Conquer all things personalization with 4-Tell’s Personalization Dictionary! From general terms to metrics to personalization objects on a website – we provide all the terms and definitions to make you a personalization wizard.
360-degree customer view
A 360-degree view of the customer is a single, end-to-end picture of the customer’s journey
and experience with a company.
Securing a 360-degree view of the customer is essential to providing a fluid, personalized customer experience. By leveraging a unified view of each shopper, every customer-facing employee in an organization can create a personalized experience that meets shoppers immediate, real-time needs.
While “clienteling” once traditionally meant “catering to clients,” the modern clienteling meaning is more extensive. The most widely accepted clienteling definition refers specifically to the processes or tools used to promote customer satisfaction through the personalization of the shopping experience.
By focusing on high-value repeat customers who cost less and buy more, businesses are able to grow sustainable revenue while increasing customer lifetime value.
Collaboration / Collaborative Commerce
The definition for collaboration is when two or more people work together toward a shared goal.
If we apply this definition to digital commerce as a whole, we can expand upon it to explain how a business works with customers to create a positive brand experience. Today this interaction can, and should, involve the use of technology.
4-Tell calls this ‘Collaborative Commerce,’ and defines it as a new way of building customer relationships, whereby omnichannel technologies allow consumer-facing employees to connect and engage with customers through digital channels.
A Customer Relationships Management (CRM) system is a technology that helps businesses organize and manage their relationship with individual people – including customers, prospects, partners and/or suppliers – over the lifetime of the relationship.
Considering the critical role CRM systems play in tracking customer data, it’s crucial that a personalization solution integrates data from the CRM to create a deeply personalized experience.
According to Forrester, customer experience – commonly abbreviated CX – is how customers perceive their interactions with your company.
Forrester defines good customer experiences as being made up of three things from the customer’s perspective:
1. Useful—they deliver value
2. Usable—the value is easy to find and engage with
3. Enjoyable—they’re emotionally engaging and people want to use them
Personalization plays a key role in creating an exceptional CX that is useful, usable and enjoyable.
As defined by Gartner, digital commerce is the buying and selling of goods and services using the Internet, mobile networks and commerce infrastructure.
As customers now use digital tools fluidly throughout their journey, digital commerce is commonly referred to as the modern ecosystem that businesses and consumers are always interacting in.
The application of digital capabilities to processes, products, and assets to improve efficiency, enhance customer value, and uncover new monetization opportunities.
To elaborate on this definition, digital transformation typically involves the coupling of real-time data from across channels (POS, Ecommerce, CRM, etc.) with modern technology (i.e. cloud apps, machine-learning, etc.).
For example, businesses who implement sophisticated personalization solutions to arm teams with predictive analytics and unified shopper profiles are said to be taking steps toward digital transformation, or are digitally transforming.
If we define this through a personalization lens, machine learning utilizes algorithms and predictive analytics to present the most relevant experience for each unique visitor.
Machine learning is more or less the engine or brains behind the results surfaced for each unique individual user.
Across personalization competitors, every algorithm powering personalized results is different. Some algorithms only account for individual behavior to drive results, while other solutions take only the wisdom of the crowd (or shopper trends as a whole) to build personalized experiences. Some others – like 4-Tell’s machine-learning algorithm – use a combination of individual and crowd behavior to personalize experiences.
Hubspot defines omnichannel as a “multi-channel approach to marketing, selling and serving customers in a way that creates an integrated and cohesive customer experience no matter how or where a customer reaches out.”
Personalizing experiences with an omnichannel approach requires businesses to surface real-time data and predictive analytics to teams across the organization. In this way, sales, in-store associates, customer service reps and ecommerce teams can always pick-up wherever the customer engages with the brand – no matter the channel.
As defined by Forrester, personalization is “an experience that uses customer data and understanding to frame, guide, extend, and enhance interactions based on that person’s history, preferences, context, and intent.”
Personalization enables merchants to fuel profits by creating 1:1 experiences that are tailored to the individual. These highly relevant experiences are more impactful and engaging, driving conversion and loyalty.
Traditionally, marketers have used a segmented approach to engage customers. A segmented approach uses aggregated data to split a target market into groups according to their shared attributes. Based on these shared characteristics, marketers then create a marketing mix for each segment.
As we know, however, individuals are more complex. While shared characteristics such as location, age or gender may increase the likelihood of providing a tailored experience to your audience, it doesn’t take into account real-time user intent or unique customer history and preferences – all which play a critical role in shaping individual’s purchasing decisions.
Point-of-sale systems, commonly abbreviated POS, enable the business transaction between the client and the company to be completed.
Traditionally, POS systems referred to the cash registers in retail stores where customers would buy and make payments for goods.
However, as technology has become more sophisticated, POS systems can now mean any combination of hardware and software that is used to report sales, manage customers, inventory and employees.
To create holistic and fluid customer experiences, it’s critical that data within a POS system is used to power personalization efforts.
Businesses collect vast amounts of real-time customer data. And with machine learning as its backbone, predictive analytics uses this historical data and customer insight to predict future products and content that customers are likely to purchase.
Predictive analytics enables organizations to use big data (both stored and real-time) to move from a historical view to a forward-looking perspective of the customer.
In the hands of teams, these analytics can enable sales associates and customer service teams to provide valuable recommendations, unearth product trends and opportunities for merchandisers. Analytics are also key in surfacing customer segments likely to respond positively to particular products or promotions for marketers.
Proactive selling is when businesses use information about a customer – either based on past interactions or collected from external sources – to instantaneously customize the customer experience. Remembering customer preferences is a basic example of this capability, but it extends to personalizing and optimizing the next steps in a customer’s journey.
For example, by using sophisticated technology that integrates real-time data to surface 360 customer profiles and predictive analytics, customer service agents and in-store sales associates can reach across channels to actively create moments of engagement and dialogue through personalized, predictive suggestions.
Recommendation engines are information filtering tools utilizing algorithms and data to suggest the most relevant items or content to a particular user in a given context.
According to Forrester, “Sales enablement is a strategic, ongoing process that equips all client-facing employees with the ability to consistently and systematically have a valuable conversation with the right set of customer stakeholders at each stage of the customer’s problem-solving life cycle to optimize the return of investment of the selling system.”
How does it impact businesses? Sales enablement allows businesses to grow their bottom-line by providing every salesperson with the necessary tools and information to sell more productively, efficiently and successfully. Research by Aberdeen has shown that 84% of sales reps at companies with best-in-class sales enablement strategies achieve their quotas, compared with 55% at companies with average strategies and 15% for laggard companies.
Stands for average order value. AOV is an ecommerce metric measuring the average total of every order placed with a merchant over a defined period of time.
Deeply personalized online experiences often increase shopper’s AOV, as they are able to more efficiently find other products aligned with their preferences and needs.
The percentage of visitors who take a desired action. The ‘desired action’ usually refers to customers completing a purchase process. In google analytics, it’s automatically calculated by dividing the number of transactions (orders) by sessions for a given time period.
Much like AOV, personalized ecommerce experiences tend to increase the percentage of customers who convert.
Customer Acquisition Cost (CAC)
CAC stands for customer acquisition cost. A company’s CAC is the total sales and marketing cost required to earn a new customer over a specific time period.
It’s usually an ongoing goal for businesses to regain their CAC investment, as keeping and growing loyalty with current customers is more cost-effective than constantly generating new customers to recoup the cost of lost ones.
Customer Lifetime Value (LTV)
Customer lifetime value (CLV) is a metric representing the total net profit a company makes from any given customer. CLV is a projection to estimate a customer’s monetary worth to a business after factoring in the value of the relationship with a customer over time.
Like conversion rate, cross-channel conversion is the percentage of visitors who take a desired action as they use different channels or devices to complete the action.
For example, customers may begin browsing on their mobile device and continue browsing on their desktop computer as their context and needs change. The cross-channel conversion would be the percentage of customers who ultimately complete the purchase on any given device.
This metric signifies how fluid a brand’s digital experience is. If customers often have trouble picking up where they left off as they switch devices, chances are cross-channel conversion will be low.
Time on site / Average time on site
In web analytics, average time on site is a type of visitor report providing data on the amount of time (in minutes or seconds) visitors have spent on your website.
Pages per session
In web analytics, this refers to the average number of pages a user looks at on your ecommerce website during a single session.
When ecommerce websites are highly personalized, they tend to be much ‘stickier.’ Meaning, users will spend more time on site and view more pages since they are continually seeing products and content that are personalized and relevant to their preferences.
Return on advertisement spend
ROAS is used to determine media effectiveness. It can be calculated for online and offline media campaigns. ROAS can also focus on campaign elements such as Google AdWords, Ad Groups or even individual keywords within PPC advertising.
Calculating the ROAS allows online marketers to see whether their advertising budget generates sufficient revenue.
Google Analytics: users vs session reporting
In Google Analytics, you can choose to measure behavior based on users or sessions. There’s a definite distinction between the two, so it’s important to be aware of them.
Users = “Unique Visitors”, or a person who has come to your website.
Sessions = “Visits”, or different times a person came to your site.
Unique visitors refers to the actual number of people who have come to your website or webpage at least once during a reporting period — this number does not increase if a previous visitor returns to a page multiple times.
A session is defined as a group of interactions a user takes while on your website. One session also refers to anything a customer does before they ultimately leave your website. Google Analytics automatically defaults to starting a new session if there has been no activity for 30 minutes.
360 profile / Shopper profile
A 360 profile condenses customer’s online and offline behavior to surface a unified view of each shoppers’ real-time behavior, purchase history and preferences.
These profiles should also include predictive recommendations to enable team members across the organization to create a fluid AND personalized customer experience.
Cross-sell product recommendations display complementary items to the products shoppers have recently purchased or are currently shopping for.
A microsite is a website operating distinct and separate from an organization’s main site, though it may function in tandem with activities on the main website and is contained within the main site’s URL. These microsites deliver more focused, relevant content about a specific topic or to a targeted audience.
In the case of 4-Tell’s personalized microsite, the entire experience is built around the behavior, history, and preferences of every unique shopper and can be navigated to within the main ecommerce website.
Similar to product recommendations, as shoppers’ signal their unique preferences through their real-time online activity and purchase history, personalization solutions can sift through your entire content library to display the most relevant – and personalized – blogs to each customer.
Coupled with personalized product recommendations, content recommendations create an even more engaging ecommerce experience causing your customers to view more pages and stay on your website longer.
Personalized product recommendations
Personalized product recommendations display similar, complementary, trending, etc. products based on the relationship between products and product categories, individual user behavior and/or the audience’s behavior as a whole.
Product recommendation blocks can live anywhere on the ecommerce site – on the home page, product pages, 404 pages, check out, etc.
Cross-sell product recommendations display complementary items to the products shoppers are currently shopping for or have recently purchased. For example, if a customer has recently viewed a rain jacket, cross-sell product recommendations may display rain boots or an umbrella.
Similar recommendations provide customers with similar products suggestions based on products shoppers are currently shopping for or have recently purchased. For example, if a customer has recently viewed a white summer dress, similar recommendations would display other summer dresses that are in a similar color, style, etc.
Predictive / Personalized / Inline search
There are many words used to describe predictive site search. It’s commonly also referred to as Personalized Search or Inline Search.
Whatever label is applied, this personalization object uses real-time behavior to display personalized product and content recommendations as the customer executes a search query. These objects also correct misspellings and connect to synonyms to display recommendations that are aligned with shoppers intended search query. Personalizing in this way creates a more efficient and visual search experience, and improves product discovery.
Behavioral cues the customer gives to indicate they are ready to buy a product.
Online, ‘buying signals’ include things like a shopper adding products to their cart, viewing a large number of items, adding items to their wishlist, or searching for a specific product.
A data silo is a repository of fixed data under the control of one department and is isolated from the rest of the organization.
Data silos tend to arise naturally in large organizations because each organizational unit has different goals, priorities, and responsibilities.
These data silos are detrimental to the customer experience. In order to provide the caliber of CX customers demand, organizations must have free-flowing, real-time data to piece together a relevant and complete shopper profile.
Inventory data is information about the finished goods a company accumulates before selling them to end users. Simply, it’s data about the tracking of goods and stock.
First party data
First party data is the information you collect directly from your audience or customers. Examples of this type of data include data from behaviors, actions or interests demonstrated across your website/app, or data you have in your CRM.
Of all data available, first-party data is the highest quality. This is because the information is coming directly from the source – your customers.
Most personalization engines primarily use first party data to personalize experiences, as the accuracy and relevancy of first party data allows businesses to predict future behavior with the most precision.
Third party data
Third party data is data purchased from outside sources. In this case, businesses are not the original collectors of the data. Rather, merchants buy it from large data aggregators that pull it from various other platforms and websites.
Transactional data is information that documents an exchange, agreement or transfer that occurs between organizations and/or individuals.
Common examples of transactional data include purchases and returns.
The basic definition of real-time data is that it is data not kept or stored, but is passed along to the end user as quickly as it is gathered.
In personalization, access to real-time data makes all the difference in providing relevant customer experiences. After all, something customers need right now may not be something they need – or want – even 20 minutes later.
The accumulation of all of the customer’s behavior. This can include overall purchases and behavior trends or recent items purchased, viewed or added to wishlist on the ecommerce website.