Using Personalisation To Create Return Customers

Using Personalisation To Create Return Customers
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Getting new customers is one thing, but how do you get them to keep coming back? In this guest post, Lucidworks ANZ head of engineering Jag Dhillon explains how a touch of personalisation can go a long way when it comes to drawing in return customers.

The last few months have been frantic for businesses. In November, Black Friday and Cyber Monday dominated the sales game with Australians spending $5 billion on bargains. Next, there was Christmas. As yet another sales heavy period, this was arguably one of the busiest times for retailers. In December alone, Australia Post delivered a record 40 million parcels, and that only accounts for parcels coming from within Australia. Just as brands had time to take a breather in January, many had to back up just as quickly for Valentine’s Day in February.

As Black Friday, Cyber Monday and other click frenzy events continute to dominate the retail calendar, brands across the nation are struggling with the lack of consistent spending over quieter months each year. Frequent peaks aren’t compensating for the lack of consumer spend outside of these sales events, and the consequences of this have been felt by multiple retailers across Australia. A number of brands went into voluntary administration and/or receivership, while others were forced to shut multiple stores across the nation. Consumers are demanding and brands are feeling the heat.

To thrive in such a competitive market outside of these sales periods, brands need to know their customers, invest in the right technology that will serve them well and create deeper relationships that aren’t purely transaction-based.

Data is like a trail of breadcrumbs

While these past few months may have been manic, the treasure trove of data left behind online represents an opportunity for business leaders to review their strategies and assess where there is room to improve before the next peak season. By analysing the data and investing in the right technology, brands can improve the personalised experience they deliver and create long-term return customers.

Knowing what data to analyse can be tricky; here are the key metrics to pay close attention to:

  • Click Through Rate (CTR): Track customers’ online movements as much as possible. This shows their journey and can help businesses identify where potential issues are or what’s working well
  • Add To Cart (ATC) percentage: Look at how many customers are adding items to their cart but not checking out, then assess why
  • Product returns: This will indicate if customers were satisfied with their purchase or not. Where possible, delve as deep as possible to find the reason for the product return
  • Bounce rates: If a website is showing a high bounce rate, it’s probably not providing the right information to keep customers engaged. If they’re not engaged, make changes that give them what they’re looking for
  • Overall traffic: Assess what the overall traffic was and compare it to the same time last year. What was done differently, and did it work?
  • Page load speeds: It’s always a good idea to check in on page load speeds, especially if images have recently been added or the content has changed. Today’s consumers are impatient and page load speed could be the difference between you and a competitor.

Use the data wisely

In this digital age, customer satisfaction and loyalty hinges on personalisation, so it’s important businesses use data to get to know their audience and understand their preferences.

A website is one of the first interactions a consumer will have with a brand, so organisations need to make sure it’s up to scratch. In such a time-poor society, customers don’t want to click through multiple pages to find what they’re looking for; in most cases, they’ll head straight to the search-bar and if it’s not there, they’ll move along to a competitor; so it’s important to deliver the results the first time.

Reading the data and understanding customers is key to making them stay. Organisations need to take the time to analyse the results from peak periods, then leverage them to be better equipped next time around.

 

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