Analyzing Relationships in Your Data

When analyzing your data on homeowner purchases you always want to see if there is a relationship in what they are buying from your HVAC company. For example, you may want to see if there is a relationship between whenever a homeowner purchases a new air conditioning unit if they often also purchase a duct cleaning service to go with it. Understanding relationship characteristics in your data will help you market more efficiently to your customer base.

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Source: Blazent

Existence & Strength

When looking at relationships in your sales data you will want to determine the strength of the relationship. You will want to see if the pairing of the items sold together is a consistent job after job, sale after sale. Knowing this information will help your sales team pair top-selling items together and provide upselling opportunities. Also, it will provide your sales team the main focus on the services and products they sell. Time is important and if your sales team can focus on the services that sell well together, then you will see an uptick in revenue.

Direction

You will also want to see if the relationships are positive or negative. Most of the time these will be positive relationships as they help drive more business to your company. This is because when a homeowner purchases a piece of HVAC equipment they will most of the time have a corresponding service or other product that compliments it. In essence, they can’t have one piece of equipment without the other.

Conclusion

According to Bizibl (Links to an external site.), “Research shows that 84% of businesses believe data is an integral part of forming a business strategy and that by 2020, 79% of sales decisions will be driven by customer data.” Understanding relationships in your sales data not only provide avenues for increased revenue potentials but also a clear picture of what your bread and butter are so to speak for your sales team. Click here (Links to an external site.) for last week’s blog post on understanding the significance of your data testing.

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