How do you make decisions when the data does not support what you intuitively know. Data-driven decision making is a great idea and it works really well when there’s stability. But how do you deal with things when stability doesn’t exist?
There is always a lack of perfect data. Those that can find data and minimize ego, bias and guessing are often those that climb the ladder of success.
So what are some of the things you can do to address this head on:
We saw the extreme importance of online and mobile well before the herd. So much so we built the first online platform in 1997. Today, we create and leverage our own digital research solutions.
It is said that all decisions are emotional. We quantify emotions. Quantifying data that’s hard to get is one good place to start.
We solve consumer psychology questions for our customers. What personality behaviors, emotions, and traits are driving revenue? We can answer that. using linguistic empowered technology, You can start to create predictive data sets were no data existed before. Sometimes this data is sitting right inside of your own company it’s just hidden and needs to be unlocked. For example, customer service data has a lot of language usage to uncover. Predicting what people do connected to what they buy from you is also critically important.
Our 10 years of research have shown that traits are responsible for 80% of your company’s revenue and profits. By focusing on this group of people, the user experience goes up, bad ratings go down because you’re aligned.
Branding: Who gets you is a matter of connecting. What does connecting mean? It’s the emotional ties that we formed between people. As a brand more and more, you are a personality.
Creative: It is said that all buying decisions are emotional, then quantifying emotion can be beneficial to defining creativity, language usage, marketing, and markets.
Apps and websites: The right language, down to verb usage, can impact conversion rates in a profound way. We make that data actionable. We also help you with tonality and language using computational linguistics. Our research connects to decades of university work to the realities of revenue and profit.
Product: Certain features and functionality work well with certain personalities and emotions while others don’t. Having the research about which product features will impact how your product is used and perceived can make a product launch work. For example, a major retailer was using technical language to describe products to a non-technical audience. Simple but who was measuring to say anything different? The language was subtly different enough to fail several product launches.
Advertising and messaging performs best when there is alignment between the writer and the reader. Just like reading a good book that your friend can’t get their head around, how language works for one person may not work for the other. By understanding who will be reading your material including video, color, and design, you can better flow the right customer through advertising and messaging.
Market to unmet needs: Is it just a media issue? Something tells me there is a better way that briefs potential customers on some ‘trust and respect’ issues. Trust and respect of your brand It’s seriously impacted when the wrong personalities meet up. It’s kind of like blood types. (https://www.businessinsider.com/harvard-psychologist-amy-cuddy-how-people-judge-you-2016-1) If you have the data, why would you attempt to acquire people at a profoundly higher cost or would you drop that audience, focusing on the best potential customers.
Modeling: An example: I recently ran a study for a major chain and polled 22 million vitamin buyers from my data set. There are so many different reasons to buy vitamins and you can see it in the results. For example, in New Jersey where there is a high density of vitamin purchases, overall people in that state see vitamins as “little batteries”. It’s the lifestyle that powers the need. In Wyoming, which has the lowest per capita vitamin consumption, all the signals came back as biological. I believe it’s because of the environment. Using massive data sets, you can discover much more accurate ways to reach an audience and determine a realistic market share and cost of acquisition.
Sampling: This could help with defining where to best get primary data. It can also be used to compare to primary data results.
Media: One of my favorite areas of work is looking at market share density maps. People who are expected to buy based on personality traits versus who is buying (if existing sales) Indicates markets to put resources against and where to avoid.
Pricing: If you have access to sales data, you can clearly see the best customer versus not so good against trait differences. In my 10 years of operating this software for myself, only a handful of traits drive the majority of sales. The differential from the best customer to worse customer is at least 10 to 1 in terms of profits. Price elasticity is not about averages. When you fine-tune who is the best customer you have more of a chance to understanding where price elasticity truly works
I get accurate assessments based on personality traits, along with a number of other data sources. There are a number of ways to acquire the data but step one is vital – get something that is informative, so you can use your exec brain to execute.
Stop averaging your work. Missing data implies make you risk averaging.
When we attract the wrong customer is when we get bad reviews. There’s no reason to attempt to align your mission and vision with someone that does not agree. You’re trying to solve a problem they have and sometimes products don’t match the personalities of the buyer. Find your audience, execute what is important and go.