Today’s fast pace requires a digitised approach that can build unique and effective offers with profile data

We see Facebook campaigns personalised to seasons; Google ads customised to keywords, but what about the actual offers? While their advertising look varies, their true selves remain the same.

Campaigns may be based on a pile of research for each segmentation. However, it’s not enough. Such an effort requires priceless resources (time, human resources, and human intelligence). Plus, these intensive hours of work don’t guarantee success either.

This is the moment when technology and AI step in to streamline the pitching process to seconds. Not only that, but today’s technology can personalise business offers to each prospect.

How can a company accomplish that?

A concept that blends data with technology, personalisation entails taking out all unnecessary parts. What remains is an effective offer, packed with informative and helpful details.

Such a modern sales strategy has become a necessity. It’s easy to customise a pitch to one client only. 

However, when companies handle thousands of clients every day, teams can’t secure the same distinctive experience for each lead. This is why they need the help of technology.

We’re talking about transforming offer creation into a cycle: data collecting, the decision stage, the design process, and multi-channel distribution. 

Data collecting

Personalisation at scale starts with centralised data. In many cases, companies have all the puzzle pieces under their grasp. Unfortunately, they fail at coordinating them into the correct picture.

Also Read: Top 5 skills needed to carve a niche in big data

In other words, different silos of data don’t move at all outside their original channels.

For instance, customer care agents may lack a protocol for redirecting their customer profiles to their marketing department. Similarly, call centres may neglect the value their daily insights can bring to their sales colleagues.

Consequently, the first step to personalisation at scale consists of data transparency. Populate one system with all available data and analysis and watch how customer profiles take shape. 

The decision stage

Back in the outdated segmentation cycle, the decision stage was missing. Marketers were transitioning from data collection to design without a template to work on.

However, the AI emergence made it possible to systemize data into a logical pattern. Nowadays, machine learning is capable of analysing millions of behavioural reports.

Afterwards, it uses the acquired knowledge to partition prospects according to an in-house system of templates. 

At the decision stage, marketers work on moulding behavioural data into patterns. More than that, based on the identified micro-etiquettes, companies can predict what their next interests are going to be.

Within these patterns, there are certain highlighted incentives with the power to sway those who fit the profile. In the end, marketers come into the possession of valuable knowledge of how to connect with their audiences.

The design process 

Personalisation at scale needs a flexible design of the product offering to accommodate the decision stage. The solution is a recent trend that is reinvigorating the tech world: modularity. 

This means that an entire marketing cycle is broken up into small parts. Each campaign element (copy, product images, e-mails, videos, banners, etc.) needs a format that works well in any combination with the others. 

Also Read: What role does big data play in the insurance industry?

With each campaign element in standby, marketers can change their order, improve, and adapt them to new scenarios at a fast pace. Thanks to the modularity, companies are able to shuffle sections between them and get unique campaigns catered to the right profile. 

For example, let’s say the system identifies a new lead in search of a great offer. Based on the profile data, the design process customises a Facebook ad by swapping a discount code with a free delivery sign.

It turns out; the prospect was willing to purchase the product only if transportation fees were free of charge.

In the end, companies come to connect each behavioural pattern identified during the decision stage to a uniquely designed offer.

To sum up, personalisation at scale begins with centralising data, moulding intel into behavioural templates at a micro-level, and populating these templates with modular design elements.

Multi-channel distribution

The last stage is about sending your customised offers away. Nonetheless, this operation isn’t without its setbacks. An efficient campaign at scale requires a fluid cross-channel coordination.

To proceed, companies need to build an automated delivery system. Such a program is meant to link leads with their customised experience. The existence of this last piece activates an efficient sequence where product offers reach those who want to hear about them. 

The old way was about launching campaigns according to a cold in-house schedule. By contrast, personalisation at scale distributes relevant offers via the right channels to prospects who need them.

This is where a multichannel strategy evolves into omnichannel communication. At this point, a new offer is updated in real-time on all platforms whether it’s an in-store display, digital billboard or e-Commerce store. 

For instance, ProductLead is a smart tool that fits this stage exactly. Its omnichannel solution delivers content simultaneously on social media, website, and any digital board around the world.

This way, the customer journey avoids lapses and shoppers enjoy a seamless experience no matter the channel they use.

Conclusion

All in all, the primary marketing toolkit (Analytics, CRM, Social Media, G Suite, etc.) can’t cope with the modern customer anymore. Now is the time for marketers to seize all the innovative solutions modern technology puts at their disposal.

If you think about it, personalisation at scale fixes a real issue that has been choking the digital world for far too long now.

Instead of the daily excess of irrelevant CTAs that makes users turn blind to the benefits of today’s market, technology can redirect meaningful messages only to those who are seeking them.

In the end, customised offers return satisfactory results to companies and build a clean digital environment for consumers as well.

Also Read: Setting up your marketing anatomy by measuring what really matters

This can be the answer to a harmonious e-commerce landscape where consumers get offers they truly love, and marketers unlock full technological potential. 

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Image Credit: Maarten van den Heuvel

 

 

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