It’s clear that this resurgence isn’t coming in-store anytime soon, so physical retailers will need to explore how to meet their customers in the comfort of their own homes. But launching a new commerce channel isn’t easy, it needs to be planned, executed and integrated into a retailer’s existing business model, with minimal disruption to business-as-usual. It’s too competitive out there to make a mistake.

The E-Commerce space is becoming increasingly competitive, seeing retailers diversify their offerings quickly to survive. Customers are now spoilt for choice, meaning retailers need to understand their customers and fulfil their demands or risk losing them elsewhere for good.

The understanding of a customer’s behaviours wants and needs come from the data you collect and analyse regarding that customer and their segment. Therefore, I spoke to one of our Data Scientists, Stephanie Seiermann, to talk in more detail about the data opportunities and strategies for retailers looking to harness the second rise of online.

What Kind of Data Would a Retailer Need to Ensure They Craft a Compelling Business Case for an
e-Commerce Channel?

The decision to move into E-Commerce should first and foremost be driven by market opportunities which can be assessed through extensive market research. This requires understanding the current market state, including market size and competitor landscape, but also existing and especially potential customers and their behaviours. If your target customers aren’t purchasing online, it might provide hard to establish a profitable online channel.

Once a market opportunity has been identified, retailers need to understand the readiness of their operational processes for E-Commerce. Do they have the right logistics and operational infrastructure in place to provide an online shopping experience? This is just the first step in the mission to create both satisfied and ultimately loyal customers, as well as generating healthy profits in the long run.

How Would a Retailer Go About Understanding the Datasets it Needs from e-Commerce and How to Capture them?

The key thing here is your data strategy, and that strategy should be two-fold. While it’s possible and recommended to leverage existing data sets, it’s also essential to gather external market data.

Existing data around historical offline sales performance, customer demographics and behaviours will provide interesting insights. It is, however, absolutely crucial for the retailer to not blindly assume online customers will show the same attributes and shopping behaviour as offline or, even worse, think every offline customer will automatically turn into an online or multi-channel customer. The insights coming from in-store data must be enhanced by data that allows the retailer to understand the potential differences between offline and online customers and ultimately, the impact of those differences on the performance of E-Commerce.

We've Mentioned Understanding Your Customers, But How can a Retailer Truly Provide Personalisation Online? What Data is Required?

Personalisation in E-Commerce is driven by the same guiding principle as in-store: “Get to know your customers”. Getting to know your online customers starts by tracking and analysing their online behaviour as early and as thoroughly as possible: “How do customers engage with my website? Which items are they viewing? For how long? What are they buying? What are they not buying?”.

Real personalisation before this data is available is very challenging and can only be achieved if it is possible to link online and offline customers to create a single view of the customer. Only in these cases might potentially be possible to re-use past data and insights to enhance, but not replace online personalisation.

Looking More at Range and Pricing Now, How Should a Retailer Decide What Products to Sell Through Their Online Channel and How to Price Them?

The starting point for deciding which products to sell online and how to price them can be the in-store assortment. However, these existing product ranges and pricing policies have to be reviewed in the context of any changes likely to occur in either existing cost structures especially around warehousing and distribution, or estimated demand shifts coming from the E-commerce channel. You also can’t forget about your competitors’ pricing structures!

Retailers need to understand how the predicted online demand profiles affect a product’s cost structure. Especially early on, when those estimates might not be 100% spot-on.

Now the e-Commerce Channel is Setup, What Kind of Web Analytics Would we Want to be Running?

The more data a retailer collects about the online behaviour of its customers, the better. This should at least span the traditional form of web analytics that captures, for example, entry points, navigation journeys, product search, dwell time, conversion and basket abandonment. Going forward, this data can be enriched with information around product returns and the reasons why customers are returning products or even sentiment analysis from social media behaviour where appropriate. This combination allows retailers to build up the right database for effectively evaluating campaigns, optimising product assortment and building sophisticated personalised product recommendation engines.

Within the current conditions, it’s also essential to understand any specific customer nuances that may have developed. For example, given the heightened level of urgency around purchasing some goods, fewer customers will be looking to spend time on sites, browsing for inspiration before making a purchase. Retailers need to recognise this and adapt their user experience accordingly. A great example of this is through 3rd party payment engine Shopify, whose ‘stripped-back functionality provides a quick and simple way for customers to choose their products, check-out and pay.

Finally, What Ai Opportunities are there in this Space?How Would you go about Understanding them?

The opportunities for retailers to benefit from the latest developments in the AI space are numerous. Typically, AI has been deployed in the customer-facing area of personalisation, helping customers to find the right products or using a chatbot to resolve general customer questions and feedback. However, AI can also make a difference internally and help cost-saving initiatives by making operational processes more efficient. A good example is the usage of sophisticated demand forecasting AI to optimise supply chains or mitigate risks of out-of-stocks. Being aware of the latest market trends is crucial for retailers. This knowledge of the latest or upcoming trends, as well as a detailed cost-benefit analysis for implementing these tools, will help retailers to shape their own tailored AI roadmap.

As you can see, there are a wealth of opportunities in the E-Commerce space and data plays a huge part in enabling retailers to make strides in this area.

Next, we’ll hear from Lizzie Willett who will share her insights on how to successfully engage with your customers through your online channel, developing customer loyalty and enhancing lifetime value along the way.