SABi is a LogicShoe R&D project designed to simplify consumer intelligence that drives incremental management decisions. Being built upon a brand valuation methodology and system dynamics techniques that connect cause-and-effect consumption behaviors, SABi is basically an adaptive simulation model that properly calibrated can deliver instant insights regarding various decision scenarios.
It was 2010 when, along with two of my friends, we were running a small branding agency. We worked with different sized clients to craft brands for new and existing markets, but for those companies to succeed, the biggest challenge was the need to prioritize specific knowledge types inside their companies.
Having some previous start-up experience and interacting with various businesses, made us realize that there are 3 essential things on top of the“to know/understand” list:
1. revenue streams
2. main consumption drivers
3. primary key success factors of marketing and communication efforts
Yes, it’s easier said than done but to actually get those, was pretty hard for most companies and, for us, it started to become quite a curiosity.
While looking deeper into it, around a year later, we actively began to question if there’s some sort of connection between branding expenses (holistic marketing and communication budgets) and their return. Companies are persuaded to allocate budgets but it felt that they do it just because everyone does the same and not because those expenses are really valuable and can drive return (no disrespect, didn't know much).
Even if there would be a way to improve the situation, the problem’s that, unfortunately, few of the things that make up for the situation are quantifiable. And even fewer are explained logically, as a cause and effect relationship.
There are clusters of cultures and attitudes with traceable consumption habits, certain market capacities, a lot of raw data and reports about product positioning, consumer segments, dashboards with key success factors about the financial activity of companies and their competitors.
Patterns are everywhere.
And the most surprising fact is that most of this data is public. It seemed like few people know what to do with it. What if we could solve that? What if we could build a tool that could ease understanding of the financial reality of a market? What if we could somehow make sense of how consumers act regarding brands and their buying 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.
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.
Our efforts resulted in a simple to use software with two roles:
- forecasting revenue streams related to a brand value
- predicting consumer behavior patterns in a certain market
The software is built upon:
- a unique brand valuation methodology, using more than 210 mathematical equations
- 3 dynamic simulation models, that describe the velocity over time of:
- the financial health of a company (revenues, sales volume, market share etc.)
- the industry capacity development (saturation, market share, branch trends etc.)
- the consumer generic consumption behaviors
The generic consumer model is built upon:
- the decision-to-buying-to-loyalty funnel that implies 6 major attitudinal consumption stages: Awareness, Empathy, Consideration, Purchase Desire, Satisfaction, Loyalty.
- the impact of 23 behavior factors that for now are quantified as a result of a 53 item-questionnaire specifically applied for each market segment that it is used in a simulation scenario.
The main lesson is that we cannot stop now that we reached the above results. We want to take our research further, to build meaningful tech-products that can be used by all kinds of companies and to create new innovation opportunities.
- we are designing small tools that can assist our models and support the required continuous data flows
- we are thinking about how to create as much consumer intelligence it is possible through our models dynamic variables, considering an organic brand experience
- we want to deliver our knowledge further, to enthusiasts and specialists, for free, in order to collaboratively build improved perspectives about intangible assets valuation.
Because dreaming never hurts, and who knows who can help us make them a reality, here are some crazy ideas yet to be thought through:
- we think our consumer behavior simulation can be used to provide proper consumer intelligence to improve the recommendation systems of online retailers
- we dare to say that our software could be used as a financial literacy tool for young professionals from various economic fields, a tool that could be fed with real market data and actual consumption insights from the local economic environment
- we want to imagine a world where the value of a brand, the intangible side of it, can be taken seriously into account when the company that owns it applies for a loan at a bank.