Driving Real-Time Personalization with Optimizely: Insights from Mårten Bokedal

1.With only 9% of organizations currently implementing real-time personalisation, what challenges do you foresee in adopting these new technologies within your team?

The true customer value of real-time personalization is all about the context. Today, we live in two parallel worlds:

A: Expectation economy: Customers now expect to be treated as individuals and it does not matter which company or organization that interacts with them. The bar has been raised.

B: The Attention economy: We are now being exposed to more digital messages across various channels than ever before. As humans, we can´t process all of them in which leaves our brain to focus on whatever information that is most relevant and timely important.

The best chance of interacting with your customers would be when they are open for a dialogue, and that usually means that you need to be able to gather information from one channel, and instantly in real-time use that intelligence to continue the dialogue in another channel. 

For this to happen, we need to better understand what part of the customer journey we can impact, for what reason and in what channel. The challenges we see implementing real-time personalization is both to understand what data you should collect from various channels (customer support, in-store, online) and how you could swiftly use that information to continue the dialogue in another channel, leading to the following main challenges:

•        Data silos
•        Legacy tech not integrated between channels
•         Misalignment in different teams, and how they are being measured
•         Privacy compliance

2.What role will machine learning models play in helping you optimize real-time user interactions based on behavioral data?

Machine learning has already played an instrumental part in increasing both efficiency and effectiveness of personalized marketing, far most in the shape of recommendation engines. Looking back, it has provided companies the tools to draw better conclusions from previous transactions and interactions and by that scaling personalization to provide better recommendations to customers.

Looking ahead, machine learning will take the next step by not only providing recommendations based on what has happened in the past, but also predicting what will happen next. By gathering more data points, predictions will provide more accurate recommendations down to an individualized level in context leading to personalization is not just delivered to the right person in the right channel, but also at the right time with more granular precision. 

3. How will the collaboration tool streamline the management of personalisation efforts, especially for large-scale marketing programs across multiple teams?

When speaking about personalization, most companies refer to data. The ability to collect, connect and segment data from different sources. But to deliver a great personalized experience, you also need content that speaks to the different segments or groups of people (or even individuals) you want to engage with. Personalization sits in the intersection between content and data.

For personalization not to become fragmented, you need an overview of the customer journey and the different personalization and you also need a content engine to deliver personalized content and messaging at scale.

A collaboration provides the different capabilities you need to:

•        Unified workspace with project overview, task assignments and cross-channel coordination.
•     Content development and audience segmentation collaboration
•     Insights of results and analytics for better planning
  
  

4. As personalisation continues to grow in importance, how do you intend to collaborate with data and marketing teams to maintain a customer-centric approach at all times?

Personalization is a topic that is moving up the C-Suite. It needs to become a management topic, since personalization rely on different dependencies like data, content, analytics and omnichannel execution. Removing silos is a first step, but also focusing on customer-centric KPIs to move towards  relationship building dialogues in opposite of mass communication and a shorter term campaign focused mindset. 

5.Given the advancements in personalisation technology, how do you plan to balance automating personal experiences with maintaining human oversight and creativity?

Successful personalization is complex, and you would need to run multiple experiments to not only deliver a personalized experience, but a meaningful personalized experience. Automation and scale can only be the results of personalization that matters, that is delivering value for the customer while also having a positive impact of the business KPIs.