Customer Data & Analytics Blog

How to Make Customer-Centric Decisions in Retail

Iqbal Kaur | 3 minute read


A decade ago, when I worked at a multi-billion-dollar retailer, I was surprised to see how little analytics were being used across the organization. Having only worked in the data centric, financial services domain, I was shocked at the general lack of data, as well as the poor quality and inability to use the customer data that did exist. However, this was not an aberration.  The retail industry, in general, wasn’t data or analytics focused at that time. 

But, a lot has changed in the last decade, forcing retailers to change as well.  Amazon, smartphones, chatbots, and other big leaps in technology  have all contributed to retailers becoming more data and analytics centric. And now the time has come to go a step further—to become customer centric. 

Gone are the days when merchants could afford to look only at sales, profit margins, and inventory turns to make assortment decisions. Gone are the days when marketers could care only about campaign response rates. Today, the focus must be on the customer.  Otherwise you’ll lose them.  But, the good news is that when you know what your customers want, how much they are willing to pay, and how often they buy, you can not only maximize revenues & profits, but significantly outperform your competitors. 

But, it’s not easy.  Today’s retailers need to serve multitudes of customers, in diverse locations, and across channels.  These customers are ever more informed and demanding, competition is more intense, and volatile economic cycles continue to impact customers’ buying power and disrupt behavior patterns. All this makes the job of a retailer far tougher than before. 

This is where customer data and ensuing insights can help. However, the amount of data can be overwhelming, and many companies lack the analytical skills to tame this data, and derive the nuggets of valuable customer insights they contain. And one may even ask:  Do the benefits really outweigh the costs? 

What are some of benefits of customer centric decisions?

  1. Effective, focused promotions on your best customers enable you to run fewer programs while increasing the sales lift from each campaign. (We’ve seen clients reduce promotion events by as much as 60%)
  2. Understanding price preferences of your customers can significantly reduce mark downs.
  3. Knowledge of what your customers buy together can inform the co-promotion strategies and even store display decisions
  4. Understanding which customers responded to which promotion can help fine-tune promotion terms and conditions to maximize response, and hence increase sales
  5. Marketers can increase average transaction size by understanding product affinities and market basket analysis
  6. Companies can move beyond the ad hoc, or seasonal, segmentation to true 1:1 customer marketing. 

These are just a handful of examples that support the adoption of customer-centric retailing, and one could go as far as to say that customer-centric decisions form the keystone of the next generation business model for retailers. 

Roadmap to Customer Centric Decisions

  1. Organize data: Often, companies have lots of data – generated by internal systems like POS, bought from external vendors like Acxiom, provided by marketing automation engines, social data…the list is endless. So, the challenge isn’t obtaining this data, but in organizing and consuming all this “big data” for use in day-to-day decision making. While IT infrastructure and Master Data Management (MDM) are needed long-term to get to the single source of truth, it makes sense to invest in tools or platforms that meet the need for timely customer data in a much shorter time frame, say weeks, if not in near real time.
  2. Profile your customers into segments or micro-segments:Once all the relevant data is integrated and organized, the next step is to profile your customers and understand their buying preferences – what, when, how, where, and why. This is where the customer data is translated into customer insights. Predictive Modelling coupled with Descriptive Statistics create the special lens that helps marketers understand their customers better, and in far more detail, than ever before.
  3. Generate & Execute action plans by touch points and channel: Insights from customer data help enhance marketing activities ensuring the maximization of marketing return on investment (MROI). Today, high-performance companies develop loyalty programs tailored to dynamic customer segments, with loyalty insight activities that proactively reward customers for their positive responses, or trigger 1:1 automated marketing with evolving behavior based on individual customer insights—rather than broad segments.
  4. Measure results and refine:As with any business activity, there needs to be a continuous learning cycle. KPIs help measure the success and reveal opportunity areas. As markets evolve, and customers’ tastes change, retailers need to continuously analyze their data in order to anticipate customer needs and retain their competitive edge. And the systems need to be able to continuously learn and improve on their own.  In essence machine learning.

While it is easier said than done, with advancement in AI technology, open source revolution, and the required focus on customer analytics, it is possible to be customer-centric without spending millions of dollars or ripping apart your existing IT & Analytics infrastructure. Leading companies are already doing it.  To know how you can do it too, in a scalable way, please reach out to us at ZyloTech.

Topics: Personalization Customer Intelligence