Brands like Amazon have set the gold standard for a modern ecommerce shopping experience. Customers expect brands to show them the right product at the right price — and they don’t want to wait weeks to receive it. What’s more, with Gen AI tools now in the fray, customers are likely to want instant product guidance, review summaries and more.
Many retailers are eager to replicate such experiences, but they often stumble on a recurring barrier: data. Without a plan to prepare and leverage the data they have (and gather the data they need), it’s tough to meet customers’ ecommerce expectations — much less exceed them.
The good news? In this piece, we’ll lay out key steps every retailer can take to wrest control of their data and pave the way for a modernized shopping experience.
1. Clarify Your Data Strategy
Today’s retailers face a common set of data challenges. For many, the problem isn’t a lack of data — it’s an excess of it. In fact, retailers often have so much data that they struggle to make sense of it. And the data they can interpret is primarily used for reporting purposes and little else. That means there’s an abundance of untapped data that isn’t being used to enhance the customer experience.
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To overcome these hurdles, it’s important to craft a clear data strategy. Three tips that can help:
- Define your data goals — clarify what you want to achieve with your data. Are you aiming to reduce customer support friction, make better product recommendations, optimize your supply chain or enhance your loyalty program? Clarifying your goals will help you narrow down the data you need;
- Identify digital tools that can make your vision a reality — if your priority is to improve your customer support experience, for instance, you might choose to build a Gen AI-powered chatbot that assists customer-facing agents and focus on gathering data from support interactions, product documentation, etc.; and
- Prioritize data governance — your software needs clean, relevant and high-quality data to deliver meaningful insights. One cautionary tale — a food and beverage retailer noticed a sudden spike in breakfast sausage sales. As it turned out, this was due to a critical data quality error: Every recent sale had been tagged as a breakfast sausage. Had they based business decisions off this perceived interest, there could have been costly mistakes.
The result of taking these steps is a roadmap that can help you take advantage of your data to benefit customers and your bottom line.
2. Use Customer Data to Enable Personalization
Customer data is one of your biggest assets, especially when it comes to personalization. But this data tends to be scattered across social media, customer support interactions, etc.
With a best-of-breed customer data platform (CDP), you can centralize data from across your tech stack and draw on third-party sources. Get creative with your data sources, too: your retail mobile app, for instance, can be a wealth of customer information and even incorporate data from first-party sources.
A robust CDP strategy can help you build a 360-degree view of each customer. And you can use customer profiles to:
- Build more effective loyalty programs — for instance, you might create a tiered loyalty program based on shopping frequency, with early access to products you know they’ll love;
- Optimize customer rewards and promotions — you might offer custom discounts based on shopping habits (e.g., 20% off sustainable products for climate-conscious customers). It’s something 48% of customers expect; and
- Unify your physical and digital experiences — in a “phygital” retail environment, in-store associates might use customer data to offer tailored product suggestions.
Want to create even richer customer profiles? Consider another helpful technology: the data clean room. It’s a secure way to exchange aggregate, anonymized data with CPG partners. You have full control over the data you contribute and who gets to see it, of course. And with access to shared datasets, you can tap into each partner’s insights on sales trends, consumer behavior patterns and more. This means more data for more tailored customer experiences.
This degree of personalization can have a huge impact on customer satisfaction. And you can continually run A/B tests and iterate to fine-tune your approach. Over time, you’ll get closer to delivering the experience customers expect — and boost customer loyalty in the process.
3. Use Operational Data to Improve Forecasting
Your retail operations can make or break the customer experience. But by leveraging data about your logistics, fulfillment and inventory processes, you can keep things running smoothly.
Predictive analytics tools are a powerful option here. They can help you forecast supply chain and fulfillment disruptions so you can proactively adapt.
But it’s also worth considering emerging innovations like digital twins. This software uses data from various sources, such as your inventory management system or IoT sensors, to virtually simulate anything from a single packaging line to an entire warehouse. What’s more, you can test out thousands of fulfillment strategies and gauge the hypothetical customer impact for each. This way, you can de-risk new operational changes to maximize the chances of success and lower the cost of failure.
The bottom line: Tapping into your operational data can help you make data-driven decisions that enhance efficiency and reduce costs, leading to better customer outcomes.
Let Data Lead the Way
Your data is key to unlocking a great customer experience. It’s also crucial groundwork for the next generation of ecommerce technology. If recent experiences with AI mean anything, it’s that data matters now more than ever.
With the recommendations we’ve shared, you’ll be prepared to gain control of your data, modernize your shopping experience and propel your business forward.
Marcelo Vessoni is SVP, Digital and Head of Retail at CI&T. A global technology transformation specialist for large enterprises and fast growth clients, CI&T helps retailers engage customers, increase sales and drive greater operational efficiencies.
