How to create an optimization culture
Adriano Mucciardi is a Senior Manager at digital consultancy Converteo. He specializes in helping brands to develop their digital strategy and optimizing their digital ecosystems.
Based on his experience, we asked him to share some of his tips for creating a culture of experimentation and optimization.
How would you define an optimization culture?
First and foremost, it focuses on continually reassessing the customer relationship and how it is delivered.
An optimization culture must be based on concrete information and requires teams to take an objective approach. That is why I believe very strongly in structuring everything around data.
Adopting a continuous optimization approach also means accepting failures in order to learn as many lessons as possible from them. For example, it is possible that some new customer journeys will fall short of delivering expected results, despite the efforts put involved in designing and implementing them. To effectively refocus or improve the strategy, we need to be able to draw positive lessons from these results.
Do you think that today’s brands are taking concrete steps to foster an internal culture of experimentation?
Today, there are plenty of examples of digital players who have embraced systematic experimentation (via A/B tests) when improving their customer journeys.
These brands are often structured around “feature teams” devoted to experimentation. Essentially, they slightly slow down the speed of delivering updates to their websites and apps, allowing them to monitor more accurately any variation in performance linked to each modification. Through this approach, brands can gain an in-depth understanding of the factors that contribute to the overall performance of their digital processes.
These A/B testing feature teams are the bedrock of an optimization culture: they have extensive knowledge of their chosen optimization tool, from both a functional and technical point of view, and they centralize initiatives to deliver consistent testing. These teams also develop, and ensure compliance with, test qualification and implementation processes.
What are the main difficulties marketers encounter when wanting to roll out personalization strategies?
Personalization is based on ‘hot’ behavioral data that can be cross-referenced with cold data (CRM, transactional, product, and so on). The first difficulty lies in collecting good enough quality data and then interpreting and using it in a meaningful way.
The second challenge for brands lies in providing a consistent experience across all channels. It is possible to utilize digital behavioral data in order to personalize a website or an app. But once we go beyond the digital core, such as into the contact center, store and sales team rolling out a personalization strategy becomes much more complex to execute, on both the technical and the organizational front.
These two difficulties are compounded by obligation that brands have to comply with the GDPR.
Do you have any advice to help brands to develop processes and a culture of experimentation?
For A/B testing, we advise our clients to begin with a detailed analysis of their customer journeys, based on their analytics data. This can clearly identify the location of any pockets of value and friction points, and thus help design a consistent testing roadmap.
It’s also essential to analyze the results of any tests in detail. It’s not enough just to know that one version outperforms another overall - it’s vital to be able to accurately identify the elements that contribute to this better performance. These analyses may, for example, show whether a version that is a better performer overall is right for a high-value customer segment. If this isn’t the case, using personalization to target this specific segment could enable you to increase conversion rates.
What new roles are you seeing emerge in the field of personalization?
I haven’t seen the arrival of completely new roles - what I have seen are new skills becoming important to existing jobs.
Digital marketers focusing on personalization are becoming increasingly specialized in data and web analysis, for example. This is because personalization requires a very good understanding of the logic behind the collection and utilization of customer information, primarily digital behavioral data. Additionally, marketers need to be skilled in the use of the personalization tools themselves and possess a good knowledge of digital performance.
What do you see as the next challenges in the field of optimization?
Today, everyone is talking about personalization, but these practices are still too frequently siloed. It’s essential to personalize at an omnichannel level. This guarantees complete consistency in customer-centric communications across all touchpoints, such as the web, app, media, CRM, store, contact center etc.
Finally, rather than a new challenge, there is an underlying trend that brands need to act on. Thanks to the increase of in-house data teams, companies can see that integrating their own data models into personalization tools can deliver large-scale benefits. So, the most mature players in the market are now looking to integrate in-house algorithms into personalization tools.
