Building a Personalized Customer Journey: Stage 2 – Engagement
Many digital and marketing leaders are trying to develop their personalization strategy in a way that caters to the individual site visitor across the customer journey. But their progress is often hampered by an overwhelming amount of advice, gimmicks, buzzwords, and excited talk about full-journey personalization.
This can be frustrating for digital and marketing teams when looking for serious solutions to the real problems their brand’s face. Attempting to craft individual experiences at a scale that caters to thousands, or even hundreds of thousands of customers and site visitors is no easy task.
Consider this: 89% of companies surveyed consider the customer experience to be a key factor in driving both customer loyalty and retention. Therefore, it’s more important than ever for customers to feel heard and for retailers to get the customer journey right.
But, what can retailers do to build the personalized, engaging online experiences that shoppers demand?
As your customers move through the customer journey, you can start to build up a fuller picture of who they are and what they want when interacting with your eCommerce site.
With personalization, you can take the data and information you have on your site visitors and customers and begin to add this information to what we already know about how they’ve previously interacted with our eCommerce site. For example, looking at what products they search for and view, as well as the pre-click data we already had from our earlier landing stage.
In an earlier blog, we talked about this important first stage of the customer journey – Landing. You can check out this blog here for a refresher.
But now in this blog, we’re going to focus on the second stage of the personalized customer journey – Engagement.
Using Engagement Data to Build Compelling Experiences
Based on the data we have already collected during the Landing stage, we can intelligently engage with our customers, tailoring the things we display to them based on their behavior up to this point. It means we can present products and experiences with a high degree of certainty that they will be of interest.
With more data, retailers can go beyond simple, one-dimensional product recommendations to create a more rounded and satisfactory customer experience, drawing the visitor on through the customer journey and hopefully toward making a purchase.
For example, if a site visitor has arrived via an ad, an intelligent personalization system will display an item or creative asset linked to the ad campaign in one recommendation slot – using a frequently bought together algorithm, for example, and recommend a new product or line in another slot.
An advanced personalization engine integrates all the data available for a customer and can carry over and deploy this integrated view at the next stage of the customer journey – purchase.
This information allows retailers to use their data in various personalization tactics to make smarter decisions about what product recommendations to make and in what combination, increasing the likelihood of conversions for a particular customer.
And this layering of data is not confined to the individual customer’s visit.
Insights gained from observing how a customer responds to a particular combination of marketing messages can be built up over time to give you a much better understanding of what messaging resonates with particular types of customers.
This means that retailers can improve their approach to customer segments in general, while at the same time fine-tuning their personalization efforts to the individual visitor.
Automated Personalization Powered by Engagement
Using machine learning in this way is called Automated Personalization. It doesn’t just display experiences based on what type of customer is visiting your site, it combines insights from customer segmentation testing with data points on the individual site visitor, tailoring a digital experience to the customer’s unique profile.
By combining customer data with out-of-the-box behavioral targets, Automated Personalization can present much more relevant, hyper-personalized experiences to the customer, increasing revenue per session and add-to-cart rates.
Here are a few examples:
Slotted Recommendations: Combine different algorithms together in one container, so ‘popular’ or ‘bestselling’ sits next to ‘relevant based on products viewed.’
Frequently Bought Together Algorithm: Test the performance of a ‘frequently bought together’ algorithm vs a ‘frequently carted together’ algorithm and go with the best performing.
So, how does this concept work in the real world? Let’s check out the story of Monetate customer Helly Hansen, who combined 1-to-1 Personalization and advanced recommendation techniques to create unique experiences across 19 countries and 7 languages.
Helly Hansen: 1-1 Personalization and Advanced Product Recommendations
Helly Hansen, a Norwegian outdoor apparel retailer, realized they needed to craft a more personalized experience for their varied customer base. With customers visiting their site from across the world, they needed to tailor their messaging to different visitors in different countries.
Before Helly Hansen turned to Monetate Personalization, they were displaying static images and promotions on their site, relying on standard seasonal messaging to entice visitors into the customer journey.
But because the needs of fashion-conscious shoppers in Paris are very different from semi-professional mountaineers in the US, they were failing to maximize revenue and conversions.
They decided to orient to a data-driven approach, letting machine learning algorithms decide what to display to different customer segments.
With this approach, they were able to display much more relevant recommendations and personalized banners on their homepage for new and returning customers.
Past session behavior, seasonality, age, gender, and geographic location were among the data points Helly Hansen used to fill slotted recommendations for returning customers, serving up much more relevant products as soon as their visitors hit their site.
For new customers, the recommendations were chosen according to trending and popular items.
Operating internationally, with different languages, currencies, and cultures to consider, delivering this degree of personalization was only made possible by the use of AI and machine learning. It helped their eCommerce teams make changes quickly, keeping them on top of trends and customer data insights.
Helly Hansen saw the following results from their personalization and advanced product recommendations:
The Role of Machine Learning in Boosting Personalisation During Engagement
Ready to learn more about Monetate Personalization for building out your customer journey during the engagement stage?
Schedule a demo with a member of the Kibo team to see how the solution works and can fit into your plans for building out personalized customer experiences.
Also, download “Delivering a Personalized Customer Journey: A Step-by-Step No Nonsense Guide,” for a more in-depth look at building out a personalized customer journey at each of the four stages.