The One number you need to grow

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Many companies and brands strive for exponential growth by way of cultivating intense loyalty amongst their customers. They invest considerable time and money in quantifying the customer satisfaction levels. But most of the methods they use are complex and ambiguous and do not result into meaningful co-relation with growth of top line and bottom line.

 

The answer is to rely on a single reliable yardstick to measure the customer loyalty. Extensive customer research has figured out one question – you need to ask: “How likely is it that you would recommend our company to your friend or colleague?” The more “promoters” your company is able to build the bigger will be its growth. When customers recommend your products or services to their friends or colleagues they are putting their reputation on the stake. And they will do so only if they feel intensely favorable.

 

In fact the percentage of customers who were enthusiastic enough to refer a friend or colleague – correlated directly with differences in growth rate amongst competitors. True royalty clearly affects profitability. The tendency of customers to stick with a product or service reduces the company’s customer acquisition costs. The tendency of royal customer to bring in new customers – at no charge to the company – is extremely beneficial especially in mature industries. In fact, the only sustainable part to profitable growth may lie in a company’s ability to get its loyal customers to become its marketing department.

 

Taking a clue from the above findings Softpillar Technologies created a powerful tool to accurately measure customer royalty amongst the existing customers by way of a confidential survey that would be thrown automatically once a sales event was successfully executed. The process was manipulation proof as it was system driven with minimum human intervention. Implemented in a leading kids wear brand, it lead to astonishing results both in terms of Sales growth as well as margin improvement. Further, giving strength to the above stated hypothesis.