The struggles of high street stalwarts such as House of Fraser and BHS have brought the challenges facing traditional retailers into sharp focus. In an era where the customer calls the tune by which retailers must play, many brands have struggled to provide the personalised, convenient services that their audiences desire.
The study has detailed the strength of retailers’ online offerings, according to UK customers, identifying the best and worst performers in the sector. There is a common thread running through the struggling companies.
Many have websites that customers find hard to navigate, lack details about stock and generally deliver bad value for money for consumers. This is not an isolated problem. Huge swathes of the retail sector are encountering these issues.
To survive and prosper, brands need to clearly understand their customers’ digital pain points, develop a customer-centric philosophy to solving their problems and anticipating their needs, and follow-through on the strategy and implementation. They need to do this now.
What needs to happen?
The biggest step that retailers need to take to future proof themselves, is to actually understand who their customers are. Many organisations currently don’t have this level of insight because they have implemented analytics tools poorly.
As a result, their data is hard to read and harder to interpret. There is only one outcome from this scenario; the data is perennially distrusted within the business. Whenever analysts propose bold new ideas backed by data, data distrust hobbles their ability to get any changes done. With this being the case, retailers don’t stand a chance versus customer-centric and digitally savvy competitors.
Beyond successful implementation, businesses need to address the lack of skills currently resting within their teams. Too often analytics is just a small part of a member of the product team’s role. The most capable analysts are primarily focused on compiling monthly reports for senior leadership. This isn’t helping businesses pursue new insights such as which audience segments to focus, where to identify them and how to tailor their experience.
If this gap is to be bridged, data scientists have to be hired to deliver meaningful personalisation analysis. For businesses to be progressive with their use of analytics, it must be driven by the need for new insights, not additional reporting.
There are so many possibilities for brands when it comes to gathering data, that some can become blind to the big issues they need to solve. What these brands don’t often do, is spend enough time thinking about how to have the right data available at the moment that they want to target customers. Brands may know who a customer is after they have completed a purchase, but by this point 97 percent of the audience is lost.
Yet, taking advantage of this data will prove invaluable. According to a recent report from Econsultancy, 93% of companies are seeing conversion rates increase as a result of a focus on personalisation. This is not an opportunity that retailers can afford to miss. However, they cannot just expect to turn on a personalisation tap to solve their problems. A clear structure and plan must be in place for how organisations approach their customers.
Best laid plans
In the first instance, retailers need to fully appreciate the extent to which they can get into the mindset of their customers. Purely relying on past-purchase history has never been enough to get an accurate view of a customer and ultimately to deliver effective conversion.
Companies that rely on past purchase behaviour and averages are failing to connect the dots between the users who nearly purchased but ultimately didn’t. For retailers that sell to a high percentage of first-time or not logged-in users, it is essential to evaluate customers at all stages of the journey and cluster their behaviours into larger, targetable groups.Thankfully, online retailers now have the option of going beyond simply analysing the demographics of their customer base.
Modern analytics solutions are actually able to cluster users from broad sets of data into audiences defined by behaviour and interest to make it easy to rapidly size, analyse, and target them for future campaigns.
Beyond this, analytics solutions that use machine learning to generate sentiment scores from thousands of data points allow brands to more rapidly sense when pages fail to deliver their users’ needs. They can then click into action and create new KPIs that brands can focus on for pages that don’t drive traditional conversions or revenue.
There is also the option for brands to generate a deep understanding of their customers without significant research effort. Some analytics solutions allow companies to rapidly search user sessions based off key on-site behaviours and audience characteristics, before linking them to session replays and heat maps.
Product managers who are armed with these techniques can quickly size problems, identify urgency, and observe and empathise with the customer. Companies without these solutions or available data scientists will instead seek to guess based on heuristics or “best practice” advice that far too often misses the mark. This is a strong edge in an era when most brands are inundated with poorly structured data that requires substantial hours to clean and analyse.
The real winners and losers will be established when it comes to applying such intelligence to the benefit of the business. Yet, to do so successfully, there are five key questions that businesses must always ask themselves:
How do I capture and structure my user analytics data effectively?
How do I ensure that I can analyse this through either access to data scientists who can peruse our massive data warehouse or advanced analytics tools that automatically surface key insights?
How do I ensure that I size my audiences, measure their reaction to key business KPIs, prioritise their largest problems, and make it easy to discover their pain points and issues via surveys, insights-driven analytics and user interviews?
How do I ensure that the behaviours I find most important for targeting users are available to me when I need to target them, instead of long after they’ve made a purchase?
How do I build a continuous feedback loop of research, ideation, experimentation, and reiteration so that I can learn from these cycles continuously?
Less rhetoric, more action
Constantly asking these questions will be pivotal to applying analytics effectively in any business. A model followed by the world’s biggest businesses, such as Amazon and Google, is experimentation. The process of constantly running tests will enable retailers to make decisions based on scientific evidence as opposed to a hunch.
By taking a large part of the risk out of trying something new with a product or service, organisations can flourish and become the innovative, customer-centric organisations that all pertain to be. If this insight then becomes the driving force behind business decision making, then retailers will give themselves the best chance of staying ahead of both their customers and their competitors.
If it doesn’t, talking about customer-centricity will only take a business so far. Today’s customer will not spend time wondering if a brand will live up to its rhetoric. Tomorrow’s will be even less patient.
Hazjier Pourkhalkhali, global director strategy and value, Optimizely