Not too long ago the customer journey was fairly simple to track and evaluate.
There were a limited number of channels for customers to browse or make purchases via. Nowadays, the utilisation of smart tools that make the customer’s journey easier, coupled with the fact that purchasing is no longer a one-size-fits-all experience, means more and more retailers are waking up to the need to up their customer journey mapping game.
Limitations of traditional analytics tools
Developing a core understanding of the people who matter most to your business is at the root of delivering remarkable Customer Experience, so user experience (UX) analytics is perhaps the most important technology for all retail brands to adopt, if they have not done so already, as it identifies why visitors behave in the way that they do.
Being able to not only identify where visitors are struggling on your site but why is essential, so using traditional web analytics tools like Google Analytics and Adobe Analytics to answer this question is like using a fork to eat soup.
While retailers may already have traditional analytics like Google and Adobe and a testing or personalisation tool in place, these systems are limited and simply not built for purpose in today’s environment. They may still be collecting information about clicks, bounces, and site exits, but they do not capture the UX insights needed to determine where your visitors are having issues, what pages they respond to most and why they are leaving/staying.
Rather than trying to run before they can walk, retailers should use UX analytics to gather all of the valuable actionable insights they can about their consumers’ experiences in order to make profitable changes to website layout, content, and images, etc.
Exploring personalisation, or at least customisation, without having a robust and in-depth overview of visitor behaviour is ineffective, which is why UX analytics is such a fast-growing marketplace in the retail technology sector. Thanks to real-time analytics that do not require a specialist to decipher, plus ease of use and simplicity of the data available, UX analytics is a good tool for customer journey mapping but there are still other common errors that can often render customer journey maps ineffective.
Here are six common errors that can make customer journey mapping fail:
Get your team and anyone who needs to know the results involved, so they are invested enough to ensure they implement customer-focused actions based on their insights too.
The customer journey includes interactions with many different areas and teams, so a joined-up approach means your customer journey map will include data and insights from all areas of the business.
Don’t forget to involve your customers.
It is them who will provide a depth of understanding. Different customers will have different journeys, so trying to reflect all of your customer segments in a single, generalised map could mean you miss important insights, and fail to make valuable customer experience improvements.
Try not to map every customer and every journey at once. Instead, focus on one at a time, done right, to put your insights into action successfully.
While there are website behaviour tools that offer a vast sum of information, that is just the tip of the iceberg when it comes to the customer journey data available.
In addition to knowing how your customers journeyed across your website and the number of clicks they made on a hero product, it’s useful to go beyond that initial website data to also understand what they were trying to do that your site didn’t let them do and how frustrated that made them.
Don’t use assumptions to build your map rather than research and don’t structure your map according to your own brand’s internal process priorities, such as sales, only.
You’re after an insightful depiction of your customer’s journey, not your brand’s sales capabilities. No-one knows more about your customers than those customers themselves, so open up to what they’re trying to tell you, even if it differs from what you were expecting/planning for.
Customer journey maps investigate every point of contact between a customer and your brand, so don’t forget to include touchpoints such as post-purchase engagement, which could cause damage if overlooked.
Customer journey maps are only as good as the actions they inform and the results their development and deployment drive, so don’t think of the map as being done.
It is now time to start making the changes needed, which is where the real work begins. Even when you think your customer journey map is complete, you’re still not done. Remember to allocate the time needed to make the changes.
With upwards of one hundred times the traffic of a standard day, Black Friday should have provided online retailers with an amazing depth of insight over the past few years – but the reality has been very different.
Not only are retailers failing to mine the volume of data generated during usual trading volumes, but Black Friday is hardly an a-typical day.
So, does data analytics offer any value at all on the busiest shopping day of the year? Most definitely, if you want to deliver the fastest, slickest and most effective shopping experience.
Consumers shop very differently on Black Friday; it is all about speed – finding products fast, ensuring delivery options fit, and checking out smoothly.
There is no tolerance for confusing offers, for convoluted delivery messaging, check-out processes slowed down by add-on offers, or options to sign up for loyalty schemes.
That is a different message for a different day. To make the most of Black Friday, retailers need to get the fundamentals of the experience as slick as possible – and nothing more.
It sounds so straightforward, yet in a market where user experience teams have little insight into problems due to the sheer volume of data, and test based on best practice and intuition at best, ensuring the fundamentals are working perfectly is difficult.
How well prepared, for example, is the mobile site? During the holiday season, the conversion rate more than doubles on mobile, signalling that more users buy this way when they have a feeling of urgency – and it doesn’t get much more urgent than Black Friday.
What is required therefore is a way to gain rapid insight from the existing data resources.
And that is where AI and machine learning are set to play a vital role in transforming the day-to-day activity of e-commerce teams.
In contrast to manual data mining techniques that can barely scratch the surface of e-commerce data, AI can transform speed to insight.
Whether through mining the entire checkout process and then surfacing immediately at both a problem and its location – or looking at different areas of the page to identify those that don’t get clicked on very often but convert well when they do – AI can provide rapid insight into the priority areas that need to be tested.
Essentially, AI can find the issues quickly – enabling organisations to focus on delivering the right Black Friday experience, from reducing journey length to improving signposting and ensuring the guest check-out is easy to find and use.
And with Black Friday in the UK fast evolving from a day to a period to a week-long event, it is becoming increasingly important to understand different trends in behaviour across the longer timeframe and ensure the experience matches up.
With good processes in place, retailers can turn their attention to the best ways of capturing shoppers’ interest. Shoppers are ready to buy – with conversion rates rising by 89 percent between the first three weeks of November and Black Friday events, retailers have a short opportunity to attract a huge audience.
While Black Friday is not a day to attempt to boost the loyalty programme, with vast numbers of individuals arriving on a site for the first time – often from Google Shopping – how can a retailer make it compelling and reduce bounce rates?
This is where analysis of this year’s Black Friday activity will provide invaluable insight not only for next year but any other peak trading time, including Christmas.
Understanding what worked, what didn’t, and being able to monetise content is incredibly valuable – especially from a merchandising point of view.
Which of the many offers on the homepage generated the most revenue? Where was it located? Did the less prominent offer outperform one located higher up the page, despite not being seen by the majority of shoppers too impatient to scroll down? Of those that did click on the offer, how many went on to make a purchase?
Instead of ditching all this insight into the Black Friday black hole, understanding the way content performs, the segments of traffic new to the site, and their journey, will deliver retailers invaluable insight to ensure they are ready to make the most of the next big trading day.