Personalization is big business.
McKinsey, for example, estimates that those brands that excel at personalization generate 40% more revenue than average players. Providing experiences tailored ever closer to customer preferences has been a major marketing battleground for years. But the game has changed.
Generative AI has brought hyper-personalized customer journeys at previously undreamt of scale and precision within brands’ reach. By crunching through huge quantities of demographic and behavioral data, brands can now serve up tailor-made recommendations, offers, and experiences before we even think to ask.
This ‘predictive personalization’ is one of the transformative CX trends examined in our new report ‘Where AI and Marketing Collide: 2024 CX Predictions’. The study highlights the enormous benefits of the technology, but also investigates a slow uptake by brands. Really slow. Like 13% slow.
How and why is predictive personalization being used?
The poster children for ‘predictive personalization’ are perhaps those ‘movies we thought you’d like’ recommendations common to most media streaming platforms. However new applications are being dreamt up every day across every sector.
These use cases include customizing email content, tailoring special offers or discounts, dynamic pricing based on predicted demand, product recommendations, adapting web content and layout, and personalizing ad content.
People like to be listened to, they like to be understood, and they like brands to treat them as individuals. Our report provides powerful new insight into how well such initiatives resonate with customers, including:
- 57% of consumers say personalized ads and recommendations help them find relevant products and services more easily.
- 51% say they like receiving product or service recommendations tailored to their personal preferences.
- 47% say they are more likely to click on an advert or email if it contains personalized content.
The report also shows how different types of ad personalization have different levels of impact on consumers’ willingness to provide their data to brands.
| Type of advert personalization | % Consumers willing to share data |
| Personalized offers as part of a loyalty scheme | 56% |
| Personalized to interests and online behaviors | 53% |
| Personalized to location | 53% |
| Personalized to personal characteristics | 47% |
But despite these clear benefits, only 13% of the 200 major brands contributing to the report say they use AI-driven predictive personalization. So where’s the disconnect?
Why aren’t more brands using predictive personalization?
To begin with, the technology is still relatively new – at least in terms of the widespread democratization of generative AI. Until recently the level of technology required to marshal, analyze, and activate customer data at enterprise scale was the preserve of large digital native technology firms. Now, predictive personalization is within reach for a far broader spread of organizations, so expect to see levels of implementation rise sharply in the coming months.
The brands we interviewed that don’t currently employ predictive personalization point to two other key reasons:
- 42% say their analytics capabilities aren’t sophisticated enough to support predictive personalization with sufficient accuracy.
- 33% say it’s too expensive to implement systems that will allow them to use AI effectively in this way.
No question, successful implementation of AI requires an upfront investment of resources. It requires time. It requires money. It also requires expertise to prepare the data architecture ahead of implementation, to integrate the AI, and to manage it.
But while this may feel daunting, there are ways to make the path towards predictive personalization a lot more manageable.
The path to predictive personalization
It helps to think of AI implementation not as a one-and-done single event, but as a journey with multiple steps. And wherever you are on that journey, the next step probably isn’t about AI at all. It’s about data.
The correct preparation and management of your customer data is critical for generative AI success. That’s because customer information is both the fuel and the training manual for AIs. Give machine learning systems accurate, high quality data from which to learn and the personalized messaging they produce is equally accurate and high quality. Miss this step and predictive personalization becomes the CX equivalent of throwing darts at a dartboard blindfold.
Achieving this level of data quality means collating and aligning all customer touchpoint data from across your business to form a desiloed, highly detailed, and accurate single source of customer truth. This process can be accelerated using a Customer Data Platform, or CDP. These are invaluable tools for unifying data from disparate systems across the martech stack – particularly when supported by identity solutions that match, cleanse and align customer records.
Even if you believe AI to be financially out of your reach right now, investing in your data architecture is a sensible step. Not only will it lend all marketing engagements greater relevance, it also ensures your business is ready for any future investments in AI.
With a solid data foundation in place, every other step towards predictive personalization is easier to navigate. You can make informed decisions about your organization’s readiness to implement an AI solution effectively, considering factors such as:
- Data depth: does your optimized data have the depth or nuance required to predict customer preferences accurately? If not, it could be time to augment your data with fresh insights from second and third-party sources.
- Expertise: does your business have the experience to extract maximum AI value? If not it’s either time to upskill existing teams, recruit AI specialists, or call in expert consultancy help.
- Platform capability: define your AI ambitions clearly and conduct thorough due diligence on the solutions that match these ambitions (and your budget) most closely. If you haven’t got the means to make this assessment, call in expert help.
The AI sector is developing so quickly that brands are in constant catch-up mode. So wherever you are on your path to predictive personalization there is a strong case for seeking expert advice from data and AI specialists – not only for implementation, but also management and governance.
The future’s coming…ready or not
A familiar trajectory lies ahead. Like radio, TV, the Internet or social media before it, AI (and in particular predictive personalization) was at one point a fringe technology. Like these antecedents did, it has become familiar if not broadly adopted. Before you know it, it will be standard commercial practice – as commonplace as having a website. We’re not there yet, but with the rewards on offer so high, it’s just a matter of time.
So now’s a smart time to get prepared. A great approach is to think big, think where you want to go, think how you want to apply AI in the future…but start small, prove value, prove value and performance quickly. Success in a more modest use case paves the way for broader, more comprehensive implementations. When budgets are tight, nothing loosens purse strings faster than proven performance.
For more information on the drivers of success in predictive personalization plus real-world examples and insights, make sure you download our report ‘Where AI and Marketing Collide: 2024 CX Predictions.
