Understanding Customers' Needs and Preferences
You’re browsing a retailer’s website looking for BBQs. The next time you login to the site you get a discount offer on BBQs. But in the meantime, you’ve bought one for full price at a physical store. Sounds familiar?
Imagine instead that the retailer recognised your interest in BBQs and immediately sent an offer to your mobile encouraging you to purchase one either online or in one of their physical stores. Helpful to you, and sales, plus loyalty driving to them.
Or better still, knowing that you have now redeemed the BBQ offer, they then see BBQ food in your online or in store basket. However, knowing the forecast for the next few days is for rain, they now send you a real time offer for a gazebo, which can be delivered to your home in time for the weekend.
In all three of these examples the retailers are using data, but the first one is only thinking about driving sales, while the others are also improving the customer experience. And we all know which we’d be more likely to buy from.
Today, experience is one of the real differentiators in retail. To win, retailers must be ready give relevant support to each customer at every touchpoint throughout the customer journey in store or online, through personalised engagement or offers at the right moment.
This demands that the retailers have very deep and real time customer insights and activation capabilities. Customers demand this too, with 76% of customers expecting a company to understand their needs and preferences
To build this understanding retailers must harness not only purchasing behaviour, but insights from every interaction, in all channels, and knit them together. Only then will they be able to determine what each customer may be interested in buying and, crucially, what will motivate them to buy.
Retail Data Analytics
Currently too much data sits in silos though, driving disconnected processes that do more to irritate than enhance the customer experience. Many personalisation campaigns just look for event triggers in data, and the more triggers the more marketing messages, but not necessarily more customer understanding or support.
But predictive analytics isn’t solely about spotting occasions to push messages of course, it's also about creating opportunities for customers to engage in conversations. It’s not enough to just amass lots of data, or even to run clever AI algorithms to generate interesting insights.
Instead, retailers should focus on using data to create scenarios that encourage the customer to engage with them, and then ensure that they act appropriately when they do. This requires a fundamental change for most retailers, not only in systems and data architecture, but in the organisation and overall approach of marketing teams.
Many retailers create heads of customer experience, but how many are rewarded for building detailed customer understanding, long-term sales and customer lifetime loyalty, rather than simple short-term campaign conversion rates?
From a retail data analytics perspective, it is a shift from inferring behaviour from past transactions to understanding current and future motivations in real time. To create engaging and relevant interactions that drive enhanced customer experience, retailers must be able to access a wide variety of data, be able to blend real time activity with contextual insights and combine all this with everything they already know about a customer.
Analysing big data in retail at this scale demands a step change in analytical capabilities. To succeed, retailers must identify the moments that matter most by using not just a handful of algorithms and some basic segmentations, but by running millions of queries every day to create in depth one-to-one insights on every single customer.
Having the insights is only half the battle though. Retailers must then have the ability to deploy them in real time to deliver a frictionless customer shopping experience across all channels.
The digitalisation of stores with beacons, interactive displays, videos and mobile apps makes it possible collect and use a data in physical stores in much the same way as online. Instore staff can be empowered with digital devices that alert them when a customer enters the store and provide insights into their instore and on-line purchase and browsing history. Seeing the full picture of the customer, they can provide truly personalised advice, offers and assistance contributing to a totally differentiated customer shopping experience.
Consistent Data Analytics Platform
Clearly this requires far more than a new CRM system or set of MarTech tools. Making departmental decisions and investing in point solutions that aim to solve single issue problems will not deliver the necessary improvements in customer experience. Instead, retailers need to act now to create the data and best practice foundations upon which intelligent conversations and frictionless customer shopping experiences can be built.
The sheer volume and variety of data, plus the millions of queries necessary and the near-real-time decisions demanded mean that automation, machine-learning and AI are the only realistic approaches to predictive analytics in retail. They need a consistent data platform
that makes data accessible across the entire organisation, right down to individual instore colleagues and to the customers themselves.
Leaders are already doing this. Creating a better understanding of their customers through analysis of historical, predicted and contextual insights, one retailer has added £100 million in incremental revenue simply by calculating the next best offer in real time. In addition, it has seen higher levels of engagement with marketing initiatives, including a 50-70% increase in content clicked and 15x uplift in email open rates.
Retailers like this are forging ahead. Instead of using silos of data to make assumptions that undercut customer experience, they orchestrate data to build real customer understanding that drives intelligent conversations, and thereby build trust. These retailers of the future are already delivering data driven customer experiences – perhaps it’s time others stopped assuming what their customers want and start engaging them in truly intelligent conversations to find out for sure.