Move from Fast Promising to Smart Promising in Retail

View the original RETAIL TOUCHPOINTS article: Move from Fast Promising to Smart Promising in Retail

The rise of search-based and social selling has heightened retailers’ desire to offer aggressive estimated delivery dates (EDD). In addition to a host of other factors, the ability to offer fast delivery when customers search for a product or see it in a social ad has driven many retailers to prioritize promising as a key capability of their tech stacks. At the same time, many retailers are reporting higher and higher shipping costs, which are affecting profits on their balance sheets.

According to a recent survey, 38% of retailers are worried about meeting consumers’ fast delivery expectations. As they begin to address these expectations, many retail IT teams are finding that the process is costly and complex. And although omnichannel fulfillment is growing in importance, they still want to drive offline sales and bring people into the store, while also minimizing the impact of shipping costs on their bottom line.

As we know, in this day and age, most retail sales are influenced by the digital experience or executed online. And the retailer’s ability to serve up an “In Stock” button and the speed of delivery promise when consumers browse online has been shown to drive both online and in-store purchases.

In fact, often, the retailer could be showing an “In Stock” button – and a longer delivery promise – to drive traffic to the store, which often results in additional add-on purchases. This can be especially effective when the item is not widely available on other sites, constrained or is in a non-competitive category.

Today’s promise engines have gotten smarter about transit time, carrier rates, inventory location and other factors. As a result, they are now allowing retailers to make accurate and faster shipping promises, which in turn helps to drive conversion and improve the customer experience.

Now, the time has come to think about flipping this on its head and using all of that rich data to make smarter decisions. Retailers can and should evaluate whether it is better to NOT make a promise at all, dynamically change the shipping cost, or slow down delivery so that it is more profitable and actually works to meet their goals.

The Trouble With Promise Engines

The promise engine market is filled with innovative solutions that excel at generating precise promises. By leveraging enhanced visibility, carrier transit data and shipping node estimation, these capabilities have become a basic requirement.

With the advanced promising engines available today, you have the flexibility to fine-tune various parameters and calculate an accurate Estimated Delivery Date (EDD). These engines empower you to speed up or slow down the promise as needed.

Additionally, you can configure different promise speeds for different categories, as well as determine where the promise is displayed, such as on the product detail page or in the shopping cart. As a retailer, you now have multiple options to create promises tailored to specific SKUs, specific times and specific customer zip codes. Your sourcing system likely offers its own set of adjustable controls, or “dials,” to further optimize the promise.

There are an abundance of variables to consider, but determining the optimal settings in relation to one another and knowing when to adjust them up or down requires extensive analysis to achieve the least cost, best fit and ideal customer experience. Ecommerce merchant teams invest significant time in analyzing factors such as free shipping thresholds, promotions, pricing and markdowns. The emergence of Estimated Delivery Dates (EDDs) has introduced additional complexity to the profitability analysis, making it an area with untapped potential.

The real complexity comes when we are looking at thousands of SKUs. Today the only conversion variable that is monitored at scale is price, and retailers are already using automated systems to crawl the web and react in real time to price changes. Nobody is doing this as it relates to promising speed, which makes it incredibly difficult to react in a scalable way. 

A New Approach: Beyond AI and Machine Learning

Even though things have gotten much more complex, promising and sourcing engines typically push “dials” as the best approach to smart or profitable promising. Unfortunately, this “set a dial and the problem is solved” approach no longer works. Today, retailers are finding that too many dials makes the process unmanageable.

While some solutions rely on machine learning “black boxes” to address this challenge, actionable data is the real key to making smart promises. The black box approach does not provide the transparency or explainability needed to truly analyze and act upon data to improve processes for the long term. 

Imagine a scenario where businesses have access to the perfect promising conversion insights, empowering them to continuously refine their promises and drive profitable omnichannel sales. What if they could explore various sourcing scenarios through “what if” analyses, modeling new sourcing decisions based on historical orders? Then, what if they had the flexibility to introduce machine learning capabilities to specific aspects of these decisions when they felt ready after robust testing and learning?

Getting Smart About Promising

More dials aren’t the answer. Today’s complex retail environment requires understanding and experimentation. The ideal promise engine should enable retailers to make precise network-aware promises, ensure their ability to fulfill those promises and then optimize them using profitability controls. By providing these capabilities, smart omnichannel promise engines empower retailers to deliver accurate promises while maintaining profitability.

When assessing a promise engine, it’s important to evaluate your own capabilities and readiness to make faster and more accurate promises – based on real data. Once you have done that, seek out providers that can enhance your promising capabilities by offering a comprehensive set of analysis tools.

By adding actionable insights, key learnings and dynamic capabilities to the promising process, retailers can deliver a promise that is driven by their own goals and business requirements and not by a general “need for speed.”

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