Personalization in marketing has been an attractive yet somewhat elusive grail for years.
Since the very first personally addressed bulk email caused huge ripples in the marketing community, brands have been striving to win the race to consumers’ hearts and minds through increasingly deeper levels of engagement.
Today, we find marketers able to target hyper-relevant messages and offers across a dizzyingly diverse range of online and offline touchpoints, yet these advances come with a new set of personalization challenges.
Gartner predicts that 80% of marketing firms will abandon personalization efforts by 2025, but not because it doesn’t work. Quite the contrary, it’s due to the complexities of managing data to effectively produce the desired results.
For this reason alone, building a personalization engine in-house is not an option for most brands.
Knowing that personalization is so vital today, many brands seek easy, out-of-the-box personalization software and therefore more predictable methods to draw in consumers.
However, the net result of taking a half measure into personalization tools is that you don’t create the deeply tailored experience consumers want.
Personalization is about more than just addressing customers directly, it’s about acknowledging them with a message that resonates with each individual and guides them along a tailor-made user experience that considers their history, preferences, and needs.
Mistakes in the industry have led to famous personalization fails from major consumer brands trying to tap into personalized experiences.
However, when properly implemented, personalization enables a truly unique experience - it draws customers in by creating moments that feel magical. And, it’s what goes on behind the scenes that makes personalized marketing work seamlessly.
Requiring robust data sets, first-party data as well as third-party data, cross channel analysis, behavioral targeting, and predictive analytics that can be customized to meet the needs of your specific business.
So how can marketers continue to provide hyper-relevant experiences, but on an ever more personal device? Let’s take a look at some of the new challenges that come with personalization technology solutions and how marketers can meet them head-on.
What Is Personalization Technology?
Personalization technology is software that allows companies to connect with their consumers on an individual level. A list of data helps this software to identify what each consumer feels about the specific brand and collect preferences at the same time. This takes the usual one-on-one interactions of marketing and makes it even more individual for each customer involved.
Can Personalization Create a Seamless Experience?
Short answer – absolutely. A survey from Infosys shows that over 85% of consumers are influenced by personalized marketing efforts.
Amazon is a great example of successful personalization. This is a business that collects enormous volumes of customer data and utilizes machine learning to generate personalized product recommendations.
Features like the "customers who bought this item also bought" and "frequently bought together" categories enable Amazon to create a fully tailored experience that feels like magic for the end-user.
The results speak for themselves – 44% of customers buy from these customized recommendations.
Netflix is another example of a brand that wields marketing personalization with skill. Everything it does is supercharged with data and smart AI.
Each user’s account is programmed to show content they are likely to find interesting or enjoyable based on previous viewing behavior.
The powerful Netflix recommendation engine uses a selection of viewing data, search history, and ratings to create unique homepages for users.
Even each film cover is personalized so that the actor featured on the cover is someone the user is likely to recognize. And the result is that this trail-blazing streaming service consistently sees a revenue increase year on year.
GrubHub is another business that successfully uses personalization, harnessing its customer and contextual data to power its recommendation engine.
The more a consumer orders from GrubHub, the more it learns about their tastes and preferences to create a unique taste profile, which enables it to provide recommendations from its 4,000 dish taxonomy.
Personalization Technology Roadblocks
Personalization can be challenging to get right for a variety of reasons, one of the primaries being an inability to properly utilize customer data, which can be overwhelming in its magnitude, variety, and quality.
Knowing how to mobilize and make sense of this information requires a skilled data science team; not something many brands have the luxury of.
Indeed, while marketing teams want to harness their customer data to create truly relevant, personal interactions with customers, only 18% are very confident in their ability to successfully execute personalization.
A range of challenges from not having the right internal resources and a complete view of the consumer to outdated technology that’s not well-optimized for mobile causes marketers to struggle to perfect smart segmentation and personalization, or indeed uncover insights from consumer inclination and behavior.
While some brands can interpret data and learn something about their customers, not all are able to compile a fulsome picture of their customer base.
Without the right technology, information gathered about consumer preferences and behavior can create a chaotic picture. And ultimately, this makes the execution of a personalized marketing campaign virtually impossible.
Getting Personalization Technology Right
Technology is the key enabler of personalization, but as we have discussed previously, it must be underpinned by the right strategy, people, and processes.
The tech required to execute personalization ranges from Artificial Intelligence (AI) and Machine Learning (ML) to data science to analytics and marketing automation, and all must work together for a personalization marketing campaign to be successful.
If these systems are disparate, this is where accidents like mistaken emails or misinterpreted data occur.
Dynamic and relevant content is the foundation of personalization. The datasets needed to deliver this are developed with machines that analyze data from a range of internal and external sources to create offers and messaging tailored to the individual.
The more accurate the data used to build this content, the better chance brands have to delight customers with personalization. Poorly executed personalization has little chance of delighting or building loyalty.
The emerging use of AI and ML for personalization is taking the technology gap to a new level, as well as lowering costs and improving efficiency.
By effectively leveraging this underlying technology, brands can collect data from customer behavior and preferences without requiring intensive human intervention to compile it – the key finding is that machines learn faster than humans (and at a lower cost).
The key determining factor in delivering an effective personalization strategy is speed.
A brand can move faster than its competitors to deliver relevant offers that the customer wants, which improves the probability of conversion and also allows it to monetize data more effectively.
The Customer Journey
A holistic view of the customer journey is not only key to a deeper understanding of what customers prefer, but also how they might want to be served personalized recommendations differently at different moments.
On average, brands are really good at collecting data about their customers – but not using it very well.
The challenge is to develop a full 360-degree view of your user throughout their entire journey on an individual level.
This provides the control necessary for delivering one message to some users and another set of messages to others.
In practice, most companies don't have a complete view of their customers – the focus is often on the last mile. But this isn't enough.
With most companies having a partial view of their customers and making decisions about them based on partial data sets, it's no surprise that there are problems with customer understanding and engagement.
This is where personalization technologies come into play – they can use AI to analyze global trends in people’s preferences and behavior, regardless of whether these individuals interacted with the brand before or came from third-party sources such as social media or mobile devices.
Technology Used for Personalization
Because marketers are now working with hundreds of millions of data points a day, AI and machine learning powered platforms are increasingly being used to do the heavy lifting; the sheer scale of data makes it impossible to handle manually.
Instead of trying to update spreadsheets and get reports out of the data team, marketers use intelligent platforms that generate insights and make real-time marketing recommendations based on the data they take in.
You don’t have to watch the platform work (and in many cases may not even need to train it), you just let it handle the data for you. Most of this happens automatically, as the platform recognizes trends and shifts in the data and adjusts its recommendations accordingly.
Marketers can use AI and ML to refine personalized marketing based on a constant stream of customer data, which is great.
But what’s more important is where that takes you next: what does the data help you to understand about consumer behavior? How can you use those insights to make something awesome happen? And how can AI and ML help?
An AI-enabled analytics platform should be able to learn what matters most to you: based on your data, how you use the insights, the reports you typically run, and any other information that feeds its algorithm.
It’ll not only report back on your data; it’ll also be able to alert you to critical information you didn’t know you needed.
Maybe something you didn’t know you could look for.
Is there a sudden spike in loyalty card registrations? Is today’s redemption activity outside the established pattern of behavior? What’s caused it and what does it mean for marketing? Is one piece of content under-performing others in a specific region?
If you double down on an unexpectedly successful offer, what will the impact be on your bottom line?
If you have all these insights delivered to you, then the hard data work is already done. No more spreadsheets, no more relying on data scientists to unlock the information behind the numbers.
Marketing’s job becomes easier and making data-driven marketing decisions becomes a lot faster.
What is the difference between Artificial Intelligence & Machine Learning?
Artificial Intelligence (AI) is a type of intelligence exhibited by machines. Machine Learning (ML) is a subset within the wider realm of Artificial Intelligence (AI) which gives computers the ability to learn without being explicitly programmed.
Can You Really Achieve Personalized Experiences With One-Size-Fits-All Solutions?
As more consumers adopt and depend on mobile devices for almost every aspect of their daily lives, the ability to deliver relevant content within an optimal timeframe becomes increasingly important.
For many consumers, mobile devices are the first (and sometimes only) screen they will use to interact with your brand.
While there is a growing number of marketing automation vendors with a range of capabilities and functionality, many are one-size-fits-all (OSFA) solutions.
Considering all the crucial elements of an effective personalized marketing strategy, the idea of executing this with an OSFA technology solution is rather ironic.
A solution like Plexure’s evaluates all kinds of data, both structured and unstructured, and from multiple sources to extrapolate valuable insights and patterns.
It can seamlessly integrate into your existing tech stack to add value to core business processes with the purpose of enabling you to craft smart and consistent marketing campaigns across every stage of your customer lifecycle.
Putting the personal into personalization is as much about tailoring to your business as it is tailoring to your customers.
The other thing to consider when looking for a personalization partner is ongoing support. Technology may be easy to buy but getting the most out of it often requires a helping hand.
The support required will vary from brand to brand, which is where managed service engagements can be a huge benefit.
Similarly, partners with a team of data scientists to solve complex business challenges can be incredibly important, as is equally a 24/7 support desk where there is a real person on hand to help.
But, with the proper technology, help, and support, personalization can be executed extremely smoothly and produce incredible results.
- Personalization is about more than just addressing customers directly – it’s about acknowledging them with a message that resonates with each individual and guides them along a tailor-made user experience that considers their history, preferences, and needs.
- When properly implemented, personalization enables a truly unique experience - it draws customers in.
- Technology is the key enabler of personalization, but it must be underpinned by the right strategy, people, and processes.
- The tech required to execute personalization ranges from Artificial Intelligence (AI) and Machine Learning (ML) to data science to analytics and marketing automation, and all must work together for a personalization marketing campaign to be successful.
- Personalized marketing is crucial for brands as more and more consumers adopt mobile devices.
- Many marketing automation vendors offer one-size-fits-all (OSFA) solutions which may not be effective.