AI, Sales & Marketing
AI, analytics, sales and marketing

Marketers’ Roadmap to Customer-Centric Decisions

A decade ago, when I had joined a multi-billion dollar retailer, I was surprised to see how little analytics was 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, just wasn’t data or analytics focused then.

A lot has changed in the last decade, forcing retailers to change. Amazon, smartphones, chatbots and big leaps in technology – all have contributed to retailers becoming more data and analytics-centric. The time has come to go a step further and be customer-centric with a true 360-degree view of your customer in mind.

Today, the focus must be on the customer or you’ll lose them. 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 and 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 all channels; customers are more informed and demanding than ever; competition is more intense; and volatile economic cycles continue to impact customers’ buying power and disrupt behavior patterns.

The solution? Utilizing all of your customer data paired with actionable insights. However, the amount of data can be overwhelming, and many lack the analytical skills to tame the data and derive the nuggets of valuable customer insights they contain.

Need a place to start? Follow this roadmap to customer-centric decisions:

Organize data

Often, companies today have lot of data, generated by internal systems like Point Of Sale (POS) systems, bought from external vendors like Acxiom, provided by marketing automation engines, social data … the list is endless. The challenge isn’t really obtaining the data, but 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 for getting to the single source of truth in the long run, 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 near real time.

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 Modeling coupled with Descriptive Statistics create the special lens that helps marketers understand the customers better and in far more detail than ever before.

Generate and 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. Loyalty insights activities proactively reward the customers for their positive responses or trigger 1:1 automated marketing with evolving behavior, based on individual customer insights rather than broad segments.

Measure results and refine

As with any business activity, there needs to be a continuous learning cycle. Key Performance Indicators (KPIs) help measure the success and reveal opportunity areas. As markets evolve and customers’ tastes change, marketers need to continuously analyze the data to anticipate customer needs and retain their competitive edge.

Ultimately, these systems need to be able to continuously learn and improve on their own. In essence, this is machine learning.

While it is easier said than done, with advancement in AI technology, open source revolution and with the required focus on customer analytics, it is possible to be customer-centric without spending millions of dollars or without ripping apart your existing IT and Analytics infrastructure. And customer-centric decisions form the keystone of the next generation business model for retailers.

Iqbal Kaur is co-founder and Chief Analytics Officer at ZyloTech, an AI-powered customer remarketing platform designed for omnichannel marketers. With more than 17 years of experience leading retail and customer analytics at GE, Target and Lowes, Iqbal is an expert in building innovative analytics practices for enterprise. Follow

ZyloTech co-founder and CEO Abhi Yadav will moderate a Tech Talk on “Applying AI techniques to drive efficiency, optimization and scaling” at the MassTLC and MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) collaboration, MassINTELLIGENCE, The Age of Machine Learning.

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