In a perfect world, companies offer the right product at the exact moment customers look for it.

Although this world is far from perfect, the retail industry is gradually approaching this ideal scenario as consumer behavior analysis becomes more accurate. This is mostly thanks to modern predictive analytics.

As a data analytics category, predictive analytics offers insights into consumer behavior based on techniques like machine learning and statistical modeling. From there, it predicts possible outcomes according to historical data with a higher degree of precision.

It lets companies get enough data to predict purchase patterns using methods like footfall analytics and customer journey map. These are then used to improve engagement, boost sales and retention, and, ultimately, foster company growth.

That being said, analytics could also help determine the elements that could affect the future of retail in more ways than you can imagine, starting with these four trends:

1. More Personalized Customer Experiences

Predictive analytics has and will always be at the core of understanding customer behavior. When combined with audience demographics, retail companies can use it to create highly personalized offers that target specific shoppers for better conversion.

Before analytics, there was no option to create offers that are specially designed for certain customers. In fact, businesses were just running on retail marketing born from a few overlapping characteristics across large groups of customers.

While that may have worked in the past, the emergence of online shopping and data analytics has rendered that particular setup outdated.

With the help of these two innovations, you can already track consumer behavior, monitor the shopper’s journey, and get insights on how to create a highly personalized customer experience.

An excellent example of this is incentivizing frequent buying to drive more purchases and achieve higher sales.

Of course, predictive analytics does more than that. It can also be used to upsell or cross-sell products.

For instance, a customer has been found to have a monthly habit of buying a specific brand of chocolates. Using predictive analytics, a retailer offering the same brand can offer the customer a buy-two-get-one-free coupon. Given the customer’s purchase history and behavior, they will most likely take advantage of this deal.

2. Omnichannel Transactions

Operating on multiple channels has become a vital driving force in sales in most retail companies today. In fact, outstanding customer experiences depend on how retail businesses bridge any gap between brick-and-mortar stores and e-commerce platforms.

One challenge that poses a threat to this goal is the neglect of back-office operations, particularly mismanaged inventory. Considered one of the worst retailer nightmares, this issue needs to be resolved to boost operational efficiency.

Thankfully, analytics that understand and predict customer behavior can also forecast the demand for certain products. This makes it easier to know what to store and when, and what needs to be discarded as soon as possible.

In other words, businesses can stock up on popular and up-and-coming products and reduce the restocking of slow-moving and soon-to-be-phased-out items.

Besides, retail analytics also helps sales personnel and even customers themselves check the availability of stock online. This removes the guesswork on the part of the sales staff and sets the right expectations to avoid disappointing consumers, which could potentially hurt their loyalty to the brand.

3. Enhanced Customer Journey

Consumers are the core of any retail business, which means understanding their shopping journey helps make sure they get the best possible experience.

A customer journey begins from the very first point of contact with the brand. But make no mistake; customer mapping doesn’t end after customers place their orders.

You see, it’s all about maintaining a healthy long-term relationship. By monitoring consumer behavior even after a purchase has been made, retail companies get to see areas of improvement.

Through predictive analytics, companies can also monitor how leads move through the sales funnel. Companies can examine marketing stimulus and consumer responses to see what works and what doesn’t with response modeling.

4. Consumer Data Platform Growth

More companies will need consumer data platforms (CDP) in the decade marked by 2021. Thanks to the immense digitization of the retail industry in 2020, consumer data platforms became more popular. More than that, it also transformed from a “want” into a “need” in the retail space.

A CDP serves as a major data resource for consumer information gathered from any means of communication made with a certain brand. As long as customers browse websites, buy in-store or purchase from online stores, they leave a digital footprint that could be analyzed and used as a guide in making important decisions that will shape that company’s future.

When used with predictive analytics, the CDP is a treasure trove of data that can help companies build a strong relationship with their loyal customers. It lets businesses know what products and promotions would click with their target market.

History Shapes the Future of Retail

There’s so much that history can teach us, especially when it comes to retail. What has passed holds clues to things that are just about to happen.

By using predictive analytics, you can take advantage of these four trends to shape the future of your retail business.

Talk to us about how you can usher in a better future for your company today.