In marketing, finding the golden ticket to increase sales is key. Marketers know that successful cross-selling is the suggestion of another, often related product to the customer to lift sales. Up-selling is a suggestion to the customer of a product that they would enjoy more than the current product they have or are about to purchase, but is a higher price. Both of these marketing techniques add revenue to the bottom line. But how do you successfully make these happen in your day-to-day marketing plan?
There are many tools to use data that you already have to make these strategies successful. These strategies are often combined on a web page and care must be taken to develop and test rules that are optimized for the situation. Luckily for us, there are many analytics tools available to perform these tests. Let’s look at some examples of successful cross-selling and up-selling scenarios that increased sales.
Real World Examples
In an online example, a coffee distributor is seeking to increase value to their customer and prompts a recommended purchase to increase sales. To provide the best value to their customers and ensure an increase in sales, this should be as targeted as possible. You can use an analytics tool to analyze multiple criteria to your recommendations; such as price point, product line, product preferences, number of recommendations to make; what similar customers also purchased, frequency of purchase, and more. Providing the most targeted cross-sell recommendation will ensure a higher conversion.
According to Fortune, Amazon uses multiple criteria for their suggestions; including past purchases, current cart contents, items the customer rated and what other customers have purchased. This can also be combined with additional rules. For example, when upselling, do not recommend items that are more than 25% higher than the original item as this may be more than the customer is planning to spend and you risk losing the sale.
Another large online retailer limits the number of recommendations to three, so as not to overwhelm the customer. A Food Manufacturer / Distributor I am familiar with wanted to optimize product placement in displays used on the store aisle end cap. By using models in Regression, Classification and Market Basket analysis, they determined the optimal product mix for the end cap with a Cross-Selling strategy. The key is to know which items to place at the right time for the right customers to increase sales.
Pulling it All Together
The amount of data to analyze for these results to be accurate and meaningful for your business can be daunting. SAP Predictive Analytics eases the detailed work required to cleanse data, develop and test the models and typically reduces the development time by 40%. This enables you to create more models in the same time. SAP Predictive Analytics provides a feature rich tool with automated features to minimize time-consuming tasks which utilizes SAP and external data. This allows you to analyze large amounts of customer data to gain maximum value for cross-sell and up-sell opportunities, as well as other targeted marketing and sales efforts.
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