Speaker 1: Hello and welcome to understanding under five market research concepts in less than five minutes. Today, I will talk about a very useful market research technique called conjoined analysis. Conjoined analysis is one of the most powerful methodologies in optimizing product features. It's especially useful for decisions when limited resources only permit a handful of product features to be included in a product, and the company needs to decide which features to prioritize. Because of this, conjoined analysis is often referred to as trade-off analysis. It's lesser known that conjoined analysis is also very effective when looking to understand a product's price elasticity, in finding the most optimal price point, in understanding what customers are willing to pay for a new service or a new feature in a product, in forecasting a new product's demand, and in understanding how valuable a brand name is. So let's take a deeper dive on how conjoined analysis works. Conjoined analysis is a survey-based research technique. The data which is used for the conjoined analysis comes from an exercise done by survey takers called the choice task. This here is a choice task example in which survey takers are asked to compare three credit cards. They're given relevant information about the three cards, what brands issue the card, what type of card it is, whether there is an annual fee, if yes, how much, whether you have cash back, and many other relevant attributes. When survey takers review these, they select the one they'd prefer, and then a set of new credit cards are shown, again, with all the information. And it goes on and on. Each survey taker makes about eight to ten choices. So how does the choice task exercise allow us to calculate price elasticity, optimize product features, forecast demand, and the many fantastic business answers we get from conjoined analysis? How can we do all of that from simply having people pick from options? Well, here's how it works. Let's suppose the product we want to analyze, and the products in the category, have three main important attributes. It may be its brand, package size, and price. For illustration, we'll call them triangle, square, and circle. And each attribute can have different levels, just like brand can be different brands, size can be of different sizes. Let's just say our shapes can be of different colors. These colors represent the different levels of the attributes. In the choice task, a survey taker is shown three products side by side. And while the survey taker thinks she is shown three different products, for the purposes of the conjoined analysis, it's really just almost random combinations of the three shapes' colors shown three times side by side. Because conjoined analysis is a probabilistic methodology, after hundreds of people making altogether thousands of choices among the different combinations of the colors of the various shapes, the analysis will look for patterns in those thousands of choices. It will look at patterns on which color in each shape makes it more or less likely for survey takers to select a product when it is shown. In fact, it will calculate a score for each shape's color, indicating whether that color increases or decreases the probability that a product will be selected. This score is called part-worth utility score. In our example of shapes and colors, among the shape of triangle, for example, it looked like green was the most appealing. Conjoined analysis calculated a utility score of 1.52, followed by yellow, then blue, and then orange. That means in the choice task, the product in which the triangle was green tended to be selected by survey takers more than when it was yellow, and quite a bit more than when it was blue or orange. Now, if we make the assumption that the total appeal of a product is the sum of appeals of its parts, then we can construct any product from these shapes and colors, and then we can sum up these part-worth utilities to get the total utility of that product. Think about it as its total appeal. And we can use a statistical formula and estimate how much more or less likely that particular product would be to be selected by customers than other products it may be competing against. This is called preference share, and by calculating preference share, we can estimate potential demand for a new product launch. The part-worth utility scores can also be used to calculate an optimal product. We can construct a product simply by selecting the colors from each shape that maximize probability of selecting that product in the market. In other words, the optimal product. Now, for example, if one of the shapes were price, the part-worth utilities could simply be used to understand the product's price elasticity or its demand curve. Conjoint analysis is an extremely useful market research technique, and I've not even scratched the surface on all that's possible. For example, it can be used to segment your customer base. But more on that in another video. If you like this video, please hit like and don't forget to subscribe. That way you won't miss future Understand in Under 5 videos. See ya.