Market penetration is one of those invariables we deal with when designing and implementing a marketing plan. Under estimate and your manufacturer builds too little product and you suffer. Overestimate and you end up with a glut of widgets that are not selling and have a limited shelf life. Market share on the other hand is post -penetration and building a position and image in the marketplace. POET will have more interest in the former than the latter, in the beginning.

So I must tell you, we had a heck of a time with this first aspect. How much penetration, consumption, and at what revenue. We made some phone calls to colleagues in the hardware business, and dug into a set of Intels financial statements. We also had a look at a couple of other semiconductor company facts and figures, and a look at our own parts and pieces. One other thing about market penetration, we felt POET would have little or no problem with this issue. Between a roll out advertising program by end users and social media firestorm, the world will know what this can do in short order.

After some degree of debate, we settled on a market share model for calculations. There are more than a few methods for doing so and a few specialized for start ups. projecting sales, market share and penetration is difficult. The first year is particularly hard to estimate. We had in the back of our minds, that again, word would not take long to get out especially if the end user company was promoting the top 3 or 4 main aspects of POET. As someone said, even just the power saving aspect is a potential marketing dynamo.

For the first year, (2015) we used a combination of sales estimates including numbers from a campaign we had done a few years ago on a new product.

2015 ---478M Revenue

We assumed the first entry level deal would be for someone like a handset maker, followed by a more integrated manufacturer, later in the year who would put them in their tablets for example. We also assumed a limit model run would have POET inside at first. This way the end marketer could gauge and measure public response to performance. Later in the year, a further or on another production run, the orders would increase for POET as results from customer feedback and social media begin to effect demand. I can not say much more on how we priced the wholesale value of the POET product, just that we borrowed a pricng matrix. You can do this if your working along with it. There is unit sales numbers on the internet, make sure as you go through the 5 year plan that price per unit goes up due to demand and could decrease on large volume orders as well. Its fluid to say the least. In additon, this would discount or price increase would also be dependent on end user size in the community and negotiating power.

NRE was not factored in for this reason.

NRE charged to a customer *is* revenue. Typically you won't take this as contra-R&D unless there is something funky going on, such as you've seconded your staff to that company and the staff is getting paid by them.

You will likely want to separate the NRE from the Semiconductor sales for reporting purposes...makes things clearer. However, if you enter a contract to do NRE work then sell the resulting work product to the customer, you will have to allocate that revenue properly which could result in deferred revenue.

An example would be, if POET was selling for $25 a unit. The end user has a XX% market share and in the first run uses a POET chip in say 12% of that runs handsets as a controlled test market. Figure out their market share value, estimate the handset sales for the year, take 7% and multiply by $25 a unit.

Example: Apple has 41% market share in the handset market. 120 million handsets in 2013. 12% of 120 million is 14.4 million multiplied by $25 is 360 million. The remaining revenue can be aggregated from the second licensee for use in tablets as mentioned above. One last consideration, We looked at one of our latest product launches and the resulting graph. Market penetration was slow at first until we had the public convinced there were no compatibility issues. Then sales went up. I dont think (in fact I know) POET will have those issues with consumers.

You then can extrapolate a value per share for year one. You can use an industry acceptable PE ratio in your calculations as well.

NOTATION If you see a specific percentage or figure or dollar amount used suffice to say it came from (somewhere).

Some specific first year notes that we felt would be of major impact.

-The beginnings of a mania are not always recognizeable, one must monitor the press, social media, video media etc to get the vibe.

-Once the vibe has started, it will be picked and packed for larger distribution in Twitter, Facebook, Snapchat etc. Literally over night the world will come to know POET as the power saver, the computing monster, and whatever the marketers want to have it known as.

-Demand will rise exponentially for products with POET inside and will also render existing on shelf products as has beens, so the retailer will also clamor for new product in response to consumer demands.

-The foundry, the end manufacturer, the retailer will have to and will move fast to fill the void. there is every real possibility you will watch this happen is speed and stealth modes. Meaning the underlying current will be a torrent and the above waterline view will be steady and increasing amount of media exposure fueling the behind the scenes frenzy. This will be mania, year one.

-Meanwhile PTK with few outbound expenses, will start to see a cash flow increase and substantially so. Marketing costs will be bourne by the end manufacturer and retailer and PTK will and can continue in the development track with more than enough cash for future development and operation.

-Understand that the once the market gets hold of the advantageous nature of the POET product, a new paradigm shift will begin. This will be phase 2 of the mania. Marketers will jump on with full aplomb. The king caps will be watching closely at this point.

-Obviously a synergy (planned or unplanned-preferably planned) will have to be developed between all the players in the process to maximize commercial advantages.

An interesting reading below on market share and market preference.

When business decision-makers look at conjoint and discrete choice model output, how should the share results be interpreted and used? In this post, we share our thoughts about preference share and market share, based on decades of practical experience.

If you used a conjoint-based (or discrete choice) simulation model and saw the following output:

you might assume the figures represent market shares. But, the output of a conjoint simulation model is known as a “share of preference.” In this post, we will discuss the important differences between preference share and market share, and provide our thoughts about how to interpret and use these figures to help with business decisions.

In previous posts, we have described the basics of conjoint analysis and simulation modeling. In this post, we pick up at the point of using output. [But, if you don’t have time to read the prior posts, work with this: A conjoint model compares respondent preferences for the building blocks of a decision – such as which TV to buy – with the offerings in the market. The output predicts the level of preference any product might achieve.]

Most model users might prefer to simplify and assume that preference share equals market share. That would seem to make life—and revenue projections—easy. But, conjoint and discrete choice simulation models are based on important assumptions, some of which are basic to market research (e.g., representative sample, accurate answers, stated preferences will translate into behavior) and others which are unique to conjoint: All products have equal levels of distribution; Consumers are fully aware of and understand the specifications of all products; All products have reached their equilibrium shares (that is, full adoption has occurred); All relevant attributes that contribute to share have been included.

What’s a Researcher To Do? Take a close look at share of preference AND market share.

So far, this post may seem focused on caveats. Let’s switch gears. The good news: Shares of preference are very powerful. They help us understand what a product’s potential is, how well-liked it is versus competitive products, and what aspects of the product (such as brand, a particular feature, or a competitive price) are most contributing to preference. In addition, by simulating changes to the product (such as changing the mix of features, or raising or lowering price), we can determine the relative benefit of our strategic options. We can see which changes most increase or decrease preference, and which have only slight impacts.

But, the inevitable question arises: Why don’t these shares look like our market shares? Skittish researchers will apply external effects, which are simply multipliers that can make preference shares look like market shares. In our experience, applying external effects robs the business decision-maker of valuable information. We recommend comparing preference shares to market shares. Why? Because there is information hiding there!

If our product has a preference share that is lower than its known market share, what might be going on? Let’s start by realizing this suggests that the product is selling at a higher level than “preference” would indicate it should be. Possible underlying causes:

• Distribution or awareness of our products is particularly strong. Therefore, we may be vulnerable. If competitors gain distribution or awareness, it would appear buyers will prefer their products.
• A market shift is occurring and other products have not yet reached their potential. Again, we’re in a vulnerable position. But, with the conjoint model, we can see what actions are needed to stave off competition.

If our product has a preference share that is higher than its known market share, what might be happening? Start by realizing that our product is not selling up to its potential. Possible underlying causes:

• Distribution or awareness of our products is low. Therefore, we have an opportunity. We may have been considering costly product changes or price decreases, but we need to allocate our resources to letting the market or channels know about our product.
• A market shift in our direction may be coming. Perhaps existing products have been capitalizing on consumer loyalty or inertia. In this case, giving consumers the chance to experience or sample our product may be needed.

When preference share does not equal market share, the conjoint model won’t necessarily tell you whether distribution, awareness, loyalty, or inertia are at work. But, you will have important information about whether your product is in a vulnerable position (preference is less than market share) or whether you have an opportunity (preference is greater than market share), even before any product or price changes are made.

What Else Can Be Done? Look at conjoint share results over time.

As noted above, time can be a factor in modeling… the conjoint model assumes equilibrium shares have been achieved. To see how time may figure into the equation, two approaches can be used:

• Track results over time and compare predicted to actual. Let’s say the conjoint model predicts that a new product will have 20% share of preference. After the product is introduced and some time has passed (perhaps one year), compare the 20% model prediction to the actual share achieved. This will result in an approximate “discount factor” to apply to future model runs.
• Model a historical action and compare to the model results. Let’s say your product was priced 10% higher one year ago, and everything else in the market was the same. The conjoint model can be run as if it were one year ago (with the higher price). Then it can be run at the current, lower price. The resulting predicted change in share of preference can then be compared to actual change in market share. As in the previous example, this will result in an approximate “discount factor” to apply to additional model runs.

Bottom Line: Use the power of shares produced by conjoint analysis.

Instead of approaching preference shares with trepidation (“will they look like market shares?”), we suggest conducting a thorough and open exploration into the differences between preference and market shares. There’s information that can lead the way to vital understandings and profitable strategies.