We’ve already talked that companies have one main objetive, earning money. If we add the variable ‘t’ we can split this main objetive in two, present and future earnings. The present earnings is the Company’s current economic result, while the future earnings are determined by the buying intention.
On the other hand the above mentioned buying intention it’s the result of several variables that occurs together like:
1. The person have the need or intention to buy something in particular.
2. The company have the product that can satisfy the generic need.
3. The person have the money to buy the product.
4. The brand communicates something that make the person feel special.
5. The company service makes people feel special.
In few words, the product/brand have a balance of attributes that makes a person want it. The buying intention is based on personal experiences and marketing communications.
That’s why it’s important to know what combinations of attributes and their magnitudes are the closest one to satisfy an specific type/segment of client.
Conjoint Analysis is a statistical technique used in market research to determine how people value different features that make up an individual product or service. The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to respondents and by analyzing how they make preferences between these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new product designs or services.
Conjoint Analysis steps:
1. Desegregate the product or service into attributes. For example in flight tickets some attributes are price, if the ticket is refundable, if there is any cost on changing the departure date, etc. If we are talking about laptops those attributes can be price, screen size, ram memory, hard drive, weight, time of battery, etc.
Each attribute can be broken in levels like regular hard drive or flash memory.
2. Then a range of products (with different attribute combinations) are shown to the respondents in different ways. It can be the product (or service) it self, a picture of it, a prototype, etc.
3. Respondents rank the products or services.
As the number of combinations of attributes and levels increases the number of potential profiles increases exponentially. Consequently, fractional factorial design is commonly used to reduce the number of profiles that have to be evaluated, while ensuring enough data are available for statistical analysis, resulting in a carefully controlled set of “profiles” for the respondent to consider.
Get it? Good. Now imagine the conjoint analysis potencial with Social Analytics or pols in your website (Integrated with you web analytics platform). You can release to the internet several images or competitive products and do conjoint analysis by classifying the comments left by the internet (Facebook, twitter, etc) users and generate a product with the set of attributes and magnitudes that fit best your market requirements with a plus advantage.
The regular conjoint analysis is based on a short list of attributes that you or a previous market research defined. With social media or with pols you can define the set of attributes based on your clients requirements and then select the magnitude and level of attributes that will become the best option for your client.
You can use almost real time conjoint analysis for:
1. Flight tickets.
2. Mobile phones.
3. Phone plans.
4. Financial services.
5. Retail (delivery, quality of products, price, etc).
6. And even for web analytics platforms (real time, olap cubes, custom variables, integration with other platforms, free vs paid, etc).