Choose Message Predictor(CMP) scores messages predictively

No surveys.
No respondents.
No fieldwork.

predictive accuracy vs. primary market research

How It Works

CMP predicts accurately because of extremely high-quality training inputs & advanced algorithms.

Etymological database of 2.7 million heuristics-based words and phrases.
DNLU – Deep Natural Language Understanding algorithms that analyze heuristics embedded in language.
Research database of 2.4 million respondent choices from past message testing studies.

CMP is an ideal Message Predicting AI solution for when you are in a message testing pickle.

Not enough time to conduct primary market research?
Short on market research budgets?
Need gut check answers, not 100-page research reports?
Can’t recruit enough customers for quant research?
Need to cut down messages for research?

Use CMP on your data

CMP, the proven Message Predictor, delivers almost everything you get from a quant message testing study.

Message Hierarchy
Message Bundles
Message Substitutions
Message Diagnostics

CMP checks all the boxes on your vendor checklist.

Cost Less than one-third the cost of a quant study.
Reliable Matches primary research 80% of the time.
Fast Get results in days.
Easy Just provide messages to test.

CMP as Message Predictor Solution can be customized to your product category and brand by training the algorithms on your data.

Share data from past message testing studies.
CMP is further trained on your custom data to improve predictive accuracy.
CMP is deployed as an easy-to-use message scoring tool for you to use directly.

CMP choose message predictor

First & only Message Predicting AI solution to “test” messages without conducting any primary market research.

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Video Time : 34 Mins

How Actor-Observer Bias and Disappointment Aversion are used in pharma marketing campaigns

Pharmaceutical Heuristics

Video Time : 32 Mins

How Numerosity Effect and Ambiguity Aversion are used in pharma marketing campaigns

Pharmaceutical Heuristics

Infographics Time : 5 Mins

Time to give up qualitative message testing

Market Research Pharmaceutical

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What is Choose Message Predictor?

Choose Message Predictor (CMP) is the first and one-of-its-kind machine learning algorithm aiming to replace market research altogether and predict message scores. It delivers everything similar to the quant message testing but minus the fieldwork. CMP features the following for message testing:

Message hierarchy/ranking
Message bundle/story flow
Message substitutes
Message drivers of appeal

Choose Message Predictor replaces the primary market research as it's trained on high-quality data and algorithms.

2.5 million data points from past message testing research studies
The only database of almost 3 million words and phrases from pharma messages
Deep learning algorithms that can understand the content in the language

CMP stands to be the greatest solution for message testing when you are running out of time or budget, and it's normal these days!

How can enterprises leverage the benefits of CMO?

CMO benefits the enterprises in several scenarios of marketing:

The marketing team can use the developed new messages.
It is perfect for the business that wants to test messages but does not have the budget.
When the agency needs to start its production now.
When the marketers end up doing some IDIs/ TDIs

Choose Message predictor helps marketers to save hours on primary market research, reduce market budgets, and cut down messages for research. Brands just need to provide their previous messages to test and get the predictive analysis for new messaging. CMO predicts 80% more accurately than any primary market research.

How does Choose Message Predictor work?

Message Predictor is an extremely powerful tool when it comes to message testing — it can even learn from past message testing events to predict how well messages will perform in future tests!

First, the program analyzes language patterns in each message and reverse engineers the heuristic embedded in the language.
Then, it will create all possible pairs of these messages and predict the outcomes
Next, it creates a hierarchy of the most effective and least effective messages based on the scored messages that have been vetted.