Business Scenario: You currently offer a premium product in the market place. A competitor recently launched a “knock-off” version of your premium product at a lower price point and it’s eating away at your revenue. To counteract this you want to release a lower cost version of your premium product to compete with your competitor. However, you have one big concern, “Will releasing a lower-cost version of my premium product eat away at the revenue of the premium product – and ultimately reduce overall revenue?”
Good news – this is a common business problem. Even better news – there is a research approach that fits this business problem to a tee. The approach is called Conjoint Analysis. Instead of talking in general terms about how the approach works, we think it would be more valuable to show real-life applications of how this approach is used to address important business problems.
Let’s look at Conjoint Analysis directly through the lens of the business problem – “How do I avoid cannibalization in new product development?”
At the heart of this business problem are two key questions:
- What product features or marketing messages signify “high value” for the premium product and “low value” for the lower-priced product?
NOTE: There needs to be enough differentiation between the two products in order to de-value the lower-priced product. The goal is to use the lower-priced product as a flanker to protect the value of the premium product.
- What are the price points that minimize cannibalization and maximize overall revenue?
Why Conjoint Analysis?
Why not do a survey or focus group? The problem with these approaches in this scenario is that they suffer from a bias towards rational responses.
“If you ask a rational person a rational question… you’ll get a rational answer.”
These approaches are particularly misleading in developing pricing information. Who would want to pay more?
Pricing Analysis Methods
There are generally two modeling approaches for pricing analysis:
- “Econometric price modeling” – an approach which uses statistical modeling tools to understand the relationship between pricing and sales using historical data.
- “Experimental price modeling” – a specific application of conjoint-type marketing research which develops a statistical relationship between customer interest and various price points on a forward-looking predictive basis.
In this particular business scenario there is no previous data to go from (econometric price modeling). We’re introducing a new product. There is no historical data to use as a reference so we must use an experimental approach – such as Conjoint Analysis.
Conjoint Analysis is also called “Trade-off Analysis.” As consumers, we make trade-offs every day when purchasing products. The classic example is purchasing flights. When looking at flights we might go to a travel site like Orbitz and type in the travel details. We’re presented with several flight options. Each option is slightly different. Some are direct flights, while some have 1-2 stops. Things like price, airline, departure and arrival time, and many other factors all play in to our decision. These are all rational factors. The problem is we don’t take the time to weigh out each factor in our head (i.e. “how much more important to me is price vs. arrival time?”) and make a completely rational decision. We simply choose. The point being – we all make trade-offs when making purchase decisions. The trade-offs are often both conscious and sub-conscious decisions. Conjoint (trade-off) analysis is great at understanding the drivers behind these complex decisions.
Remember, at the heart of this business scenario are two key factors – price and product value statement (features and benefits). Price sensitivity is related to the value proposition.
- What people are willing to pay (price sensitivity) is based on how they perceive the benefits.
- Changing elements of the concept can change their willingness to pay (i.e. Product/service attributes and communications)
By using Conjoint Analysis we are able to systematically alternate the product elements (i.e. features, price, etc.) and measure the impact they have on ‘willingness to pay.’
Conjoint Analysis has many different flavors. The flavor most appropriate for this problem is called Choice-Based Conjoint (CBC). Using the CBC approach, respondents express their preferences by choosing products from sets of concepts rather than by rating or ranking them. For example, a respondent might see three test concepts and be asked to choose the concept they would purchase – or if they don’t like any of them they can choose “none.” These test concepts are made up of the different elements or components of a product offering (i.e. Features, Promotions, Price, etc). In Conjoint Analysis, these elements or components of a product offer are called attributes. Within each attribute are levels – alternative statements or features. For example, levels underneath the attribute “Price” may include different price points.
Just like in the example of purchasing flights, respondents are given a set of options (test concepts) and are asked to choose.
The analytical output of a CBC study includes attribute utility values, purchase likelihood, and simulated preference share. The utility scores are a relative measure of the impact of each level within an attribute. Purchase likelihood and simulated purchase preference enables comparisons to be made in the appeal of a specific combination of attributes/levels to each other.
Example – Cameras
To help bring this business scenario to life, let’s attach it to a particular product so we can make up specific attributes and test concepts. If we restate the business scenario for a camera company it might look like this…
“A camera company offers a single premium product. They want to introduce a lower-cost product to combat the revenue being taken away by a competitor’s knock-off product, but they’re worried about cannibalization and reducing overall revenue.”
Let’s assume that if given the choice between choosing the “premium” camera or not choosing a camera at all, 20% of consumers would choose the premium camera (this number was derived from the actual CBC study but we won’t go into detail today).
Ideally, introducing a new lower-cost product would tap into the 80% of people who currently would not choose the existing premium product. The challenge in this scenario is to find the right mix of product offerings that optimize overall preference share and total revenue. In other words, when introducing the new lower-cost product, the goal should be to pull in some of the 80% who currently would not purchase the premium product rather than shifting some of the 20% of premium buyers down to the lower-cost product.
In the CBC study their product attributes might include:
- Battery Life
The Conjoint Analysis software systematically mixes and matches these product attributes and creates a series of test concepts. These concept variations are tested inside the CBC study and the output is a list of utility scores – which are used to simulate purchase likelihood and preference share.
Starting point for simulations
The cornerstone of the choice simulations is the “baseline scenario.” This is a construct of the most likely mix of features and price points for the two product levels (low-cost and premium).
In the simulator, we can configure the two products, setting the level of each attribute in the design to represent a likely purchase environment. From this starting point, we can investigate the “what-ifs” based on making changes to different attributes and see how they impact preference share and simulated revenue. The baseline is not “good” or “bad”, it’s just a starting point.
The baseline scenario for the lower cost and premium products look like this….
Share of preference – Baseline scenario
The simulated share of preference under this baseline scenario is:
- Lower-cost: 30% (meaning 30% of people would choose the “lower cost” camera if asked to choose between these configurations of “lower cost” and “premium”, or neither).
- Premium: 16%
- None: 54% (people who wouldn’t choose either of these options)
Revenue – Baseline scenario
Simulated revenue is calculated as: preference share of camera X price point X 1,000 units. (We use “1,000” units as a reference point but it can be any number of units you choose)
The simulated revenue for the baseline scenario (per 1,000 units sold) is:
- Lower cost = $30,000 (0.30 x $100 x 1,000 units)
- Premium = $40,000 (0.16 x $250 x 1,000 units)
- TOTAL = $70,000
“What If…?” analysis
We have a starting point (baseline scenario). The next step is to run a series of simulations. These simulations alter one element at a time while keeping everything else constant. For example, to measure price elasticity, we would test these product scenarios under different price points and measure the impact it has on purchase likelihood.
Let’s say the price of the “lower cost” product is raised from $100 to $150. How does this impact cannibalization and revenue? Let’s do the math…
Share of preference – Simulation #1
The simulated share of preference under simulation #1 is:
- Lower-cost: 24% (6% less than the baseline scenario)
- Premium: 20% (4% more than the baseline scenario)
- None: 56% (2% more than the baseline scenario)
Overall, people shifted away from the “lower cost” camera and moved into “premium” or “none.”
Revenue – Simulation #1
- Lower cost = $36,000 (0.24 x $150 x 1,000 units)
- Premium = $50,000 (0.20 x $250 x 1,000 units)
- TOTAL = $86,000 ($16,000 more than the baseline scenario)
Raising the price of the “lower-cost” camera resulted in less people choosing either of their products (lower cost or premium); however, it produced a higher overall revenue. By moving the price point of the “lower cost” camera closer to the “premium” camera, it made more people willing to fork out an extra $100 for the additional benefits the “premium” camera offered.
These simulations would continue until we identify the right mix of product features and price that minimize cannibalization and maximize revenue.
Putting it all together
The business problem – “How do I avoid cannibalization in new product development?”
In the camera company example, Choice-Based Conjoint Analysis (CBC) was used to help optimize the differentiation between two camera products by testing and simulating a variety of product features and price points. Remember, we wanted to identify the product features that signify “high value” for the premium product and “low value” for the lower-priced product.
The objective of the lower-priced product was to function as a flanker that protects the value of the “premium” product and, if possible, competes with the competitor’s knock-off product.
The revenue growth opportunity came by creating a “lower cost” product that motived people to upgrade to the “premium” product and also competed to bring in new, “low-cost” customers (pulling from the “none” category).
Ultimately, the camera company maximized revenue and avoided cannibalization by using Conjoint Analysis to construct the optimal product offerings (features AND price).