“Data-driven marketing” is one of the bigger industry buzzwords. But here’s the thing: Just about all marketing in today’s world is data driven. From social marketing to content marketing, data is playing bigger and more influential role in how marketers develop strategies and evaluate performance.
And then there’s personalization, where marketers and data scientists work together to craft a one-to-one experience for customers by predicting their wants and interests. This is data-driven marketing at its best, putting marketers in a position to directly address consumer needs.
Jeff Rosenfeld, VP of customer insight and analytics for The Neiman Marcus Group, recently wrote for CMO.com, “You can’t personalize [the customer experience] if you don’t have a complete picture of how each customer interacts with your brand.” What’s more, you can’t get a complete picture of how customers interact with your brand if you don’t understand the basics of how personalization works.
To that end, we offer you 20 personalization terms, both basic and advanced, that every marketer should know.
Key performance metrics
1. Average Order Value (AOV)
Dividing your sales revenue by the number of orders gives you the AOV, a metric useful for strategizing ways to increase profit.
Average Order Value = Revenue / Number of orders
According to BigCommerce, “AOV is determined using sales per order, not sales per customer. Although one customer may come back multiple times to make a purchase, each order would be factored into AOV separately.”
While it doesn’t help you determine “gross profit or profit margins, [… it] offers insight into how those figures come to be. For example: an online clothing retailer selling three shirts priced at $15, $21, and $29 with an AOV of 19 … [tells you] customers are not buying multiple items [and] the low-end shirts represent the majority of sales.”
Inspired to try new ways to cross-sell or bundle products? Thinking about creating a threshold for free shipping? AOV can help measure before-and-after results.
2. Revenue Per Visitor (RPV)
How profitable is each customer visit to your website? Divide total revenue by the total number of visitors to measure your RPV.
Revenue Per Visitor = Total revenue earned [in time period] / Number of visitors [in time period]
“Like other online business metrics, RPV helps you see what is working and not working in your company’s overall sales efforts,” Optimizely says. “The revenue per visitor metric helps you evaluate new visitor acquisition efforts to see which strategies are working. RPV can also be used to determine how much you can afford to spend on paid user acquisition.”
This is an important metric and offers greater insight than AOV. It’s possible to have a very high AOV but only be converting a small percentage of the visitors to your website.
Finding ways to raise your RPV can grow profits through loyalty tactics without increased spending on attracting new customers. A drop in RPV may signal you need to analyze why current marketing is attracting unqualified visitors. It may also be time to test and optimize your website pages.
3. Average Items Per Order (IPO)
The average number of items a customer orders in one transaction is another indicator of performance. This metric can give you insight into how well you’re generating demand. For the visitor who only planned to get one item, how effectively are you creating opportunities to stay on your site and buy more? Is your recommendation engine doing its job?
A conversion is usually defined as when a visitor to your site becomes a paying customer, but it can also mean other activities like subscribing, registering, downloading, or clicking a Where to Buy button. You can measure how well your recommendations, targeted offers, reviews, email personalization, etc., is working by measuring conversions. The conversion rate equals the number of defined activity achieved by the number of visitors.
A hot topic now is the power of mobile conversions. When Google and Nielsen analyzed 6,000 mobile searches, they found that 55% of conversions, such as a store visit, phone call, or purchase, happen within an hour after a mobile search.
Separating and segmenting shoppers
5. Shopper profile
Who is your core audience, and how do you best reach them? Answering this question means analyzing your customers and their needs. Traditionally, shopper profiles are a way to group consumers by similarities, based on demographic data on age, gender, region, employment, and lifestyle.
But today’s shopper isn’t so easily categorized. Capturing and analyzing sophisticated data like “buying signals” to segment shoppers according to shifting online behaviors is now more and more necessary to stay in touch with customers. For more advanced personalization providers, shopper profiles include data on what shoppers are actively shopping for in real time.
Traditionally, shopper profiles have included demographic and behavior data, which personalization providers have leveraged with predictive algorithms to serve up relevant content to consumers.
Segments are the defined categories you put your customers into so you can interact with them more effectively. Segmentation can determine your most valuable customers or identify an opportunity for a targeted campaign.
For instance, children’s clothing store Carter’s might categorize their visitors into new moms or parents with toddlers. These segments could include demographics, but, more importantly, they should be based on behavioral data like clicks, views, channel preferences, or activity on site. For example, a 30-year-old woman who has visited the site multiple times over the last year and looked at and purchased different baby items for ages 0-12 months might be classified into the “new mom” segment.
Brands and retailers can use these segments to better target shoppers with advertising or product recommendations that fit their needs.
As the industry evolves beyond the marketing campaign, we now seek to realize marketing orchestration. This means engaging customers across multiple channels with personalized content in real-time. According to AdExchanger, “Orchestration implies that the decision to place an ad or create an interaction is decided by the marketer, and it occurs in conjunction with all other marketing actions across the ecosystem on a one-to-one basis.”
This is challenging as you must integrate technology, data, and campaigns so you can automatically recognize customers no matter where and how they are interacting, and immediately deliver tailored, responsive content, creating a seamless customer journey.
Personalization is the use of data to individually tailor customer experience, for example by curating the products a customer sees on a website. This goes beyond something like Coca-Cola’s Share a Coke campaign.
Today’s shoppers expect to see much more than their name on a product or in an email subject line. All of the content in an email, for instance, should make a meaningful connection and feel like it’s addressed uniquely to them. Achieving personalization like that requires you to integrate and analyze all the customer data you can get your hands on, not just transactional data.
See our last section for different ways to offer personalization to your shoppers.
Data types that drive personalization
9. Buying signals
When do you know a prospect is leaning toward a purchase and it’s time to close the sale? Buying signals. Back in the day, a good salesperson watched for subtleties in tone of voice, gesture, and gaze. In the digital era, customer behavior online can also send you clues about their buying mood. As already mentioned, buying signals should inform how you’re marketing to shoppers.
Here are a few examples of online behavior that might indicate a shopper is about to make a purchase: increased time on the site looking at products, digging deeper into product reviews, viewing your curated social content, and looking at similar products.
Every interaction a potential customer has with your business is a touchpoint. From email, to social media, to live chat, it is important to track the various interactions visitors have with you on their journey. The data you collect can show which touchpoints are most effective and where you might need some tweaking.
11. Onsite data
If you have the right tools in place, you can monitor consumer behavior on your site through onsite data. This comes from a number of places — on-site search, product relationships (so if a consumer looks at one product, you can anticipate what other products they might like), on-page actions, etc. — and can be used to enable a basic personalized experience.
After someone has spent long enough on your site, you can begin feeding them relevant content and product recommendations, filtering up the things that are more likely to appeal to them. However, if your personalization is only based on onsite data, when a new consumer first lands on your site, it’s difficult to do more than guess at their general demographics and provide them content based on that.
12. Off-site data
The really interesting piece of the personalization puzzle is what consumers are doing before they come to your site. Information like where site visitors come from, what they were previously looking at, whether they just were in the middle of shopping for a product but didn’t buy it — this is the kind of data that marketers’ dreams are made of.
Rudimentary data — such as where a visitor is arriving from — can be accessed through tools like Google Analytics. More complex data such as what products a consumer might have been looking at before arriving on your site is more difficult to come by. Third-party service providers can provide you with this type of data (see acquired data below).
13. Offline data
Offline data is what brands know about their customers’ offline behaviors.
Ideally, offline data is sourced through your own call-center data, CRM data files, NPS surveys, and point-of-sale transactions in brick-and-mortar purchases. But brands will sometimes also use third-party vendors to get data on “retail transactions from credit card networks, aggregated offline catalog transactions, syndicated loyalty-card data, and more,” according to The Digital Marketing Glossary.
Brands often use this type of information to build out targeted advertising campaigns and more relevant — and customized — personalized on-site experiences.
14. Third-party data
Third-party data refers to data that you purchase to augment your own data. More specifically, third-party data is often aggregated across a variety of websites and platforms and is then packaged together by a third party data provider. The provider, like Experian, Nielson, or other data management platforms, acts as a vendor between you and other parties.
Third-party providers offer marketers the ability to better understand consumer behaviors. The benefits include building out more advanced product recommendations and offering first-time visitors, as well as repeat visitors, a more personalized on-site experience. However, due to the degrees of separation between your business and third-party data, there are often questions around sourcing, transparency, and reliability of the data. If you have an opportunity to instead purchase first-party data, do so.
15. First-party data
The data you collect directly from visitors to your site — or sites — is first-party data (see on-site data above).
This contrasts with third-party data, which is sold to you by platforms that gather data or aggregate it from other companies. The advantage of first-party data is that you know exactly where it comes from and have a greater grasp of its quality and integrity.
Second-party data is when you obtain another company’s first-party data, perhaps from a trusted partner who has a demographic overlap with your customer base.
Personalization Use Cases
16. Product recommendations
When a shopper visits your homepage, a category page, or a product page, there is an opportunity to display other products in a highlighted section of the page.
If you aren’t using personalization on your site or if someone arrives that you have no data on, the shopper will see generic recommendations, like new arrivals, best sellers, or discounted items.
If you are personalizing your product recommendations feed and a shopper you have data on arrives to your site, they will see personalized product recommendations. These product recommendations are specific to each individual shopper, according to data collected, and might include similar, complementary, or recently viewed items.
As e-commerce giant Netflix proves, websites that do product recommendations well can be big revenue generators. The key is to get better at personalizing these recommendations. According to Spendsetter, 39% of consumers “said they buy more from retailers who personalize web recommendations.”
When customers visit your site, they will often visit category pages, product pages, and maybe even place items in the shopping cart without purchasing. Retargeting is a way to bring them back. A cookie placed on the visitor’s device enables your ad to be displayed after they’ve moved on to other websites. See examples such as a Twitter ad for Kelley Blue Book and a Facebook ad for Expedia.
18. Universal product catalogs
Composed of merged e-catalogs from multiple vendors, a universal product catalog gives retailers a global view of items that participating companies have in common.
The benefits of having an up-to-date and complete universal product catalog include connecting related products and product categories to one another, which can lead to better product recommendations.
Whether at the store, during a phone call, on your website, or across social media, is your messaging and customer experience consistent? Omnichannel refers to actively managing your customer’s journey across any touchpoint to create a seamless whole.
Customer experience has always been important, but, now, brands and retailers must think about the customer experience across many different touchpoints. A critical component of customer experience is treating each shopper like an individual. When a customer shops in-store, they are able to receive personalized guidance from store associates. When your incorporate personalization into your digital strategy, you can offer this same tailored experience online and on mobile.
This kind of integration can be a tall order for companies, according to eMarketer. As of “Q1 2017, only 3% of respondents said all of their automation, engagement, and deployment tools are fully connected, with data, metrics and insights traveling freely between different technologies.”
20. 1:1 Profiling
One-to-one profiling targets individual customers instead of groups. Combining rich data that details unique characteristics and preferences with artificial intelligence, you can deliver an especially personal touch at each step of the journey.
Whether you’re serving up a personalized home page with specific content and product recommendations or offering personalized messaging sequences along the shopper journey, one-to-one profiling is the ultimate personalization goal. When done right, it combines messaging, product recommendations, and shopper assistance to imbue the shopper journey with a personal touch.
Use this list of terms as a starting point to understand how you can use data to better reach your consumers. In today’s connected world, consumers are comfortable sharing their data with companies, as long as it leads to a more efficient, entertaining, and personal shopping experience.
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