Personalisation and hyperpersonalisation are as old the dinosaurs. In the same year Jurassic Park arrived in our cinemas (1993), Don Peppers and Martha Rogers predicted in their great book,The One to One Future, the end of mass marketing and a shift to personalisation. Marketers, and enterprise software vendors from dta and analytics to voice of customer, have spent the past 20 to 30 years trying to work out how to get it right – with mostly disappointing results. We need an honesty jar here. True personalisation is unworkable.
In reality, there are inherent, and regulatory, limits to what you can achieve. It’s impossible to personalise all the details about a person, to understand their personality, tastes, culture and preferences (even with partly understanding their preferences in their online behaviour and some shallow preferences), when you don’t know them. Next, your customers have become your biggest regulators and they aren’t impressed with just any personalisation. They are also cautious about sharing their data. Add in the fact some B2B, and certainly B2C companies, are still struggling with data management. Then, the regulators are becoming more stringent on data privacy and tech companies are building stronger tracking barriers. This all adds up to one thing. Personalisation needs to change. I would argue that we’ve been using the wrong framing. What we should be talking about relevancy.
AI and GenAI are the capital of everything possible
In this article I want to take a step back from the glare and wall of noise about personalisation. I walk you through the practical steps you can take to overcome these challenges and personalise experiences in a way that delivers value for the customer and the business. Customers want personalisation everywhere – without being invasive – in Europe and North America, in parts of APAC – and in China it’s a different story But, what are they really asking for? And how do you set about delivering it? The answers are rooted in AI-powered customer data platforms (CDPs). As discussed in a recent post, CX leaders have moved beyond personalisation and account-based marketing. They are investing in personalisation technology to hyperpersonalise experiences at scale, enhance their credibility with customers, improve efficiency and reduce waste.
Before we look at solutions and tactics, we need a deeper understanding of the problem. So, what is missing from personalisation – why is it unachievable as we’ve known it?
To beyond – but not infinity
Four factors make true personalisation unworkable.
You don’t know me – marketing teams have been using first names in emails and messaging for years. But using a first name doesn’t mean a person will find any value in the message or content. It also doesn’t mean you’re on first name terms. You don’t know who that person is on an individual level like a friend or family member does. Generalisations, being clichéd or outdated and superficial messaging are also switching customers off. Personalisation in B2B means leveraging insights on what we know, and what we can predict. It means tailoring your offer, content and messaging to meet the needs of customers – in their role and for the company. Relevancy is your strongest currency (more on this in a moment).
Customer are your biggest regulators and they aren’t impressed with just any personalisation – customers expect businesses to do something with the insights from their data that’s in their interests. They often know what their data’s worth. And they want something tailored, meaningful and relevant in return for sharing it. Using blunt tools often provokes blunt reactions. Brands who overuse personalisation or misuse data risk a customer backlash, consent withdrawal or an exodus to competitors who get it right.
Poor data management – by their own admission, almost a third of marketers (27%) believe that their data strategy is the biggest obstacle to personalisation, according to Gartner. This is not new, but incomplete, vaulted or siloed data is still diluting the effectiveness of personalisation strategies.
Regulators are getting more stringent and tech companies are building stronger tracking barriers – this is self explanatory. We all work in regulatory environments which govern the way we use data and protect customer privacy. Browser developers are creating a cookieless world and changing the way marketers do their job. Teams are having to update their data strategies as cookies and third-party data are erased from the playbook. McKinsey calculates that companies will have to spend 10 to 20% more to generate the same returns if they don’t work out how to grow their access to their first-party data. This is a big challenge, but leaders have found the solution.
A CDP can partly help you overcome these challenges. Platforms unify and analyse all of your first, second and any third-party data (historic and real-time). Users have a single, 360-degree view of customers’ offline and online behaviors, interests, needs, and preferences. Some CDPs share real-time unified data with execution systems to deliver the right content to the right customers on the right channel. Same save marketers time and effort on the last mile and have added advanced support for personalisation, journey orchestration and much more. Learn more here. There are hundreds of CDPs on the market: enterprise, mid-size and SMEs. Later, we pair some of the use cases with enterprise-grade solutions.
How to get started
If you are evaluating how to add a CDP into your MarTech stack to drive your personalisation plan, here are some steps leaders can take to get started.
Get executive buy-in, build a cross-functional team and bring data to the C-Suite and leadership teams
Get senior buy-in and align stakeholders around the new CDP / personalisation roadmap. CX leaders often appoint a Chief Data and Analytics Officer (CDAO) who collaborates with their C-suite colleagues to build a cross-functional team to develop and drive the change. CDPs ingest data from across the business. Every department with a stake needs to be involved. An effective personalisation strategy is about more than enabling technology and being data-smart. What internal resources, skills and processes are needed to design and implement the plan?
Create shared KPIs
What are your primary use cases? Establish a shared understanding across your organisation about what your priority use cases are, and align everyone around the shared definition. Don’t try to boil the ocean. Model two or three simple use cases to track and measure. Organisation-wide collaboration makes it easier to measure the impact of investment if there are shared goals and KPIs. This makes it easier to prove ROI. I talk about how to prove the value of your initial CDP efforts later.
Develop shared use cases
Remember the customer should be at the centre of everything. They’re not data points. Always put the customer and their needs, wants and preferences first. Keep in mind that personalisation or hyperpersonalisation are progressing but still limited, even with all available data, there are still limits for our B2B customer understanding.
Leverage real-time, unified customer profiles to personalise experiences
CDPs aggregate customer data and update profiles in real time. Profiles include demographics, preferences, past interactions, and transaction history – enabling companies to better anticipate customer needs and deliver personalised experiences at scale. A unified customer profile also helps agents identify cross-sell and upsell opportunities by making next-best product recommendations, and suggesting additional services – professional services for an enterprise software client, for example. That said, I believe that CDPs will be even better in the future as GenAI evolves. We are just starting in the road of real personalisation in my opinion.
Create a stronger bat signal with intent data
The sales funnel has become a complex network of tunnels as buyers criss cross touchpoints and channels. The journey is anything but linear. Many prospects don’t hit radars until they are ready to buy. AI analyses data points: web searches, pages people visit and the content they read to gauge when someone is likely to purchase. The insights can be used to create value with targeted and customised content to orchestrate the journey – personalised offers, messages, content. Forrester found that 50% of marketers using intent data experienced more successful sales prospecting.
How many buyers are in the market for your product or service at any given time? The ball park figure is around 3% to 5%. Are you ready to serve up the right content at the right moment?
Sophisticated customer segmentation
Segment journeys beyond basic personalisation. With a CDP you have the speed and flexibility to finesse your segmentation parameters using demographics, firmographics, behavioural and transactional data. Meaningless content and messaging kills loyalty. Sophisticated segmentation helps you target relevant customers with relevant content, offers and messaging at the relevant time.
Identity resolution
Look for a CDP which includes patented identity resolution technology to fill in any identity gaps and attributes data from your first, second and third-party sources to specific customers.
Real-time lead scoring
Using AI and machine learning, a CDP can identify leads are most likely to convert, those who need more nurturing and those who are likely to churn. Look for a solution that provides B2B lead prediction and account scoring, and surfaces customer churn and conversion predictions.
AI and machine learning can be applied to surface next-best action recommendations which help marketers identify how to best orchestrate journeys based on their wants, needs, or preferences.
A CDP can help you set rules to create scores based on individuals within accounts. Insights can be used to trigger campaigns or communications to create value through relevant, up-to-the-moment content, messaging and offers.
Scattering loss is a huge drain on campaigns. Platforms can also help you minimise the number of contacts that aren’t in a target group and not interested in your products and services.
Harness contextual information
GenAI can surface information to give front line staff full customer context to personalise responses and solve issues, share tailored recommendations and anticipate needs. The technology can understand intent, sentiment and emotion. Sales teams are also equipped with real-time contextual information to inform interactions at all stages of the customer journey.
These use cases are not exhaustive. The possibilities are vast. Be clear on your primary use cases, your integration needs, compliance factors, the technology capabilities you need, the capacity to scale as you grow, and regulatory requirements.
Test and keep it simple
This sounds obvious – but test a simple use case during any proof-of-concept pilot. The evidence is a powerful reminder that getting personalisation right (and wrong) is a high stakes business. Data transformations are tough. Customers are sceptical about the wave of ‘personalised’ content and offers they see every day.
Keep the pilot simple so that all stakeholders can understand the use case to prove value from initial CDP efforts. Focus on complex use cases too early, you could struggle to show the value of investment.
Where do we go from here?
We know that customers want personalisation everywhere. The status quo isn’t delivering on their expectations – or yours. Gartner predicts that within two years, 80% of marketers who have invested in personalisation will abandon their efforts due to lack of ROI and / or the difficulties in managing data. This is staggering and in many cases the fault line is strategic. Thinking around personalisation needs to change if we are to overcome the inherent and regulatory limits holding us back, and frustrating customers who are growing more and more sceptical about the ‘personalisation’ overtures from brands. Personalisation as we know it is going the way of the dinosaurs. AI and GenAI are now the capital of everything possible. Leaders already report strong ROI.
It will only get better.
What are your thoughts? Where do we go from here?
The article was originally posted on CMSWire: https://www.cmswire.com/customer-experience/redefining-personalization-in-marketing-how-ai-is-changing-the-game/
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