Would you have invested $100k on Amazon stock in 2010? What about on Netflix? As of December 2020, that $100k would have been worth about $2.3mm and $6.3mm, respectively. I don’t know about you, but I would be very satisfied with those returns.
It is impossible to predict the future with 100% certainty, but with today’s technology advancements and limitless access to data, we have the ability to rapidly analyze information to better predict what may happen in the next 10 seconds or in the next 10 years. In fact, everywhere we look, and across nearly every industry, people are using data to make predictions. Weathermen use it to give us a forecast, investors to pick stocks, economists to recommend policy, and gamblers to bet on sports games.
Regardless of industry, the key to making better predictions is to analyze the right data and information. The more accurate, timely, and actionable that information is, the better insights we can derive, and in turn, the better decisions we can make.
The ROI of Retail Analytics Explained
In the CPG world, the biggest and most successful companies, such as P&G, Unilever, and RB, are leveraging data as their “crystal ball” for driving strategic decisions. For example, data is regularly used by R&D teams to gauge where consumer behavior/preferences are moving. R&D will layer in years of historical sales data across multiple sales channels to generate a predictive model for informing decisions on what new product launches will have the highest probability of succeeding in the market.
At the same time we’re seeing these forward-thinking brands leverage data for decision-making, there also continue to be countless companies who are still “flying blind” without data. They may have had prior success launching products through “gut” instincts, or may simply “wait and see” what trends become mainstream before taking new products to market. However, in today’s retail landscape, this approach is simply not sustainable long-term. As more companies leverage data and analytics, it’s imperative for others to follow suit, or risk significant financial setbacks.
Let’s walk through two scenarios demonstrating why.
Say your company is launching 6 new SKUs next year, and you are allocating $50k per product for R&D, initial manufacturing runs, marketing, and administrative expenses. If my math is right, that is $300k in spending.
SCENARIO A
Your company uses no retail data or analytics for product development and creates products based on “gut” assumptions. Under this scenario, one of those products is successful, two are good enough to keep, and the other three fall flat and are eventually discontinued. Below are some financial assumptions:
Successful Product = $150k in profit/year x 1 = $150k in profit/year
Mediocre Product = $30k in profit/year x 2 = $60k in profit/year
Failing Product = $20k in losses/year x 3 = $60k in losses/year
Grand Total = $150k in profit/year for new product launches
SCENARIO B
Your company leverages actionable and predictive retail analytics to make better decisions for product development. Under this scenario, three of those products are successful, one is good enough to keep, and the other two fall flat and are eventually discontinued. Below are some financial assumptions:
Successful Product = $150k in profit/year x 3 = $450k in profit/year
Mediocre Product = $30k in profit/year x 1 = $30k in profit/year
Failing Product = $20k in losses/year x 2 = $40k in losses/year
Grand Total = $440k in profit/year for new product launches
Now, let’s say that your company spends $30k for the year in that retail analytics solution described in Scenario B. That $30k spend generated an additional $270k in gross profit. Subtract the cost of analytics from your gross profit and your $30k spend converts to a 9x return on your money. As you scale and launch more products, the return on retail analytics gets even more amplified.
Diving Deeper Into the Applications of Retail Data & Analytics
So how does retail sales data actually help your business make better predictions? Good retail data and analytics data should instantly show you:
- Size of category
- Growth rate of category
- Sales by product
- Sales by product attributes (e.g. flavor, size, average price, form/delivery method)
- Sales and market share by competing brands
This information is valuable when deciding what categories to enter (or avoid), with what price points, and what product attributes. It can also help a brand understand the size of the prize and the ultimate potential revenue that can be generated by having a product crack into the top 10 or top 50 of a category, and so on. If you can fully understand a category and the winning characteristics, such as flavor, delivery form, size, price, and other attributes, you are much more likely to have a new product that sticks and is accepted by consumers. The right data can additionally help you cut products from a new line that were bound to fail, which can save you hundreds of thousands of dollars a year in inefficient spending.
The bottom line? When making large investments, company leaders have a fiduciary responsibility to make the best decisions possible. The best decisions are made by using the right information. That is why retail data and analytics should be a top priority. Those that adopt analytics will always be at the forefront of consumer demand and making strategic moves based on high-ROI predictions, while those that fail to adopt analytics will likely be left behind.
Want to take ClearCut’s retail analytics for a test drive? Contact us here today.