IdentityNovember 20, 2017

14 eCommerce audiences marketers need to succeed

Reaching these 14 eCommerce audiences with the right content can make all the difference in terms of engagement, conversion, and satisfaction.

Online shopping has completely changed the game for retailers. While many retailers still have their brick and mortar locations, many are seeing their online stores continue to gain year over year. This new shopping experience calls for new strategies across many channels including social, ad platforms, email, and more. All of this is for naught, however, if you can’t reach the right audience, at the right time, with the right content.

Reaching the right audience with the right content can make all the difference in terms of customer engagement, conversion, and satisfaction. To achieve these goals, brands need to be able to group customers into segments determined by certain characteristics, called audiences. These audiences can then be used across platforms to reach a wide range of customers that will respond positively to a brand’s content. For e-commerce, audience marketing means more purchases and more revenue. So, how should you divide up your customer base?

This article will cover 14 of the most useful types of audiences for ecommerce marketers, including suppression, shopping cart abandonment, and latent users among others, and how you can use them to get the most out of interactions with your customers.

Acquisition

Installs and crashes

Applications are as much a part of everyday life as mobile devices are, creating a prime opportunity for brands to engage further with customers that install an app through marketing campaigns. Creating an audience segment for customers that have recently installed your application, ideally in real time, makes further brand engagement easy. For example, a customer that installs a travel app and is placed into an audience segment and targeted, may receive promotional emails and ultimately book a vacation from that campaign.

Customers not only rely on applications performing properly, they expect it. When an app crashes, customers can become frustrated and become less likely to continue using the app to interact with that brand. Creating an audience comprised of customers that have had a negative experience, like an app crash, can help brands turn it into a net positive experience and retain that customer. A simple application of a crash audience is to send them an email with a Zendesk link to alert your team of potential issues. That way the customer not only feels supported, they also feel that the brand is willing to take action to prevent them from having that same negative experience again.

Channel opt-out

While email is an incredibly effective form of engaging with customers, it has also become one of the least liked by customers. Between automatic categorization of emails as “promos” as seen in Gmail’s Inbox offering, unsubscribe services like Unroll.me, and spam filters, fewer and fewer ecommerce emails are reaching customers. So what is a brand to do when a customer decides to unsubscribe from emails? As the old saying goes, when a door closes, another opens; channel opt-out audiences take note of these customer communication preferences and reroute brand engagement to other channels. That means that a segment of customers that have indicated they do not want to receive emails, may instead receive more Facebook retargeting, display ads, or app pushes.

Suppression

Suppression lists are highly defined audience segments used to exclude customers from receiving content from specific marketing campaigns, ads, and more. Suppression lists help to minimize exposure fatigue and ensure you are only sending relevant, interesting content to customer populations across channels. Creating and employing suppression audiences is ideal for controlling audience exposure, making intended content resonate further when received.

Product affinity

Brands can divide customers into segments according to product affinity, which is the likelihood that based on products they have already invested time searching for. This is somewhat similar to lookalike segment modeling, but with a focus on customer actions rather than personal characteristics. This loose correlation between the customer and the types of products they buy provides the basis for predicting the kind of products or content that will resonate with customers.

So if a customer has searched for a pair of blue sneakers and spent time looking at a specific pair, you could add this customer to the larger segment of customers that have searched for similar sneakers and retarget them through Facebook ads by displaying different types of the same brand or color sneaker to encourage them to purchase.

Active no-purchase

Your traffic numbers may be showing a steady increase, but your sales numbers aren’t reflecting the increased traffic, so what gives? Brands often have many customers visiting their apps and sites, sometimes on a regular basis, without ever making a purchase. By creating a segment of these customers, marketers are able to create content that will resonate with these hesitant customers and push them to complete their first purchase. For example, if customers in an active no-purchase audience have visited the bedding section of a retailer’s site, then a marketer might direct a campaign that offers a discount or a perk for purchasing bedding within a certain timeframe. Creating a sense of urgency as well as offering an additional benefit may be just the thing to compel these active customers to purchase.

Shopping cart abandonment

According to research, 90% of shopping carts are abandoned. That represents millions of dollars lost for ecommerce. So, how do you make sure that you avoid your platform from becoming part of the statistic? One way to address this issue is to create audiences based on customer actions, like adding an item to a cart, but not purchasing. Depending on the customer data platform (CDP) you are using, you may be able to make this more granular and add secondary aspects like timeframes.

With this kind of segmenting, you’re able to automate retargeting in the form of, say, an email reminding customers of the item they added to their cart but did not purchase within two hours of doing so. While it is not a surefire solution, this kind of retargeting increases the possibility these customers complete transactions that might otherwise have been completely abandoned.

Dormant users

A common problem brands face is dormant users: they install an app, then stop using it. Dormant users present a unique opportunity for brands because they already have an in—the customer has already installed the app, they just need to be re-engaged with in the right way. A dormant user audience makes it easy to target these users with compelling content geared towards them, which could be just the push needed for them to start using the app once again.

Leverage existing customers

Cross-selling and upsell

As any iPhone user can attest, customers have a tendency to want the newest, most advanced product. Brands benefit from tracking what products customers purchase and then marketing towards them when an updated version of the product becomes available. This segmentation is often referred as an upsell audience and is particularly useful for brands that consistently produce new or updated versions of the same product.

This same segmentation can also be used as a cross-sell audience. Creating this type of audience allows brands to automatically recommend products that complement the product the customer has already purchased. In the case of the iPhone, a retailer may look to cross-sell headphones, phone cases, screen protectors, or even iTunes gift cards. Offering these additional products makes it more likely that a customer will purchase them, even if they hadn’t considered purchasing before, making it an easy way to bolster sales.

Replenishment

More often than not, when a customer finds a product that they like and that works for them, they will continue using it. This is especially true for personal care products, pet products, and household goods. Identifying users that purchase certain goods at regular intervals and placing them into a replenishment audience allows marketers to target them more effectively. As customers that are already accustomed to having a replenishing order, they may be more open towards purchasing other replenishable goods, increasing the stream of recurring sales.

Lifetime value (LTV)-based lookalikes

High-value customers are not only great for your business and profit goals, they also are a shining beacon of user advocacy that could bring in additional customers. The more high-value customers you have, the more likely more people will recognize and trust your brand. The trouble lies in finding more high-value customers—often, brands find themselves grasping at straws with little progress made towards identifying and converting customers into high LTV customers.

That’s where audiences come into play; by creating an LTV audience you can use the characteristics of these customers to identify “lookalikes,” or rather, people that share similar characteristics to your high LTV audience. Using this tactic can help both marketing and sales to target the right kinds of people and businesses. Besides using it as a base for lookalikes, a high LTV audience allows you to cost-effectively target your highest value customers with highly tailored content so you can retain or upsell to these customers in a cost- and time-efficient manner.

Near high-value shoppers

Brands need to keep track of the different tiers of customers over time, including that of high-value or high-engagement customers. While a high-value segment is a no-brainer, brands can benefit greatly from a less obvious audience segment: near high-value customers. Depending on your definition of high-value customers, your near high-value customers will be customers that are very close to completing the number of actions or frequency of actions that make them “sticky.”

A prime example of near high-value customer segmentation is Starbucks’ app loyalty point program. For Starbucks, the threshold for high-value seems to be $150, which is tracked by the app as customers purchase products. For each dollar spent, two stars are added to the customer’s account. Once the customer reaches the high-value threshold of 300 stars, they are able to earn points towards free products. In this case, the audience segment has already been pre-qualified to an extent because they have downloaded the app, registered, and entered their information. From there, it’s a matter of incentivizing these customers to reach the monetary threshold. With a series of app pushes and emails Starbucks very cleverly is able to guide these near high-value customers directly into becoming high-value customers.

Event-motivated shoppers

Black Friday is the busiest shopping day for retailers in the United States, with 2016’s sales totaling $655.8 billion. And Black Friday is just the beginning of the holiday sales season. Customers are now primed to purchase on these days, thinking they will be able to get a better deal than at any other time. In fact, a portion of customers will hold off from purchasing a product during the year in order to buy it on Black Friday or Cyber Monday. Reaching these customers is a critical part of any retailer’s yearly sales goals, but with so much content coming from every direction, it can be difficult to stand out.

Enter the event-motivated audience. Online retailers are able to create an audience of customers that purchased products during one or more of the holiday season sales the previous year. These customers may be more likely to make purchases again the following year, especially if they receive some enticing communication in the shape of special promotions or a sneak peek into deals before Black Friday or Cyber Monday. By creating this type of audience, retailers are able to tap into the excitement and desire to score a great deal

Similarly, websites like 1-800-Flowers have had success creating audience segments made up of customers that indicated their purchases were for birthdays or anniversaries. Because these events are annual, customers are likely to be interested in purchasing flowers as gifts the following year when reminded by a well-timed email.

Web users, not app users

Converting web users to app users is no easy task, especially because customers largely do not want to add applications to their phones. Even if they do decide to download your application, it’s highly likely that they may not use it. In fact, according to Forrester, 84% of consumers’ time is spent in only five apps. Needless to say, the odds are stacked against marketers trying to increase app adoption, but the reward could be worth the trouble. While web is great at reaching people, apps are better for creating deeply loyal customers. If customers are already engaging with your brand’s site on a regular basis, they are already one step closer to becoming app customers. Defining an audience of customers that are regular web users, but don’t use the app allows brands to create content to entice these customers to download and regularly use the app, making them more loyal in the process. Amazon, for example, used this audience segment type to try and get customers to use their newly launched Amazon app by offering an app-only version of their popular Gold Box Deals. These deals were only able to be accessed and purchased in the Amazon app, via mobile and Amazon Kindle.

Measuring impact

A/B testing and lift analysis

As it turns out, the scientific method does have a place in ecommerce marketing; A/B testing allows marketers to test different creative, channels, and platforms to see which has the best return. A/B testing is particularly useful for allocating money and time. To A/B test, however, you don’t just need something to test, you need someone to test it on. That’s where audiences come in—marketers can create three randomized audience segments for each A/B test. One audience is exposed to version A of the test, one is exposed to version B of the test, and the last segment is used as a control. You can read more about A/B testing here.

Lift analysis is similar to A/B testing; to perform a lift analysis, you compare an audience that received a campaigns with a control group. Lift analyses typically home in on specific metrics to see if the campaign has any effect on results. Basically, A/B testing helps you determine which platform, content, and channel is best for your campaign and lift analysis audience segmenting helps you measure the effect of your campaigns.

 

How do you create audiences?

Now that you’ve learned all about these audiences, you may be wondering how you can employ this in your brand’s customer database. There are many customer data platforms (CDP) that look to make audience creation simple, among addressing other customer data needs.

AudienceSync, mParticle’s identity-based marketing offering, enables you to create, define, and send your audiences to 175+ of the leading marketing services. Once you have your audiences in place, AudienceSync offers automated subscription management; the list automatically updates based on the user actions for efficient resource usage. If a user clicks on a Facebook ad, they are removed from your audience so you don’t waste time and resources targeting them on Twitter, for example. If you’d like to learn more about AudienceSync and the many additional CDP features mParticle provides, please contact us!

AuthorAbril McCloudDirector of Content
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