Top 6 predictive analytics software benefits for the Retail industry in 2019

The retail industry is extremely competitive with traditional brick-and-mortar retailers under pressure from online services such as Amazon. In this environment, anything that delivers advantage has to be quickly seized. Predictive analytics offers huge benefits for retailers as it helps them turn Big Data into actionable insight to better understand and connect with their customers. Research shows that retailers who can use their data properly can improve operating margins by over 60%. So what do retailers need to consider when selecting their Predictive Analytics tools?

First, let’s start by defining predictive analytics. You can think of it as one step up from traditional Business Intelligence (BI) solutions. Traditional analytics have been designed to give an idea of the present based on past information. With access to Big Data, predictive analytics software uses past data to predict future events. It does this by creating predictive models powered by advanced predictive analytics algorithms. These models are fed by structured and unstructured data from a wide range of internal and external data sources. This vast amount of data facilitates the predictive modelling to be trained and deliver increasingly accurate predictions.

In the context of retail, the best predictive analytics software helps companies understand customer behavior, know what kinds of product will be successful, improve marketing performance and reduce inventory risk. A key reason for this is the ability of predictive analytics to analyze the data at a micro rather than macro level. Predictive analytics tools study individual customer interactions rather than simply an average behaviour pattern across a customer segment.

According to Research and Markets, the global predictive and prescriptive analytics market was valued at USD 5.52 billion in 2017 and is projected to reach a value of USD 16.84 billion by the end of 2023. The retail sector was identified as one of the three key markets driving this growth.

The research organization states that predictive analytics software is popular in Retail because “increasing competition, a wide array of product offerings, multiple touch points for customers, and increasing customer complexities enable retailers to use analytics. Applied to relatively large sets of customer data, it can enable marketers to predict future behavior, customize best customer offers, or interact with their clients or suppliers based on such forecasts.”

The Key Benefits of Predictive Analytics Software

The best Predictive Analytics software should help deliver:

Improved customer experience

McKinsey suggests that companies that can improve their customer journey can see revenues increase by as much as 15% and lower the cost to serve by up to 20%. The best predictive analytics software lets you gain a much clearer and granular understanding of your customer – what they like, their attitudes and behaviors. All this data can be fed into your predictive analytics models that can begin to tell you what products they would like, how and when they’d like to be communicated with and which marketing campaigns are likely to have the greatest resonance with each individual customer.

Reduced customer churn

We all know that it costs far more to attract a new customer than keep an existing one. A 5% reduction in customer churn can equate to 125% increase in profitability.

What retailers need to know is when a loyal customer begins to disengage with the brand. Through predictive modelling, you can understand which customer is straying, which person has the most potential to be a long-term profitable customer and when the customer is likely to purchase again. The best predictive analytics software can identify customers likely to leave as well as predicting the remedial actions most likely to be effective – such as targeted and personalized promotions and incentives.

Optimized and flexible pricing

Predictive analytics is now being used as part of an optimized pricing strategy where a product is priced differently according to a range of variables such as channel, location or time of year. The best predictive analytics tools allow you to develop highly accurate predictive models that study competitor prices, inventory levels and historic pricing patterns and customer demand to ensure that your pricing is correct for each situation. Companies that are leveraging predictive analytics solutions to facilitate advanced pricing capabilities are reporting as much as 30% gains in operating profit and Return on Investment (ROI) up to 800%.

Personalized and targeted marketing

Personalization has long since moved from a ‘nice to have’ to essential for retailers. Forbes suggests that personalization builds both profit and loyalty, while other research in the US found that 63% of Millennials and 58% of GenX customers would gladly share their data in return for personalized offers and discounts. Retails are uniquely positioning to collect a range of data on individual customers, including preferences, buying history and shopping patterns. This is a goldmine for predictive analytics. With the best predictive analytics software you can begin to personalize every aspect of your marketing and engagement strategies with customers. You can create predictive models that take granular customer information and turn it into insights that inform your promotion and incentive strategies.

Improved inventory management

Similar to Artificial Intelligence, predictive analytics is increasingly applied to improve inventory and store management. In the past, companies have held excess stock to cover the risk of stock-outs. However, the days of a fully stocked inventory are quickly diminishing as retailers realize that less stock equals more profit. The best predictive analytics software gives retailers a far better understanding of customer demand. Using advanced predictive analytics modelling and algorithms, you can highlight areas of high demand, quickly identify sales trends and optimize delivery so the right inventory goes to the right location. Predictive analytics tools can keep you ahead of customer demand so that you can streamline your supply chain, reduce storage costs and expand your margins.

Improved store location

With all the comment about eCommerce destroying main street, it’s worth remembering that in 2017, 94% of all retail sales were in brick-and-mortar stores. So, choosing a store location remains one of the most strategic long-term decisions in the retail industry. It’s an area where the best predictive analytics solutions can play a significant role. Predictive analytics tools can be used to forecast the potential revenue for a selected store location based on data such as demographics, property market, competitive activity, market conditions, customer purchase power and purchase behaviours. In addition, these Predictive analytics models and algorithms can also be used to analyze and manage your existing locations.

Why choose OpenText for Predictive Analytics?

OpenText™ is at the forefront of the development of a wide range of analytics solutions. OpenText™ Magellan™ is an AI-powered analytics platform with predictive analytics capabilities that provides users with a 360 degree view of their business. It enables them to access, blend and explore data quickly without depending on IT or data experts. It gives companies the ability to manage and analyze all the Big Data and Big Content stored in their Enterprise Information Management (EIM) systems.

Editor’s note: This is an installment in our “AI Glossary” series of blog posts, offering guidance on key areas of artificial intelligence and analytics. Look for future posts in this series over the months to come.

Zachary Jarvinen

Zachary is the Product Marketing Lead for Analytics and Artificial Intelligence at OpenText. He previously worked at Global Fortune 500 Epson and the U.S. State Department, and was part of the 2008 Obama Campaign Digital Team. Zachary speaks fluent Spanish and Portuguese, and holds an MBA/MSc from UCLA and the London School of Economics.

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