Defining the Problem
Major Issues
1. Uncertain Attractive Yet Challenging Music Industry Landscape The music industry is a 32 billion dollar industry, offering a huge opportunity if it can be penetrated. However, it is fraught with challenges. ArtistsArtists, producers, and record companies have little idea on how to find and create success in the industrygo abo. The dominant players, i.e. record companies utfind pursuing the production of thethe next big hit by producing in masses and hopingin hope that one turns out good, . This is shown evidenced by the low hit success rate of 10%. of a song topping music charts (Pg. 1, P2). What entails is an industrial marketing practice of huge inefficiencies and unwise budget expenditure (Pg. 8, P3&4). These problems stem from the market interactions of the industry, such as thewhich is dictated by popular culture. There is constant and rapid evolution due to both volatility of popular music culture and lack of fail-safe apparatus to accurately predict upcoming trends. Hence, all segments need a tool that improvea tool that improves their chances of landing a hit song and reduce the need for excessive marketing expenditure.. The satisfaction gratification of this need is ever more urgent due to falling sales and increasing music piracy (Pg. 5, P4). Can HSS be the answer to the industry needs?
2. Harnessing Overcoming Polyphonic HMI Barriers’s Opportunities Polyphonic HMI has significant barriers to overcome. Upon failing to impress hardware providers, HMI is lacking capital and time. In an industry that is normally associated with art, they have not found a way to make science and music mesh. However, they possess the potential to revolutionize the music industry. Polyphonic’s HMI’s core competencies lie in its technical expertise in artificial intelligence and natural science applications (Pg. 2, P3). Even though it does not provide specific feedback on how to improve a song, HSSThe HSS scientific product has the ability to revolutionize the music industry byhelps to eliminateeliminating the uncertainties of relying on instinct while incorporating humanistic music preferences in predicting future successes.. It will serve to reduce marketing inefficiencies, thereby allocating concentrated efforts and budgethelp to refocus efforts and budgets towards hits that have a a highhigher likelihood of success of making the next big hit. Below are further issues of consideration, specific to HMI.What stand in its way is the minor issues detailed below. Minor Issues
1. Shoestring Marketing Budget
HMI operates on a limited budget of $150,000 and this creates two implications. First, HMI is unable to reach out to the entire market and it limits them to target only 1 segment at this juncture.. Pricing is critical as well, given that it needs to ensure that it recovers its operating fixed costs. Secondly, HMI is in dire need of a of a cost efficient marketing plan is unable to do advertising and has to strategically come up with a feasible selling process. What is HSS’s ideal marketing mix? 2. Dealing with Competition
In addition to the budget constraints, HMI cannot simply ignorethere are competitors that HMI cannot ignore. Using Porter’s vertical axis (See Appendix), the main competitioncompeting forces, which lies in the substitutes of HSS which are traditional cCall-out studies and individuals’ gut instincts (preference surveys). These conventional substitutes depended on human instincts and thus it makes consumers highly skceptical of the abrupt replacement with machines. Depending on the different uses of substitutesHowever, it is pertinent to note that levels of scepticism would differ across segments and in turn affect the adoptability of HSS. Thus, based on the target market, how should HMI needs to position itself using an optimal marketing strategy to minimize skcepticism and ensure highest possible profits?. Strategic Options:
Option 1: Target Unsigned artists (UA).The UA segment comprises of hundreds of thousands of hopefuls that dream of a shot at fame and who are willing to spend money to further their dreams. Value Drivers: For UAs, nothing gets in the way of pursuing their dreams. In addition, they form a mostly technologically savvy crowd who are willing to adopt new technologies like HSS. The potential of this segment is huge where 300-400 demos are sent to a record label per week. Approximately 187,200 demos (350 demos per week) are sent in per year to the dozen or so RCs under Universal Music Group for the US market (Pg. 5, P6). Risk Drivers: The core needs of UA is to get a contract from RCs, but as RCs might not align their tastes and preferences to the scoring criteria of HSS (Pg.12, P5), a good report from HSS does not directly translate to the UA being signed by the RC. UAs have financial constraints which have prevented them from seeking available avenues like Internet Polls and Call-out surveys that costs above $1000 (Pg.10, P1). The shoestring budget does not allow for extensive advertising as it is costly for outreach to the massive number of UAs (Pg.11, P4). Lastly, the reports may be too complex for UAs to understand, resulting in low adoptability. Option 2: Target Producers. This segment comprises 20-30 successful producers, few hundred producers with occasional hits and thousands of minor producers.
Value Drivers: The use of HSS can help to diminish uncertainty to a certain extent, giving producers a better gauge of the potential of their songs. Risk Drivers: There are a few noteworthy risks. TFirst, there is a disconnection with the primary needs of the producers. Producers need to create hit songs but HSS merely suggests whether the songs that have the potential to become hits, showing limited effectiveness in tweaking songs. Therefore, HSS can only be seen as a subordinate tool in meeting producers’ needs. Secondly, thereThere is a high level of skcepticism as producers pride themselves as artists with skills that cannot be replaced by a machine. Lastly, they might feel threatened that the software will replace their job and hence refuse to adopt it. Risk Drivers: The core needs of UA is to get a contract from RCs, but as RCs might not align their tastes and preferences to the scoring criteria of HSS (Pg.12, P5), a good report from HSS does not directly translate to the UA being signed by the RC. Thus, HSS is unable to meet the needs of this segment. Next, UA have financial constraints which had prevented them from seeking available avenues like Internet Polls and Call-out surveys that costs above $1000 (Pg.10, P1). Thus, this is a highly price sensitive segment. In addition, Polyphonic does not have the full registry of UA. The shoestring budget does not allow for estensive advertising as it is costly for outreach to the massive number of UA (Pg.11, P4). Lastly, the reports may be too complex for UA to understand, resulting in low adoptability.
Option 3: Target Record Companies(RC). This segment comprises of five major RCs with combined share of 84% in the U.S. market, each being home to at least a dozen labels. In addition, there are also tens of thousands of other small and midsized RCs (Pg. 6, P1). Value Drivers: The core needs for of RCs is to reduce fixed costs that mainly stem from marketing initiatives(Pg.6, P3). Hence, the core competency of HSS can directly address the by helping them filter hits and needs of RCs, which is to predict hit songs with high accuracy for better allocation of budgetallocate budget to support likely hits and generate higher expected revenues. In addition, RCs areis more a price insensitive, segment predisposed with higher budget and resources. A partial ACCORD analysis shows that this segment has high adoptability: HSS with an accuracy of 80% would be perceived to be more superior to traditional research methods of fairly low accuracy of 10% (Pg.10, P2&3). HSS is highly compatible as it suits the labels’ current behavior of sending songs for in-depth analysis in the form of reports. And since RCs have familiarity with intepreting reports, complexity is low. The massive use of HSS in the music industry will definitely allow HSS to get noticed and tributes will spread by word-of-mouth, ensuring high communicability. Risk Drivers: However, Record labelsCs under the same record company have significant collective buyer power to congregate and pressurize Polyphonic to reduce prices for their reports. In addition, major RCs are hierarchical and operate with red tape, so the process of implementing HSS would be cumbersome. Recommendation:
Option 3 is recommended. As the music industry faces a decline in album sales, the RCs are forced to practice caution in launching albums. Consumers have become more discerning with their product expenditure. Every album launch requires a gamble on the part of the RC – marketing expenditure of $300,000 and upwards do not guarantee success in recouping the costs and profitability. An axiom of the industry is that less than 15% of released music titles generate profit, meaning the bulk of marketing investment are moot. Therefore, HSS brings about intervention to support the inner workings of RCs. HSS sifts out the albums that do not make the cut from the manufacturing line, which avoids wastage of marketing budget. On the other hand, HSS could direct the RCs to focus their endeavors on potential hit albums by allocating the bulk of budget to support likely hits. As such, HSS helps the RCs to streamline marketing initiatives, and extract higher values from marketing investments. The RCs can then reap higher margins from a reduction of fixed marketing costs. With predictive accuracy of about 80%, RCs will perceive HSS as instrumental in turning their performance around. Justification of Recommendation
Positioning Statement: Balance between Science and Art
“Within the business of research technologies in the pop music industry available to RCs, Hit Song Science deviates from subjective preference sampling of limited individuals by juxtaposing the test song and mathematical attributes of past hits validated by masses. HSS is thus the new-edge scientific humanistic tool that engages “a million cultured ears” to increase the likelihood of producing hits and reduce marketing expenditure.generate unbiased and technical assessments of songs, regardless of the reputation of the artiste. ” Financial Attractiveness
Option 1: UA
Option 2: Producers
Option 3: RC
Target Volume
10,000 songs
6,500 songs
11,000 songs
Marketing Cost
$1.5/ Unsigned artist
$98.4/ Producer
$2,500/ label
(target top 5 first)
Break Even Price (BEP)
$95/ song
$130/ song
$89.1/ song
UC(10% above VC)
$30,000
$19,500
$33,000
Assumptions for all segments: Annual Fixed Cost=$500,000, Marketing budget= $150,000,VC per song = $30, 10 songs/artist, Figures are for North America.
Assumptions for UA: 100,000 unsigned artists (Pg.5, P6), Penetration Rate (PR)=1% Assumptions for Producers: 25 top producers(20 artists each) (Pg.7 P2),500 one hit producers(10 artists each)1,000 aspiring producers (1 artist each) PR=1% Assumptions for RC: 12 labels per record company (Pg.5, P7), 5 major RCs, 10,000 small RCs, 2,500 unique albums and 3,000 unique singles, PR=50% Unit Contribution
$30,000
$19,500
$33,000
Analyzing the financials of each segment, it corroborates that targeting major RCs (Option 3) is the optimal choice. The $150,000 marketing budget is sufficient due to the least outreach that needs to be marketed to (12 labels or 5 RCs). It justifies the higher arbitrary market penetration rate set at 50%. Comparing with Option 2 and 3, marketing budget will be overstretched due to the much larger base of interested parties. Eg: only $1.5 marketing dollars can be spent per unsigned artist which is probably close to production cost of a leaflet for promotion. Even though Option 1 and 2 have higher gross song volumes, the higher market penetration in Option 3 ensures the highest effective volume of songs that HSS technology captures (11,000 songs). Option 3 is also the most financially viable because it delivers the lowest Break-Even Price ($89/song) and highest Total Unit ContributionUC ($33,000).), assuming that each song is priced 10% above Variable Cost of $30 for all 3 options. Value Sharing between RCs and HMICreated by HSS for Record Companies To justify the premium pricing of HSS(see later), we first considered HSS’s value add for to the entire RC industry. It is based on Gross Value Add (GVA) to the industry ($1.32B) from two main sources: (1) Marketing Cost Savings and (2) Increase in Expected Revenues. Marketing Cost Savings
Before HSS
After HSS
Success rate of marketing to get hits1
10%
50%
Singles to “market” 2
1,500
300
Marketing and Promotion cost/single3
400,000
400,000
Total marketing cost
600,000,000
120,000,000
Marketing cost savings
480,000,000
Assumptions: (1) Pg.10 P3, Assume conservative 50% success rate instead of 80%. (2) Pg.9 P3, Assume HSS capture only 50% of 3000 new singles. (3) Pg.8 P3, Assume $300,000 marketing cost and $100,000 promotion fees (1): Usage of HSS dramatically reduces Marketing Expenses of newly released singles. Traditionally, there are about 3000 singles released per year, of which HSS technology captures half due to the 50% market penetration rate. Given that the HSS software increases success rate of marketing hits from 10% to 50%, we only have to promote 300 singles (instead of 1,500) to get 150 hits. Assuming each single is marketed and promoted at $400,000 per single (Pg.8 P3), tThis results in 80% decrease in marketing expenditure from $600M to $120M, generating potential cost savings of $480M. (2): Assuming hit success rate remains at 10%, there will also be an increase of expected revenues of $842M. With the implementation of HSS technology, there is now 80%there is 40% probabilityhigher predictability (50% instead of 10% based on A&R) that 1 out of 10 albums/singles marketed will become hits, generating increased probability-weighted expected revenues of $776.5M for albums and $65.8M for singles. Increase in Expected Revenues
Before HSS
After HSS
Hit Success Rate (Fixed)
10%
Success rate of marketing to get hits
10%
50%
Number of hit albums
125
125
Weighted Probability expected weighted revenues from hit albums (Table A) 176,250,000
881,250,000
Number of non-hit albums
1,375
1,375
Probability weighted revenues from non-hit albums (Table A)
17,875,000
89,375,000
Increase in expected revenues from albums
776,500,000
Number of non-hit singles
150
150
Probability weighted revenues from hit singles (Table A)Weighted expected revenues from hit singles 11,500,000
57,500,000
Number of non-hit singles
1,350
1,350
Probability weighted revenues from non-hit singles (Table A)Weighted expected revenues from non-hit singles 4,950,000
24,750,000
Total Increase in Expected Revenues
842,300,000
Assumptions: Hit albums = Hit Success Rate(10%)*PR(50%)*2500 unique albums=125 (Non-hit albums=2500-125) Hit singles = Hit Success Rate(10%) * PR(50%) *3000 unique singles=150 (Non-hit singles=3000-150) Probability weighted revenues from hit-singles/albums= Success rate of marketing
*[(1/3)*Low Estimate+(1/3)*Med Est+(1/3)*High Est] *Hit singles/albumsIncrease in expected revenues from singles 65,800,000
Sensitivity Analysis to Determine Optimal PricingAssuming hit success rate remains at 10%, there will also be an increase of expected revenues of $842M. With the implementation of HSS technology, there is now 80% probability (instead of 10% based on A&R) that 1 out of 10 albums/singles marketed will become hits, generating increased probability-weighted expected revenues of $776.5M for albums and $65.8M for singles. Sensitizing GVA
Market Penetration Rate
1%
25%
50%
75%
100%
Success Rate of Marketing to get hits
10%
0.00
0.00
0.00
0.00
0.00
11%
1,512,059
37,801,477
75,602,955
113,404,432
151,205,909
25%
13,517,250
337,931,250
675,862,500
1,013,793,750
1,351,725,000
50%
26,446,000
661,150,000
1,322,300,000
1,983,450,000
2,644,600,000
60%
31,057,500
776,437,500
1,552,875,000
2,329,312,500
3,105,750,000
80%
39,980,500
999,512,500
1,999,025,000
2,998,537,500
3,998,050,000
The sensitivity table proves that implementing HSS technology improves success rate of marketing, which in turn increases the GVA for the RCs. However, we observe that GVA is limited by the market penetration rate, depending on how best HMI utilizes its marketing budget to capture the largest possible portion of the market, reinforcing the importance of Promotion and Distribution strategies.its marketing mix. GVA was then utilized to derive the price ceiling, price floor and target price of HSS technology on a per song basis. Based on target price of $60,105 per song or $600,105 per album, HMI is able to achieve a profit margin of 99.85%.
Price Ceiling
Price Floor
Target Price
Assumptions
100% Penetration Rate
80% Success Rate
1% Penetration Rate
11% Success Rate
50% Penetration Rate
50% Success Rate
50% Share of GVA3
Price/song1
$181,730
$6,873
$60,105
Net Value Add to RCs2
($2.68 bil)
$1.32 bil
$661 mil
Profit Margin of HMI(PM)
99.97%
56.58%
99.85%
Assumptions: (1) GVA divided by target volume(market penetration * total volume of songs) (2) Net Value Add= Gross Value Add- Cost of Reports; Cost of Reports = Price per song * Market Penetration Rate * Volume of Songs (3) Assume that HMI will share value created with record companies equally The sensitivity table proves that implementing HSS technology improves success rate of marketing, which in turn increases the GVA for the RCs. However, we observe that GVA is limited by the market penetration rate, depending on how best HMI utilizes its marketing budget to capture the largest possible portion of the market, reinforcing the importance of Promotion and Distribution strategies. GVA was then utilized to derive the price ceiling, price floor and target price of HSS technology on a per song basis. Based on target price of $60,105 per song or $600,105 per album, HMI is able to
achieve a profit margin of 99.85%. To analyze the mutual benefit of implementing HSS, we analyzed HSS’s impact on Universal’s 2002 profit margin assuming it to be originally 30% after deducting all the relevant costs (Pg.8 P6). With the implementation of HSS technology target price and assuming that Universal claims 32% the $661M net value created (See Pricing of HSS), this results in 8% increase in profit margin. PM. Suggested Marketing Mix for HSS
Short Term: HMI should first target one major RC to the top Major RCs and hope to achievee industry awareness and accreditation of the technology and capture the dealing with at least one major Record Company. Patenting the HSS technlogy and giving competitive exclusive rights to one RC will diminish buyer power that major RCs hold when they congregate. SThe suggested Pricing is standardized at $60,000 per song. The Product is homogeneous and utilized on master recordings of established artists. Pertaining Promotion and Place, HMI would seek the management of RCs’ management via professional connections of HMI board members. Advertising leaflets are published to inform about the product. Salesmen and technical staffpersonnel would execute On-site Demonstrations to entice and enlighten interested RCs. Follow-ups are conducted to update RCs on changing market trends and consumer preferences. For further market awareness, “HSS guaranteed” stickers will be placed on record label albums to enhance visibility of product. Mid-term and Beyond:HMI would now target small/medium sized RCs, Producers and Unsigned Artists. The aim is to increase sales volume in other segments using the Market and Product Development strategy. Pricing is discriminated between Producers and Unsigned Artists. The Product is licensed as a software to major RCs, while reports are personalized and simplified for UAs and aspiring producers. Promotion and Place initiatives are done via personal selling and one-to-one consultation with RCs with the incentive of volume discounts. HMI can also offer trials of one unreleased song and four recently released songs to validate predictive and post-dictive accuracy. A chart release of Top 100 songs screened through by HSS technology and online retailers’ reviews increases product visibility. HMI will pursue the development of mobile apps and DIY online websites for unsigned artists and producers to encourage easier consumer adoption.
