of prospects and customers. Everyone is talking about big data, but how can it be leveraged for digital marketing campaigns and marketing spend optimization?
Data, in particular external data, is becoming an essential tool in the marketing decision-making process. Marketers not only need to make sure that the data is relevant but also GDPR and CCPA compliant. This post will go over how external data can help digital marketers at eCommerce companies build more successful and accurate data-driven marketing strategies. The use cases this article covers are customer lifetime value (LTV), ad retargeting, customer segmentation, and personalization.
How can external data help predict customer lifetime value (LTV)?
Targeting the right audiences is an essential aspect of a marketer‘s job. The customer journey can vary greatly depending on the type of customer. Some are low-maintenance, some are loyal, repeat customers, while some are neither. The goal is to target the customers that will spend the most, but cost the least to retain. Predicting user behavior early on in the funnel and identifying those users most likely to have a high LTV can transform both acquisition as well as retention marketing campaigns and drive a significant improvement in marketing efficiency. However, identifying who the high lifetime value customers are isn’t always obvious. Building a system to determine different customers’ lifetime values helps marketers understand who repeat customers are, and who is worth focusing more marketing spend on.
Marketers typically rely on historic data to try and calculate which customers will have the highest LTV. Internally sourced historical data can provide part of the picture on whether a user is expected to have a high LTV. Understanding the right target audiences for eCommerce companies can be especially challenging given the broad range of potential customers that they can reach online through a multitude of marketing channels.
Customer data need not only come from within the organization; it should also come from other data sources. Incorporating third-party data will create a better picture of the customer and help produce more accurate predictive models.
Some examples of data from external sources that can be used for repeat customer forecasting and LTV prediction are:
- Customer purchase trends in terms of business vs. personal
- Individual online shopping behavior and purchase history
- Social media activity and reviews that show positive attitudes towards products
- Alternative credit scores that can indicate purchasing potential
- Online search queries related to certain products
- Online behavior on review sites indicating positive attitudes
- Demographic information about potential new customers
Predictive model optimization for repeat customers and LTV means that marketers can better understand if, following an initial sale, the buyer will make another purchase. This allows them to place customers on different tracks for marketing campaigns and email marketing (nurture campaigns). For repeat buyers (customers who are more likely to continue spending) the marketing team can prioritize them and put them on more aggressive tracks, while taking longer, more subtle tracks with those whose LTV is not as high. The potential results of these focused marketing efforts are increased repeat buyers, improved customer retention, and reduced cost per lead.
How can external data help with ad retargeting optimization?
Online ads play a vital role in attracting potential customers. They are especially important for eCommerce companies, where potential customers can click on an ad and make a purchase instantaneously. Retargeting is a powerful marketing strategy, especially when coupled with product catalog based ad types such as Facebook Dynamic Product ads and Google RLSA. Understanding which behaviors, actions, and interactions could indicate a potential customer drives where to best allocate marketing spend. Ad retargeting campaigns can also boost conversion rates, as customers typically need to see an ad more than once before making a purchase from a new brand. The “cardinal” rule of advertising, known as the “The Seven Times Factors“, is that potential customers need to see an ad seven times or more before they buy. Experienced marketers know that the chances of converting a prospect into a customer at their first interaction with their brand are extremely low. Retargeting website visitors that didn’t complete their purchase can increase customer engagement and conversions.
Choosing who to focus retargeting efforts on is an important marketing decision. The most successful retargeting campaigns are the ones that target the visitors most likely to convert. eCommerce companies see a lot of abandoned carts, and customers who only make one purchase, however not all of these opportunities are worth spending money on retargeting with ads. Internal historic data doesn’t provide all of the information required to decide which customers to retarget. Most eCommerce companies’ data collection consists of their website visitor behavior, customer email addresses, IP addresses, and additional contact info (if a form was completed). When building a predictive model for ad retargeting purposes, including external data will make it more accurate. Companies can combine their internal data with external datasets to identify the customers most likely to become repeat shoppers and prioritize marketing spend towards these high-value targets. Companies can take the data they have on their website visitors and enrich it with external data to learn more about their existing and potential customers.
Some examples of data from external sources that can be used for ad retargeting are:
- Location-based data such, as zip code, to understand high-value targets based on property values and income levels
- Geospatial financial indicators that show a propensity to make more than one purchase
- Time-series data that indicates repeat visitors and the likelihood to purchase based on clicks
- Social media habits such as the likelihood to be influenced by it
- Spending habits such as the most frequent retail purchase category
Retargeting the right customers will increase conversion rates. In fact, at Explorium, we have seen customers increase their conversion rates by upwards of 300% by incorporating external data into their ad retargeting models.
How can external data help with customer segmentation and personalization?
80% of repeat shoppers only purchase from brands that provide personalized experiences, while 66% of consumers would deliberately not purchase from a company that failed to personalize their buying experience.
Amazon, Netflix, and Google have forever changed the game. Personalization is now an expectation—not a nice-to-have. There’s more competition than ever before to provide seamless, fully optimized, and personalized customer experiences.
Website personalization means adapting the site dynamically depending on who is viewing it. Each visitor sees a version that reflects their needs, wants, and behaviors. For example, Amazon has a recommendation section for each product for other items that customers frequently buy together. Retailers can make suggestions based on things like demographic data, location, season, and past browsing or purchasing history.
Before creating a successfully personalized online customer experience, eCommerce companies must segment their audiences. One of the biggest challenges marketers face is understanding who their customers are and why they buy. It is important to know this in order to send the right messages and create customized experiences. This is especially important for the eCommerce companies serving a variety of audiences with different products and/or brands. eCommerce companies typically segment their customers based on information they can collect internally such as which ads customers clicked on, the products viewed on the website, their level of engagement on the site, if they signed up for a newsletter, or any other form submissions. eCommerce companies might already have an abundance of website marketing data, transactional data, and some data from google analytics. By enriching their existing data with external data, they gain more nuanced and accurate insights. Better insights help create more comprehensive customer profiles and improve customer segmentation.
Some of examples of data from external sources that can be used for customer segmentation:
- Social media interactions with the product and others in the category
- Number of previous purchases in the same category
- Spending potential and financial stability metrics
- Demographic data including cohort group preferences
- Search engine queries in related fields
- Online purchases within a certain amount during a specified time period i.e. number of purchase over $30 made in the previous 6 months
Better segmentation means better personalization when it comes to website experiences, ad campaigns, and email campaigns.
Case Study: How glassesUSA.com used external data to improve customer segmentation
GlassesUSA.com is a fast-growing online retailer, serving a mass market with a broad variety of products. Over the years, they have made growth and innovation a priority, but wanted to improve their customer segmentation to provide a better user experience. They were interested in leveraging external data and wanted to use a tool that would help with the automation of external data discovery and the integration of it with their existing data. They were already using data from customer interactions with their website, marketing platforms, transactions, and Google Analytics to help them segment based on age, price-point, use, and predicted user intent and needs. They were making data-driven decisions, but wanted to create more accurate predictive models that they could scale.
They decided to use Explorium for their external data acquisition and to train new machine learning models. They were able to use various types of geospatial data (demographic data and proximity to alternatives/competition) which gave them the ability to segment their audiences based on different parameters. They could then provide a different experience for each audience and offer smarter shopping experiences in terms of what products, services, and upgrades a particular user sees. It also helped them build better customer relationships.
This mechanism is at the heart of their business, essentially dividing their pool of customers into groups that behave differently. This has increased both conversion rates and order values leading to a 15-20% increase in the per-session value. GlassesUSA.com used Explorium and external data to build many segmentation models which help them provide a personalized shopping experience for every type of client.
Making the case for external data in data-driven marketing
“Data-driven marketing” is certainly an industry buzzword. It sounds great in theory, but will only be beneficial with relevant, comprehensive data that is GDPR and CCPA compliant. Accurate predictive models need to be trained on the right data for the specific business problem. As they say, garbage in, garbage out. Internal data can be useful, but needs to be enriched with external data to get the full picture. With the right external data platform, organizations can gain access to all of the relevant external data needed to build more accurate predictive models in-house. It is beneficial for marketers to build analytical models in-house, because it can help them build high-performing marketing campaigns faster. Predictive models need to be updated, and with the right platform, external data sources should be updated in real-time. When it comes to data-driven marketing, it is not always about the amount of data, but the quality of the data. High-quality data comes from outside the organization, and gaining access to it can be a huge asset for digital marketing and eCommerce.
Explorium provides the first External Data Platform to improve Analytics and Machine Learning. Explorium enables organizations to automatically discover and use thousands of relevant data signals to improve predictions and ML model performance. Explorium External Data Platform empowers data scientists and analysts to acquire and integrate third-party data efficiently, cost-effectively, and in compliance with regulations. With faster, better insights from their models, organizations across fintech, insurance, consumer goods, retail, and e-commerce can increase revenue, streamline operations and reduce risks. Learn more at www.explorium.ai