This post discusses the concept of web personalisation, potential benefits of this methodology from a digital marketing standpoint and some of the implications of a personalised web from the perspectives of society as a whole.
Web personalization is an umbrella term for methodologies used to tailor web content to a specific consumer or target audience (demographic, psychographic) and falls into two categories:
- 1. Implicit data aggregated from user patterns such as:
- History of purchases, recommendations, page views, clicks and visits.
- Location, type of device, time.
- Referring URL, searches.
- 2. Explicit data aggregated from user actions such as:
- Newsletter signups, website forms, surveys.
- Logins, purchases-in-process.
- CRM-data aggregated through customer/user communication including customer support and bug-tracking.
How web personalization works.
In simple terms, web personalization works by tracking web users, aggregating data about their behaviours and using algorithms to parsing the data with variables, parameters and conditionals defined by the objectives of the personalization efforts. This data then is used to create content and user experiences tailored to the needs and wants of the tracked user (Skiena, 2012; Segaran, 2007).
At the core of web-personalization is the s k ‘cookie’ which is a snippet of data associated with a specific user and types of cookies can be divided in four main categories:
- 1. Session cookies
- Session cookies stay in the browser memory only during a visitor session and are, for example, used for “remembering” shopping cart content in e-commerce web-sites.
- 2. Persistent cookies
- Persistent cookies stay in the browser even after a session is closed and are commonly used for web advertising and to track users and their behaviour as they travel the web.
- 3. First-party cookies
- First-party cookies are created by the site currently visited by a user.
- 4. Third-party cookies
- Third-party cookies are added in a visitors browser by a domain other than the currently visited and are commonly used in web advertising where banners are associated with a referring domain.
At a minimum, a cookie comprises two bits of data: a unique identifier and information about the user. It also contains attributes that inform the browser what to do with the cookie (Cahn et al., 2016). A simple cookie process can be defined as:
- A person visits a website.
- The visitor gets attributed a cookie which is saved in the browser of the visitor.
- If the visitor leaves the site and comes back on a later occasion the user then will be recognised by the cookie and identified as a previous user.
PERSONALISATION IN ACTION
E-commerce has long been a driving force in the development web personalisation solutions, and all major e-commerce platforms (Shopify, 2017; Magento, 2017; Bigcommerce, 2017; WooCommerce, 2017; Volusion.com, 2017) offers functionality that enables merchants to deliver personalised product offers. Many of these platforms also can be connected to sophisticated marketing(automation)- and CRM platforms such Marketo.com (2017), Salesforce Pardot (2017) and Hubspot.com (2017) which can further help to plan, coordinate, manage and measure sales- and marketing efforts.
The following section presents three types of web personalization; dynamic content, e-mail marketing and search. Case stories from each respective category also are presented together with examples of commercial tools that offers solutions to achieve the outcome presented in each case story.
#1. Dynamic web content
The idea behind dynamic web content is to adapt web content to the preference model of a defined user persona based on, for example, whether the user is logged in or not, purchase history or position in a sales funnel. In the following section four case stories are presented of companies utilising this technique.
- With the aim of selling more products, web retailer Amazon (2017) offers a highly personalised UX to existing customers based on data from previous purchases, browsing history, wish-lists, customer lifecycles and rich customer profiles (nectarOM, 2017) which can be seen in the top Amazon screen shot below with no product recommendations shown to new visitors compared to existing customers which get presented with a range of recommendations and product offers as illustrated in the second image below:
- SBI is a management consulting firm which uses Hubspot (Hubspot.com, 2017) to create a personalised customer experience. Having implemented an ‘elective’ personalization model (Meghan, 2017) SBI ask first-time visitors to choose a role that best describes them which enables the digital marketing team of SBI to present content relevant specifically to each user. The upper images below illustrates how their site looks for a first-time visitor while the lower screen shot shows how their front-page is presented after the user has defined itself as a “marketing leader”.
- Pardot (Salesforce Pardot, 2017) is a leading digital marketing platform which as part of their feature-set offer organisations and digital marketers tools and solutions for creating personalised forms, landing pages, emails and websites. Having defined a range of buyer personas, the marketing team at Pardot uses their own platform to offer personalised user experiences adapted to defined user profiles/personas (Dynamic Content, 2017) of their customer segments. If, for example, a sales prospect is identified as a user of Sugar CRM (Sugarcrm.com, 2017) as illustrated in the top image below, the person gets presented with information on how to integrate Sugar CRM with Pardot, while a user of SalesForce (Salesforce.com, 2017), as seen in the lower image will get information relevant to SalesForce.
#2. Automated email marketing
Automated email campaigns can be divided into two categories:
- 1. Proactive leads nurturing
- Proactive leads nurturing or mail shots can be defined as a large number of identical emails sent to a defined email list. A common use case are newsletters.
- 2. Reactive leads nurturing
- Reactive leads nurturing can be defined as personalised and individual emails sent to specific leads or customers after a defined event is triggered and form the basis of what has become known as ‘Marketing Automation’. While all leading Marketing Automation platforms offers tools and solutions not only limited to email marketing; reactive leads nurturing is at the core of their product offering.
Below, five examples of email automation triggers are presented together with real-world examples of personalised emails generated by each presented trigger type.
- Abandoned shopping cart
- The image below displays a personalised email from fashion retailer Frank and Oak (Frankandoak.com, 2017) triggered by an abandoned shopping in which the customer are offered free shipping and a 25% discount if finalising the purchase. In addition to all major marketing automation platforms, MailChimp (2017) also offer this feature.
- Welcome mail
- As displayed in the image below the craft store Michaels (Michaels.com, 2017) send visitors who sign up for their newsletter a welcome email and a discount next time they visit their store. This is a basic feature of all e-mail marketing platforms of which Campaignmonitor (2017) is one of the most popular.
- Cross- and Upsells
- When Sony released the PlayStation Vita they created a personalised e-mail campaign to existing PlayStation Plus and PS3 owners to buy the new platform (Econsultancy, 2017). The campaign message was also segmented offering online customers an Amazon (2017) voucher while Gulf state countries received email vouchers for in-store purchases. The image below illustrates one of these emails which were sent using Pardot (2017).
- Visitor thresholds (lots- or few returning visits)
- US retailer Pinkberry has built their business by selling dessert made of yoghurt and currently has more than 150 stores throughout the US. A big part of their success is attributed to a loyalty program built around an app where customers earn points when buying Pinkberry products (Pinkberry, 2017). The image below shows the reminder emails Pinkberry send to customers who have been inactive for some time and which offer these customers a free product next time they visit a Pinkberry store; a feature offered by Marketo (2017).
- Success metrics
- Often used by companies selling digital applications is the use of success metrics as a trigger for automatic and personalised customer emails. Success metrics works by tracking how customers are using a product and, for example, providing customers with learning resources and help if they do not fulfil stated OKRs/success metrics. The image below shows such an email from Paymo (2017) which was sent to new users who after a few days after signing up for Paymo’s project management app still had not created a first Paymo project.
One of the most sophisticated areas of web personalization is ‘search’ of which Google has been a driving force in developing algorithms that predict the needs and wants of the person who uses their search engine. Users of Google (and other search engines) now gets highly personalised search results not only based search terms, but also on previous searches, location, search history, purchases, recommendations, used device and activity in social media to name a few, resulting in two users searching using the same phrase might get two completely different search results (Schmidt, 2016; Official Google Blog, 2017).
For digital marketers, personalised search brings opportunities as well as implications. As an example; with personalised search results ‘on-site SEO’ need to be less ‘broad’ and highly relevant to a particular search phrase resulting in considerable more inbound marketing assets such as blog-posts, white papers and web-site pages adapted to long-tail keywords and phrases having to be produced. While this means higher costs and more work for a digital marketing team, content and paid search optimised for long-tail keywords have a considerable higher CTR by being highly relevant also for a limited market segment, which in a traditional ‘broad’ marketing campaign would not be economically feasible. This also can be seen in the screenshot below; displaying the Google SERP for the term “yellow shoe strings” resulting in multiple organic search results optimized for this arguably obscure search phrase and also a paid search advert from Snowleader.com (2017).
This report has discussed the concept of web personalisation from a digital marketing perspective. As pointed out, web personalisation can help digital marketers in creating marketing assets and campaigns that are more efficient and with a higher ROI than traditional marketing strategies by being highly relevant to the end customer in both contents (visual and copy) and time of delivery which is also why I personally am a strong proponent of this methodology in marketing.
However; web personalization in the domain of ‘search’ do come with some serious implications clearly visible in the image below:
As shown a Google search for the term ‘Egypt’ made during the Arabic spring uproar, for one user yielded information about the conflict (left), while the same search made by another user returned mainly travelling tips (right). This s k ‘filter bubble’ clearly is a threat to the democratic process by leaving it up profit driven corporations to decide what constitute as truth which also is discussed by Pariser (2012). Web personalization clearly does come with benefits both for organisations, marketers and consumers; the question is at what price?
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