Consider your daily life as a customer: you don’t want to be bothered with irrelevant coupons, emails, or texts, but you do want to be informed of offers that meet your specific needs.

Personalisation is an approach to customer engagement—almost a philosophy—that focuses on delivering tailored, meaningful, and relevant customer communication. On the business side, personalisation allows a company to activate all the customer data available to deliver more relevant experiences for your existing customers and for future prospects as well.

In today’s marketplace, it’s not enough to send each customer an email that addresses simply by name and offers a discount based on a past purchase. Segmentation is not personalisation either. You have to design and deliver tailored messages to thousands of customers in multiple interactions. That’s where technology comes in.

It is now possible for companies to truly interact on a personal basis with all their customers all the time. That’s why it’s important to always think and talk in terms not just of personalisation, but personalisation at scale.

Why should personalisation at scale matter to marketers?

When done well, personalisation can deliver impact and growth quickly while creating better experiences for your customers. Personalisation plays a critical role across the full customer lifecycle—acquisition, customer engagement, basket size, frequency of purchase, cross-sell, and/or churn prevention, among other things.

Because the future of marketing is in data analytics, agile, and digital—and personalisation at scale is where they all intersect! A majority of classical marketing disciplines—advertising, messages, prices, services—will become much more personalised. And it builds real value.

Research by respected research consultants McKinsey shows that personalisation, fully implemented, can unlock significant near-term value for businesses—such as 10 – 20 percent more efficient marketing and greater cost savings and a 10 – 30 percent uplift in revenue and retention. What’s more, even though immediate results can be achieved in a matter of months, adopting personalisation as a practice can have a long-term positive effect on customer satisfaction.

So what is the right approach to making personalisation at scale work?

The reality is that consumers want better personalization: approximately 80 percent of them say it’s important to them. But, while 95 percent of the marketing professionals McKinsey questioned at the World Retail Congress, in industries from energy to banking, said they recognise the need and potential for personalisation, only 20 percent say retailers are doing a good job at it.

Understanding the importance of data and analytics is the key value generator for all personalisation attempts, and it has to be at the heart of your thinking. First of all, you need to make personalisation a priority and develop a strategy to build the right foundations and operational capabilities.

Establishing the strategy doesn’t have to be a lengthy project that takes weeks or months, just a deliberate top-management decision to create a path to more personalised customer experience.

Many marketers believe the first priority is to fully understand the quality of their data, build capabilities in analytics, or find the right tools. But most of them can start making personalisation work quickly with what they already have.

The next step, decisioning, is also not as complicated as at first it might seem. You typically start by understanding who the customer is, looking at their behaviour, and identifying the key triggers to act on, or KPIs of value.  This might come as a surprise, but a lot of the initial data mining is simply hypothesis driven, and a lot of the low-hanging fruit to drive momentum in the organisation is common sense. 

What prevents companies from successfully personalising at scale?

Many companies are collecting and storing massive amounts of data but are having trouble finding and merging the most relevant subsets. Instead of generating and assembling more and more data, companies should focus on identifying and collecting the right data. Sometimes less data actually put into action is more effective than adding the most sophisticated external data set. 

Second, many companies still think in terms of seasons or general events rather than appropriate triggers. It was believed on a few years ago that triggers are the specific occasions when a particular message will be most valuable to a customer. These days this has been overtaken by AI and is more empathetic to the whole customer journey instead of measurements at set points along the way.

Third, personalization at scale requires agile, cross-functional teams, and many companies are still stuck in a siloed way of working. Running an agile project once is relatively easy, but making it stick and scale is difficult.

Those cross-functional teams make it easier to apply a test-and-learn approach, as all relevant experts are in the room and insights can be shared instantly, which is a prerequisite for personalisation at scale. As a result, the number of campaigns brought live can easily increase by a factor of ten or more. Test-and-learn or not being afraid to fail can be a significant cultural shift for traditional companies. And it’s a lot of fun for employees—something we always find amazing. 

Finally, the right tech tools and infrastructure have to be in place to test successfully on a large scale across the entire customer base, and this can feel overwhelming. However, technology has advanced a lot, and there are several simple and powerful solutions available.

It is observed that there are four common roadblocks:
1. Many companies are collecting and storing massive amounts of data but are having trouble finding and merging the most relevant subsets. Instead of generating and assembling more and more data, companies should focus on identifying and collecting the right data. Sometimes less data actually put into action is more effective than adding the most sophisticated external data set. 

2. Many companies still think in terms of seasons or general events rather than appropriate triggers. Triggers are the specific occasions when a particular message will be most valuable to a customer. A customer moving to a new home, for example, is a trigger for an energy company. In my experience, trigger-based actions have three to four times the effect of standard communication. 

3. Personalisation at scale requires agile, cross-functional teams, and many companies are still stuck in a siloed way of working. Running an agile project once is relatively easy, but making it stick and scale is difficult. Those cross-functional teams make it easier to apply a test-and-learn approach, as all relevant experts are in the room and insights can be shared instantly, which is a prerequisite for personalisation at scale.

As a result, the number of campaigns brought live can easily increase by a factor of ten or more. Test-and-learn or not being afraid to fail can be a significant cultural shift for traditional companies. And it’s a lot of fun for employees—something we always find amazing. 

4. The right tech tools and infrastructure have to be in place to test successfully on a large scale across the entire customer base, and this can feel overwhelming. However, technology has advanced a lot, and there are several simple and powerful solutions available.

Companies really need to engage with marketing, operations, and tech experts to build organizational capabilities that can sustain change and establish new ways of working. That comes from both training existing staff and recruiting new top talent. For companies that need to fill multiple roles with specific skill sets, specialist competence is essential. But it’s not enough.

Specialists also need to be “translators,” who can communicate insights comfortably and effectively across business functions. It’s understandably difficult to make the right technology choices, since the landscape is very dynamic, complex, and not particularly transparent, and it’s unclear what different providers are doing and what advantages they offer.

Predictive personalisation Software (PPS), is a marketers dream solution, selecting products populating and sending as email marketing, items identified as having the greatest likelihood of being bought next that individual consumer. Without need of any human involvement whatsoever it delivers that 20x higher ROI mentioned. Plus because it runs perpetually it is always data-accurate, with no down time ever.

But one way to start to simplify the technology side is to understand that it has to enable three things: 

1. Integration of consumer data to develop a clear and complete view of your customers, ultimately through customer-data platforms
2. Decision making based on signals given during the customer journey. It can be as simple as an Excel VBA-macro that helps a call-center agent shape a personalised offer to a customer or as sophisticated as a centralised decisioning engine, or “brain,” that can interact with outlying systems to make real-time decisions based on consumer data being captured.
3. Distribution of coordinated content offers across audiences and channels in real time, with room for teams to adjust them based on feedback

Simply put, technology is crucial to scaling personalisation, and a customer decision platform has to be at the heart of it. We found that 50 percent of companies that outperform the market feel they have the tech tools they need, compared with only 16 percent of their poorer-performing peers.

In the long run, the real value comes not just in developing the three elements of the operating model but in getting them to work together seamlessly. Once consumer data has been collected, it needs to be prepared and transformed to uncover additional insights: the more comprehensive the customer view, the more accurate the predictions of the decision-engine models will be. But while technology is very important, technology alone won’t solve all the personalisation challenges, but it’s damn close.

Technology and analytics play a big role in driving impact beyond pilots and scale, but at the end of the day, companies don’t achieve the impact without changing their internal operating model to be agile, focused on key customer KPIs, cross-functional, and driven by rapid decision making.