Retailers in today’s multichannel landscape must track and analyse consumer data across multiple touchpoints to win loyalty through personalisation.

Online consumer

Applying big data to the multichannel shopping experience, information analysts like to tell us, is like teenage sex – everyone’s talking about it but no one knows how to do it.

Rarely a day goes by without another industry pundit urging retailers to make the most of big data to improve customer experiences across multiple channels – but how many are actually doing that?

A poll of 100 large retailers in the UK and US, carried out by Accenture, found that even though two thirds of respondents said they believed their unique data could help their company to differentiate their products, only 25% of active analytics users said they used their data ‘to a great extent’.

In the retail sphere, suppliers are doing their bit to help retailers crack the big data nut and aid them on the journey towards the ultimate goal of predicting what each individual customer desires. New tools are appearing all the time which, if retailers can integrate them with their existing data supply, will build an ever more detailed picture.

But more and more tools can also complicate things for retailers – for whom, in some cases, merely deciding whose responsibility it is to carry out data analysis can provoke a major organisational restructure.

In fact, according to Retail Week’s Data Management report, produced in association with eClerx, 36% of respondents complain of a lack of technical ability to analyse all data sources; while 33% want more cross-functional collaboration between departments (see box).

Analysing customer returns

Where does the customer journey end? Is it with the completion of the sale? The delivery of the product? Or, as one research company believes, after the customer has had their product for around 15 days – the average time it takes for them to get about to returning an item they don’t want.

Glasgow-based Clear Returns looks at data on returned goods and returning customers to help retailers improve the customer experience and reduce the level of returns.

It recently worked with a high street fashion retailer, which sells globally. Segmenting customers who returned goods, Clear Returns discovered that just 1% of ‘bad’ returners were responsible for driving 10% of returns’ costs. On the surface they appear to be ‘good’ customers, because they are particularly responsive to marketing campaigns. Once they are identified, they can be added to a suppression file, so they are no longer actively targeted.

Previous research has found that up to 50% of returns come from first-time customers, and that of these people, more than 80% never shop again with the store – adding up to a huge amount of lost business. If these customers are identified early in the process, they can be contacted with a care message in a bid to prevent the experience being seen as negative.

As well as helping customers, Clear Returns says its returns data can highlight content and product issues. Its research has found that between 5% and 10% of products drive as much as 50% of returns. If these can be identified after just a few sales, the problem can be tackled before it has a big impact on business.

For the retailer with turnover of £10m, Clear Returns reckons it can save the business in the region of £100,000 a month from better targeting and reduced fraud.

Ellie Turner, business development manager at Clear Returns, says: “The sale online is not the end of the transaction, or the customer journey. Shifts in purchasing behaviour mean that the new point of sale is when the customer actually decides to keep their purchase, in the comfort of their own home and with minimal retailer influence. Retailers must begin to focus on the keep value of their customers, not simply their purchase behaviour, in order to maintain profitability.”

In the UK, Tesco is held up as one of the star performers when it comes to data analysis. This is because it has been working since 1995 to develop its Clubcard scheme. Its investment in data has been huge, having acquired customer science firm Dunnhumby in 2006, which in turn acquired ad tech company Sociomantic this year.

It is inching closer to the individual customer-targeting dream, with Tesco chief executive Philip Clarke revealing in March this year that the retailing giant plans to introduce a personal, digitised Clubcard that will let customers tailor their loyalty programmes. Having years of in-depth customer data on millions of items is clearly paying off for Tesco.

House of Fraser has also been working hard on tracking customer journeys across multiple channels and devices. As a retailer that sells everything from nail polish to washing machines, it has taken an interesting approach, which is to focus on how marketing affects the journey.

Customer insights

Margaret Herrera, House of Fraser’s affiliate manager, explains: “We need to integrate as much data as possible and to have a complete picture of our customers’ journeys. In order to consolidate our data sources, both online and offline, into a single measurement hub, we focus on marketing attribution.”

This attribution system is managed by DC Storm, which works alongside Rakuten Marketing. Using the DC Storm universal user function, House of Fraser can connect user activity across multiple channels and devices and then analyse this journey.

Herrera says the tools give House of Fraser a better handle on the customer journey, removing previous customer blind spots and targeting shoppers across multiple channels. “For example, the popular store card has been advantageous to House of Fraser, as it makes it more straightforward to connect our offline sales with the online user journey. This is a great tool in helping us to understand the online/offline relationship,” she says.

Of course, gathering data from loyalty schemes and online shoppers isn’t the only challenge. What is even trickier, but becoming an increasingly vital part of creating a seamless multichannel experience, is finding out what these customers are doing when they’re out on the high street.

Within the store, there are various ways of capturing customer data – from the old faithful of a comments box to emerging new technology. Among the latest developments are the iBeacon, Apple’s new personalised, micro-location-based notification and alerts system; and the Connected Fitting Room, an in-store intelligence system powered by Microsoft and developed by Accenture.

Dan Mortimer, chief executive of digital consultancy Red Ant – which is working on iBeacon trials with leading retailers – says the system can be used to help retailers understand what customers are doing in store, and provide real-time information.

“This gets even more interesting when we layer that information with other sets of data – from the online store, stock levels or even promotional data – to serve unique, relevant notifications direct to shoppers’ devices,” says Mortimer. “For example, being able to match a specific consumer’s previous online shopping habit with current stock levels places the power in the hands of the staff on the shopfloor, with clear insights into how best to approach the customer.”

Microsoft began trialling Connected Fitting Rooms this year. In a pilot with US clothing retailer Kohl’s, garments are tagged with an RFID chip that triggers a screen in the fitting room when the customer enters showing other items that might be of interest. The customer benefits by being able to request clothes in other sizes or colours without leaving the dressing room. This data is tracked and analysed.

“The data that the Connected Fitting Rooms are now producing gives businesses, especially retail, an opportunity to not only capture that data but bring it into the company, and bring some intelligence and analytics to it and feed it back to improve profit margins and customer satisfaction,” said Steve Dunbar, Windows-embedded lead at Microsoft, of the Connected Fitting Room when it was unveiled.

But what of the future? Although analytics seems to be dominated by external suppliers at the moment, Accenture’s research found that among the retailers it surveyed, 54% were hiring and developing analytical talent in house. Just over half have already invested in analytical tools and software.

Not many retailers are in a position to go as far as Tesco and buy their own insight firm of course, but there is lots to think about – at the very least getting to grips with the fact that it is time to start transforming your business into a data-led business if you haven’t already.

As Mortimer concludes: “Retailers need to prepare their business for the future today by adopting a smart approach to understanding the customer journey and preparing for the influence that data is having on the future of retail.”

Big data potential

Retailers are aware of big data and its potential, but the quantity of data, and especially unstructured data, is a massive challenge. Not only are organisations grappling with finding relevant data, they also need the right skills, structures and strategies in place to deal with the explosion of big data, finds Retail Week’s Data Management report, in association with eClerx.


of respondents say product data is still more advanced than customer data.


rate their preparedness to use data as five out of 10 (with 10 being fully prepared).


of interviewees say complexity of the data and diversity of sources are “considerable” obstacles.


complain of a lack of technical ability to analyse all data sources.


of interviewees struggle with capacity in their team with regards to data management.


say the marketing department is responsible for managing big data; 47% say IT is.

The Data Management report is free to subscribers and is available from