Big data creates a unique opportunity for retail marketers looking to better understand their consumers. With greater opportunities to gather insights about shoppers, marketers can more accurately predict customer behavior and quickly act on insights in real time.

While big data is rich with information, retailers have long struggled to analyze it in an efficient manner. However, as new tools emerge that are leaps and bounds beyond traditional analytics, this data is being used to inform a wide range of business decisions. Retailers must adapt or risk falling behind to more tech-savvy competitors.

The Limits of Traditional Analytics

The problem with traditional analytics is in the name. The amount of data being analyzed is far too big to use classic methods. While marketers have mined useful insights from big data, it’s impossible to gain the most complete, accurate analysis using dated methods.

With manual analysis, we’re forced to rely on broadly segmenting consumers based on simplistic data points that often miss the bigger picture. For example, a strategy that markets to a woman solely based on her one-time purchase of luxury perfume might ignore the fact that she had a coupon, and would otherwise choose to purchase a more affordable product.

Additionally, traditional analysis takes far too long, resulting in inefficiencies from the bottom-up that cause even broad insights to lag behind consumer trends. It takes weeks or longer to analyze data manually, paralyzing decision makers as they wait for information that might not even be complete or accurate.

The fact is, retailers that still rely on traditional analytics will always be one step behind their technologically advanced competitors. A more efficient solution for understanding and responding to customer behavior is machine learning.

The Benefits of Machine Learning

Machine learning platforms in the retail industry use complex algorithms that ingest and analyze an extensive set of data points, providing retail marketers with comprehensive, contextual information about consumers. While many retail marketers still use a hodgepodge of outdated legacy software systems for marketing automation, customer segmentation and price elasticity, machine learning platforms combine this data into one, easily accessible database, allowing users to pull more accurate and actionable insights.

This holistic approach allows retailers to develop a more nuanced understanding of customers based on multiple perspectives. A machine learning platform would recognize that the woman who purchased luxury perfume only did so because of a steep discount, and could factor that into their data analysis. Retailers that have a wide variety of information about customers can serve them much more effectively, because they can target individual shoppers with highly personalized discounts and offers, through the right channels at the right time.

Continuing with our perfume example, a retailer with machine learning capabilities could entice the shopper by sending her another coupon, or engage her with advertisements for related but more affordable products.

Machine learning platforms are constantly adapting and improving based on both online and offline customer interactions. These platforms process years of online and offline data across all channels, enabling marketers to make informed predictions about a customer’s purchase down to the price, location and device.

How To Kickstart Machine Learning With a Third-Party Platform

Machine learning can be complex and highly technical, which makes it difficult for retailers (especially smaller companies) to implement effective strategies. This causes many retailers to shy away from the field and lose valuable insights by sticking to traditional analysis strategies that lack technical depth. However, with the help of third-party machine learning platforms, retailers can establish a more effect data strategy and hit the ground running.

Third-party platforms allow companies to quickly incorporate machine learning tools into their marketing strategies. With a third-party platform, retailers don’t need to invest the time and money into starting from scratch. Machine learning platforms are also supported by experienced data analysis experts, who can help retailers understand new findings and continue to improve marketing strategies. A third-party offering also provides constant operational support and troubleshooting, while also eliminating otherwise steep learning curves.

When transitioning to a machine learning platform, here are some initial steps to take:

Define your goal: There are many ways machine learning can help your business, so you must first determine your “why.” Whether you want to increase revenue or boost sales of a certain product, defining what you hope to gain from machine learning will ensure you select the right approach for maximum ROI.

Identify your pain points: Once you’ve determined your goal, figure out what’s preventing you from reaching it. Machine learning can’t solve everything, so make sure that it’s the right solution for your specific set of challenges before diving in.

Choose a vendor that can help you meet your goals: Once you’ve determined what you’re looking for in a machine learning platform, find a partner best suited to help you reach those goals. Take time to research so you can be certain that you choose the machine learning platform that best meets your specific needs.

As machine learning technology becomes increasingly popular in the retail industry, marketers will have to develop their own strategies or risk making crucial decisions based on undeveloped and inaccurate information. The insights gleaned from big data and machine learning algorithms help establish strategies that increase customer engagement, loyalty and, ultimately, profit. The sooner companies adopt a machine learning strategy, the better.

(About the author: Kerry Liu is chief executive officer and cofounder of Rubikloud)

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