In a world where the customer is king, it’s a retailer’s job to give them the keys to their own personal kingdom. But whereas shoppers can easily create tailored experiences in-store, they need guidance through the online customer journey.
Artificial Intelligence is shaping the future of personalisation in ecommerce and enabling online shopping to move away from being purely functional to becoming an experience. Rather than viewing it as a single solution for a specific moment of interaction, savvy brands view personalisation as a thread that runs across the entire customer journey. Where each customer touchpoint is enriched from search right through to purchase based on a combination of profiling, historical data and in-the moment behaviour.
However, it’s important to think carefully about your overall trading strategy and actions you want customers to take. The objective is not to personalise everything, but to integrate personalisation where it is of most value to your customers.
Let’s take a closer look at getting the basics right as a starting point to drive closer online relationships and tailored engagement.
Put the right data to work
Data is the raw material for a successful personalisation strategy. Collecting, unifying and understanding how to make best use of product and customer data is central to building a personalisation strategy.
The quality of the data is important – it’s important to feed your journey with data that has been enriched and is fit for use. Therefore, it’s better to take a quality over quantity approach to utilising data. Rather than try and throw large data sets around, identify and qualify a few strategic data points, activate them and then add new data points gradually. Taking an iterative approach and constant testing is how to turn your data into a valuable resource for both your brand and the shopper on the receiving end of a personalised experience.
Individualising experiences also requires thinking outside of the traditional data sets. Harnessing data from multiple sources, also means developing and interacting with new types of unstructured data such as voice and visual or image-based data.
Use data to drive personalised engagement
The biggest advantage that retailers have online over bricks-and-mortar is the vast volume of data available to underpin personalisation strategies. This makes it much easier to create targeted customer engagement, driving site traffic in a tailored and meaningful manner.
A great example of this is La Redoute. The French retailer works with Attraqt to run its fashion marketing campaigns, sending consumers personalised emails at key moments, using insights from their data footprint. These include:
- Sharing a selection of products customers might like, based on previous purchases.
- Following up with them by email after a rebound.
- Sending them a special message on their birthday.
One reason this technique is so successful is AI can help recognise shopper intent and adapt content based on browsing or purchasing behaviour, to make visual recommendations that match trading goals.
Every email campaign La Redoute sends links through to a personalised landing page, containing 1:1 recommendations. This customised approach has generated a click- through rate of up to 40% on some campaigns.
La Redoute and Attraqt: read the case study
Create promotions that are meaningful to the customer
According to eConsultancy, only 8% of consumers decide to engage with a retail brand because of an email that addresses them by their first name. However, 50% would respond to an offer that is of specific interest to them. Effective personalisation is only as good as what data retailers have available around customer preferences and how this connects with product data. Factors such as gender, size, favourite colour and style can influence what people buy, and this information is gathered and enhanced every time someone shops on your website.
The more you know about consumers, the better you can target not only content, but the promotions they are offered, to increase engagement and trigger a purchase state of mind.
Feelunique uses customer data in an effective way. The beauty website encourages users to create their own profile sharing not only standard information, but also details of their hair type, skin type and body goals.
In addition, Feelunique asks people to choose their favourite cosmetics brand, offering platinum customers a 10% discount every time they purchase any product from that range for life – and shoppers have the opportunity to change that brand every six months.
Combined, these techniques provide a wealth of data for personalised recommendations, and an ongoing promotional offer that is always relevant to the customer – embodying the ‘feel unique’ concept.
Guide customers to think out of their comfort zone
Showing that you understand each customer’s preferences allows helps to instil trust in your brand. Where the right data helps to get the basics right, machine learning helps to order product hierarchies and tailor recommendations for each customer, based on items you know they will like and feel comfortable purchasing. Then, over time with deeper understanding of past and in the moment behaviour, add more adventurous picks to the homepage or product detail page. This encourages consumers to discover new ranges based on the sense that you as a brand has an individual understanding of them. This way you can increase order values with products they would never have chosen to look at themselves.
There are techniques you can use to enhance the authority of these ‘outside the box’ recommendations. Behavioural principles such as social proof, scarcity and authority are effective tactics for getting customers to buy now; if you can show great feedback from someone who has bought that item, or warn them that it’s only available for a limited time period, you can guide consumers with wisdom of the crowd or by the influence of others and more. Pushing the envelope is also an effective way of stopping customers from ‘self-filtering’, diversifying the products they view and continuing to ensure you inspire them.
Emotions drive behaviour. If the right message and interaction occurs at the right moment for a shopper, it’s more likely to appeal to that person’s subconscious emotions. This in turn will help motivate the shopper in a preferred direction and make them more likely to commit to a purchase. In this regard understanding consumer psychology and why people buy is key to effective personalisation.
Putting customer insights to work
Successful ecommerce brands have taken the leap from understanding what people to why people buy. Understanding customer behaviour on an individual level is key to nurturing lifelong relationships, and there are many techniques that retailers can deploy in the ecommerce space that use personalised insights effectively.
Data is the foundation of all well-executed personalisation, and quality of information is critical to success. The better information you collect about a customer from engagement in the past and engagement in the moment, the better you can act on it.
Visual merchandisers can enhance these data-driven opportunities, by combining the capabilities of algorithmic technology with their qualitative understanding of the consumer, to create experiences that feel both individual and authentic. Done properly this is a mutual value exchange, not an imposition to the shopper, and one that will cultivate deeper connections and loyalty to your brand.
Attraqt empowers retailers to tailor the online customer journey by optimising visual recommendations, navigation and other elements of the ecommerce experience. Discover more about personalisation with Attraqt.