How to Win with Optimal Product Assortment

Getting product assortment right isn’t easy, but it’s a major key to success for both brand and retailer. There’s an abundance of data available to support pricing and inventory management, but optimising a store’s assortment is still more often than not based on gut feel and many assumptions. As the brand, shouldn’t you have a significant voice, founded on data, on how retailers are stocking your products?

Customers prefer less cluttered stores, but removing SKUs from them often causes a decline in sales
When stores try to replace lower-selling items, they often find out (too late) that many of the eliminated products are essential to their customers, who then take business elsewhere
Localized assortments that cater to tastes can cause revenue lifts, but trying to apply the same efforts to different categories doesn’t always work
When prices shift from low to high, even with increased quality, sometimes retailers learn the hard way that price matters more

How to identify winners now

It’s not hard to spot and remove poor selling lines using data provided by retailers and 3rd-party sources. But that slow seller could be an essential to your most profitable customers. Simple changes in category and assortment can have a shockwave effect.

For years, CPGs have been using software that promised to optimize the mix of products in a category. But, this technology is largely reliant on historical sales data and is based on manual data inputs. What these systems can’t do is forecast demand for new products, entirely new categories, or account for how shoppers would behave if a particular product were dropped. If you remove one of your SKUs, will demand transfer to another product in your range, or will it be scooped up by a competitor?

The best CPGs work closely with their retailers, adding value through rigorous category management research and recommendation. They can best help retailers by answering questions like:

If slow-selling products are replaced with new ones, what will be the demand for these new SKUs?
If customers don’t find the product they are looking for, will they move to a competitor brand?
How will category sales change depending on the number of products carried?
What is the optimal way to implement store clusters: by customer demand, by geography, by size of store, by store format, or through socioeconomic factors?

For CPG companies, Insite AI can accurately answer these questions, enabling the world’s leading players to maximise the ROI of their R&D, sales, and marketing efforts.

By combining multiple data sources in real time, critical strategic decisions can be made, reducing risk, maximizing revenue, and ensuring value is added for the retailer. Insite AI’s machine learning engine processes millions of data points and combines CPG sales data, retailer EPOS, loyalty data, and 3rd-party sources. It combines this with billions of other data points such as weather forecasts, social media insights, flavor trends, website data, local happenings, and sporting events.

Using this data, Insite AI runs millions of automated what-if experiments, doing what no human category or assortment team could ever accomplish Instantly.

The outcome is accurate predictions and data, down to the most granular level.

You’ll be able to predict sales of brand new products and brand extensions with a high degree of accuracy, right down to the individual store level. You’ll understand the implications of adding or removing a product from a category. You’ll be able to game plan how your competitors will respond. Your CPG will be able to model and test millions of pricing hypotheses, knowing how one change would affect your own brand and those of your competitor.