Deliver a Better E-Commerce Customer Experience: Optimize Delivery Data, Analysis and Timing

ECommerce

Shipping and delivery have become a critical part of the overall eCommerce customer experience. Providing a good experience can increase repeat business: 87% of consumers report that a positive delivery experience makes them more likely to shop with a merchant again. On the flip side, 37% of consumers report that a bad experience will lead them to blackball a merchant and never shop with them again.

Companies, such as Amazon, have turned fulfillment into a source of competitive differentiation equal in importance to brand, selection, and price. Amazon, in particular, has invested years and billions of dollars into building an eCommerce customer experience that is second to none, even – or especially – as regards fulfillment and delivery. But most companies are wholly reliant on their carriers both for estimates of shipping times and for providing tracking information and alerts.

This reliance can lead to a loss of control over the customer experience and any time anything goes wrong, you will be blamed: a recent study showed that 94% of consumers blame the merchant for any shipping problems they experience. You own the shipping and fulfillment experience, whether you control it or not.

Achieving full control is a multi-step process and the first step is obtaining full visibility into the shipping network. There are three major types of impediment to getting that needed visibility, involving data, time, and meaning, but they can be overcome with the right supporting technology.

The first obstacle to overcome is the complexity and volume of the data required. Each of the carriers presents data in a slightly different way, making aggregation or direct comparison difficult. The sheer volume of the data can be a challenge: with thousands of shipments spread across a variety of carriers, modes, services, and zones, manual processes and simple tools such as Excel are quickly overwhelmed.

The next obstacle is extracting meaning from the welter of information provided by your carriers and internal systems so you can answer three key questions: Am I using the right services? What are these services costing me? And most important, what is the impact of my service choices on my customers’ experience?

The final obstacle is time itself. If you are relying on carrier SLAs, especially for guaranteed services, your customers’ experience may be favorable but at what cost?  If you have chosen other more economical but less consistent services  and adding a safety buffer of additional time in order to avoid overpromising and angering customers, you may be losing sales because increased delivery times significantly decrease shopping cart conversion rates. To control your shipping network and the customer shipping experience, you need to know the actual days-to-deliver for all of your markets. Also, active management of shipments in transit requires real-time data, but performance analysis (“how did my network do last quarter?”) and the formulation of shipping strategies requires a longer view.

What is needed to overcome these obstacles? Let’s look at each in turn.

Handling the problems related to data requires a two-part solution: normalization of data to overcome non-uniformity and application of Big Data techniques to manage its volume.  Normalization is the process of mapping the disparate data types and formats provided by the carriers and your internal systems to a single canonical standard, making it possible to perform analyses aggregating shipping across all of your carriers, or to make comparisons across carriers.

For any company with even moderate levels of shipping, the volume of data generated can be daunting. Fortunately, the rise of Big Data tools makes the task of dealing with large volumes of data (relatively) simple. And with direct data feeds via EDI or APIs, the data can be made as timely as needed.

What is required to obtain the meaning you need to control your delivery network and experience? Robust, comprehensive data, used to feed analytics informed by deep domain knowledge and further reinforced by the application of the latest machine learning and other advanced techniques can provide operations and logistics professionals with the answers they need.

Finally, the ability to use real-time data but also to store that data and supplement it with historical information is critical to enabling multiple time lenses, from the real-time updates needed to actively manage by exception to the historical performance analysis required to answer critical questions around services used, markets served, and the time and cost required to get your goods into the hands of your customers.

We have seen that fulfillment and delivery have become a critical piece of the overall eCommerce customer experience, but many companies have little control over that experience and instead rely upon their carriers or other third parties.

Three major impediments were preventing companies from controlling their shipping network: data, meaning, and time. Overcoming these obstacles requires the right data and the technology needed to make that data useful.

Fortunately for those companies without the billions of dollars and years of effort that market leaders such as Amazon have invested, there are now commercial SaaS applications available which provide the platform and analytical applications necessary to control the delivery network and customer delivery experience.



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