Email Marketing Isn’t Over – But It’s Not What It Used To Be

How many times a day do you go into your email inbox and clear out all the spam? For many people, this is a frequent practice. In fact, the average consumer receives over 120 emails a day, and according to Data Prot, on average 85% of these emails are spam. Findings like these have sparked an ongoing debate about the success of email marketing for retailers. Is the age of email marketing really over?

Despite the increased number of spam emails and the growing demand for other retailer touchpoints like SMS and app notifications, email marketing is very much alive. The average email open rate in retail is 32%, and retailers have several opportunities to grow this percentage.

The best way to increase open rates and click-through rates for emails is personalization. In a recent study, Adobe found that emails with personalized subject lines receive almost 20% higher open rates than mass emails. That said, subject lines are not the only way that retailers can personalize their marketing emails.

Let’s dive into four ways retailers can improve their email marketing efforts with customer data and personalization.

Implement Personalized Messaging

Personalization is a must-have for all retailers, as customers have now come to expect it at every step of the buying journey. In fact, Salesforce found that 66% of shoppers “expect companies to understand their unique needs and expectations.” This trend has added increased pressure on retailers, especially smaller local companies that are fighting against industry goliaths like Amazon and Walmart. In response, these smaller retailers can incorporate personalized messaging in their email marketing to give customers an individualized experience without decreasing efficiency.

Some examples of this personalized outreach can include outfit or recipe recommendations based on products the shopper has bought before or notifications of new products the retailer has brought in. These businesses can also incorporate personalized customer surveys into their email outreach to better target unique needs across the organization, from customer service to buying and inventory management.

Ultimately, when retailers implement individualized messaging in emails, customers feel valued and understood, leading to higher open rates, click rates and ongoing loyalty.

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Send Relevant Recommendations

This personalized messaging can also be used to improve basket sizes and the number of visits to the online or brick-and-mortar store. By using customer data, such as purchase behavior, retailers can ensure that their product recommendations are relevant to the shopper, making them more inclined to buy. There are a few ways that retailers can source recommendations.

One opportunity is to send an email that reminds an individual shopper about an item they viewed or even added to their cart but did not buy. This is known as an “abandoned cart” email and in 2021, nearly 40% of abandoned cart emails led to a conversion. What’s more, by proactively learning why shoppers abandon carts, retailers can help relieve the issue. For example, if a shopper was deterred by a delivery timeline, the personalized email could include nearby pickup options for shoppers who enabled geolocations. Or, if a shopper was on the fence about the product itself, the personalized email could include recent reviews from satisfied customers.

Other opportunities for email recommendations include suggestions for people who have similar buying behaviors, top-selling items in the shopper’s area, almost sold-out items in the shopper’s preferred product categories and more.

Offer Engaging Discounts Based On Transaction History

As concerns of a pending recession continue, retailers have the opportunity to engage their customer base through personalized discounts. For example, when a grocer shares a weekly ad email, they can use preferences and cart history to see what the shopper is already buying and offer a targeted coupon. If a shopper used to buy eggs frequently before the price went up and then slowed their consumption, they might benefit from a coupon. Or, if a shopper often buys dresses, the retailer could share an email with a discount on a similar skirt to widen the shopper’s categories of interest within the brand.

Furthermore, customer data can be used to avoid over-discounting as well. If a shopper is easily convinced to increase their basket size and a 10% off coupon has been historically sufficient, the retailer can avoid sending 30% discounts to that shopper, keeping margins at a higher level. The better the retailer’s understanding of their shoppers, the more opportunities they have to personalize email content and build stronger customer relationships.

Email Marketing Is Alive and Well

At the end of the day, shoppers just want to be heard and supported. They want their customer journey to be efficient, with as few roadblocks as possible. With email marketing, retailers have an easily established point of contact with these shoppers that can be used to show relevant products, discounts and messaging related to their needs and their typical customer journey. By implementing AI-enabled technology, retailers don’t have to manually create individualized offers for their wide array of customers. This saves the retailers time and money and ensures every shopper is receiving unique attention through personalized emails.

By truly understanding their customers, retailers have an opportunity to boost email open rates, increasing overall engagement and revenue. This level of individualized attention and customization will keep email marketing flourishing for years to come.

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Picture of Shekar Raman

Shekar Raman

Shekar Raman is CEO and co-founder of Birdzi, a grocery retail AI solutions company that was inspired by an idea his 11-year-old daughter had about locating products in the supermarket. He is passionate about building data-driven technologies leveraging AI and machine learning to help retailers and brands elevate the customer experience. Shekar began his career working on the Human Genome Project at the Dept. of Human Genetics, Univ. of Pennsylvania, developing algorithms for protein modeling. He then continued onto AT&T Bell Labs, working in the Speech Recognition group and later to Systems Engineering, architecting and implementing infrastructure solutions for a large Fortune 500 company working in both consulting and management roles.

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