This is the valuing of your own labor rate that takes place after your third or fourth trip back from Ikea. You know you have saved some money buying a set of shelves that you need to assemble but part of the decision was made by assessing how much time it would take to put the item together vs how much you saved.

Ikea is in the business in devaluing our self-perceived labor rate so that they can charge the most for a flatpack item such that the discount achieved justifies the hours needed to assemble the item. (This is the same model used by meal-kit businesses, or at least it should be.)

For items that do not require assembly then the trade-off people have to make is their willingness to buy products that do not comply with the standards for that type of product bought everywhere else. For example, a desk lamp from Ikea requires no assembly so there is no time-cost to save. To justify the lower price there must be an investment from the customer. In this case it is the willingness to accept a non-standard shade fitting. The same is true of other non-assembly products sold at Ikea.

For those folks interested in alternative data, there could be a macro signal regarding wage earnings and wage growth buried in this data. Comparing the price of an Ikea product with a similar (assembled) non-Ikea product over time could be a useful economic indicator.

Beware AI Homogenization

Many firms (Amazon, Google, etc) are touting their plug-and-play AI and Machine Learning tool kits as being a quick way for firms to adopt these new technologies without having to invest resources building their own.

Sound like a good idea but I challenge that. If data is going to drive the new economy, it will be a firm's analytics capabilities that will give it a competitive advantage. In the short-term adopting a third-party framework for analytics will move a firm up the learning curve faster. Over time this competitive edge becomes blunter, as more firms in a sector start to use the same frameworks in the race to be "first".

This homogenization will be good for a sector but pretty rapidly firms competing in that sector will be soon locked back in to trench warfare with their competitors. Retail distribution is a good example, do retailers use a 3rd party distribution network or do they buy and maintain their own fleet. Using a 3rd party distributer saves upfront capex but it voids an area of competitive advantage. Building their own fleet, while more costly, gives a retailer optionality about growth and expansion plans.

The same is true in the rush for AI/ML capabilities. While the concepts of AI / ML will be the same for all firms, their integration and application has to vary from firm-to-firm to preserve their potential for providing lasting competitive advantage. The majority of firms we have spoken to are developing their own tool kit, they might use established infrastructure providers but everything else is custom and proprietary. This seems to be the smart way to go.

Conagra Vs Pinnacle Foods

Reproduced Courtesy of Market Watch.

We dug in to this story using our grocery data sets. By tracking promotional sales up to, and after, the announcement we saw a 2% year-on-year decline in average price paid for Pinnacle Foods' products. Following Conagra's announcement, there was an immediate price hike that took average prices  6% higher in the following six months.  As per this article, our findings suggest a lack of due diligence was performed during the transaction.

To see our full report, send an email to support@knowledgeleaps.com.

M&A Data Base & API, Complete!

We have just released version 1.2 of our UPC  to ISIN / Stock Ticker mapping API and data base.

The data set covers 1,500 brands in 424 categories spanning 50,000 individual products.  We have also made our data  point-in-time accurate, going back 10 years.  Submitting a UPC and a date to the API and it will return the ISIN number of the company that owns product at that point in time - provided the company was public. For private companies the API can return customizable values.

In time, we will provide access to the underlying data so that firms can analyze the characteristics of firms that are acquiring packaged-goods brands as well as those brands that get disposed of.