In retail, the ability to predict customer behavior, forecast trends, and anticipate sales can be a major advantage. Yet, without the right technology, this level of insight can be nearly impossible to achieve.
Predictive analytics software is one technology that can help retail businesses take their operations to the next level, offering them opportunities to boost sales, improve the customer experience, and grow their bottom line.
Today, we’re taking a closer look at how predictive analytics in retail works and discussing what you can expect when you implement it at your own company.
What is Predictive Analytics in Retail?
Predictive analytics is a type of statistical modeling system. Within it, intelligent machines scrutinize current customer data and use that information to predict future business activity. In other words, these programs look at what is happening and what has happened to guess what might happen in the future.
If you’ve ever finished a Netflix movie only to have the platform offer you a few more titles that you might like based on what you just watched, then you’ve seen predictive analytics at work.
Modern technology has made it possible for retailers to anticipate upcoming patterns with incredible accuracy. Using predictive analytics, it’s easier for retailers to optimize every aspect of their operations, including:
- Forecasting upcoming sales
- Planning for potential product shortages
- Providing a more personalized retail experience
- Running promotional offers
- Improving operational efficiency
Increasingly, vendors are augmenting their ERP systems with built-in predictive analytics capabilities. This is especially true of the top ERP systems. Retailers can use ERP as their retail software solution and leverage it to analyze data as soon as it’s received, while ensuring everyone has access to real-time updates.
Retail ERP Success Story
A large global retailer successfully implemented ERP software by prioritizing customized, on-site training.
With so many tools and technologies available, why should retailers consider predictive analytics during ERP selection?
For one, it’s a primary way to stay competitive. In the wake of the COVID-19 pandemic, retailers are looking for ways to automate and simplify their operations. This includes incorporating technologies that allow them to meet increased demand and facilitate influxes of e-commerce traffic.
Research shows that in 2020 alone, U.S. e-commerce sales grew to more than three times the former rate, and this trend shows no sign of slowing down.
With predictive analytics, retailers can look at historical buying patterns to determine which items are top sellers with their target audience and which factors motivate them to buy.
Then, they can use that data to strategize future campaigns that appeal to those catalysts.
Beyond predicting future sales, how else can this technology benefit your retail company? Let’s look at some of the top benefits of predictive analytics in this industry.
5 Benefits of Retail Predictive Analytics Technology
1. Improve Inventory and Store Management
Retailers must carefully balance their inventory, ensuring they have enough products to meet customer demand while avoiding costly overruns.
With predictive analytics, they can highlight areas of highest demand, analyze new and emerging sales trends, and optimize their delivery approach to equip stores with the exact inventory they require.
This software uses a ranking system to grade each product’s performance based on indicators such as sales margins or number of units sold. This helps stores correlate how each product contributes to their overall profitability and revenue so they can stock their shelves more effectively.
2. Anticipate Changes in Buying Behavior
The retail industry is constantly in flux. Trends and fads dictate what’s hot one minute and out the next. Then, there are periods where seasonal demand is exceptionally high.
If retailers know in advance that their sales are about to increase dramatically, they can put gears into action and ensure their stores are ready. They might not always be able to plan for such spikes or dips based on historical data alone, but the right intelligent systems can allow for a more accurate analysis.
3. Personalize the In-Store Experience
According to one report, 44% of consumers will take their business elsewhere if a brand does not offer a personalized experience.
One way that retailers can turn casual browsers into long-term buyers is to offer in-store experiences that are tailored to each shopper. They can do so by assessing buyer history to better understand the deals that pique their interest. Then, they can create campaigns that utilize this knowledge.
For instance, they may send a text with a custom offer as soon as a buyer gets within a certain range of the store. Or they may equip floor associates with smart devices, like tablets and phones, so they can access relevant shopper data. When associates recognize that a certain shopper is in the store, these associates can approach them with offers and recommendations that may catch their eye based on their buying history.
4. Create Targeted Marketing Campaigns
In their quest to appeal to new buyers, brands historically deployed generic, one-size-fits-all campaigns. While this approach may be successful to some degree, consider how much waste is generated when entire segments of leads fail to convert.
When using predictive analytics, retailers can create custom marketing and outreach campaigns. This allows them to tailor their content and positioning based on a range of buyer characteristics, such as:
- Shopping preferences
- Search histories
- Spending habits
- Buying patterns
With this data in hand, they can go from a bird’s eye view of their audience to a more granular perspective. Then, they can create multiple, smaller-scale campaigns that appeal to those sectors directly, rather than one massive campaign that misses the mark.
5. Make Better-Informed Pricing Decisions
The art of setting prices can elude some retailers. This is especially the case in the e-commerce realm where numbers don’t usually shift in response to external factors, such as seasonal demand.
With predictive analytics, companies can more clearly see when it’s time to gradually start increasing or decreasing their prices. These smart systems employ a range of different pricing functions, allowing retailers to:
- Track inventory levels
- Compare competitor prices
- Collate demand data
With these insights, companies can slowly shift their pricing in one direction or another. This helps them avoid sudden spikes, which can be jarring for customers and detrimental to brand loyalty.
The Power of Predictive Analytics at Work
As retailers continuously seek ways to innovate and bounce back from the COVID-19 pandemic, they’re wanting to set their brands apart from the rest.
If you’re thinking about adopting new enterprise software for your retail operations, don’t underestimate the power of predictive analytics in retail. These systems store and organize historical business data and use it to predict future behavior. As they do, they take into account the various factors that could affect those trends over time.
Interested in learning how data analytics can increase the benefits you realize from your CRM or ERP implementation? Contact our ERP software consultants below for a free consultation.