In my pre­vi­ous arti­cles, I explained a bit about how an inspired appli­ca­tion of dig­i­tal ana­lyt­ics to any firm’s mar­ket­ing strat­egy demands a broad-lens view of the con­sumer, a focus on more than just whether or not some­one con­verts, but who, when, why, and what may stand in their way dur­ing their con­sumer journey.

Com­monly, we look at mar­ket­ing as a fun­nel, an ever-narrowing chan­nel that cap­tures as many con­sumers as is pos­si­ble on one end and deliv­ers them some­where spe­cific on the other. While many mar­ket­ing orga­ni­za­tions real­ize that the fun­nel is wide at the top and nar­row at the bot­tom, sur­pris­ingly few strive to under­stand the intri­ca­cies of the unin­tended out­flow: where loss occurs in the fun­nel, espe­cially in the con­text of dif­fer­ent types of customers.

A funnel-type break­down is help­ful because, in the most ideal use of this idea, it helps us orga­nize our efforts into tiers—picture a series of hor­i­zon­tal cuts across your fun­nel. At the top of the fun­nel, we just want to be noticed. As things progress, we retain inter­est by sat­is­fy­ing the needs of the con­sumer (earn­ing our­selves a rela­tion­ship), before finally look­ing to close at the funnel’s bot­tom. Even after the sale is com­plete, the fol­low up and ser­vice we pro­vide con­tin­ues to serve the fun­nel, as repeat pur­chases, upgrades, etc. come into view.

This is well trav­eled ground, and it’s just one form of the mar­ket­ing life­cy­cle (not every­one has a fun­nel). Despite hav­ing a deep under­stand­ing of these con­cepts, tra­di­tional mar­ket­ing tools, espe­cially those designed to mea­sure the per­for­mance of mar­ket­ing chan­nels like email, dis­play, search, and social, don’t allow us to see into the fun­nel at all. We’re stuck know­ing where the con­sumers go in and ulti­mately how many come out on the other side. As the fun­nel nar­rows, there are read­ily acces­si­ble data—where our sales are being lost or gained, rela­tion­ships weak­ened or strengthened—that beg to be brought into an envi­ron­ment where it can be used to under­stand and opti­mize chan­nels more effectively.

A Hypo­thet­i­cal Example

Of course, it’s much more com­pli­cated than merely under­stand­ing where con­ver­sion is lost out­right by assum­ing the fun­nel is telling us the truth. Let’s say we are an e-commerce site with only two prod­ucts for sale, and one mas­sively out­sells the other. Is this is sign that the more suc­cess­ful prod­uct is intrin­si­cally bet­ter suited to our audi­ence? Maybe or maybe not.

It’s equally or even poten­tially much more a result of our mar­ket­ing design that’s shap­ing that out­come. How are we mes­sag­ing? How is the expe­ri­ence for each prod­uct com­pet­i­tive with other sites where these prod­ucts are offered? How is our pric­ing? How is the prod­uct pho­tog­ra­phy, impres­sion that sup­port is avail­able, ship­ping costs, qual­ity of the site, etc. ver­sus the other places a con­sumer will look for this item? No doubt we can manip­u­late those num­bers if we know what’s pro­pelling our con­sumers towards one prod­uct over the other, espe­cially if it’s all a self-fulfilling prophecy.

To bring this exam­ple home, I was once work­ing on a cam­paign where sales for one company’s prod­uct plum­meted for a whole month and its other prod­ucts con­tin­ued to per­form. The search agency could not fig­ure out the prob­lem, and the client thought the prod­uct was out of style (despite mas­sive click vol­ume in their search cam­paign). What was the prob­lem? The prod­uct was out of stock. Yes, you read that right. OUT OF STOCK for a month and nobody fig­ured it out, and every­one wanted to blame the search cam­paign for not pro­duc­ing sales that were impos­si­ble to produce.

Don’t Lose Your SaaS

One of the best exam­ples of how mar­ket­ing affects sales can be seen in soft­ware as a ser­vice (SaaS), where prod­ucts are tiered rather than indi­vid­u­ally pack­aged (think Base­camp, Zen­desk, or some­thing sim­i­lar with basic, pre­mium etc. plans). What pro­por­tion of sub­scribers are set­tling for the basic pack­age? Is that num­ber, that ratio of low-margin to high-margin buy­ers really opti­mal? And I don’t just mean opti­mal for the com­pany; if the higher pack­age is bet­ter suited to the cus­tomer, why aren’t they buy­ing it? Are we los­ing buy­ers in higher tiered pack­ages because our mar­ket­ing is fail­ing to engage with one or more audi­ences at some crit­i­cal point in the fun­nel, or is the lan­guage we use to describe our fea­tures pre­sented as our own flurry of buzz­words rather than con­nectible ver­biage that res­onates with the con­sumer? When it comes to the foren­sics of why the basic plan is pre­ferred, can we shift the lens to see each audi­ence indi­vid­u­ally, which pref­er­ences are moti­vat­ing the slices of our cus­tomer base? Can we then pin­point the exact moment in the fun­nel where the deci­sion to set­tle for the basic plan was made?

Tra­di­tion­ally, no. This is data beyond our reach. Part of under­stand­ing what’s hap­pen­ing in the mar­ket­ing life­cy­cle is exactly this: being able to break our con­sumers down into “per­sonas,” sorted by the unique fac­tors that bring any given cus­tomer base together. If we want to fully under­stand the mar­ket­ing life­cy­cle, we have to under­stand the seg­ments of that life­cy­cle and, most impor­tantly, how each type of consumer—each persona—progresses through those seg­ments. Today, almost zero cam­paign man­age­ment plat­forms offer this func­tion­al­ity (but it is avail­able in your ana­lyt­ics tool data).

In part two of this arti­cle, we will look more closely at the sales fun­nel to under­stand the steps in the process.