As the last installment in this review series of our final webinar from 2021, Leveraging Data to Drive Increased Personalisation and Empathy, we are going to see what is considered a good approach for creating a personalized journey in any industry.
Our guest speaker for the webinar’s third and final presentation was Armin Gulber, co-founder and Design, and Digitization Lead at Nextwit, an agile start-up that helps companies big and small from a broad spectrum of industries in delivering intuitive data and digital initiatives.
And that’s the topic he chose to discuss quite elaborately, providing an insightful perspective on how to effectively personalize digital (and non-digital) services.
To better illustrate how most companies view personalization schemes, Armin used the analogy of a ship cruising the sea to find a bountiful island where it could land.
Once it locates such an island on the horizon, it first has to navigate some treacherous, rocky waters before arriving at its destination.
For a company initiating a personalization journey, the island is a combination of lucrative prospects, increased revenue, and improved organizational efficiency. And the rocks surrounding this place are the challenges and perceived obstacles that, if surmounted, will lead the company to achieve its business objectives. Such “obstacles” also include figuring out appealing offers for customers and designing a UI that users will love.
However, most ships and their crews can’t see what’s going on under the waves and are oblivious to such factors.
The same applies to most companies that are ready to embark on a personalization journey but are not mindful enough of such “hidden” challenges and aspects of personalization.
So, what’s under the water, exactly?
Now that we’ve scouted the waters surrounding the island and know what lies beneath, we need a course of action to reach our destination safely.
Armin brought forward Nextwit’s suggested order for approaching personalization:
Armin brought some case studies as well to show how the failure to meet these personalization perspectives can derail, delay, or disrupt a personalization journey entirely.
Missing aspects: Open-minded culture, Robust and clean data, Explored customer needs
Two service providers went through a merger and began looking for synergies straight away. As a business objective, they wanted to increase revenue by targeting and reaching each other’s customers. They implemented a rushed communication campaign and tried to provide appealing offers but didn’t realize that they’d had raw, unrefined data about their customers’ needs and interests. Offers were dispatched to the wrong segments, some customers received the same offer over and over again, which ruined personalization.
Their personalized eDM campaigns eventually failed due to low conversion rates and bad CX.
Digital Content Provider
Missing aspects: Future-proof infrastructure, Robust and clean data, Explored customer needs
The company sought to launch a new app and was very confident about its understanding of personalization. They were fully focused on creating a shiny frontend for the app but didn’t do much with the spaghetti architecture in the back. A jumbled IT stack makes the use of data in personalization schemes difficult and overcomplicates processes of measuring and monitoring results and KPIs. Furthermore, as customer needs were not explored, the company failed to realize that their users were more interested in the quantity of content than how it was presented through recommendations.
In the end, the management seemed doubtful if the new app would deliver the financial results they’d hoped for.
Financial Service Provider
Missing aspects: Future-proof infrastructure, Robust and clean data
A mammoth financial service provider wanted to increase operational efficiency by moving all its data to digital channels, as it was struggling with its legacy systems that also severely limited customer options. They intended to launch digital self-care solutions which required the presentation of personalized information, however, due to technological debt incurred through said legacy systems, it led to a host of issues, including cases where customers found misleading or incorrect data about services (e.g. billing). The company meant well, but its systems were so obsolete that it made the smooth transition to digitally personalized solutions all but impossible. Instead, they should have started the process with data cleansing; using data virtualization tools that could bundle up all the fragmented data and organize it, which wouldn’t be a huge investment when done smartly.
Physical Industry (Agriculture)
Missing aspects: Open-minded culture, Robust and clean data, Explored customer needs
Slowly, most companies in physical operations are starting to realize they generate enormous amounts of data that can be utilized in their sales processes. Such was the case with a company dealing in agricultural products; at first, they didn’t want to understand that personalization could help their efforts. They knew they had the data and sought to provide a digital solution with great UI but overlooked that they could take it a step further, see what their customers are doing, and tailor the solution to their needs. In this case, by processing data about generations of seeds, soil composition, weather, and more, then comparing such patterns to data gathered from the fields of neighbouring farmers, they could help their customers pick the best seed, soil, equipment, and agricultural technique that best suits their unique circumstances.
Data can be easily applied to digital surfaces for differentiating experiences , so prototyping with real data can help in these situations to get a better grasp on customer needs.
All in all, if you consider all seven perspectives of personalization, even those that are “hidden” and can’t be perceived as easily as frontend and coming up with good offers, the journey itself from inception to implementation and launch shouldn’t become a multi-year project.
As a closing, Armin explained Nextwit’s tried and proven approach that they suggest to their clients:
Nextwit can help in all three stages: workshop facilitation, research, and data modeling during the solution health check, by providing prototype construction and data virtualization when prototyping, as well as creating or tweaking decision-supporting tools and providing training post-launch.
If this last blog post on our final 2021 webinar piqued your interest and you’d like to learn more, head over here and watch the presentations at your own leisure!