Part 4: A 360º view – Going the distance for data

A 360º view: Going the distance for data

Data as a retail treasure map

In 2014, sudden, mass retail competition gave customers endless product options. The key to success, especially for brick and mortar, was learning to offer a better experience

But what did that mean? How were retailers supposed to figure out what customers actually wanted? 

Today, there’s no doubt that this priceless vision of customers and demands can only come from data. And retailers are going to great lengths to learn what it’s trying to say.

2014: Shoppers to retailers: “It’s like you don’t even know me anymore”

In 2014, traditional brick and mortar retailers were struggling to understand customers, at a moment when it was more important than ever to offer a personalized experience. 

Many were only just starting to implement omnichannel and D2C strategies, if at all. This meant their understanding of customer needs and demands was limited to whatever data they could get their hands on, which wasn’t much.

E-commerce and D2C retailers, on the other hand, were strengthening their customer bases thanks to customer data they got right from the source. This data empowered them to offer a better, more personalized experience. 

But even retailers that were able to collect all that customer “big data” were only at an advantage if they knew how to use it. In 2015, retail executives reported that they were better able to capture data than draw insights from it.

An increasingly blurred online-offline customer journey made it difficult for them to connect multiple data points left by consumers. And how could they interpret data that was often siloed in different data pools?

Losing influence over the path to purchase

The proliferation of mobile devices and social media usage also meant that retail was becoming more pull rather than push.

30% customers looking for inspiration from retailers' ads and communications from DeloitteBy 2015, only 30% of customers were looking for inspiration directly from retailers’ ads and other communications.

Deloitte also found that shoppers in 2014 expressed a desire to curate their own personal assortment even before visiting a store, though were often frustrated due disparate online and in-store stock assortments – another example of the toils of a weak or non-existent omnichannel strategy.

The loss of control left retailers scratching their heads about how to meet customers on their territory and with the right products.

Looking at the numbers, we can see that many traditional retailers weren’t sure how to engage with customers to figure out what they wanted and needed.

Challenges with connecting with customers and data
Source: The rise of the customer-ed economy | The Economist Intelligence Unit

Many companies still relied heavily on websites (51%) and email (23%), with only 23% using social media and 10% on mobile apps to engage with customers.  One Capgemini report even reported that the importance of social media (Twitter and Facebook) seemed to be dropping among shoppers and that it was likely “hype”, while the importance of retailer apps and the internet continued to rise. 

It’s not hard to understand retailer confusion; things were changing faster than they could keep up with. Certainly, they were not anticipating the level of importance that influencers and shopping on apps like Instagram would soon take on.

Overlooking a key indicator

McKinsey predicted that by 2019, retailers would have, or would be able to obtain, a 360º view of customers and the ability to combine unstructured data. Moreover, they would need to become more agile across the organization, conducting several small-scale experiments to determine investment priorities, and hiring in new types of retail profiles, namely in analytics and digital marketing. 

However, at that time, most retailers focused on implementing marketing and e-commerce initiatives. That being the case, the view of customers was not truly 360º.

Experience is two-fold: getting something new, and getting something easily.

In 2014, retailers were largely overlooking the importance of data coming from their own back of the house operations regarding true, real-time, local demand.

Without data guiding some of the most basic aspects of their decision-making, retailers – especially merchandisers – were left blind to changes in demand and therefore, unable to meet it.

At the same time, merchandising decisions, especially regarding distribution, were intuition-based without data informing merchandisers of the needs of each store, but often rather by cluster or other such grouping.

But the thought of overhauling standard procedure was daunting, as supply-chain transformation with the technology available at the time typically took anywhere from 18-24 months.

2019: Data-forward retailers have a real 360º vision of customers 

+ 2-5 pp EBIT increase for retail tech leaders according to McKinsey

In 2019, data is pretty much all retailers talk about. Not surprising, since those that become tech leaders for capturing and using data can increase EBIT by up to 2-5 pp over those who don’t.

Not only does it let retailers get personal with customers, it lets them get hyper-personal. 

Today’s customers, especially those under 40, are looking for an emotional connection to brands rather than a transactional one, a sentiment shared by the panelists at our recent Nextail Live event in Barcelona. The basis of competition has gone from product price and superiority to customer insights and experience. 

“Deep retail” even makes it possible to even understand customers on an emotional level through tech like AI image recognition and mood analysis. This information helps retailers understand attitudes through reactions and behavioral patterns. Thus, the way to customers’ hearts and minds is through their data. 

And while being quasi-mindreaders is certainly an incredible possibility, it’s only half the picture. 

A customer-centric and personalized experience consists of both how brands address customers through unique, immersive shopping experiences and ensuring the ultimate level of convenience and product availability.

While omnichannel offerings have come a long way in five years, they’re undeniably hard on supply chains and back-office operations. Retailers are using advanced technology to collect and process granular data to further transform in-store and online experiences by making sure they can offer the right products to the right people at any given moment.

By applying advanced analytics and machine learning (more on this in Part 5), retailers are obtaining a true 360º understanding of customers allowing them to anticipate product demand. In other words, they’re becoming more “data-forward” and agile in their approach as opposed to just data-centric.

New tech and professionals in a blended retail tech space

But this transformative retail tech doesn’t just drop out of thin air. We’re seeing retailers quickly blend into the tech space in several ways. 

Notable is the growing adoption of CRM tools used by many retailers to collect customer data regardless of the channel. While this is a step in the right direction, it doesn’t necessarily allow them to interpret the data they collect.

Chart of CRM software revenue
Source: Statista 2019

How do they go about translating this information into insights?

50% increase in number of data scientists retailers plan to hireAll you have to do is watch the flood of data scientists and software developers joining the retail ranks, bringing with them expertise from other industries. Research from Salesforce and Deloitte Digital found retailers were planning to employ nearly 50% more data scientists leading up to 2020. 

These professionals, and other tech-related profiles, are acting as translators and connectors between the company and their data. They can help brands do everything from spot trends to understanding real-time demand at SKU level instead of relying on guesswork and inflexible stock management methods of the past.

In an extreme example, Nike recently acquired predictive analytics and demand sensing firm Celect to build tech in house and/or interpret their massive amounts of unstructured data. That way, they can strengthen their inventory management through localized demand and tactical channel allocations. 

Other brands, like River Island, partner up with providers of tech solutions like Nextail since scalable SaaS models allow them to implement technology quickly and with lower CAPEX than developing in house. Now instead of one to two years, retailers can completely transform their supply chain within a matter of just a few months. 

What it all means

Thanks to their intelligent use of data, 70% of best-performing retailers (+10% revenue increase in the past fiscal year) are agile enough to respond quickly to consumer demands and insights, versus 30% of laggers.

Whether or not brands innovate in-house or with the help of others to tap into their data will depend on the needs and constraints of each retailer, but one thing is clear: those who use the data to collect to transform themselves into customer-centric retailers are the best positioned to find success in the coming years.

Deloitte Digital and Salesforce sum it up nicely, “Actionable consumer data in the hands of headquarter and store personnel is nothing short of a brand’s superpower, as it enables companies to know shoppers in the moment and grow intelligence with every step.”

We couldn’t agree more. 

Coming up…

Artificial intelligence makes everything possible: from data collection and analysis to D2C strategies, omnichannel, and ultimately a fully customer-centric customer experience, in store and online.

In our fifth and last article of this series, we discuss how it’s gone from a great idea to the motor behind full retail transformation. 

Miss a post? Check out our earlier posts, below!

Part 1: How D2C strategies get retailers and customers to cozy up

Part 2: From multi-channel to omnichannel – A fluid path to purchase

Part 3: The new role of the store in the customer experience

Did you know that Nextail uses artificial intelligence and machine learning to crunch and interpret massive amounts of data fast? Visit our product page to find out more about how it works.

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