7 Metrics Key to Conducting Good Sales Analytics
A lack of available data is certainly not a problem the manufacturing and retail sectors are facing today. Quite the opposite, the emerging challenge is figuring out how to best wrangle that data into formats a variety of stakeholders can understand and use — and what information is most relevant to their decision-making in terms of driving performance goals.
In other words, there are metrics and there are key performance indicators (KPIs). Each team must define for themselves what is most important to quantify. This is a key step in avoiding “analysis paralysis” in which there’s so much data to wade through and so many insights to consider that it actually starts to impede effective decision-making.
While the exact KPIs your sales team defines will depend on your business objectives and niche, here are seven common key metrics worth considering when it comes to conducting good data analytics.
Sales Volume/Revenue By Product Line/SKU
One of the biggest advantages of modern sales analytics is the granularity of the insights provided. That is, users can keep drilling down into data to understand even the smallest parts of the whole — like sales volume and revenue by individual SKU. Users can also zoom out to gauge performance of broader product lines as needed.
Analyzing broad and specific product numbers helps decision-makers make wise decisions pertaining to areas such as inventory, lineup, design, marketing and production.
Sales Volume/Revenue By Location
Understanding sales performance by locale also helps retailers optimize their approach to storage, order fulfillment and stocking. There are a few ways to view sales performance by location, including breaking it down by volume and revenue. This will ensure decision-makers understand how many products are being sold by location, as well as how much the company is earning as a result.
Revenue From New Business
An evergreen question in the minds of retailers is how best to balance customer acquisition efforts with customer retention initiatives. It’s important to understand how products are selling, as well as to which types of customers — new or existing.
This is why the experts at HubSpot recommend sales teams measure and compare their percentage of revenue from new business against—
Revenue From Existing Customers
Analyzing your company’s percentage of revenue from existing customers will provide insights into the rate at which people are making repeat purchases and how it’s affecting your bottom line. Sales and marketing teams can then make tweaks to optimize customer loyalty.
Customer Lifetime Value
A major component in the balancing act that is acquisition vs. retention, average customer lifetime value (CLV) helps sales teams understand how valuable they can expect the relationship with a buyer to be — and tracking this metric over time is important to understanding how well efforts are optimizing the expected value.
Percentage Of Market Share
In a bustling industry filled with disruption, on top of already thin margins, companies need to analyze their own performances against those of competitors. Looking at percentage of market share is a good foundation for understanding how your company stacks up against others vying for the same sales.
As Investopedia points out, looking at progression of market share over time — how it increases, decreases or both within a given period — can help your team understand its competitiveness.
Upsell/Cross-Sell Rates
How successful is your company at getting people to buy complementary products or better versions of the products they’re considering? As Business 2 Community cites, ecommerce giant Amazon has cross-selling to thank for about one-third of its overall revenue.
This metric can help sales teams determine which product offerings complement each other best, as well as how to best position products in the funnel to maximize up- and cross-selling.
There’s a lot of information pertaining to sales today, but defining the right metrics will help companies get the most from their data analytics efforts.
