Alex Kibblewhite takes a step back from the data to think about how eCommerce personalisation can be improved with a broader understanding of context.
Personalisation is key
Personalisation is everything in today’s eCommerce world. It encapsulates the strategy of providing content and experiences that speak individually to customers across marketing channels. It is becoming an increasingly key factor to success with companies recognising it as an important feature of their eCommerce marketing strategy.
Easy to get metrics are only part of the picture
Creating relevant brand experiences allows your customers to feel like you know them as individuals, and importantly, they know you. Tools such as Google and Adobe Analytics have facilitated easy data analysis by making behavioural and transactional data more accessible. They surface the key information needed to support a web personalisation strategy, such as location or browsing behaviour in user-friendly dashboards and reports. The easy to access and analyse nature of these tools means that behavioural data has taken the limelight and overshadowed another key a factor of Personalisation. It is hugely important. However, it is one piece of the puzzle.
Personalisation requires connection with both data and context – the world in which your customers are living. How much more powerful would your strategy be if you were able to understand not just how your customers behave and act online, but how they talk and engage online outside of the somewhat limited view of your website?
The importance of language
Language analysis is largely overlooked in reference to personalisation. This is a big oversight as language, when properly analysed, contributes a huge amount to your strategy. Language sources such as reviews, forums, blogs, comments, follows, reviews, posts, surveys and tweets are all real-time data sources that hold valuable insights about your customers. This data is where you can uncover valuable information such as your customers’ interests, purchase intentions, favoured brand attributes, what they don’t like, what they talk about, what makes them angry, what makes them happy. This information is hugely important for informing a Personalisation strategy.
For example, let’s say you own a women’s clothing brand, a typical Personalisation approach would be to put returning customers into segments based on previous purchase behaviour. Through language analysis, you could compare how those different segments of your audience differ so that when they land on your page not only is the experience, layout, product type suitable for that segment but the language is also personalised – this could be the product descriptions, product description or brand voice helping them feel more comfortable and further personalising their experience!
The forum gold-rush
There really are numerous questions that can be answered by comparing the language of your customers and all this information should be working in conjunction with behavioural data to support your personalisation strategy.
The beauty is that this data is readily available and plentiful. Forums are a perfect example of where you can source this valuable data, and are often overlooked. For instance, Styleforum, as of November 2018, has over 3 million messages on menswear alone. Consider the many other mediums where the consumer voice can be heard online, and you have a vast amount of data that you could be capturing and comparing to add that layer of audience understanding to your personalisation strategy.
Even if you don’t have all the bells and whistles of a multi-channel personalisation strategy, comparative linguistics with Relative Insight is an important part of your eCommerce activity. So don’t miss the boat – get onboard and in tune with what your customers are saying, not just what they are doing.
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Technology born from criminal linguistics research
Relative Insight was born out of a 10-year research project with Lancaster University’s linguistic and cybersecurity departments. In the beginning, we used language analysis to help law enforcement identify criminals masquerading as young people in chat rooms.
Today, we use the same methods to help brands communicate more authentically with their audiences—focusing on statistically significant differences in the way people speak, and deriving insights from them that fuel sharper strategy and smarter communication.