A client told me this just the other day, and it struck a chord; not because it is a pithy play on words, but because it is so very consistent with many of my recent client discussions.
“There is data in that system, but we haven’t applied the resources to harvest it and do anything with it.”
“We are sitting on a mountain of customer loyalty data. It is all over. We use it sparingly with our suppliers to drive down our sourcing costs. Our next step is to find a way to use it to make better operational decisions.”
“Our inventory data is all over the place. The view we have from the front lines is different from the view we have from headquarters. Both views are correct and both views are incomplete.”
Another one I’ve heard, but not quite so recently, is: “Data is the new crude oil. All we have to do is refine it.”
I always thought highly of this quip, but I’m beginning to think it needs work. Data is a valuable natural resource of our digital enterprise, indeed.
It also has the potential to be refined and monetized, yes.
But isn’t there a more pressing need? To find it, harvest it, and corral it so it can never again escape your grasp?
Tackling the sprawl of data
The metaphor is a useful tool to explain what I believe to be a growing, perhaps unrecognized challenge for retailers (and direct-to-consumer manufacturers): the unmitigated and unyielding sprawl of data, exacerbated by the ongoing expansion of channels through which you sell and fulfill your products and services.
When I think about the most important data a retailer has—the highest grade—I believe it is customer data, and inventory data. Few would disagree with that, however, there is a nuance that I have not yet seen explored.
Customer Data: Usually held in a loyalty system or CRM system. Utilized primarily by marketing, accessed by front-line employees for customer look-up.
Inventory Data: Usually held in an ERP, supply chain, or merchandise system. Utilized primarily by buyers, accessed by front-line employees for inventory look-up, replenishment, and fulfillment.
If these data sets are not already contained each in a single repository, watch out! It’s long overdue time to get ahold of it.
Customer and inventory data - a perfect partnership
Here is another thought: why don’t these two data sets live together? They are potentially symbiotic. Here is how:
Sally is a customer you hold in great reverence. As many studies have detailed, your most valuable customers are customers who engage / purchase through multiple channels.
She is a multi-channel shopper in Boise, Idaho and an advocate for your brand on social media—she is a subscriber to your loyalty program that offers free shipping, discounts, and special events at her store just five miles from her home.
She spends amongst the top 10 percent of your entire customer base. She is a driver of your top-line and therefore falls into a ‘highly valued’ category.
For Sally, and the others of your customer base in Sally’s category, would it be beneficial to optimize her behavioral impact on your top-line sales and profitability performance? What about her behavioral impact on your bottom-line cost of operational performance?
With customer data and inventory data working in unison, we could:
- Anticipate Sally’s needs and position inventory in a store or distribution center that offers the lowest possible cost to fulfill based on her shopping behavioral preferences. For example, clothing re-order vs new category exploration--one a destination visit (better to check the fit & style in person) while another is seasonal wardrobe re-vamp
- Influence Sally to purchase in a store channel over a digital channel (or vice versa) dynamically, which effectively benefits both your top line (more selling opportunities in-store) and bottom-line performance (cost of fulfillment because she’s coming into your store)
- Incentivize certain behaviors, like returning digital channel sales to the store channel again eliminating your cost for a return shipment and getting Sally back into the store, possibly with a digital promotion for more potential selling opportunities
Further, consider the possibilities if you are mixing complementary products with differing margin profiles (e.g. apparel and accessories).
In this example, the retailer could consider mechanisms to optimize basket-level margin profile by also incorporating least cost to fulfill at the item level while still converting the full basket. This is sophisticated stuff, but the point to be made is this:
There is untapped power (and profit) available now to bringing together customer data and inventory data within your enterprise, but first, you have the find it, harvest it and corral it.
Why is this important?
Mobility-as-a-service - the new data-driven frontier for Retail?
In my last blog post, I explored Peggy’s interaction with retailers through a Mobility as a Service (MaaS) provider, and posited that a future / upcoming selling channel through which a retailer will engage a consumer is through the MaaS provider operating a fleet of autonomous vehicles.
In this future, the MaaS will be the primary interface to the consumer, the ordering ‘vehicle’ for the product or service, and potentially part of the fulfillment execution and cost to the retailer or consumer.
As such, the MaaS provider will have greater power to decide which retailers are featured, and will look for partners who can maximize its potential value to the consumer marketplace.
In my view, a MaaS provider will seek two things in a partnership with a retailer:
- A MaaS provider will prefer to work with a retailer that can gain the advantage of having deep customer awareness, order history, and next best offer so the MaaS provider can enhance the personalization it provides to its customer.
- A MaaS provider will prefer to work with a retailer that can provide transparent access to hyper-localized inventory assortment so that, together, the fulfillment of that inventory is convenient, befitting of the consumer’s requested service level, and cost-optimized.
In fact, I would argue that these two specific factors above will be the most important considerations for all future selling channels, not only MaaS providers.
First, you have to find your data, harvest it, and corral it. Hurry up!