3 ways customer data can increase online sales conversion

James Bennett

James is the experience chief at Kalido, a new breed of customer experience consulting and delivery firm which uses data to drive growth and customer value through marketing automation, Web and mobile experiences, and content creation. Kalido is the leading Salesforce Marketing Cloud Partner in the Asia Pacific region and part of the ASX-listed IVE Group, a diversified marketing communications business with annual revenues in excess of $600 million and over 1500 employees.

Data has been an increasingly critical factor in improving the efficiency and effectiveness of marketing and business operations.  

But with so much customer data available, what should you be collecting, analysing and using to your advantage?  

When looking to improve online behaviour and conversions, many marketers focus solely on website data but this is only one part of your customer’s story. Customers now interact with businesses through a number of different channels such as your retail store, call centre, website and mobile app. So, if you are just looking at basic customer profile data, you may not be getting the full picture.  

Here are three examples of what data marketers should be focusing on.

Accessible and consumable data

Data does not replace the need for the creative or the psychology aspects of marketing, however data does provide context that has not been previously available. That said, using data in marketing can be difficult, so focus first on customer data that is readily accessible and consumable.

Plan the customer experiences you want to deliver, and then work on enabling the data to activate the most valuable of these experiences. You can build a single view of a customer in a data lake to conduct data science studies later. There is always low hanging fruit, go get that first.

Behavioural data

Behavioural data focuses on consumer buying habits, brand preferences and product usage. It is hard to collect in a meaningful way and even harder to derive meaning from but it is a vital piece of the puzzle for marketers.

Common sources of behavioural data include websites, mobile apps, marketing automation systems, CRM systems, call centres, and billing systems. This type of data should not be neglected if you want to understand and plan relevant experiences for your audiences.

Although it may seem obvious, many organisations do not track website customer behaviour well, especially if the activity doesn’t lead directly to a conversion. Every click and activity outside of a website purchase is important, and collecting this type of data will help you understand and reach customers better.

Integrated and modelled data

Having a broader, contextual view of your customer provides more opportunities for personalisation. By integrating and modelling your data, you can understand your customer behaviours and patterns, and can create targeted offers or content to increase engagement and conversion.

For example, if a customer uses a loyalty card when making purchases from your business, you can display personalised content and recommendations for other products based on what they’ve purchased in-store before or may have added to an online cart but not yet purchased.  

This same personalised content can be delivered to them in an email or via social channels, encouraging them to click through to your website, or return in-store. By providing relevant, personalised content you are more likely to engage them.  

Integration of other data sources can also be used to evaluate and provide a more accurate view on channel performance.  

For example, you may find that in isolation, your website has a low sales conversion. However, when viewed in conjunction with your call centre data, you can see customers visiting your website are actually contacting your call centre to complete the sale.  

Data has changed the way we as marketers can understand the customer. You just have to use it in a way that will make a difference and create a positive customer experience.

Tags: digital marketing, data-driven marketing

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