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If you work in marketing, then you will know that we love a model, a matrix, or a manifesto. Back in 2019, I wrote an article on the three Ps of marketing. But as time, technology, and data progressed, one of these Ps has risen to the top of consumer expectations: personalisation.
So, what does personalisation mean in 2020? What’s possible and what’s a pipe dream? Above all, what do you need to consider when it comes to data-driven marketing with personalisation at its core?
First, it's important to set the scene and observe a shift in consumer buying patterns and expectations. According to the sixth edition of Salesforce State of Marketing report, 73% of customers expect companies to understand their needs and expectations. This fact, coupled with the realisation that ALL purchases are now considered (buying a toothbrush requires research), puts consumers firmly in the driving seat when it comes to expectations and buying power.
We need to be ready to react, personalise, and respond in real-time if we want to attract and maintain loyal customers. To do so, we need to consider a framework to shift the marketing mindset. Welcome to the three Ts of personalisation in 2020:
Targeting has been top of mind for marketers for decades. But how we target has seen a seismic shift from broad categories, to personas, to segments, and now individuals.
Marketing has gone full circle, starting with the local shop, where the owner would know you, your name, and your preferences. Then to industrialisation, when branding was crucial and mass communication prevailed. Today, we’re back to personalised 1-to-1 experiences.
So, how do we target in the world of personalisation? There are two fundamental options: rule-based or machine learning algorithms (or a blend). The latter has seen a huge rise in recent times, with 84% of marketers planning to use artificial intelligence (AI) in 2020 and by 2025, 95% of interactions with consumers will be driven by AI.
Rule-based targeting allows marketers to target segments based on the creation and manual building of business rules. The simplest articulation would be IF/THEN statements. For example, if a user falls into segment X, then show Y. These segments can be broad or narrow, and be based on multiple data points such as behaviour or attributes.
Machine learning targeting, on the other hand, utilises algorithms and predictive analysis to dynamically present the most relevant content or experience to each individual. This allows for true personalised experiences with a target of one.
Both rule-based and machine learning targeting have their pros and cons. The real key to effective personalisation targeting? Matching the strategy to the goal.
The number one challenge for marketers in 2020? Engaging customers in real-time.* But as I have previously written, the more important question is real time or right time?
This can be answered by creating a quadrant: inbound versus outbound, and proactive versus reactive. Timing needs to be based on the need of a customer (reactive/inbound) or a need created by the brand (proactive/outbound). The correct timing needs to be aligned with the need, real time in terms of reactive/inbound, or right time in terms of proactive/outbound. By building this quadrant based on use cases, expectation, and empathy, the answers to real time or right time will become apparent very quickly.
Once this is decided upon and aligned, the process and relevant technology that is designed to deliver that timing will need to be implemented and managed. Real time activation needs the right data, infrastructure, and architecture to succeed at scale.
As Karl Wirth, CEO of Evergage says, “Always be testing”. But testing, learning, and importantly failing, requires bravery and a shift in mindset from the top down.
As we move to the “test and learn” way of working, it is firstly crucial to set the objectives of the test. Why are we testing? What key performance indicator or business metric are we trying to move the dial in, and by how much? As soon as that is clear, then the variables, and all-important control group can be set.
When it comes to the results, have confidence in your learnings. But remember to dig deep into the data and consider all the factors, such as business cycles, time of year, and campaign length. If things still don’t look right, test again.
The future of marketing is testing. Thanks to AI and machine learning, companies will be able to automate testing and optimisation without the heavy reliance on human resources. But it is important to learn from the failures, just as much as the successes. As Thomas A. Edison famously said, “I have not failed. I’ve just found 10,000 ways that won’t work”.
There is a fourth silent T that should not be forgotten: trust. This should go without saying in the world we live in today. Our research shows that 65% of customers have stopped buying from brands that have done something distrustful.* As Rachel Botsman discusses in her book, Who Can You Trust?, trust is the force needed to make the leap from known to unknown, and to cross the sea of uncertainty. We have seen a fundamental shift in trust, from local to institutional, and now to distributed. In order to build trust with prospects and customers, we need to allow users to control their data for personalisation with explicit consent and understand reviews from their peers, friends, and communities. Trust and transparency must not be overlooked.
So, where to from here?
Marketing is about asking the right questions constantly. Perhaps it is time to start asking, what am I doing with my targeting? Do I have the right strategy in place to deliver true personalisation at a 1-to-1 level? Is my timing right and contextual? Am I testing, failing, and learning? Do I have the right process and people in place to deliver this? Finally, does my technology deliver my desired outcomes?
These trends, customer expectations, and much more will be discussed at an upcoming Salesforce webinar on Wednesday, August 5, 2020, to help transform your marketing strategy beyond the new normal.
Sign up for our webinar, Trends From Nearly 7,000 Marketing Leaders to Help You Lead Through Change, here.
