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Reduce complexity. Grow better, faster, and cheaper than ever before using the power of language.
Here are some examples of what can be achieved using our software. By focusing on the underserved needs of your future customers, you reduce complexity and focus your efforts on those who need you. You avoid ‘selling’ to people who don’t want you.
In this example, you can see how one company that was once was media focused becomes Product-Market focused.
Before
Marketing efforts are spread across many people not based on underserved needs but based on those who click and visit certain websites.
Good customers find this company a very reasonable cost but there are too many people that click and never buy.
After
Marketing it’s now focused on customers based on underserved needs. Efforts to reduce marketing to people without those needs are achieved. Because the company is focused on the ideal customer, there are more of them.
While no system is ever perfect, by improving the focus on new ways to segment people who desire your product, you reduce the effort on people that just don’t get you. You can worry about them later – maybe when you have a different product that suits them. I like to think of it like the coffee business. The very best beans can’t be all sold to you because there’s not enough of them. Coffee companies blend a variety of lower grade coffee that reduces the overall cost while maintaining sales volume needs. Make it too distasteful, people quit buying. Make it perfect, you go out of business.
Publishers have to make money in a very similar way. They can’t sell you all the golden clicks to just you. They have to blend. In my experience, most companies are blending not only media but their CRM ends up blended as well. The CRM becomes a reflection of the publisher’s CRM. The publisher creates lists that look like buyers but they’re not selling a list to you that is based on quantified underserved needs.
Once you understand the addressable Market you can back into how to reach them in a much more precise way. You’re beating the media-based system. It’s like you’re separating the coffee beans before you pay for them. For the example of coffee, it would be really hard to do this – you would need some kind of special tool to separate. We like to think that we made a tool that does this based on underserved needs.
How do we do this?
The first effort is understanding there are a lot of people that have unique sets of real-world, people activities. In the United States, there are 243,300,000 people that make up the vast majority of the economy. Well over 95% of all buying can be attributed to these people.
Having a list of people is not the answer. We tried to just improve the old way but it wasn’t working.
People activity (HA) will contain common linguistic properties across large populations. People activities are things like scuba diving, jogging, reading. There are approximately 450 of these activities. For years marketers used human activities, along with demographic data to find customers. It’s still in use today. In the days of telemarketing and direct response, this system worked well. Many businesses scaled using this method.
Using this methodology and creating density maps, made companies like RCA billions of dollars in revenue. It worked really well in the early days of digital marketing. I combined what worked really well for RCA with digital marketing improvements. I built a technology that did remarkably well but once digital media cost started grew beyond traditional media cost, it no longer worked as well.
By looking at categories of language, I was able to break down how people describe their world using the lexical hypothesis.
By measuring millions of written words, written by people who perform the segmented people activities, I found common patterns specific to each person’s activity. In essence, what we did is find a way to bridge the few people who write to the mass majority who never write down a single thing. In testing, we determined that those who don’t write make decisions just like those who do write. To prove the model, we connected it to CRM data of organizations.
We were able to map each person’s activity to a linguistic-based trait mapping.
We then Mapped linguistic traits to the entire United States buying population. What we uncovered was why people make decisions about certain products. Traits plus a few other things are causal to purchase decisions. Thus, the idea is not to even try to sell to those who don’t want you. Those who need you can be found through the same Publishers but in a different way. We protect the privacy of individuals by utilizing only the linguistic aspects of large populations.
Using the United States buying population connected to our linguistic engine, we found people traits and Big Five personality types. We found a way to prove that traits are causal to why buy.
More results: Traits and CRM
This is an example of an insurance company. You can clearly see that risk prevention is multiples more than than the US population. Risk prevention, reward and anxiety drive to purchase decision to buy a policy. Sounds pretty obvious and it is.
A more detailed view of how they think is presented here. While this is not a creative brief it’s the beginning of how to address this particular population. Clearly this is a focused group of people looking for a particular product to fulfill an underserved need.
Ultimately, you’re connecting traits to revenue and profit. Then you’re going to extrapolate and find other groups of people with the same trait patterns. Now its time to test with publishers. You can find publishers where these people are likely to be and measure the results.
In this example, you can see car brands in the state of Iowa and the different traits that are prioritized for each brand. Are we psychologists and do we understand why these brands have these different traits, no. We understand these brands attract people with these traits. And we can find more people with the same traits.
Stealth Dog Labs continue to research traits, language theory and recently connecting data to Big 5 personality types. We have found patterns that could be very useful to brands that like Big 5. This is useful when building creative briefs, products, and marketing plans.
How can I put this to work?
There are a number of areas where you can apply Stealth Dog Labs software.
One of our favorite areas is financial. Understanding resource allocation and budgeting is paramount for making this work. We often find resources pointed in many different directions that don’t result in growth. Having a way to connect theory to profit is important.
If you can create an addressable market based on underserved needs, we believe you have a list of future loyal customers. Why not treat new customers like loyal customers based on prediction.
Integrating prediction and prescription data into data analytics is vital to driving the Company. If you want to avoid disruption, predicting prescribe your future.
Obviously CRM is desperately in need of something else. Marketing, customer segmentation are also areas that need improve prediction and prescription data to better serve the underserved needs of future customers.
Finally, CMS optimization using prediction in prescription can happen here. As soon as you register a customer, you should be able to predict what they want before they are purchasing anything. Don’t just rely on what they click but determine who they are and why they’re making decisions. I used to fly a lot. I always wondered why a new Airline would make me jump through so many Hoops to become loyal when I was obviously loyal at the other Airline. Understanding the motivation in underserved needs of people can speed up the Loyalty process through CMS and CRM.
This is a basic example of how our data flows through your system.
Jeff Bezos makes Customer Delight a priority at Amazon. What we have predicts and prescribes how to achieve Customer Delight faster and better. You don’t have to be Amazon to have the same capabilities.
We don’t think shopping should just be about finding what you’re already thinking about. The real world is about suggestions. Just walk into a store and everything is a suggestion. Even your friends suggest things, don’t they? You only came in for some limited amount of what the store has yet the store understands it’s customers. Your brain is processing thousands of things as you walk to find the one thing you need. Did you just walk out with that one thing? Likely not. Digital can be a lot more like that experience – not selling but suggesting what matters. By presenting and giving them options you are helping them solve ‘jobs to be done’ in a much better way.
When you Delight your customers, you are creating higher retention and loyalty. Your customers are less concerned about price and are more likely to talk positively about you. They often spend more because there’s less hassle to who you are. Customer Delight can be achieved for any meaningful product company. If you have a product that serves a purpose, you can find the total addressable audience based on the underserved needs they have.
Customer Delight means basket sizes are larger. This is something we all need to work at in the digital world. We need to fill the shopping cart based on purpose and underserved needs. We need to improve conversion rates by connecting our product to the underserved needs of people.
Big brands started doing this years ago. There’s no reason why a small company has to settle for a less than ideal approach. In summary, it’s not about selling it’s about solving the underserved needs of customers. This is foundational to reaching product-market fit at your organization.