Faster isn’t always better: The case for smarter promising
Smart promise engines should allow retailers to make an accurate network-aware promise, ensure they can keep it, then optimize it with profitability controls.
Today’s omnichannel retailers want an aggressive and accurate estimated delivery date (EDD or “promise”) to drive customer experience and conversion. Still, many have yet to prioritize it because of the cost and complexity of executing it. At the same time, they are balancing two goals with their websites: to drive both online and offline sales.
Store availability and delivery speed are growing in importance every year. Upward of 81% of retail sales are influenced by the digital experience or executed online. Both store traffic and online conversions are directly affected by the presence of an “in stock” button and the speed of delivery promise when consumers browse online. As more people use the web as their first research tool — these attributes have even more impact on the purchase decision.
As important as these new conversion levers of certainty and speed are, we still see retailers at different points on the spectrum of promising:
Those avoiding making a fast promise, i.e., a padded promise to protect for lack of precise visibility and dials to set it.
Those trying to make a fast one but suffering for it on the shipping cost side.
The lucky few with the product margins high enough not to care.
Promise engines in the market today
Today, the promise engine market offers solutions that create an accurate promise through a mixture of better visibility, carrier transit data and shipping node estimation. These capabilities are table stakes.From there, speed can be added through a number of strategies on the carrier and network side. However, each of these has challenges.
Network speed likely comes from increased use of stores or more local inventory positioning. Parcel carrier changes and rate shopping are the other levers that are challenging to make for a variety of reasons. Once some combination of these options is chosen, a promise and sourcing engine tries to make this improved promise as profitable as it can be. This is the general toolkit and progression a retailer would take in a promising journey.
However, this approach overlooks the dual sales goals mentioned above. In many cases, a slow delivery promise is actually an outcome that retailers are seeking in order to drive that store traffic (and additional add-on purchases) when they know the item is not widely available on other sites or sitting in a non-competitive category.
Promising engines today allow you to set dials to compute an accurate EDD and give you the ability to speed the promise up or down. They may also allow you to configure a variety of speeds – slow promise in one category, fast promise in another — and locations of that promise (product detail page, cart, etc.). A retailer now has multiple options for creating a promise for a specific SKU at a specific time for a given customer's zip code. And then, the sourcing system has its own set of dials to optimize against this promise.
The dials are plentiful, but how to set them optimally in relation to each other and where it makes sense to move them up or down takes considerable analysis to find least cost, best fit and ideal experience. E-commerce merchant teams spend considerable time analyzing free shipping thresholds, promotions, pricing, and markdowns. The rise of the EDD has added more complexity to the profitability analysis, which is an untapped area.
Add to this that current solutions don’t provide any insights on when/where to do this. Nor do they provide the ability to change as business conditions change dynamically at speed and scale – product pricing, inventory velocity and conversion rate sensitivity can all affect the promise.
The real complexity comes when we are looking at thousands of SKUs. No retailer can effectively manage this. 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, much less being able to react to it in a scalable way.
Not another dial
These are the realities, yet promising and sourcing engines out there are still pushing these “dials” as the approach to smart or profitable promising. If you can set a dial — then the problem is solved! But if you have too many dials, it becomes unmanageable and even unexplainable in some cases.
Some are offering machine learning black boxes to solve this, but the rich data needed to make this effective is a huge journey that retailers must take before it can become effective. In addition, the explainability features and transparency of top drivers for the decisioning need to be included to help retailers make the tweaks they need.
What if promising and sourcing engines started thinking less about dials and more about insights and experimentation? What if retailers are able to see the impacts of faster or slower promises across products through front-end A/B testing that was enabled by the promise and sourcing engines? So far, A/B testing has yet to enable robust testing with supply chain data sets. What if they had the right promising conversion insights allowing them to iterate toward setting up different promises to drive overall profitable omnichannel sales? What if they could do “what if” sourcing scenarios (model a new set of sourcing decisions against historical orders) so they could have more confidence in setting those dials? What if they could augment certain elements of these decisions within that maturity journey to machine learning only when ready for it?
A better way
An omnichannel promise engine should support a retailer on its journey through the promising maturity curve. Smart promise engines should allow them to make an accurate network-aware promise, ensure they can keep it, then optimize it with profitability controls.
A better path exists to get retailers down the path of an aggressive and accurate delivery promise. It’s not more dials. It’s more understanding and experimentation that is required. It’s a product approach that allows you to crawl, walk and run through the promise maturity curve while meeting the multiple goals of an omnichannel retailer.
If you are considering evaluating a promise engine, ensure you’re evaluating your own capabilities and readiness to make a faster, accurate promise. Then look to providers that will make your promise smarter not just with optimizations through dials, but with a complete set of tools that will make you truly smarter about what promise to make and when to make it.
About the author
Darpan Seth is the CEO and co-founder of Nextuple Inc. The company has a mission to level the omni channel technology playing field, unlock the value of fulfillment networks and create a more sustainable future. Seth has more than 25 years of order fulfillment experience working with more than 50 retailers such as Dicks Sporting Goods, Signet Jewelers, Staples, Walgreens and Kroger. He works out of the Boston area and is responsible for all aspects of Nextuple’s global business strategy and operations.