Automated, machine-learned “nudges” have become critical for driving and improving merchandise decisions for retailers today. It is a growing trend that has also extended into consumer electronics products that remind or nudge people to do things on a daily basis (wake up, attend a conference call, pick up groceries, meet up with a friend, take 10,000 steps, etc.). Our lives are, to some extent, ruled by these nudges.
In his 2015 letter to shareholders, Amazons Jeff Bezos wrote:
Through our Selling Coach program, we generate a steady stream of automated machine-learned nudges (more than 70 million in a typical week) alerting sellers about opportunities to avoid going out-of-stock, add selection thats selling, and sharpen their prices to be more competitive. These nudges translate to billions in increased sales to sellers.
Similarly, in the real world (vs. online), consumer products like smartphones or the more recently launched Apple Watch are constantly providing nudges with advice for managing ones life and improving health.
Whether it be in the B2B or B2C world, we are all seeing an emergence of big data-driven nudges, which are in essence sophisticated algorithms processing data and attempting to influence meaningful micro decisions to create impact.
Furthermore, big data analytics are driving nudges across a whole of host of merchandizing decisions retailers make on a daily basis. Lets look at a few examples:
- Assortment: Big data drives assortment decisions and synthesizes data outside and within retail information systems to provide very specific nudges like keep product X, drop product Y, or add product Z to this assortment.
- Online Marketing: Big data algorithms process and synthesize sentiments from consumer product reviews, and nudge marketing teams not to spend too much money on online marketing for a particular product (i.e., if the reviews are poor).
- Pricing: Big data helps to drive nudges on changing prices for products based on a whole host of variables, such as competitors prices, stock quantities and projected demand.
- Promotion: Big data nudges suggest changes on the timing of promotions and can identify information that merchandisers can use to create personalized promotions.
- Local: Big data nudges help retailers learn about what is trending locally by analyzing marketplace seller data and a whole host of other data points, like a surge in demand for snow shovels or air conditioners.
- Seller: Big data nudges also help retailers make decisions about which sellers to add to maximize sales.
- Product Content: Big data provides specific nudges on which product description elements to modify to improve traffic or conversion (e.g., change meta tags or product descriptions, add more reviews and better images, change the title, or have more back-links on a page, etc.).
As Bezos calls out in his letter, these big data-driven nudges translate to billions in increased sales for sellers and a lot of earnings for Amazon. There is no doubt that these nudges are on their way to becoming mainstream, and some of these nudges will even be automatically acted upon by machines. In the retail world, however, there will still be many nudges that require the keen interpretation and judgement of a human category merchandising manager but the nudges should help them become more informed and efficient in their jobs.
The future is just around the corner and I will leave you with a nudge of my own its time to start preparing now for this new paradigm of data-driven merchandising decisions.