In the previous article, we discussed pricing myths. Let us delve deeper into creating a formidable pricing strategy for a new product and then iterating over it.

How do we create a monetization strategy that fuels growth?

Monetization strategy should be an enabler of growth by maximizing the value captured and using that to create more value for existing users or to create value for new users.

Monetization loops: An example of a monetization loop that fuels growth is shown below

This is why setting the right monetization strategy is paramount. In the current investment environment, increasingly more VCs are demanding profitability. The first step towards that process is monetizing the current users.

As we monetize our current users, we can invest part of that money to acquire new users either through SEO/SEM, outbound sales, referral etc. % of those users come to the platform and convert to engaged users who could then be monetized to keep this virtuous cycle going.

How do we identify the right price?

There is an array of pricing and market research tools that can help zero into the price points. Some common ones are:

Gabor Granger Pricing Technique: The method involves asking potential customers the likelihood of their purchasing a product or service at different price points (PP).

The model shows user a product or a package and asks ‘Would you buy product at x price’. If user says No, the next lower price point is shown, if user says yes, then a higher price point is shown. This process repeats until the model identifies an optimal price point.

We can then plot the conversion point i.e. % of users who are willing to pay at various price points and predicted revenue curve as shown below:

From the above two curves we could identify the optimal price point.

This method is helpful when there are already some price points we have identified around which we need to optimize. So if the price range is wrong then what we get as an output would also be not reflective of true willingness to pay. However, it fails to take into account the competitive environment or when there is no context for what should be the range.

Van Westendorp Max diff Analysis: This analysis could be broken down into two parts:

a. Defining the packaging (What are we giving to the user): For this we first need the packaging i.e. what features are important to the users. The best way to do this is to create a survey and ask users what they value the most and what they value the least.

Allowing users to evaluate the trade off will give us a relative scaling of the features as shown below:

b. Defining the price (How much we are charging): Once we have the packages, survey respondents are asked four questions

1. Too Expensive: At what price would you begin to think the item is too expensive to consider (in USD $)?
2. Too cheap: At what price would you begin to think the item is so inexpensive that you would question the quality and not consider it (in USD $)?
3. Cheap (Not expensive): At what price would you think the item is a bargain — a great buy for the money (in USD $)?
4. Expensive (Not a bargain): At what price would you think the item is getting expensive, but you still might consider it (in USD $)?

We shall implement a validation check to ensure the sanity of the data such as too expensive price point > expensive price point. If not then ask the respondent to reconsider their inputs.

The survey results are plotted as shown in the graph below

The point where Not a bargain and Not expensive meet is called optimal price point. The Range between the points of intersection of Too Cheap — Not a bargain meet and Too Expensive — Not Expensive meet is called the range of acceptable prices.

We can then use either Gabor Granger Pricing Technique or A/B testing to get to the optimal price point for our product.

Conjoint Analysis: Conjoint analysis may seem similar to max diff analysis however, there is a difference. The respondent doesn’t select which attribute lies at the two ends of a spectrum. Instead, respondents are given two options of various product configurations and asked to choose which one they are most likely to purchase.

We can keep changing the attributes in each package and price and compute the relative weightage of each attribute.

Conjoint analysis thus ask users to select the tradeoff between the complete pricing and packaging in one go. This could be useful once we have defined the packaging and price range and now want to dive deeper into the analysis.

Live testing with actual product: Of course you could at some point in time launch an A/B test and see how users convert with different packages and pricing. This is often done after you have done your research based on the previous analyses methods suggested.

Pricing using AI: These days ML is being used to price products in real time. The idea is to achieve first degree price discrimination by taking into account multiple cues such as user’s shopping behavior, seasonality, demand from other users, availability of inventory, etc. No wonder pricing in retail is moving to data science team. Will discuss this in more detail in another article.

What tool should I use?

While there are many tools out there to get the results, I have used Qualtrics to run these surveys. However, a simple google search will yield many results. You could use any of them.

Once we have identified the initial pricing, it is important we keep reviewing the packages and pricing. As I have found from my experience:

Pricing is not set in stone but is in motion

In the next article, we will discuss more on how to measure the success of a monetization strategy.

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