Data Science Usecase (Part-5)
Cross-selling presupposes offering of the additional item to a customer who has already bought something or intends to buy.
The algorithm goes through sale transaction data and generates rules demonstrating the percentage of times the products are bought together. Thus, the role of data science is to provide fact-based recommendations from CRM data and transaction data.
These algorithms help to decide which products may be offered together or even placed on the same page in the catalog. Cross-selling also involves package deals. The analysis results help to form packages of the items offered at a discount.
How to Identify Cross-Selling Opportunities
There are two primary ways to identify a cross-selling opportunity for a customer: By auditing customer data to look for opportunities or by receiving a request in reference to your current engagement that can be expanded.
Audit your customer data to gather information that can guide recommendation conversations. The specific triggers will differ based on your product or service offering but generally look for points of friction related to your offering and opportunities where your solutions can provide an improvement.
- How is your product being utilized?
- Who uses your product and what teams are they on?
- What initiatives is your product being used for?
- Are there manual processes related to your engagement that you can help automate?
- Are there tools used in relation to your engagement that are outdated and you can provide alternatives for?
- Are there integrations with your product that you can replace with native features?
In some cases, instead of initiating the cross-sell conversation, your customer will come to you with a request that extends outside of the scope of your current solution that you can solve through a cross-sell.
You can use that request as the starting point to open communications with decision-makers outside of your point of contact to get an understanding of what the need is that your cross-sell can address.
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