Common reasons why personalisation fails

Common reasons why personalisation fails

Customer Experience

Customers want to feel valued throughout every stage of their shopping journey

In a highly competitive marketplace, a great customer experience can be the thing that sets you apart – and today’s customer wants to feel important and valued throughout every stage of their shopping journey. To meet the growing appetites of your diverse global customer base, you can now personalise their experience at scale, delivering individualised recommendations to thousands of customers in real-time, anywhere in the world.

In this article, we take a look at the most common reasons personalisation can fail and how to remedy the most common issues.

Advances in processing data mean we have the possibility of creating a deeper, more dynamic relationship with our consumers.

When done well, we (as consumers) may not even recognise how sophisticated the use of our data has become and take for granted that our favourite shops should ‘know’ us and what we like. For example, 55% of the respondents in our report area offering delivery preferences already selected and website landing pages as personalised elements on their websites - simple, but highly effective way to make life easier for your customer.

For example, supermarket giant, Tesco, has been using the data from their Clubcard for years. With this, they can collate information on every shop - showing returning customers effective product recommendations based on both their online and offline purchase history at all stages of their online shop. 

Even the most brilliant of data scientists will ultimately fail. There are simply too many rules to write, too much data to manipulate and too much content to create to ever handle personalisation at scale without AI.

Using AI, especially when combined with a CRM, brands can provide customers with personalised offers and services based on demographics, purchase history, historical purchases and browsing habits. Machine learning algorithms can run through large scale, constantly changing data sets and predict which products a user wants to see next. When used in combination, data insights and AI can help you offer relevant cross-sells, make recommendations, and show special offers at every touchpoint to increase the average order value.

We take it for granted now, but sophisticated AI has always been the secret behind Amazon's success - and let's face it, it's their influence on personalisation that now pushes all eCommerce retailers to work harder to deliver exceptional experience consumers expect as the norm. 

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