Using Conversion to Inform Labor Models

by ShopperTrak on 03-16-17

“Understanding the power of conversion was a tremendous unlock for our brand,” Catherine McCabe, a Vice President of Teavana, says in the below interview to ShopperTrak’s Brian Field.

McCabe, like many of ShopperTrak’s clients, understands how conversion is one of, if not the, most important KPI for driving in-store sales. Why? Because, as McCabe discusses, conversion is a shockingly accurate indicator of an aspect of retail that can be difficult to quantify: associate performance — and, by extension, labor models.

Many retailers mistakenly look to sales levels as a metric for employee effectiveness when, in fact, a myriad of other efforts that have nothing to do with associate performance can cause sales fluctuation. Conversion, when compared to sales, is a more precise, more targeted, method for tracking associate effectiveness.

Conversion, which is one of the four metrics found in ShopperTrak’s Sales Equation, prevents certain unpredictable factors –e.g., weather, traffic, and marketing efforts – from unfairly affecting labor models and evaluations of how associates perform their jobs.

When ShopperTrak analyzes conversion rates, the metric is, out of necessity, informed by traffic and contextualized with other KPIs – i.e., ATS (average transaction size) and sales. This sort of direct influence and contextualization ensures that conversion offers retailers a well-rounded portrait of employee effectiveness.

With this principle in mind, McCabe, Teavana’s Vice President of Sales, Operations, Learning and Development, and her team built a new labor model around existing traffic numbers and desired conversion levels. Within only six months of piloting traffic and leveraging the new model, McCabe saw significant increases in conversion.

To learn more about Teavana’s traffic model and how the brand increased conversion rates by more than 10% in six months, check out the video interview with Brian Field, ShopperTrak’s Director of Advisory Services, below:

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