How to Master the eCommerce Customer Experience through Data

Fast-paced, reactive, and constantly pushing boundaries, the retail industry has long been at the forefront of customer experience (CX) led design. The origins of CX are traceable from consumer theories of the 60s, through the glory days of direct marketing in the 80s and 90s, to the advent of internet retailing more than 30 years ago. CX defines the desired or actual perception and feelings your customer has towards your brand throughout each stage of the buying process, from inspiration (which may happen before coming into direct contact with your business) through discovery, channel, and product interactions (such as navigating your website or store, or both) to purchase, delivery, post-purchase support and beyond. It is widely understood that – as a minimum – a positive CX will be seamless and engaging, making customers feel valued and understood, encouraging loyalty and repeat business.

CX has become well established in the lexicon of retail businesses; from global omnichannel businesses to pureplay digital shops, the need to design tools, experiences and services that respond directly to the known needs and desires of the consumer is key. Whether focused on business-to-consumer (B2C) or business-to-business (B2B) every organisation needs to be able to continue to understand and adapt to customer needs to survive and succeed.

The pivotal role of data in supporting the retailer-customer relationship is not a new concept. When Amazon opened its first online store in the age of data-fueled digital retail was truly upon us, it was data that underpinned their ground-breaking offerings such as customer ratings and recommendations.

Long gone are the days when meeting customer expectations in eCommerce could be achieved with a reliable search and an efficient checkout. Over the last decade, CX skills and tools have needed to evolve exponentially to meet ever more sophisticated and nuanced customer demands.

The digital age has brought forth a new breed of savvy, fickle consumers who expect not just products, but immersive personalised experiences, seamless service, and meaningful engagement. In response, CX design is now a complex, multi-dimensional phenomenon that encompasses everything from website design and usability to personalised AI-driven interactions and cross-channel post-purchase support.

As well as the pervasive influence of social media, generative AI, machine learning, and big data analytics have all further revolutionised the CX landscape. Not only have these tools enabled businesses to better understand customer behaviours and preferences in real-time but also support retailers with anticipating future needs, leading to more personalised and predictive customer experiences.

Two watch-outs:

1. The extraordinary amount of data available for some businesses can often lead to a ‘paralysis of insight’ where the volume of data gathered is at odds with the skills and capacity available to interpret or understand it. This can be rectified by clearly defining the goals of the insights to be gathered, and ensuring he business has the resources in place to action those insights.
2. At Profound we are still seeing still seeing the basics of good UX and CX being missed or forgotten in even the most extensive digital transformations.  Even (maybe even particularly) large enterprise organisations can run towards innovative data tools and new transformative tech, without consideration for the basics of experience planning and design. In any size retail business, there must be at least a modicum of CX woven into your design as, even if there isn’t the budget or time to consider a wholescale approach, the basics must be covered for the platform to be successful. There are plenty of examples of good CX available to use as a benchmark and any good design agency or SI will have the ability to guide your decision-making.

Traditionally, eCommerce businesses have relied on key performance indicators (KPIs) such as site, page and basket conversions, Net Promoter Score (NPS), customer advocacy, and employee engagement scores to gauge their success in delivering a quality customer and brand experience. While these metrics will continue to offer valuable insights, they barely scratch the surface of what’s possible with a more holistic, modern approach to using data to inform your customer experience.

Customer data should encompass a wide spectrum, including real-time traffic data, customer feedback, behavioural analytics, and sentiment pulled from a broader field such as social networks and review sites. Data also needs to account for broader factors such as cultural shifts, social trends, and geopolitical dynamics. Finally, retailers need to embrace the opportunities emerging from unexpected quarters. For example, initially designed to help combat fraud, behavioural biometrics builds on behavioural analytics in that, as well as collecting and analysing user behaviour patterns, it also looks to verify users based on how they interact with their device compared to their past performance.

Data sources that can be used to inform CX design and decision-making include:

• Net Promoter Score (NPS) – measures the level of customers’ willingness to recommend a company, brand, product or service.
• Customer Satisfaction (CSAT) – assesses how much a company, brand, product, or service has met or exceeded customer expectations or needs.
• Customer Effort Score (CES) – measures how easily a customer has found it to get their needs met or issues resolved.
• Voice of the Customer (VOC) – tools that capture metrics and qualitative data around customer needs, expectations, and dislikes.
• Behavioural data – is the data collected that shows how customers interact with different touchpoints. This might include channels that you own and manage, and those that you don’t – such as social. You might capture web browsing behaviour, app usage, in-store interactions, purchase history, service records, and much more as well as considering behavioural biometrics and patterns of individual interactions across products.
• Transactional data – refers to the specific buying patterns of a customer, as well as any returns, deals, coupons, and vouchers used.
• Demographic data – which can range from basic information about the customer such as age, name, and location, but also includes gender, occupation, income level, or business type.

Psychographic data – which covers more subjective characteristics of the customer and might be provided or assumed by perceiving certain behaviours. This could include interests, attitudes, values, lifestyle, and personality traits.

Pull these vast fields of data together and this holistic approach can allow retailers to design highly personalised, reactive customer experiences that resonate on a deeper, individual level, driving conversion and fostering long-term brand loyalty.

Personas – Using data to create and inform customer personas, eCommerce retailers can not only design more effective and personalised customer experiences but can also give customers representation in decision making. Naming these personas and even bringing them to life with images or detailed descriptions can help make them even more real. By developing detailed personas that reflect the needs and behaviours of different customer types, you can design for and test your experiences against these models.

The lifecycle – Every element of the customer lifecycle benefits from consideration and analysis. This means gathering data as early as inspiration (such as through social media) all the way through to the post sale experience (through your customer feedback and service management channels). Often neglected, this continuous, cyclical assessment and understanding of your customer’s relationship with your brand can help to drive the greatest loyalty and value over time.

Continuous improvement – Changing customer expectations and the fast pace of innovation in digital retail means that any successful retailer will be continuously improving the customer experience. This needs to be underpinned by ongoing, up-to-date analysis of customer feedback and behaviour to make well-informed data-driven improvements.

Personalisation – A key component of the modern eCommerce customer experience, personalisation based on set preferences and previous behaviour tailors the shopping experience to an individual’s preferences, delivering content and recommendations that are relevant and engaging. High fidelity, real-time data coupled with AI and ML tools can allow you to deliver hyper-personalised experiences on the fly. These real-time, highly sensitive and targeted tools rely on accurate, trusted data sources and can boost engagement, satisfaction and, ultimately loyalty.  Ensuring that your platform and data management tools adhere to the latest privacy and security legislation is also vital to enable this.

Streamlining – ECommerce retailers can use data to remove friction from the purchase process and offer excellent personalised, customer support both during and after purchase. Data can also be used to improve operational efficiency and ensure faster more reliable delivery, collection and return services.

Prioritising customer-led change – Whether customer experience change is being delivered through large-scale projects or an ongoing improvement programme, data helps in prioritising efforts. This ensures those aspects of the customer experience that have the greatest impact on the immediate or strategic goals of the business – such as brand loyalty, sales conversion, or even market growth –   are addressed first, maximising both customer satisfaction and business outcomes. Trading activities can also be supported and informed by well-executed data strategies, allowing retailers to act faster when testing or deploying offers, promotions, or new, market-driven experiences. With the advent of AI and ML – and supported by high-fidelity data – much of this can be done in real-time.

Successful CX design and management in digital retail will require the right resources and sophisticated data and analysis tools that can capture customer feedback, analyse data, and provide actionable insights. At Profound some of the tools we use and support our customers with are:

• Customer feedback platforms such as Hyland, Qualtics, and Medallia which can capture real-time feedback across multiple channels.
• Analytics and data visualisation tools like Google Analytics, Domo, ThoughtSpot, Alteryx, Snowflake, and Tableau which can help understand your customer behaviour and preferences.
• CRM systems such as Salesforce, Oracle and HubSpot CRM to track customer interactions and provide a 360 view of the customer journey.
• Personalisation and engagement platforms such as Monetate, Nosto, Dynamic Yield, or Optimizely for personalisation capabilities to tailor the shopping experience.

Wider reach analytics and prediction tools such as Predyktable for national behaviour forecasting and Sprout Social for social management and CRM.

Remember that while data and insights play a pivotal role in shaping eCommerce customer experiences, they must always be contextualised within a broader spectrum of influences. A program of continuous improvement, informed by a well-honed mix of data, market understanding, and customer empathy, is the key to mastering the eCommerce customer experience. This holistic approach will not only enhance your customer’s immediate satisfaction and strengthen their loyalty but will also position your businesses for sustainable growth in the ever-shifting digital retail landscape.