How Reorder Models can Increase Revenue

How Reorder Models can Increase Revenue

Modern buyers expect distributors to know what they need and when they need it. In the past, outside sales reps visited customer storerooms to determine which products were running low and what needed refilling. While most distributors are using a hybrid sales approach today, their sales reps still need to have the same visibility of customer needs that they do when they’re on-site.  

AI has changed the way B2B companies do business. Instead of salespeople combing through purchase histories, advanced reorder and recommendation models automate the curation of product suggestions, boosting revenue and productivity for companies that utilize them.

What Are AI Models?

AI models are programmed to recognize specific data and patterns. This analysis enables the models to make predictions about customer behavior and product preferences. As a result, sales and reordering recommendations are much more precise. 

Amazon, for instance, has some of the most powerful AI models in use. Their systems analyze customer behavior, determining what people are buying, how they react to different products and what they are most likely to purchase next. Amazon’s AI uses this information to recommend items on its homepage, product pages and at checkout. The site’s eCommerce recommendation models have proven effective, accounting for roughly 35% of sales – nearly $70 billion in value. 

What Are Reorder Models? 

AI-powered reorder models are trained to anticipate when a customer is due to reorder consumable products. The program analyzes patterns in purchasing data to draw connections about the relationship between products and customer behavior. This information paints a profile of the customer, shaped by their order history and buying patterns, that predicts what items they will come due to reorder.  

These accurate, hyper-personalized selling models add value to every customer contact. Reorder models can help sales reps: 

  • Determine when a customer is due to reorder 

  • Identify items that need replenishing 

  • Understand possible upsell opportunities 

  • Be more consultative, productive and efficient 

How do these systems work? To effectively process data and provide accurate predictions, reorder models must be trained.  

During training, models are shown various purchasing scenarios to teach them which products companies tend to buy and how often they are reordered. The more data available to train AI models the better. AI can still identify reorder patterns even if the data is messy—which means it can contain typos, extra spaces, inconsistent formats, etc. The final step is to apply filtering to ensure the system only recommends previously purchased items for reorder. Since distributors have significant amounts of data already and their customers typically have a lot of recurring purchases, they are potentially well-positioned to use AI reorder models.

3 Ways Reorder Models Increase Revenue

Reorder models increase revenue in three ways.  

Enabling Sales Reps 

First, reorder models increase revenue by enabling sales reps to be more efficient and productive. Reorder models will identify customers due to reorder and alert your sales reps when it is time to get in touch. 

Creating Upsell Opportunity 

The second way reorder models increase revenue is to create an upsell opportunity. All upsells start with understanding your customer. Upselling allows your sales reps to make the most of any interaction with a customer and results in better margins on larger orders due to efficiencies in logistics and manpower.  

Through Cross-Selling 

The final way reorder models increase revenue is that they can be used alongside other models to enable cross-selling. This technology gives your team insight into relevant product pairings and add-ons, increasing the likelihood of customers making additional purchases. The reorder model gives a sales rep a reason to get someone on the phone which opens the door for a cross-sell. By using systems similar to eCommerce giants like Amazon your eCommerce site can provide better recommendations to customers. At checkout, other models provide “similar items,” “customers also bought,” and “recommended for you” products to encourage customers to add to their carts.   

Before including AI models into your sales strategy, it is crucial to understand the differences between distributors and B2C companies that have had so much success with reorder models. 

  1. Distributors have more products – While a B2C business may have 1,000 products, distributors may have millions of SKUs. Unfortunately, most reorder models created for B2C platforms are not powerful enough to process these kinds of inventories. 
  2. Distributor recommendations are based on different types of data – Unlike B2C customers who generally make one-time purchases, distributors’ customers regularly reorder the same products. Because of this, buyers spend less time looking through eCommerce sites. With less browsing data to analyze, reorder models must utilize different kinds of information to be effective. 
  3. B2B buyers appreciate being able to talk to a person – While B2B buyers like to do research online, they still prefer to talk to a person before making a purchase. AI models need to augment sales reps and not just live on eCommerce platforms.   

Reorder models can improve your customer’s eCommerce experience, boost the effectiveness of your sales team and increase revenue. While various AI models are available, it is crucial to find one specially designed for distributors to ensure the best results. 

 

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