What can retail analytics teach us about our customers?

11 May 2022

What can retail analytics teach us about our customers?

Identifying your core personas and maintaining customer loyalty can boast significant benefits to any retail enterprise, and for good reason. 

By incorporating retail analytics throughout your enterprise, users across a wide range of teams can gain access to insights that will help them better appeal to customers – boosting loyalty and satisfaction. For customer success managers and other buyer-facing roles, this new arena of insights promises to transform how customers are approached, nurtured, encouraged, and maintained. But how?  

Below, we’re investigating the relationship between retail analytics and customer relationship management in more detail, as well as exploring some of the current challenges facing new capabilities.  

Why loyalty matters

It’s no secret that buyers are becoming more sophisticated in their needs, and more elusive in their loyalty than ever before. With a recent survey revealing that 86% of respondents would leave a brand after as little as two poor experiences, customer relationships must be more than just satisfactory.  

The results of focusing on delivering a stellar and tailored customer experience are obvious when we learn that 82% of customers report that they feel more positive about a brand after engaging with personalised content. The benefits to your business are even clearer. Word-of-mouth recommendations have a monumental impact, with it being reported that 60% of customers will trust recommendations that come from family and friends. 

Through the use of enhanced retail analytics, this focus can be realised in practice. 

Gaining a strategic perspective 

Retail analytics can help uncover how best to approach your target personas by providing a distinct reporting dashboard on each customer touchpoint, facilitating a 360-degree view of your personas. 

With this, customer success managers can dive deeper into the insights that matter – accessing intelligence such as: 

  • Individual purchasing trends 
  • Preferred times of day 
  • Previous interactions and touchpoints 
  • Estimated budget caps 

All of these can then be used to inform more strategic, positive experiences that increase retention and loyalty. 

Data, digital storefronts, and the on-premises challenge 

Gaining this 360-view of a customer or segmented persona can be much more challenging for on-premises locations than it is for digital eCommerce storefronts, and for obvious reasons. A digital store can access a wider suite of tools aimed at collecting customer data – from offering discount codes in exchange for email addresses to using cookies to deploy retargeting efforts.  

On the other hand, on-premises stores are unable to collect as much data in the same transaction, and we have already seen a surge in efforts to resolve this issue (such as Tesco’s Clubcard or offering digital receipts sent to an inbox).  

As we look to the future of this trend, we expect to see more physical locations incorporating data collection efforts to better understand how they can appeal to customers to boost loyalty and, in the process, profits. 

ML and me: predictive analytics and customers 

While traditionally only available to the most sophisticated retailers, the ongoing commercialisation of Machine Learning capabilities is enabling more and more businesses to realise the benefits of predictive analytics. 

Combining statistical models with Big Data analysis, predictive analytics allows users to explore potential future outcomes and stay ahead of the competition. Providing a proactive, future-forward approach to decision-making, predictive analytics can forecast the effects of a wide range of critical decisions, such as:  

  • Revenue increase or loss for promotional activities 
  • The impact of product pricing changes 
  • Rise or fall in consumer sentiment based on activity 

While ongoing efforts are focused on making these advanced analytics more and more accessible to retailers, there are still core hurdles that must first be overcome.  

Any data that’s fed into predictive analytics frameworks must first be structured, centralised, and well-governed. Fail to fulfil the requirements, and the consequences could be vast – such as the introduction of scope creep, the need for extensive restructuring projects, and the possibility of introducing unwanted bias into reports (a potentially disastrous consequence). 

Learn more about predictive analytics in our guide exploring the four types of analytics and their challenges here. 

The future of harnessing consumer data 

As we see the need to deliver positive, tailored experiences grow, we expect to see two factors dominate: quality, and accessibility.  

Just as consumers continue to become more sophisticated and demanding in what they want from a retailer, so too will users of retail analytics begin to demand more from the quality of their data-driven insights, in the hopes of reaching previously unavailable intelligence. As tools become more widespread, the quality of insights gathered will play a vital difference between competition.  

We also expect to see retail analytics platforms become more accessible to everyday users than ever before – allowing those without prior technical experience to interact with complex datasets through the introduction of a streamlined UI.  

To learn more about the current retail analytics landscape, and what it means for your business, download our free eBook: “5 ways to boost your margins with retail analytics”.

Free download: 5 ways to boost your margins with retail analytics

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