This post discusses web personalisation from a digital marketing perspective, and some of the societal implications of a personalised web.
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 sign-ups, website forms, surveys.
- Login data and purchases-in-process.
- CRM-data aggregated from user communication, including customer support and bug-tracking.
How web personalization works.
Web-personalization works by tracking web users and aggregating data about their behaviours, and using algorithms to parse the data using variables, parameters and conditionals defined by the objectives of the personalization efforts. The resulting insights 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 are web-cookies which are snippets of data associated with a specific user, which can be divided in four main categories:
- 1. Session cookies
- Session cookies stay in the browser memory only for the duration of a visitor session. Example use cases include 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 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 the 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. In addition, it also contains attributes that inform the browser what to do with the cookie (Cahn et al., 2016). Simplified, cookies work as follows:
- 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.
Cookies are created either server-side as a result of a web server instructing a browser to create the cookie, usually through an HTTP-header similar to Set-Cookie: ‘cookie_name’=’cookie_value’; or client-side with JavaScript using the document.cookie method’ (Mozilla Developer Network, 2017). While both methods have their pros- and cons, one implication of client-side cookies is that they do not work if the user has JavaScript turned off.
PERSONALISATION IN ACTION
E-commerce has long been a driving force in the development web-personalisation, 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 connect 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 techniques: dynamic content, e-mail marketing, and search. Case stories from each category also are presented, together with examples of commercial tools that can be used to achieve the outcome presented in each case:
#1. Dynamic web content
The idea behind dynamic web-content is to adapt 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 the position in a sales funnel. In the following section, four case-stories are presented of companies utilising this technique:
- AMAZON
- With the aim of selling more products, 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). As can be seen in the top screen-shot, below; new visitors to Amazon will not get any product recommendations, while existing customers get presented with a range of recommendations and product offers, as illustrated in the second image:
- SBI
- 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 asks first-time visitors to choose a role that best describes them. Acquiring this information enables SBI to cater to more relevant content. The upper image below illustrate how their site looks for a first-time visitor, while the lower shows their front-page after a visitor defined itself as a “marketing leader”.
- PARDOT
- 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, Pardot offers personalised user experiences adapted to each persona (Dynamic Content, 2017) of their primary customer segments. If, for example, a sales prospect is identified as a user of Sugar CRM (Sugarcrm.com, 2017), the person gets presented with information on how to integrate Sugar CRM with Pardot (top image below); while if the user has a SalesForce account (Salesforce.com, 2017), as seen in the lower image, he or she will get information on how to connect the platform with SalesForce.
#2. Automated email marketing
At the centre of every marketing automation platform is a customer database and a technical back-end which through the use of cookies, web-logs, analytics-scripts and third-party tools keep track of individual users’ behaviours and actions. This enables marketers to create personalised- and automatic email-campaigns triggered by defined events.
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 are personalised emails sent to specific leads or customers after a defined event is triggered and often is referred to as ‘Marketing Automation’. While most marketing automation platforms offers tools and solutions not only limited to email marketing; reactive leads nurturing is at the core of their product offering.
E-mail Triggers
Below are five examples of email automation triggers presented 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 cart and where the customer are offered free shipping and a 25% discount if finalising the purchase.
- Welcome mail
- As displayed in the image below the craft store Michaels (Michaels.com, 2017) send visitors signing up for their newsletter a welcome email and a discount next time they visit the 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 their new product (Econsultancy, 2017). The message was segmented, offering online customers an Amazon voucher, while Gulf-state countries received email vouchers for in-store purchases. The image below shows one of these emails which was 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 major 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 email Pinkberry sends to customers who have been inactive for some time, and which offer these customers a free product next time they visit a Pinkberry store.
- Success metrics
- Companies selling digital applications often use success metrics as triggers for automatic and personalised customer emails. Success metrics work by tracking how customers are using a product or service and can be used to, for example, provide customers with additional learning resources if they do not fulfil stated success metrics. The image below shows such email from Paymo (2017) which is sent to new users who, after a few days after signing up for Paymo’s project management app, still have not created a first project.
#3. Search
One of the most sophisticated areas of web personalization is ‘search.’ In this domain, Google has been a driving force in developing algorithms predicting the needs and wants of anyone using their search engine. Users of Google now get highly personalised search results that—not only are based on 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 with an identical search phrase, might get two completely different search results (Schmidt, 2016; Official Google Blog, 2017).
For marketers, personalised search brings opportunities as well as implications. As an example; with personalised search results ‘on-site SEO’ has to be highly relevant to a particular search-phrase which increases the need for more inbound marketing assets to be produced, as individual assets has to be created for significant long-tail keywords and phrases.
Below is presented the Google SERP for the term “yellow shoe strings.” As can be seen, this search-phrase result in multiple organic- and paid results for what is arguably a quite obscure search term:
The price of web personalization
Web personalisation, as shown in this post, can help marketers in creating efficient strategies by delivering messages that are highly relevant in both content- and time of delivery.
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). Arguably, this “filter bubble” is a threat to the democratic process as it lets profit-driven corporations decide what constitute as truth.
Web personalization, as shown in this post, do come with many benefits—the question is at what price?
- References
- Bigcommerce. (2017). BigCommerce: Ecommerce Platform & Shopping Cart Software. [online] Available at: https://www.bigcommerce.com/ [Accessed 6 Jun. 2017].
- Biglaces.com. (2017). Big Laces sells over 1000 styles and colours of Shoelaces and Bootlaces. We love laces and pride ourselves on the quality of our products and our customer service! on Big Laces website. [online] Available at: http://www.biglaces.com [Accessed 7 Jun. 2017].
- Cahn, A., Alfeld, S., Barford, P. and Muthukrishnan, S. (2016). An Empirical Study of Web Cookies. Proceedings of the 25th International Conference on World Wide Web – WWW ‘16.
- Campaignmonitor.com. (2017). Email Marketing for Your Business | Campaign Monitor. [online] Available at: https://www.campaignmonitor.com [Accessed 7 Jun. 2017].
- Cit.nus.edu.sg. (2017). NUS Centre for Instructional Technology. [online] Available at: http://cit.nus.edu.sg/ [Accessed 7 Jun. 2017].
- Dynamic Content. (2017). Dynamic Content – Salesforce Pardot. [online] Available at: http://www.pardot.com/training/dynamic-content/ [Accessed 7 Jun. 2017].
- Econsultancy. (2017). Sony case study. [online] Available at: https://econsultancy.com/blog/65329-seven-inspirational-email-marketing-case-studies-from-the-digitals/ [Accessed 7 Jun. 2017].
- Frankandoak.com. (2017). Frank + Oak. [online] Available at: https://www.frankandoak.com/ [Accessed 6 Jun. 2017].
- Hubspot.com. (2017). HubSpot | Inbound Marketing & Sales Software. [online] Available at: https://www.hubspot.com/ [Accessed 6 Jun. 2017].
- Kusinitz, S. (2017). The Definition of Net Promoter Score [In Under 100 Words]. [online] Blog.hubspot.com. Available at: https://blog.hubspot.com/marketing/the-definition-of-net-promoter-score-nps-in-under-100-words#sm.0001wxs3o3f37fi2tk41k7u62aj21 [Accessed 6 Jun. 2017].
- Magento. (2017). Magento: eCommerce platforms and solutions for selling online. [online] Available at: https://magento.com/ [Accessed 6 Jun. 2017].
- MailChimp. (2017). Marketing Automation – Sell More Stuff | MailChimp. [online] Available at: https://mailchimp.com/ [Accessed 7 Jun. 2017].
- Marketo.com. (2017). Best-in-Class Marketing Automation Software – Marketo. [online] Available at: https://www.marketo.com/ [Accessed 6 Jun. 2017].
- Meghan, A. (2017). Website Personalization. [online] Blog.hubspot.com. Available at: https://blog.hubspot.com/marketing/website-personalization-examples-dynamic#sm.0001wxs3o3f37fi2tk41k7u62aj21 [Accessed 7 Jun. 2017].
- Michaels.com. (2017). Michaels Stores – Art Supplies, Crafts & Framing. [online] Available at: http://www.michaels.com/ [Accessed 6 Jun. 2017].
- Mozilla Developer Network. (2017). Document.cookie. [online] Available at: https://developer.mozilla.org/en-US/docs/Web/API/Document/cookie [Accessed 6 Jun. 2017].
- nectarOM. (2017). How Amazon Uses Marketing Personalization – nectarOM. [online] Available at: https://www.nectarom.com/amazon-targets-customers/ [Accessed 6 Jun. 2017].
- Nordstrom. (2017). Nordstrom Online & In Store: Shoes, Jewelry, Clothing, Makeup, Dresses. [online] Available at: http://shop.nordstrom.com/ [Accessed 7 Jun. 2017].
- Official Google Blog. (2017). Personalized Search for everyone. [online] Available at: https://googleblog.blogspot.fr/2009/12/personalized-search-for-everyone.html [Accessed 7 Jun. 2017].
- Pariser, E. (2012). The filter bubble. 1st ed. London: Penguin Books.
- Paymo. (2017). Paymo · Online Project Management App. [online] Available at: https://www.paymoapp.com/ [Accessed 7 Jun. 2017].
- Pinkberry. (2017). Home – Pinkberry. [online] Available at: http://www.pinkberry.com/ [Accessed 7 Jun. 2017].
- Salesforce Pardot. (2017). Pardot B2B Marketing Automation by Salesforce. [online] Available at: http://www.pardot.com/ [Accessed 6 Jun. 2017].
- Salesforce.com. (2017). CRM Software & Cloud Computing Solutions – Salesforce UK. [online] Available at: https://www.salesforce.com [Accessed 7 Jun. 2017].
- Schmidt, E. (2016). How google works. 1st ed. [Place of publication not identified]: Grand Central Publishing.
- Segaran, T. (2007). Programming collective intelligence. 1st ed. Beijing: O’Reilly.
- Shopify. (2017). Meilleure solution e-commerce: votre boutique en ligne avec Shopify. [online] Available at: https://fr.shopify.com/ [Accessed 6 Jun. 2017].
- Skiena, S. (2012). The algorithm design manual. 1st ed. London: Springer.
- snowleader.com. (2017). Snowleader : Equipement ski, streetwear, matériel montagne, vêtements montagne. [online] Available at: https://www.snowleader.com [Accessed 7 Jun. 2017].
- Sugarcrm.com. (2017). Customer Relationship Management Software | SugarCRM. [online] Available at: https://www.sugarcrm.com/ [Accessed 7 Jun. 2017].
- Volusion.com. (2017). Ecommerce Website Store & Shopping Cart Software | Volusion. [online] Available at: https://www.volusion.com/ [Accessed 6 Jun. 2017].
- WooCommerce. (2017). WooCommerce – eCommerce for WordPress. [online] Available at: https://woocommerce.com/ [Accessed 6 Jun. 2017].