Phygital Marketing 4.0: Making Word-of-Mouth Measurable

The biggest problem in the oft-quoted and oft-advised marketing strategies, focussing on Positive Word-of-Mouth, has been that it's more said than measured. A positive WOM impacts the business favourably, not just from making the Customer Acquisition Costs more sustainable, but also drives the holy grail of repeat business. Brands with a time-tested WOM, like if it's running shoes then it has to be Nike, to Cadbury's & Hershey's being the quintessential kids choice, to superior safety delivered by Volvo Cars; has all been decades of work building the brand and re-inforcing the strong Point's of Difference to the extent that the Consumer Value Proposition was widely known and also delivered for the longest known time.

From this perspective, marketers have often thought of WOM as a gradual, long-term effect that rubs off on the brand with years of excellence in consumer service, hence held extremely scared, but still not subject to measuring it.

Let's say, the Marginal Cost of Acquisition (MCOC or MCAC) explains the incremental cost that business incurs as it scales up, becomes imperative in ensuring the unit-economics model is sustainable.

Similarly, if Customer Life Time Value (CLTV), explains the compounded impact on revenues that a business realises against the cost incurred for acquiring such a quality consumer, highlighting the value of every customer in the lifetime of the business.

But, why as marketers, we have not delved deeper into measuring the power of WOM both in a qualitative and quantitative sense, despite it being one of the most impactful levers in the marketing strategy ??

But, before we move ahead let's lay down the premise well enough on Word-of-mouth advertising:

I. Why is WOM ever-important than ever?

Did you realise marketers options are increasingly getting cluttered & with more unsustainable unit economics? With the digital advertising network being largely oligopolized, the clutter to target the right audience in increasingly hard to avoid. The large ecosystem aggregates like Apple & maybe sometime Google too, can make it difficult for attribution models to track accurately, hence acquisition costs and conversion funnel may not show the right picture. Even more so, there's nothing that measures the consumers word holistically, both because a large part of it may be offline, even online it is largely attributed to the unaccounted Dark Social. Yes, we do have tools including the NPS, Survey's, Social Sentiment trackers, etc, but we know they don't quantify and pop-up the right levers that impact the real WOM.

II. Quantify WOM:

Let's start by laying down all that we have, which is measurable. So if the entire CAC matrix is broken down through the existing attribution tools and web analytics tools available, we do clearly know what's the Organic Acquisition Weekly (O-WAU) and what's the Paid Acquisition Weekly (P-WAU). Just like the concept of MCAC/MCOC, this has to be seen as an incremental impact within every time frame and hence the gain from the Returning Users (RU) needs to be off-setted.

Now, let's lay down the equation for estimating what's Newly Organically Acquired against what the business Paid for and what just snowballed from prior efforts.

1. Total Consumer Frame (in a WAU timeline) = Returning Users + New Paid Acquisitions

2. WOM effect = Anything that was Newly Organic in that time frame against the impact against the Total Consumers Frame

III. What the W-f? :

Let's Lay down the metric as the WOM-factor(W-f):

WOM Factor(W-f) = Newly Organic Users/ Total Consumer Frame

Having devised the WOM-factor, now the business has a clear sense, let's say it the W-f is 0.1 means that every 10 consumers have led to one acquisition from WOM. Now's that's simply splendid for a marketer to know and be empowered with.

The more you think about the W-f, the better clarity it brings in segmenting various business models, business genres, consumers propositions, etc, Like how about saying that New launches may have a high W-f which would stabilize and remain largely consistent in a Foodtech model. However, in an Ed-Tech Model it mirrors the offline education seasonality, if it's under a K-12 model or completely opposite W-f if it's focussed on professional & competitive genres.

Thus the W-f modelling can also throw a lot of insights into business cycle forecasting and predicting more sustainable unit economics models in future.

Author: Harshavardhan Chauhaan

This article is part of the Phygital series focussed at more evolutionary, radical and futuristic models in marketing & business.