Story:
Capturing brand velocity for dynamic decisions

We started to research the field to find hints about how something could be done about our curiosities.

A hint: The Brand Valuation Process

We found our first answer in the brand valuation process.

We hadn't had a clue about valuing intangible assets in any way. We first tried to learn from the best. We found the best inspiration in the biggest companies of brand valuation in the world: Interbrand, Brand Finance, BBDO etc.

After reading everything we could about methodologies, techniques, best practices, frameworks, we were surprised to see how subjective the process was. Brands are valued more or less according to each firm’s proprietary methodology. Moreover there was no connection what so ever with a cohesive ranking order between the brands. We knew that each brand valuation firm uses its own “way”, so the results are supposed to differ, but it’s odd that, not even the brand hierarchy is kept, especially for brands belonging to the same branch of an industry.

Observing that each company approaches valuation on its own terms and looking at the deep score differences of their separate results, we couldn't figure out how brand valuation is helping, if it does not reflect something real, objective and justifiable.

So we thought that if we could figure out what drives building up brand value, we could define correlations between those drivers and appropriate channels of branding, marketing, communications and advertising actions and strategies.

We knew we couldn't match the expertise of the global brand valuation companies, but our product was shaping and we didn't quite needed it.

A. First take: The financial point of view

We first took things from a financial point of view. We had to simultaneously confront two financial realities of a company:

1.       its own return and

2.       the industry’s market revenue streams

Independently, these two perspectives assess capacity, but compared they reveal power, potential and competitiveness.

We approached this purpose, by assigning the annual growth rate of a company’s return to it’s own Brand Portfolio. According to some financial performance indicators along with the cost of the invested capital and the growth rate of the revenue streams from one year to another, we extracted a financial value that we assumed it’s attributed to the fact that the company has, at that moment, a certain Brand Portfolio. Without it, the company would not be able to grow.

But the architecture of the Brand Portfolio (the generic term for either House of Brands or Branded House or even a hybrid form of them) wasn't assessed in any way. There were models to visualize it and tools to decide upon branding strategy, but what about its financial value as a whole or as a sum of many distinct brands?

In other words, you could drive $1000 by selling a lot from a very low priced brand, or very few items from 3 premium brands. How is this not relevant in a brand valuation situation!? We put our minds together and created an algorithm to position brands inside the architecture and in relation to their market. We then classified them according to the main segments of clients (price/quality relationship).

We now had a way to define the complexity of a brand portfolio’s architecture with a single number, a number that describes the fact that customers are willing to pay a certain amount of money for those precise products and services.

B. Integrating the industry

The second facet of the financial reality of a brand it is its market, the industry it develops in, the economic environment that enhances or inhibits buying behaviors.

And that market, as much as we don’t want to admit it, isn't limitless!

We weren't necessarily thinking about offer and demand, or competition. We thought about how could we figure out what industry branch and sectors would perform better in the future, in order to adjust one’s brand to that trend on the long term.

We didn't only emphasize on the future market capacity (forecasted sales in terms of volume and revenue) but actually found new ways to analyze the raw data from an industry or sector.

1.       We constructed analytics that describe the historical trend of sales, the industry power, the industry potential and the added value per product or transaction.

2.       We turned everything we could into numbers through meaningful sequences of equations and recursive algorithms that could apply specifically for industries that provide goods and services with a short and very short consumption-cycle.

3.       We chose about ten FMCG and HORECA branches of industry that addressed basic consumer needs, for easy comparison and discovering synergies.

So, after we put together a coherent mathematical pattern we had the numbers that allowed us to analyze opportunity and the financial chance of a company to further grow in one of those markets.

That was pretty cool.

Entering the CONSUMER journey

While looking at each company’s financial chance, we realized that all those predictions and forecasts were intended to purely reflect an extension of the past sales. Nothing will assure those trends, but mere inertia.

Not one market would survive without a context.

Then it struck us.

The only one element that drives context nowadays is the CONSUMER. Not the client, nor the customer, but the PERSON, beyond the end-user’s buying preferences. The way he’s like, the things that he desires or he doesn't know, the way he’s inclined to judge, discuss or gossip, the facts that make him wonder or bore him, his basic natural instinct, the stuff he wishes to do, to criticize, the way he’s going to change as he ages or what is it making him happy right now — all these deserved to be discovered in relation to a brand, when defining a brand’s value.

So we defined a second purpose: rendering human behavior using data about people identities, demographics, cultural settings, psycho-graphics, generation related trends, in order to understand what drives people from aware customers to loyal consumers.

Human behavior is driven by experiences built one after another, defining patterns of past behavior that ultimately drive recall, recognition and consumption reiteration. Therefore experience is becoming the predominant economic offering that brands can deliver online and offline.

Measuring experiences

First we had to deal with the fact that all these above characteristics of the person as a consumer are not, or hardly are, quantifiable in any way, but on the other hand they aren't that volatile either. A mature person’s preferences, perceptions and attitudes are quite stable because they spring from their innate and educated, over a long period of time, experiences.

Capturing them in a material way became our little obsession and trouble as well. With a bit of luck we came across the approach of system dynamics. As soon as we saw the mechanisms of cause-and-effect funnels of data, in a web of flows and accumulated stocks of information, we knew we could use these principles to our purposes.

Whereas the financial situation of the company and the industry’s indicators were very simple to integrate in a system dynamics model (previously computed in Excel spreadsheets), the consumer component seemed almost impossible to handle.

Fortunately, we weren't the only ones that thought system dynamics would be a good way to track the awareness-loyalty funnel of consumers. From a technical point of view, we used the simulation technique of Vensim, a piece of software that helped us a lot. We learned it in a few days, after which modeling-time began. We ultimately got to about 256 gradually evolving simulation models for the Consumer Component. We deployed endless tests and trials and corrections.

The DATA hassle:

In the end it was clear that beyond consumer data that we could freely and publicly access, we were in need of specific data as well, directly from a precise sample of consumers. With the best practices of experiment validity in mind, we made a generic questionnaire of about 53 items, all eight-level Likert scales with an even-point scale so that no neutral option would be available. The respondents had to pick a side (agree or disagree) about certain brand experiences from their life, according to specific branches of industry. We then paired the questions in 23 behavior factors that influence the 6 major attitudinal stages of random consumption: Awareness, Empathy, Consideration, Purchase Desire, Satisfaction, Loyalty.

All this data became material for our input, flows and stocks in the system dynamics model we attached to the Consumer Component. By calibrating the model and the 6 attitudinal stages of consumption we succeeded in gathering all those data flows into a single construct we called Brand Equity.

Whereas we previously figured out if customers are willing to pay a certain amount of money for brands, now we also knew WHY did they acted that way. From our point of view, Brand Equity reveals how does one brand, from a certain industry, create value for a consumer, with each new branding and advertising expense.