How Salesforce Einstein Product Recommendations Can Boost Your Sales

How Salesforce Einstein Product Recommendations Can Boost Your Sales

Salesforce Einstein Product Recommendations is an AI-driven, groundbreaking solution that could transform your business. 


Markets in today’s business climate are ferociously competitive. Companies that have the edge provide products and services that are personalized to their clients preferences and needs, a fact that makes efficient customer relationship management a business-critical activity.


In this article we explore Salesforce Einstein’s Product Recommendations, a solution that uses machine learning to provide consumers with personalized product recommendations. We’ll talk about what Salesforce Einstein Product Recommendations are and how your business can make the most of them.


We walk through how to get started with this powerful tool, and outline its various benefits including higher revenue, better customer service, better staff productivity, and enhancement of the growth of your brand.

What are the Salesforce Einstein Product Recommendations?

Salesforce Einstein Product Recommendations use machine learning to make product recommendations for your clients. The solution runs your customer data through algorithms that analyze customer behavior and use this information to predict their preferences and needs.


The quality of the suggestions generated by the solution, as with any machine learning algorithm, is determined by how good the input data used to train it is. For example, the data should be varied enough that the algorithm does not produce biased recommendations.


The platform relies on training data to be able to effectively comprehend customer behavior, make better personalizations, evolve along with changes in customer preferences, and provide more accurate input for the business’s decision making process.


In order to effectively manage customers to relevant products, the software will look at behavior data like how much time a client spent on a particular page on your website, what they clicked, search terms they entered, and so on.


The solution will also analyze demographic data, geographical location, and, of course, product attributes in order to efficiently match them to the customer preference.


Salesforce may also look at additional data such as social media activity – things like likes and shares, the user’s interaction with marketing emails, and broader customer segment information – gender, income, age, and so on.

What Businesses Need Product Recommendations?

While the software would bring lots of value to businesses in a variety of sectors, it is a great solution for those dealing in retail and e-commerce. 


These are industries where products move quickly, business is fast-paced, highly competitive, and catalogs typically feature a wide range of product offerings. 


It is most powerful in situations where customers might have difficulty finding what they need in a catalog, choosing between different options, or where the business can cross- or upsell products. 

Benefits of Using Salesforce Einstein Product Recommendations

Increased Sales Revenue

Placing the right product in front of the right customer, at the right moment dramatically increases the likelihood that you will make a sale. Also the fact that you are able to more easily cross-sell and upsell products means that you get more value from customer purchases. 

Improved Customer Experience

For the customer, the user experience is massively improved. They are able to quickly and easily find exactly what they need and will see other complementing products that they may not initially have considered. A positive, improved experience means that customers are likely to come back, resulting in more sales down the road, and  further increasing sales revenue.

Reduced Manual Effort

Marketing teams or merchandisers are not required to manually pick products to show on websites or promotional materials. Data analysis, a process that typically requires specialized skills, time, and effort, does not have to be done by human teams. Choreographing what products to promote based on stock levels also becomes much easier. The tracking and reporting of key metrics, like average order value, is greatly simplified.

Enhanced Product Visibility

New products, products that are less well known, overstocked, and slow moving products can be given more visibility by showcasing them alongside more popular, fast-moving related items.

Implementing Salesforce Einstein Product Recommendations

  1. Start by preparing your data. See that your CRM data – customer profiles, purchase history, and other relevant data are complete, accurate, and up to date. This will ensure that  the algorithm is trained on the right data to make good recommendations. 
  2. Integrate Salesforce Einstein with your e-commerce platform and any related systems to  make sure data is flowing smoothly between your platforms, and the system for recommendation is working correctly. This is often a complex, technical, but crucial task. Bringing in a specialized, experienced partner like OMI will guarantee that you get the job done right, thoroughly test the solution, and can receive the technical support that you might need later.
  3.  After implementation and testing, monitor its impact on the results that matter to you. For example, sales figures, and customer satisfaction. You can also analyze some of the customer behavior patterns to see where changes are occurring and how these affect your business.


Given how fickle customer sentiment, preferences, and even needs can be, it is of utmost importance that as a business you stay in tune with your client base on what they want and need. 


For businesses in retail, e-commerce, and consumer goods in particular, Salesforce Einstein Product Recommendations, with its ability to provide personalized recommendations, and with the tips we have provided here, will help you make that all-important leap forward. Allowing you to meet and exceed your customers expectations, and significantly improve your teams’ productivity.


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