Hyper-Personalisation During and Post Pandemic
Author: Matt Garner
Personalization delivered at scale can no longer be a future business goal. As we’ve detailed in our whitepaper, “How to be Hyper-Relevant“, partnering with Intel, COVID-19 has accelerated the need to create 1-2-1 experiences. This will be critical to engaging and retaining customers during and post-pandemic.
To give more context around our whitepaper, we’ll walk through the following in this blog:
- How online consumer behaviors have shifted due to COVID-19
- Why retention has become a top priority for the Media & Publishing sector
- How Hyper-Personalization can drive reader and viewer engagement
How has the pandemic disrupted Online Media & Publishing?
COVID-19 has forced abrupt and unparalleled changes to consumer behavior. With people in lockdown or self-isolation, the reliance on digital mediums to live, socialize and buy has never been more apparent. In particular, the Online Media & Publishing industry is dealing with unprecedented spikes in demand.
According to BARB, audiences for traditional TV viewing have now exceeded Christmas levels, reaching over 200 minutes per day in the UK*. Video on demand, such as BBC iPlayer and ITV Hub, totalled 5.4 billion minutes in March 2020, up from 4.9 billion minutes in March 2019. Online News sites have also soared globally, with traffic to The BBC, New York Times and The Guardian rising by over 25% in March year-on-year, as reported by Similarweb**.
The pandemic has spurred record readership and viewership levels, but media owners and publishers reliant on advertising dollars are seeing their incomes under pressure. 50% of UK marketers surveyed by Marketing Week plan to cut their budgets***. But almost half, at 45%, plan to change their ads or content to make these “more relevant”.
The industry is facing a difficult balancing act between soaring demand and dwindling budgets. Many need to prove the value of their audience, making the retention of readers, viewers and/or subscribers a, if not the, top priority. This is where hyper-personalisation of content and messaging enables companies to become “more relevant”, which we’ll explore in the next section.
What is Personalization?
Advertisers and publishers have been preaching the need to personalize how they communicate and sell to their customers for decades. Technological advances, particularly in AI, Machine Learning and Data Processing, are now enabling companies to do so in real-time and at scale.
Personalization is the practice of delivering 1-2-1 experiences to the individual user. This involves analyzing the user’s demographic data, such as age, gender and region, and their historical behavioral data, such as most recently viewed articles, to recommend and suggest relevant content. Other third-party data sources, such as search, social media or web traffic trends, can also be inputted to inform these recommendations.
For media owners and publishers, this automated process relies heavily on data processing to connect thousands, or even millions, of different content assets (videos, playlists, songs, podcasts, news or articles) to their thousands of different readers, viewers or subscribers.
How does Hyper-Personalization differ to Traditional Personalization?
Firstly, Machine Learning is applied to automatically learn and improve the experience through absorbing different data inputs and looking for trends to emerge. This method doesn’t rely on static rules to perform a particular task, such as “if a user clicks on this article, then recommend this article next”. Instead, the rules emerge from the data, in order to optimise towards the end goal.
Secondly, Real-time data can be injected into the mix to fine-tune content recommendations. These can include: the user’s current behaviours (what did the user just view, how far did they scroll down the page, at what moment did they pause and exit the video) and the user’s current context (what’s the user’s time, date, location or even weather which can impact their immediate needs).
Both these components ensure that content recommendations not only improve the relevance to the user; they’re also delivered in a timely manner that meet the user’s individual interests and needs.
And, is this practice GDPR-compliant? Hyper-personalisation ensures that customers are getting more value through relevant content and recommendations. This practice is then 100%-compliant if transparency and consent are in place, meaning that businesses are open with how they collect, process and store customer data, and customers have the right to ask or withdraw their consent.
Why is Hyper-Personalization critical during and post pandemic?
Hyper-Personalization can support immediate COVID-19 response efforts for both brands and publishers by:
- Improving views and conversions to generate more revenues
- Optimising budgets through limiting spend on customers with high purchase intent.
But the reality is that Personalization, in traditional or hyper form, has always been an important goal, i.e. delivering the right message, through the right channel, to the right customer, at the right time. COVID-19 has only accelerated the need to put this goal into practice.
As Google Cloud Specialization Partner of the Year, Datatonic has a proven track record in developing cutting-edge solutions in AI, Machine Learning and Cloud Modernisation. Driven by technical excellence in Data Science and in-depth industry experience, we’ve delivered Hyper-Personalisation and Recommendation System programs for top-tier clients across Publishing, Media and Retail sectors.
* BARB, What People Watch: Edition 3, 15 April 2020.
** Similarweb via Press Gazette, BBC, NY Times and Guardian among biggest winners, 17 April 2020.
*** Marketing Week, Just 7% of brands “seize the opportunity” during COVID, 28 April 2020.