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IREU Top500 The Customer Report: 2018

IREU Top500 The Customer Report: 2018

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GUEST COMMENT The ‘customer exposed’- the three stages of a data-driven ecommerce strategy

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GUEST COMMENT The ‘customer exposed’- the three stages of a data-driven ecommerce strategy
GUEST COMMENT The ‘customer exposed’- the three stages of a data-driven ecommerce strategy
Every connection we create in the course of our daily lives reveals a wealth of information about our likes, dislikes, habits and desires and this includes consumers’ communication online with retailers when browsing or purchasing goods and services. Every interaction we make is framed by a web of electronic devices, so that all our choices from the breakfast cereals we buy from supermarkets, to the car we drive to work, to the posts we ‘Like’ on Facebook, to the programmes watched on TV, are logged and stored.

But in reality, all this data is only of use to retailers and brands if they can build a robust framework that connects it together, analyses it, provides actionable insight on the behaviours of customers and enables a positive long-term change in their businesses. What they need is a three-stage data-driven ecommerce strategy.

The first stage is ‘Do’, and the aim of this is to make sure that the basics are in place. In terms of data collection, this means ensuring that robust analytics are available to the business with a clear understanding of the key objectives. Does the retailer want to know, for example, what response they get to designer products available on the site in comparison to more generic products; or whether customers are searching for single items, or items that form part of a set.

The data sources will need to be strategically integrated so they provide a complete picture of how the eCommerce site is performing. If products are being viewed but not being sold because of a limited range of sizes or colours, relevant stakeholders in the business need to be able to see this in real-time. These insights will enable changes to be made quickly and decisively.

The second stage is ‘Improve’, and this allows the business to maximise the insights from the data. If analysis tools are made available to the wider team they will be empowered to delve deeper into the data, to ask relevant questions and make optimisations based on the answers. This may require a subtle, but necessary change in company culture, so that the whole team is encouraged to test and learn with a view to continuously improving the experience for the customer – the net result for the business will be an increase in conversions.

Analytics will also support improved personalisation. The data will help to form a greater understanding of customer segments so that 1:1 personalisation of cross-sell and up-sell recommendations can be fully automated. This should not be underestimated. eCommerce retailers are at many different stages in their personalisation offerings, and the use of analytics will help in the development of accurate and effective personalised promotions and communications that are an essential element in the customer experience.

During the ‘Improve’ stage, retailers will be able to implement customer lifetime value modelling. This will allow them to identify early indicators that customers may become loyal and deploy tactics to encourage this.

Of course, retailers understand that online activities also impact purchases in brick-and-mortar stores, but data collection will enable this to be analysed and quantified, so that an understanding of the true value of ecommerce activities can be realised.

This second stage in the process of building a data-driven ecommerce strategy will be crucial in differentiating the business and the key objective must be to outperform any previous expectations.

The third part is ‘Transform’, which focuses on data science and data partnerships with a view to developing new methods and processes to take the eCommerce business to greater maturity. Data science relies on sophisticated tools that unite data mining and statistics to inform predictive analytics, and in an ecommerce environment can have a significant impact on product marketing, merchandising and inventory management. Examples of data science include the use of machine learning-driven recommendation engines, or high value behavioural analysis that can determine which parts of the site are most valuable in driving purchases.

Ecommerce businesses can also form creative data partnerships, or tie-ups with public data sources that can help to answer questions about customers. Tools are able to harness detailed patterns of human behaviour and use analytics to understand them, and adapt existing sites or even design new platforms that connect directly and personally with the target audience.

Without a strategy that allows the data to be curated and actionable, a company is simply sitting on massive amounts of information from which there are no insights and no answers to crucial customers. How else can ecommerce businesses find out how consumers behave within a certain timeframe from purchase? How their activities differ by channel or by segment? How they react at different points in their purchasing journey?

Designing, managing and getting value from a transactional website is a complex and constant challenge. But with greater clarity of what existing and future customers look like, the experiences that prompt them to return and keep them loyal, and insight into their personal likes and dislikes, ecommerce retailers are suddenly seeing customer relations through a new and very clear lens.

Meagan Hughes is Lead in the Digitas LBi Analytics team

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