How deeper Channel Analytics brings visibility to the path to purchase
In our last article, we skimmed the surface talking about Shopper Funnel Conversion, and how engagement with different types of media elicits different probabilities of a sale down the road. We also talked about how conversion events are not all created equal, and that buyers should beware. Today we want to dive deeper into the reliability and signal strength of conversion metrics, and how Channel Analytics brings important visibility to the path to purchase.
Converging Conversion Metrics: Marketing, Merchandising, and Commerce
Shoppable media has continued to gain steam over the last decade, creating a convenient bridge between impressions (marketing) and conversions (commerce). Is that shoppable ad an impression or a storefront? The answer is both. It’s tempting to over-simplify the complex, but it’s important to have a critical understanding and observation of the different stages of engagement and how those affect the probability of sale—both offline and online. Just as a marketer who drove 200M clicks would not take credit for $XM sales, an ecommerce manager who saw $200M in carts would not claim all the inbound was due to organic traffic.
A Purchase Path Laden with Data
The path to purchase for fast moving consumer goods is a circle of life: you discover, plan, buy, make, experience, and repeat. While the mechanisms differ, the stages of the journey are similar online and offline.
In brick and mortar, a flurry of marketing draws you into the store aisles, which are chock full of merchandising designed to advance you from awareness (discover), to engagement (plan), and on to conversion (buy+). The in-store path is optimized for conversion: you see the end cap signage, you stop and engage, you pick up the product, you put it into your cart, and then you checkout.
Likewise in the digital world, you cruise online during discovery (social, recipe sites, brand sites), you see an engaging ad, you click the ‘buy now’ button, you add it to your local grocery store’s cart, and then checkout. The beauty of this blended marketing/merchandising/commerce moment is that all the actions taken in the digital aisle are captured in Basketful’s Channel Analytics. Browsing, seeing, stopping, picking it up, heading to register, and checking out. This purchase funnel intelligence is powerful first party information for your brand.
Good Metrics = Numbers in Context
This rich chain of funnel data is exciting, but it’s important to discreetly understand what each event is within the purchase funnel to fully optimize over the long run. For example, you’d never consider someone who paused in front of your end cap to be a sale. That behavior certainly increases the probability of sale either now or in the future, but it’s not (yet) a sale. In the world of shoppable media, how you bridge from one activity to another greatly affects the probability. If you had a choice, would you hand your product to the shopper, or would you skip that and put it directly into their cart? That is the difference between driving someone to a product detail page (where they may or may not choose to proceed to checkout) vs adding the product directly to the shopper’s cart at a given store. The latter versus the former is apples to oranges.
The final definitive step of conversion is checkout at the register. (Beyond that, the promised land: repeat!) This last step in the purchase funnel is an important one, but happens to be the most murky. Driving shoppers through the online purchase funnel happens millions of times a day by hundreds of entities, but those final moments of the experience— the checkout process— are a walled garden owned by the retailer. High fidelity signals of whether your shopper made it to the bottom of the funnel are held close to the vest by retailers. This is where highly variable attribution methodology and sales insights provided by the retailer to the referrer kicks in. The most commonly used methodology is ‘last touch attribution’, which gives all of the credit for a cart conversion to the item that was last added/clicked/or visited. As an example, if a user added to cart a tempting shoppable ad for a birthday cake, and at the last second viewed a scooter on the retailers site before checking out their groceries, the scooter brand would get attribution credit for the cake and the advertising brands would not. Despite these signals being low fidelity and inaccurate, this information can be used to create a caricature of the bottom of the funnel activity, however directional it is. There have been attempts within the industry to take attribution signals received from these limited and questionable sample sets and project those rates across similar shoppable brand events– what is sometimes called data backflushing–in order to create a caricature of checkout activity where there are voids. While final conversion information is important, this methodology is akin to taking an exit poll from a handful of shoppers, of only the last item they looked at, and then projecting that behavior onto all known activity.
Basketful Channel Analytics
Complementing Basketful’s ecosystem of shoppable capabilities is Channel Analytics, which stitches key 1st party path to purchase metrics together, letting brands optimize their digital path to purchase on the fly. Powered by direct add to cart integrations at over 100 US retailers, Basketful’s industry leading Inventory Awareness also protects your brand fidelity along the way. From shoppable brand media and recipes, omnichannel product locators, and to entire shoppable ecommerce sites—we’re here to help drive commerce and shopper insight to make your marketing dollars go much further.