Every now and then, we have heard the phrase ‘hyper-personalised marketing’. It is one of those things that is spoken of a lot, but many aren’t really sure how to go about it. Simply put, hyper-personalisation is a data science-led marketing approach that helps brands in creating campaign or communication journeys based on real-time personal, behavioural and transactional data of their audience. It is very fascinating because it utilises behavioural and real-time data to create highly contextual, relevant communication for your audience.
This kind of granular approach enables the creation of highly relevant and specialised content which is tailor-made to individual audience’s wants and needs. Real-time data gathering, analysing and customising a highly personalised communication journey are the building blocks here, powered by machine learning and AI-powered applications such as consumer data segmentation, behavioural and predictive analysis, user experience (UX) and content creation/curation applications.
Why is it important? Because hyper-personalisation has a direct impact on marketing budgets; the instant engagement and conversions are much higher. From a long-term perspective, it builds, positions and evolves product-led brands to a purpose-led brand. On the other hand, audiences find personalised, tailored and relevant offerings for their needs, and provide a higher than usual engagement rate in return. Done right, it’s a classic win-win for both.
Hyper-personalised marketing is one of the frequently used phrases in strategy meetings; we end up talking about target audience behaviour and how relevant content marketing approach can be crafted across the consumer journeys. But the approach doesn’t evolve into proper hyper-personalised marketing applications and process integration in their Paid-Owned-Earned media ecosystem (POEM).
Currently, media agencies are the ones leveraging it most for their clients. As they have strict KPIs to meet and must derive outcomes from their campaigns, their media targeting is certainly leveraging advanced hyper-personalised tactics. It is largely skewed towards paid performance marketing for increasing sales and generating engagement and share of voice (SoV) for brands.
Although it seems logical and an obvious approach to adopt, very few brands in India are actually understanding and leveraging it well. In most scenarios, hyper-personalisation is done manually as a content marketing roadmap and mainly for social and digital content creation to cater to consumer expectation across their path to purchase journeys. Hence, theoretically it starts from crafting relevant content for social and digital media based on consumer demographics, behaviour and preferences. And then it’s being used across the entire POEM ecosystem. This leads to ticking the boxes in their marketing strategy but not actually a proper integration of data, machine learning and applications to leverage it.
Hyper-personalised marketing has to evolve across owned and earned channels as well. This will give customer engagements a longer shelf value and will eventually strengthen their customer value.
Brands do realise the need of not just relying on paid media support for a hyper personalised two-way engagement; their wish-list is to crack the code of doing it organically. And that’s why in most discussions, brand managers continuously push their marketing teams and agencies to create a holistic organic content marketing approach based on real-time, data-led hyper personalised marketing.
Amazon, Spotify, Reliance Jio, Google, Citi Bank, HDFC, OLA, Zomato, AirBnB are the some of the leading brands with a much-evolved automated hyper-personalised marketing approach built in their integrated marketing ecosystem.
The biggest obstacle is the perception of hyper-personalised marketing being highly tech advanced and expensive. It is a perfect case of putting the horse before the cart. Investing in applications certainly needs additional marketing budgets. Brands should start with aligning the processes for hyper-personalised marketing.
CONTENT INTELLIGENCE is the answer
The process is quite simple and linear. I’ve drawn up a check-list that covers the necessary steps marketers can take, such as:
- Use basic listening, databases and analytics tools
- Understand consumer behaviour and preferences (qualitative and quantitative)
- Leverage content intelligence to derive insights and communication space (planning and strategy)
- Craft campaigns and content as per consumer journeys (content production)
- Integrated channel planning for publishing (POEM)
- Track, analyse and optimise performance (Real time data and analytics.)
In conclusion, I also believe that the industry, as a whole, needs to evolve from confining hyper-personalisation to just paid media campaigns and reach a place of more holistic usage by expanding it in their owned and earned content marketing strategies. Brands need to start investing in either in-house integration of these applications and processes or through their creative agencies.
This article is a guest contribution by Nishith Srivastava - the head of marketing strategy and digital transformation at Indigo Consulting.