First, set clear goals and prioritize among conflicting goals (for instance, price to maximize revenue but ensure a 10 percent profit increase).Second, pick one of the three types of pricing strategies: maximization, skimming, or penetration.Third, set price-setting principles that define the rules of your monetization models, price differentiation, price endings, price floors, and price increases.Finally, define your promotional and competitive reaction principles to avoid knee-jerk price reactions.

A pricing strategy is your short- and long-term monetization plan. The best companies document their pricing strategies and make it a living and breathing document.

What monetization model do we envision for our new product? Why is it the right one, and how did we choose it?Which models did we not pick, and why?What are the most important trends in our industry? How do they affect our choice of a monetization model? How do we plan to monetize our product if customer usage varies significantly? Which price structures have we considered and why?Do we have the right capabilities and infrastructure to execute the chosen monetization model and price structure?

To have a chance at converting free customers to paid customers, you need to test what benefits they will pay for and ensure a functional free experience. You also need to know exactly how many customers will actually be willing to pay. What’s more, you must avoid giving the farm away for free because it will leave your premium offering with very little value.

Customers pay when they use or benefit from the product. This can be exceptionally successful if you can align the metric directly to how customers perceive value. This can be effective if you are adept at predicting future trends. The alternative pricing model makes sense when your innovation creates significant value to end customers but you cannot capture a fair share of that value using traditional monetization models.

Auctions — competition based pricing for goods and services. Google AdWords, eBay, and other two-sided marketplaces use auctions. Market determines price. If you can control inventory for an in demand product, you should consider this model.

Dynamic pricing — airline industries, Uber offer dynamic pricing for peak demand times. Dynamic pricing boosts the monetization of fixed and constrained capacity. Complex model requiring extensive data analytics.

Subscription —recurring revenue increases customer lifetime revenue, revenue predictability, cross-sell and upselling opportunities. Works well in online and offline services where the product is used continually, in competitive industries, and where it can reduce barriers to entry through large upfront payments.

What are the product configuration/bundles we plan to offer? Why did we pick these offers? Do they align with our key segments? If not, why not?What are the leader, filler, and killer features for the new product or service our company is developing? How did we find out?Have we explored a good/better/best approach to product configuration and bundling? What do we expect sales to be for each product configuration? Is the share of the basic product configuration lower than 50 percent? If not, why not?Have we explored bundling our new product with existing products? What would be the benefits for us and the customers?Have we considered unbundling as an opportunity? What would be the benefits to customers and our business (if any)?

Align offers with segmentsDon’t go beyond nine benefits or four productsCreate win-win bundles for you and the customerDon’t give away too much in the entry-level product. Look at customer distribution by product and upsell percentage to qualify a problemHard bundles might not work and an a la carte bundle may be betterPer product pricing needs to be higher than bundled pricingMessaging and communicating the value of the bundle is a sales opportunity depending on what product or feature they are afterBundle an integrated experience and charge a premium instead of a discountDon’t bundle for the sake of bundlingLook for inverse correlations and exploit both by including the nice to have inverse.

Establishing which features are leaders (must-haves), fillers (nice-to-haves), and killers (features that are deal killers), andCreating good/better/best options.

Product configuration and bundling are your key building blocks for designing the right products for the right segments at the right price points. Product configuration is about putting the appropriate features and functionality — those customers need, value, and are willing to pay for — into the product; this process has to be done for each identified segment. Systematic product configuration prevents you from loading too many features into a product and producing a feature shock.

Does our product development team have serious pricing discussions with customers in the early stages of the new product’s development process? If not, why not?What data do we have to show there’s a viable market that can and will pay for our new product?Do we know our market’s WTP range for our product concept? Do we know what price range the market considers acceptable? What’s considered expensive? How did we find out?Do we know what features customers truly value and are willing to pay for, and which ones they don’t and won’t? And have we killed or added to the features as a consequence of this data? If not, why not?What are our product’s differentiating features versus competitors’ features? How much do customers value our features over the competition’s features?

Did we segment before we designed the product? If not, why not?What were the segments? How did we get to these? Which ones would we serve initially? Do they represent a sizable market?What criteria were they based on? How different are these segments in their WTP? Can we respond differently to each segment? If so, how?How did we describe the segments? What observable criteria do we have in these descriptions? Do our descriptions and observable criteria on each segment pass our sales team’s sniff test?How many segmentation schemes do we have in our company? Can we consolidate to one segmentation across product, marketing, and sales?Who in our company is responsible for segmentation? At what point in the innovation cycle does this person (or people) get involved?

Do Segmentation Right:Begin with WTP data — By clustering individuals according to their WTP, value, and needs data, you will discover your segments — groups of people whose needs, value, and willingness to pay differ.Use common sense — Practicality and common sense are as important as statistical indicators.Create fewer segments, not more — Serving each new segment adds significant complexity for sales, marketing, product and service development, and other functions. Smart companies start with a few segments — three to four — and then expand gradually until they reach the optimal number.Don’t try to serve every segment — The products and services you develop should match your company’s overall financial and commercial goals. A segment must deliver enough customers — and enough money — to make the investment worthwhile.Describe segments in detail in order to address them — Investigate whether each segment has observable criteria for customizing your sales and marketing messages to them.

The message here is clear: You need to create segments in order to design highly attractive products for each segment. And you must base your segmentation on customers’ needs, value, and WTP. This way, segmentation becomes a driver of product design and development, not an afterthought.

New products fail for many reasons. But the root of all innovation evil is the failure to put the customer’s willingness to pay for a new product at the very core of product design.

Startup companies are much more likely to possess Feature Shock and Undead pricing problems. However, some product launches are treated as Minivations and startup companies with Hidden Gem technology may not see the need for a pivot. Regardless of the problem you may encounter, there are certain things you can do to find the solution.

Feature shocks happen when you try to cram too many features into one product, creating a confusing and often expensive mess. In a sincere effort to have it be “all things to all people,” you launch a product that pleases few. The result is the product’s value is less than the sum of the parts. Due to its multitude of features — none of them a standout — these products are costly to make, overengineered, hard to explain, and usually overpriced.

No one wants to sell his or her idea short. Minivations are products that tap neither a product concept’s full market potential nor its full price potential. Companies that fall into the minivation trap underexploit the market opportunity and the price they could have charged, thereby robbing themselves of profits. Minivations go down as undermonetized products cursed with a “what might have been” tag.

With a hidden gem product, a company has a brilliant, even revolutionary idea but fails to both recognize it and quantify the product’s value to customers. Or the company decides it lacks the capabilities to bring the unusual idea to market. Hidden gems often end up in limbo, neither launched nor killed. They often don’t make it to market, but if they do, they arrive undervalued, as freebies or deal sweeteners.

Applied to monetizing innovation, an undead product is one that still exists in the marketplace, but demand is virtually nonexistent. The product, for all intents and purposes, is dead, yet it continues to “walk around” like a zombie.

When an undead is in the making, you become delusional and detest any evidence that goes against your beliefs. You resist objectivity and keep investing time, feeling you have come too far. Once the product is in the market, your sales teams can’t sell it, and it causes them to miss their targets — by a lot.

Myth #1: If you simply build a great new product, customers will pay fair value for it. “Build it, and they will come” is the mantra. Why we believe it: because Star Wars, FedEx, Harry Potter were all rejected by directors, businessmen, and editors and were wild successes. But they are the exceptions not the rule.Myth #2: The new product or service must be controlled entirely by the innovation team working in isolation. Why we believe it: because Henry Ford said that customers would have wanted a faster horse. Indeed, the Innovators Dilemma, Different, Play Bigger, etc. put a premium on this as well. The fine line that must be walked is that of being different in a way that we are confident will resonate with customers. Confident because you have talked, consulted, argued, and shown the product to customers and more importantly, gauged their willingness to pay.Myth #3: High failure rate of innovation is normal and is even necessary. Why we believe it: sports analogies.Myth #4: Customers must experience a new product before they can say how much they’ll pay for it. Why we believe it: because its safe.Myth #5: Until the business knows precisely what it’s building, it cannot possibly assess what it is worth. Why we believe it: a cost-plus mindset that ties willingness to pay with what it cost us to build.

The most important aspect of moving to a “design the product around the price” innovation process is finding out as early as you can whether customers value your innovation and would pay for it. You can only determine customers’ WTP by actually asking them — not by imagining what they will say.Two pieces of information are important in this phase: customers’ overall WTP for a product (the price range they have in mind) and their WTP for each feature (so you know which features matter most and which features don’t matter at all).Five types of research questions will help you get these answers. Best practices in having these discussions include positioning them as “value talks” (not as pricing discussions), expecting key insights to come from the simplest questions, mixing structured with unstructured questions, and not relying solely on quantitative data.

Direct, purchase probability: “what do you think could be an acceptable price?” “What do you think would be an expensive price?” “What do you think would be a prohibitively expensive price?” “Would you buy this product at $?”Purchase probability questions: “On a scale of 1 to 5, where 1 is, I would never buy this product and 5 is, I would most definitely buy this prdouct, how would you rate this product?” 4 or 5 you stop. Less than 3 lower the price and try again.Most–least: create a subset of your features and ask them to rank the features from most valuable to least. Change the subset for 5 different sets. You will end up with rich feature by feature feedback.Build-your-own: give them a list of features and ask them to build their “ideal product.” As they add features, the price increases. See where they stop with features and price.Purchase simulations: A.K.A Conjoint analysis. This is where you test the pricing of certain bundles of features. Changing bundles and prices. Definitely the most complex option and usually requires software and/or a consultant to manage. As expected, however, the results can be game changing.