appScatter — An innovative play on the app market

appScatter — An innovative play on the app market

appScatter is an early-stage business commercialising its cloud-based platform that allows app developers to distribute, manage and gather intelligence across multiple app stores globally. The recent public launch of the platform is expected to significantly boost licensed users and with a developed pipeline of registered interest, we forecast revenues to increase rapidly, bringing the group to EBITDA break-even during H119. Visibility on key variables should improve during 2018 and evidence of a successful launch could result in an upward rating of the shares.

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appScatter

An innovative play on the app market

Initiation of coverage

Software & comp services

28 November 2017

Price

54p

Market cap

£34m

$1.29/£

Net cash (£m) at 25 September 2017

6.5

Shares in issue

63.2m

Free float

64%

Code

APPS

Primary exchange

AIM

Secondary exchange

N/A

Business description

appScatter is a B2B mobile app distribution, management and intelligence company, whose cloud-based platform helps its customers distribute to and monitor more than 50 app stores globally. It is also building one of the largest repositories of app store data and plans to launch a marketplace to support third-party app integrations in early 2018. The company was founded in 2013 and listed on AIM in September 2017.

Next event

Trading update

March 2018

Analysts

Bridie Barrett

+44 (0)20 3077 5700

Katherine Thompson

+44 (0)20 3077 5700

appScatter is a research client of Edison Investment Research Limited

appScatter is an early-stage business commercialising its cloud-based platform that allows app developers to distribute, manage and gather intelligence across multiple app stores globally. The recent public launch of the platform is expected to significantly boost licensed users and with a developed pipeline of registered interest, we forecast revenues to increase rapidly, bringing the group to EBITDA break-even during H119. Visibility on key variables should improve during 2018 and evidence of a successful launch could result in an upward rating of the shares.

Year
end

Revenue
(£m)

EBITA*
(£m)

PBT*
(£m)

EPS*
(p)

DPS
(p)

EV/sales
(x)

EV/EBITA
(x)

12/16

0.0

(3.5)

(3.6)

N/A

N/A

N/A

N/A

12/17e

2.1

(5.6)

(5.6)

(10.2)

0.0

13.3

N/A

12/18e

6.8

(3.2)

(3.2)

(5.1)

0.0

4.1

N/A

12/19e

14.0

1.5

1.5

2.2

0.0

2.0

18.3

Note: *Normalised, excluding amortisation of acquired intangibles, exceptional items and share-based payments.

First-mover advantage in a vast market

Google Play and the Apple App Store dominate the app store market in western geographies, accounting for 85% and 69% of all downloads in the US and UK. However, the scale of the remaining app store market is also vast, particularly in geographies such as China. Within this ‘other’ market, appScatter benefits from first-mover advantage, already distributing apps to and monitoring their performance across multiple app stores globally including the majors.

Strong growth and high operational leverage

appScatter started licensing the platform with a soft launch at the beginning of 2017. Early momentum is good with c 150 paying licensees and £0.9m of revenues reported in H117. The public launch of the platform was on 22 November and funds raised in its September IPO are being used to ramp up marketing efforts. We base our forecasts on the group’s experience to date but factor in an element of caution regarding marketing effectiveness, volume discounting and the cost base. Around 10k accounts have registered an interest and we assume a relatively high take up initially as pent up demand is converted post launch, falling to a normalised level in Q218. Other than sales and marketing, the cost base is fixed in nature; consequently, operational leverage is high and we forecast EBITDA break-even during H119. On this basis, the group is comfortably funded.

Valuation: Hinges on successful public launch

Delivery to forecasts implies 4.1x EV/sales in FY18, falling to 2.0x in FY19 – less than half the rating of SaaS peers. As such, evidence of a successful launch of the platform and sustained take-up during 2018 could result in a re-rating of the shares. At this early stage, customer numbers and ARR levels are the most relevant metrics to track to determine the group’s prospects. Given the limited trading history, we use a 15% WACC in our DCF, which returns a value of 67p. However, as the group’s track record becomes established we would expect the discount rate to decrease towards 12.5%, returning a value of 89p.

Investment summary

Tapping into a vast addressable app store market

appScatter is an early-stage B2B mobile app distribution, management and intelligence company. Its cloud-based platform helps its customers (app developers and publishers) to publish to and monitor more than 50 app stores globally, with more being added. Beyond the key Google and Apple app stores, the global app store market is fragmented and monitoring performance on an individual app store basis can be burdensome. appScatter’s SaaS-based platform enables customers to register each app once, for multiple distribution, thereby overcoming the technical challenges of distributing, updating and monitoring their apps across multiple stores. With its data mining capabilities, this is enabling the company to build one of the world’s largest repositories of multi-store app ranking data, which it plans to monetise from next year. The planned launch of a marketplace for add-ons in Q118 is another potential source of revenue. The group listed on AIM in September 2017, raising a net £7.4m, and the public launch of the platform was on 22 November.

Forecasts hinge on successful public launch

appScatter began to license its platform on a restricted basis in January 2017. Maiden revenues of £0.9m were reported in H117, and as of June annualised recurring revenues (ARR) were £1.7m, providing a starting point for forecasts. The group has also built a strong pipeline of potential licensees; 10,000 accounts have registered an interest in using the platform (on a free basis) and management anticipates a high conversion rate to paying licensees post the public launch of the platform. In addition it has developed an extensive database of targets. Following launch, we expect the company to ramp up its marketing efforts, and widen its network of strategic partners.

Given the limited history, we base our forecasts on the group’s experience to date but factor in an element of caution regarding marketing effectiveness, price discounting and the cost base. We assume from 2018 that the cost per acquisition (CPA) is 50% above that experienced in test marketing and that the conversion rate (ie from accounts that have registered an interest in the platform to licensees) drops from 4% to 2% after four months. In 2019, we assume that marketing spend is increased even further and to support forecast rapid growth we assume an increase in headcount from 33 to 61 and a 20% increase in other costs. The business model should be highly scalable and, due to this high degree of operational leverage, we expect the company to achieve operating margin break-even during H119, with strong operating profitability by H219 (22% margin). Although this rapid progression towards profitability is unusual among SaaS-based peers, we note that appScatter’s profile is most closely aligned to Atlassian (see page 19), which shares a similar cost structure. We summarise the key drivers underpinning our forecasts in Exhibit 1 below. Given the early stage of the company, we also demonstrate the impact on our forecast should we flex certain key assumptions: price (for instance if volume discounts are offered) and conversion rates (3% rather than 2%).

Exhibit 1: Summary forecasts and scenarios (£m)

Flex conversion rate

FY17e

FY18e

FY19e

Edison forecasts

FY17e

FY18e

FY19e

Flex pricing

FY17e

FY18e

FY19e

Revenues

2.1

7.7

18.7

Revenues

2.1

6.8

14.0

Revenues

1.8

5.1

12.5

Gross margin

(6%)

50%

73%

Gross margin

(6%)

44%

65%

Gross margin

(21%)

25%

60%

EBITDA

(5.3)

(2.1)

6.2

EBITDA

(5.3)

(2.9)

1.8

EBITDA

(5.5)

(4.5)

.2

EBITDA margin

(254%)

(27%)

33%

EBITDA margin

(254%)

(43%)

13%

EBITDA margin

(303%)

(88%)

2%

Operating profit

(5.6)

(2.4)

5.9

Operating profit

(5.6)

(3.2)

1.5

Operating profit

(5.8)

(4.8)

(0.1)

Operating margin

(269%)

(31%)

31%

Operating margin

(269%)

(48%)

11%

Operating margin

(320%)

(94%)

(1%)

ARR

2.03

12.15

24.13

ARR

2.03

9.81

17.51

ARR

1.36

8.14

16.2

Registered users

12,369

44,758

90,102

Registered users

12,369

44,758

90,102

Registered users

12,369

44,758

90,102

Licensees

210

1,129

2,195

Licensees

210

900

1,586

Licensees

210

1,129

2,195

Conversion rate

 

3.2%

3.0%

Conversion rate

2.5%

2.0%

Conversion rate

 

3.2%

3.0%

DCF 15% WACC

107p

DCF 15% WACC

67p

DCF 15% WACC

51p

DCF 12.5% WACC

103p

DCF 12.5% WACC

89p

DCF 12.5% WACC

70p

Source: Edison Investment Research

Valuation and key investment considerations

Based on these forecasts, appScatter trades on an FY18 EV/sales multiple of 4.1x, similar to its larger (more mature) SaaS peer set, although on a premium to other UK software groups. With the strong forecast revenue growth, this premium would convert to a significant discount to SaaS peers in FY19 when it trades on 2.0x sales (peers 5.1x) and 15.1x EV/EBITDA (peers 35.1x).

The valuation opportunity hinges on the successful commercial launch of its platform. As a first mover in this area, there is currently little visibility regarding the level of demand for its services. However, 10,000 accounts have registered an interest and visibility on some of the key variables will start to improve as the group moves into 2018.

We have tried to capture early-stage uncertainties by using a higher CPA than that experienced to date by management, factoring in an element of price discounting and assuming a 20% increase in the cost base in 2019. We also use a relatively high 15% discount rate in our DCF and assume that after the initial forecast period, revenue growth recedes to a CAGR of c 20% to 2027 (and 2% in perpetuity). This returns a value per share of 67p. On evidence of the demonstrable success of the platform, we would expect the discount rate to come down; we generally use a WACC of 12.5% for technology groups that have demonstrated demand but are still loss making, which would return a DCF of 89p. Investors should also consider:

Large global addressable market: appScatter’s solution is targeted at any company that publishes apps – a potentially very wide addressable market ranging from companies whose app is the basis of their services (games or m-commerce), to those that publish apps to support their service (retail, finance, etc). According to Gartner, there will be 269bn app downloads in 2017, and developers are adding approximately 400k new apps a year to the two largest app stores: Google Play and the Apple App Store. Although these two app stores dominate downloads in the US and Europe, the remaining market opportunity is considerable, particularly in other geographies such as China. appScatter’s investment case rests on its ability to license customers wishing to also tap into this ‘other’ segment of the market. Additionally, the product is able to assist with different iterations of an app (geographical, language, etc) within the same app store, which is particularly relevant for Apple App Store and Google Play

Little direct competition: The appScatter platform is distinct from current offerings that focus on the two largest app stores, Apple’s App Store (iOS) and Google Play (Android).

High operational leverage: We have built an element of caution into our forecast cost base and assume EBITDA margins peak at 25% after three years. Management believes the large majority of costs are fixed in nature, which could mean margin could continue to expand.

Additional revenue streams: Our forecasts do not factor in growth from a number of areas being explored by management. For example, we understand from management that it is at an advanced stage of negotiations with a potential strategic sales partner, which has relationships with over 200k app developers. Were this partner to convert even a small percentage of the developers, this could have a material impact on forecasts. Furthermore, our forecasts do not include potential revenues from the data API or the marketplace the group plans to launch in Q118. Over time, these products have the potential to also contribute a material amount of high-margin revenues.

Sensitivities: A nascent business with limited visibility

Given the early-stage nature of the business there is a higher than average degree of sensitivity to our forecasts, particularly in relation to the pace of adoption, the cost to acquire customers, the rate of conversion to paying customers, renewal rates and the price of the service. Other risks include potential competition in the future, as well as currency risks, as appScatter is planning to switch its pricing from sterling to US dollars. Should the business not proceed as anticipated, appScatter may require further funding to achieve cash flow break-even.

Company description

appScatter is a B2B mobile app distribution, management and intelligence company. Its SaaS-based platform helps its customers, which include app developers and app publishers, overcome the technical and fragmentation challenges of easily distributing, updating and monitoring their apps across more than 50 app stores globally.

The platform is distinct from other offerings that tend to focus on the two largest app stores, Apple’s App Store (iOS) and Google Play (Android). From a single point of registration, appScatter’s platform offers the widest reach in terms of automated app store registration and distribution. With its data mining capabilities, this is enabling the company to build one of the world’s largest repositories of multi-store app ranking data. Across the platform it tracks c 977m unique app URLs from 8.6m apps and monitors approximately 2.1m app publishers. This data could provide valuable intelligence to companies regarding their, and their competitors’, app performance and in addition to licensing the platform, management plans to start to sell this data.

Management restricted access to the paid services on the platform in anticipation of the public launch, which was on 22 November. As at 27 September 2017, c 10,000 publishers had registered an interest in using the platform. At 31 July 2017 it reported over 800 paying users with over 3,000 apps on the platform. Customers range from individual developers to large enterprises and some significant customers include Allianz, City AM, Emirates and Lab Cave.

In Q118, the group also plans to launch a marketplace for add-on services that will enable its users to integrate a wide range of third-party solutions such as in-app analytics, optimisation and monetisation solutions or revenue factoring services (among others). As well as making the appScatter product stickier, this has the potential to provide an additional source of income for the group.

Company background

CEO Philip Marcella founded the business in 2013. Philip, who is an experienced programmer, has worked in app development since 2002, running a team that has built hundreds of apps across a range of markets, including utilities, games, children’s books and augmented reality. Concurrently, he recognised the time-consuming nature of app publishing and his team began to build a tool to automate the process and which would also provide clients with access to consolidated sales data across multiple app stores. This same team has been instrumental in the development of the appScatter platform.

In 2013, appScatter LLC was formed in the US, where it began researching and testing apps on alternative app stores. The company recruited its first engineers in 2014. In 2015, the company created a proof of concept for submitting apps and gathering download data across multiple app stores, thereby harvesting data from millions of apps across multiple app stores. appScatter Limited (UK) was formed in 2015, and it acquired appScatter LLC in May 2016.

In 2016, the appScatter platform underwent beta and usability testing, and a sales and marketing team was created. The company introduced the platform at the Berlin app summit in November 2016.

September 2017 AIM IPO

Prior to IPO, approximately £12.6m had been invested in the business, largely through several private placements. On 5 September, appScatter listed on AIM, raising £9.0m gross proceeds at 65p per share (£7.4m net of IPO and fund-raising costs). Post the IPO, Philip Marcella owns 26% of the shares and the company has a 64% free-float.

The intention is to use the proceeds of the IPO for working capital (£4.8m), in particular to fund the growth of the licensed user base, as well as platform investment (£1.7m around the launch of the marketplace) and sales and marketing (£1.3m).

appScatter platform

Until recently, the platform was only available on a restricted basis, with some services offered for free and a wider range of services only available to a select number of licensees. The public launch of the platform has added a number of additional features aimed at improving the user experience and increasing subscriber retention.

Platform capabilities

The appScatter cloud-based platform, which runs across more than 600 servers, is integrated into and collects publicly available data from 50 of the world’s largest app stores (out of a total of approximately 300). It is now tracking more than 8.6m mobile apps and 977m individual app URLs1 from approximately 2.1m publishers worldwide. The platform integrates a telemetry system to power the backend. Data tracked includes revenues, downloads, ranking, territories covered, meta data, pricing and app-specific details including publisher names.

  Each app has multiple versions to cover all app stores and all geographies in which it is published – each version has its own unique URL.

In terms of platform infrastructure, Amazon Web Services is used for data storage and MongoDB for data sets. Riak Time Series databases are used for app ranking data and Amazon Elastic Search is used in enabling appScatter’s search engine.

Core services

The core services currently offered include app store distribution, app store rankings and competitor intelligence.

App store directory and search engine: The appScatter directory provides details on each of the 50 app stores that it supports. This includes descriptions of registration and submission processes, reporting procedures, territories covered, size and in-app billing capabilities. Using the appScatter search engine, users can access details on apps and publishers, including ranking position, territories covered, all metadata, pricing and app specific information such as publisher name.

App store registration and distribution: Once registered with appScatter, subscribers have a single point of access to app stores worldwide. appScatter checks for an app’s compatibility with its supported stores and enables its customers to choose to publish apps either by country, device type, language or store. Where an app is incompatible with an app store, the platform will notify the user with the specific reasons such as lack of a brief description or insufficient graphics (Exhibit 2). appScatter currently distributes to 50 stores worldwide, avoiding the requirement for individual registration on each app store. The company is adding four to five stores each month and expects that it will level out at c 100 stores (ie those that are the most efficient and productive for customers to use).

Analytics and ranking: Users can monitor their own app’s sales or download performance by store (which in turn can drive their marketing strategy). It also enables users to quickly check, in one place, an app’s territorial compliance, brand consistency, etc. Performance reports can be segmented by geography, app store or device type. This information can either be accessed via the appScatter dashboard, or via integrated third-party analytics tools (eg Google analytics).

Intelligence: Users can create a customised watch list of competitor or interesting apps or segments in order to monitor market share or other industry trends.

Monetisation: The recently launched appScatter Android software development kit (SDK) enables app developers to conveniently integrate in-app billing to all Android stores integrated on its platform: for instance, Amazon, Bemobi, Samsung and Google Play. This enables in-app purchasing across all app stores, including those that do not currently support this form of monetisation. Where a store does not specify its own in-app billing method, appScatter’s own in-app billing will be used as default. This should appeal to app developers as it has the potential to extend the monetisation of their apps. Unique to appScatter, this is an open-source initiative that management believes will support the wider use and awareness of the appScatter platform.

Exhibit 2: App store directory (top left), App publisher dashboard (top right), App ranking (bottom left), App submission dashboard (bottom right),

Source: appScatter

Freemium business model

The platform is available to use for free on a limited basis, with a wider range of services available to licensed users. appScatter segments its licensees into three categories – small, medium and large – with the categories differentiated as follows:

Exhibit 3: Client types

Client type

Number of apps

Typical number of users

Examples*

Typical annual contract value ($)

Client objectives

Free

0

Small

1

1-5

Independent developer

1,200

Smaller businesses have a primary goal of increasing downloads and sales of their app.

Medium

2-99

5-50

Dev house, marketing agency

1,200-60,000

This category includes larger independent businesses that have multiple apps, but also dedicated app development houses, marketing agencies and industry analysts. In addition to greater monetisation opportunities, these users will benefit from the ability to maintain, monitor and update their large app portfolios, eliminating significant manual effort. 

Large

100+

15+

Global insurance company, global car manufacturer

60,000-150,000

Larger institutions may gain more utility in the maintenance and monitoring of their app portfolios as opposed to monetisation. They can use the appScatter platform to ensure their apps are only distributed where they are compliant with regional regulation, while it also helps them maintain brand consistency across their app portfolio.

Source: appScatter, Edison Investment Research. Note: *Based on real customers.

Pricing: Subscription model based on ‘seats’ and apps

In keeping with other SaaS companies, appScatter charges its customers a regular subscription fee to use the platform. The charge is based on both the number of users ($12 per ‘seat’ per month) and the number of managed apps ($60 per app per month). The minimum sign-up is for a small client account, which would be charged as five users and one app for a total cost of $120 per month. Additional monetisation could occur through the marketplace of integrated third-party software, though the pricing of these products will vary, and in many cases will not generate incremental revenues but instead should boost retention.

Growth strategy

With the proceeds of the fund-raise management plans to increase its marketing spend to drive user acquisition, as well as hiring to support user growth and continue developing the platform.

Sales strategy: Direct and partner-based

Test marketing began in November 2016 to determine customer cost per acquisition (CPA) over social media, Google AdWords and select conferences such as QCon and App Promotion Summits. Social media and Google AdWords tend to attract smaller customers, whereas the conferences are more fertile ground for developers and enterprise customers. To accelerate user acquisitions, appScatter is also looking to form strategic partnerships with other analytics and mobile advertising networks that serve a similar customer base. It is in discussions with a number of potential partners, whose own users would be able to access their data through appScatter. In some cases, appScatter would pay a commission on revenues generated by these partners.

Public launch of the platform

As of 31 July, appScatter had c 150 paying licensees (which equate to c 800 users or seats managing c 3,000 apps). In addition, there are currently approximately 10,000 users that have registered an interest in the service. Following the launch of the platform, the focus is on converting the existing registered users to licensees. Management has indicated that there is a degree of ‘pent up demand’ from the existing registered user base and consequently in the first few months post launch we would hope to see a steady increase in new licensees to the platform. In addition appScatter has a database of approximately 1.7m names, which will be used to target potential users of its platform over the medium term.

Geographic expansion

appScatter currently has three employees in the US and the remainder in the UK. On 1 July, the company opened an office in Berlin, initially starting with two developers from its outsourced provider. In time, the company would like to open an office in China to access the domestic market.

Product road map

The company has plans to increase and improve the services offered on the platform, including:

Marketplace: In Q118 the company plans to open a marketplace where users can directly integrate with other services such as Adobe Analytics, Airpush, JIRA, Flurry, Salesforce, Slack, Xero, etc. By integrating with a wide range of apps, management believes that it will increase the stickiness of the core service. For instance, users could integrate their own ad-networks enabling them to monitor in-app behaviour, return on marketing investment per app store, in-app purchases, etc, to get a fuller picture of the effectiveness of an app’s marketing campaign on a store-by-store or market basis. Another add-on under consideration relates to the factoring of advertising revenues, which could appeal to publishers generating a significant share of advertising revenues from Asia where payment cycles can be protracted. In total, management plans to introduce approximately 300 third-party add-ons over the next 12 months and will target marketing at existing users based on their individual profiles. Add-ons will include a wide range of free and paid for services and over time could provide a valuable additional revenue stream for the group. For example, enterprise software company Atlassian (workflow management and collaboration tools), generates c 6% of its total revenues (c $37m last year – stated net of 75% commissions to providers) from the 3,000 or so add-ons that are available in its marketplace.

Data API: appScatter is becoming the world’s largest repository of app store ranking data and in early 2018 it plans to launch a data API. This will enable customers or data companies to access the vast amounts of publisher data it collects. This data could prove very valuable for a company’s marketing intelligence. Initially it plans to sell the raw data, which is likely to appeal to its larger enterprise customers or data companies that have their own business intelligence (BI) tools. Further down the line, it plans to develop its own BI interface, which would make this data accessible for all of its customers.

Telemetry services: Telemetry functionality is already integrated into the back end of the platform, to enable the rapid access and sorting of data. Telemetry functionality is being extended early next year to further improve the dashboard analytics capabilities for its customers (eg to enable advertising and sales overlay analysis). Ultimately management is working towards the full integration of a telemetry offering into the apps for both frontend (eg number of downloads, revenues) and backend analytics (eg load times, memory usage).

App reviews and translation: The platform is being translated into several languages, with the initial focus on German, followed by Chinese.

Market: The app economy

A vast global download market

appScatter’s solution is relevant for any company that needs to publish apps. This can range from companies where the app is their primary source of income (for instance games developers, entertainment services), or companies where apps are provided to customers to support their wider business case (eg retail companies, restaurants, financial services groups, etc).

The market for mobile goods and services is already a vast market and is forecast to continue to grow rapidly. App downloads are ultimately driven by increased smartphone usage, itself a function of continued improvements in connectivity as operators move towards universal 4G coverage, the availability of affordable handsets (particularly in the fast-growing developing markets), and improvements in the quality and range of services on offer as technologies improve (eg HTML5, Google’s Accelerated Mobile Pages). Furthermore, greater consumer acceptance of mobile payments and the deregulation of the mobile payments markets, particularly carrier billing, is opening up the market to a wider range of digital goods. Almost half the world’s population has internet access (3.4bn, eMarketer) with approximately three-quarters using a mobile connection, of which almost half is via a smartphone (47.4%), and penetration is forecast to continue to expand at a steady rate. In addition, tablets, wearables and connected TVs are all gaining in popularity.

Analytics group App Annie estimates that in 2016 consumer spend on apps was $62bn and forecasts this to increase to $139bn by 2021. The vast majority relates to games (81% of consumer spend via stores in 2016), although in-app subscription revenues for non-gaming services are forecast to grow even more quickly over the next five years (25% CAGR). Many non-game apps also monetise through advertising or m-commerce, both of which account for a significant share of industry revenues. In 2016, global mobile advertising revenues surpassed desktop to reach $99bn with 27% growth forecast in FY17 (eMarketer) and m-commerce is now almost a trillion dollar market (Euromonitor, 2016).

However, these statistics only relate to the monetisation of apps. In today’s app economy, many businesses distribute apps not for direct monetisation reasons, but to widen their reach or to improve their service offerings. According to Gartner, there will be 269bn app downloads in 2017, up from 102bn in 2013, and developers are adding approximately 400k new apps a year to the two largest app stores, Google Play and the Apple App Store (Statista). AppScatter estimates that there are c 10m apps available to download in app stores, but over 1bn unique URLs from over 6m active publishers; it is already tracking c 90% of the entire market.

Exhibit 4: Number of apps available – Apple App Store

Exhibit 5: Number of apps available – Google Play

Source: Statista2

Source: Statista3

Exhibit 4: Number of apps available – Apple App Store

Source: Statista2

Exhibit 5: Number of apps available – Google Play

Source: Statista3

Relevance of ‘other’ app stores

The Apple App Store and Google Play are clear market leaders in terms of revenues and downloads. Respectively they have 2.2m and 2.8m apps available (Statista, Exhibits 4 and 5) and, in the US and the UK they account for 85% and 69% of all downloads. However, given the scale of the market, the number of downloads from the ‘other’ category (of c 300 stores) remains significant, particularly outside the US and Europe. China is the world’s largest app market and because most of Google’s services are banned, other platforms account for approximately 75% of all downloads in China (Exhibit 6).

appScatter estimates there are over 300 legitimate app stores worldwide. The lack of independent analytics outside the App Store and Google Play makes it difficult to provide an accurate picture regarding how relevant the other app stores are to developers. However, appScatter’s research, as well as data that we have gathered from other app stores, suggests that as the two giants become over crowded, it is the ‘other’ category that is gaining in relevance. We note below various categories of stores:

Telecoms operators: MTN Play, TIM Store, Vodafone, Airtel Sri Lanka, AT&T App Centre, Turkcell T-Market.

Device platforms: iOS (2.2m apps – Statistica) and Android (2.8m apps – Statistica), Windows (670k – Microsoft by Numbers) and Symbian.

Independent stores: Getjar (0.8m – Business of Apps), Bemobi (0.3m – Wikipedia), Mobango (0.1m apps – Business of Apps 2015), Amazon (0.6m apps – Statista), Wandoujia (200m users – Business of Apps 2015), Tencent (Myapp – 250m apps downloaded in March 2017), Baidu, IMobile (0.8m apps), Aptoid (0.23m apps – March 2017, appScatter white paper), Yandex (0.1m apps).

Manufacturer specific: Samsung Apps, LG Smart World, CISCO app HQ, Lenovo.

The search driven nature of major app stores, which have millions of apps on offer, means that, for all but the leading apps, being discovered can be difficult and expensive.

Alternative stores can improve app discovery as they are less crowded, often drive higher conversions for niche or specialist apps, may have higher daily users in specific geographies (particularly in Asia) and often have less restrictive in-app advertising policies. For instance, a 2013 statistic from the One Platform Foundation suggests that the chances of an app being featured increased by 20-fold on an alternative app store. Similarly, according to the 2013 report ‘Apponomics’,4 data provider Xyo shows that 75% of apps in its search engine get 90% of the downloads, whereas for the major app stores it is more likely to be 10% of apps driving 90% of downloads.

  Apponomics – The insider’s guide to a billion dollar app business. Peggy Anne Salz, in partnership with InMobi.

Exhibit 6: App store market share by geography

Exhibit 7: Revenues from app stores by market – China larger than the US

Source: appScatter

Source: App Annie (App Annie’s Market Forecast 2016-2021)

Exhibit 8: Downloads by store

Exhibit 9: User data from selected alternative app stores

Source: App Annie (App Annie’s Market Forecast 2016-2021). Note: App Annie does not track all ‘other Android’ stores; it is not clear how many stores are included in this analysis.

Source: appScatter

Exhibit 6: App store market share by geography

Source: appScatter

Exhibit 8: Downloads by store

Source: App Annie (App Annie’s Market Forecast 2016-2021). Note: App Annie does not track all ‘other Android’ stores; it is not clear how many stores are included in this analysis.

Exhibit 7: Revenues from app stores by market – China larger than the US

Source: App Annie (App Annie’s Market Forecast 2016-2021)

Exhibit 9: User data from selected alternative app stores

Source: appScatter

Issues faced by app developers and publishers

Complex and fragmented app store process

By limiting app distribution to the largest stores, app owners are restricting their reach, but the challenges of managing the wider publication of an app are often seen as too complicated to justify the effort.

We outline what we consider to be the key stages in an app’s journey in Exhibit 10.

Exhibit 10: The complex journey of an app

Source: Edison Investment Research. Note: appScatter’s services in bold.

App store registration and distribution

For every ‘unique’ app published, there may be tens or hundreds of different versions (instances) created in order to be compatible with different programming languages, operating systems, as well as variations for different geographies (eg for language or compliance reasons) or for adherence to differing app store policies. Publishers will also need to ensure that local legal requirements are adhered to on a store by store basis (such as tax rules).

For instance, a unique app that is published across 10 markets on five different app stores could have 50 versions. For enterprises that publish more than a few apps per year, this can mean managing hundreds of versions of their apps. Developers/publishers will need to register themselves with each app store. Individual approval of each version of an app is required by app stores, a process that can take up to a week. The task of distributing to app stores is generally managed by internal teams and can become resource heavy. Consequently, they focus on stores with the largest market shares, typically just Google Play and the Apple App Store.

Once published, companies need to monitor these apps, not only in terms of performance, but also compliance to a specific company’s policies or branding.

Existing solutions that enable app owners to publish across a number of stores tend to limit services to Android environments where there are many alternative stores to Google Play. For instance, CodeNgo enables distribution to approximately 30 Android stores, and Digital Turbine distributes to eight stores. An open-source initiative was started in 2013 by Opera, SlideME and CodeNgo called The One Platform Foundation for the Android ecosystem although this does not appear to have had much impact. None of these solutions, however, provide feedback on an app’s performance, ranking data or market intelligence.

App ranking and market intelligence

A further bottleneck relates to app store ranking data and market intelligence, which again tends to be restricted to Apple’s App Store and Google Play.

App Annie’s much cited consumer index shows which apps are most frequently downloaded from App Store and Google Play. Within these stores, data is collected on apps that run the App Annie API, and hence it excludes proprietary apps made by Apple, and some other larger apps such as YouTube. Nevertheless, 90% of the top 100 global app publishers use App Annie, equating to 1,000,000 apps, which provides a deep pool of data. Due to its third-party integrations, App Annie can also offer a range of more sophisticated app analytics (in-app analytics, return on marketing investment), as well as app store optimisation (ASO) tools. However, unlike appScatter, App Annie is not involved in the distribution of an app.

Other analytics players include Yahoo’s free mobile application analytics tool for Google Play and the Apple App Store: Flurry, which is used by 170k developers across 500k+ applications. Flurry offers detailed in-app analytics, although no ranking data as far as we can tell. Other notable offerings include Applyzer, which provides ranking data across iOS, Mac apps, Apple TV and Google Play; Google Analytics (Google Play only); Sensor Tower; Apptopia; Appcodes; MetricsCat; appfigures; AppsFlyer; delta DNA; AppMonsta; and Digital Turbine.

appScatter’s solution

In summary, there does not appear to be any competing solution available that supports both the distribution into and the ranking of apps across as wide a network of stores as appScatter’s.

Through the planned marketplace, appScatter’s licensees will be able to integrate a wide range of applications, which would enable them to consolidate multiple tools into one dashboard.

Management

The appScatter team has worked together in various guises for over a decade, largely led by Philip Marcella, providing consistency and depth of knowledge to the product suite.

Philip Marcella, CEO: Philip is a serial entrepreneur and seasoned British programmer. He started his first company in 1996, RMR Plc, and listed it on the London Stock Exchange in 2000 for £70m, raising £20m, with 300 employees and offices in both the UK and the US.

Manish Kotecha, CFO: Manish has over 14 years of experience in the role of CFO/finance director for several publicly listed companies. During this time, he has been involved in numerous fund-raising initiatives both for equity and debt exceeding £200m. His key strengths include developing and implementing robust internal controls, reporting and compliance procedures suitable for growth companies.

Clive Carver, non-executive chairman: Clive is a chartered accountant and worked with Coopers & Lybrand, Kleinwort Benson and Price Waterhouse Corporate Finance before becoming head of corporate finance at Seymour Pierce, Williams de Broe and finnCap successively. He is a qualified corporate treasurer and also a non-executive director of several AIM-listed companies.

Michael Buchen, non-executive director: Michael serves as a director on the boards of a number of companies in the telecom, energy, financial services, software, automotive and food & beverage sectors. Previously he was the managing director of Dhabi Holdings PJSC.

Jason Hill, chief revenue officer: Jason is a B2B sales professional with 20 years’ experience driving fast growth digital sector technology companies. He was the sales director behind Philip Marcella’s previous listed company RMR Plc.

James Eggleston, chief technology officer: James’s previous roles include head of infrastructure and security at Attest, head of technical operations at Nektan plc, and head of IT infrastructure at HH Global.

Financials

appScatter started licensing its platform at the beginning of 2017 and the platform has been revenue generating since January this year, reporting maiden revenues in its H1 results of £0.9m on an operating loss of £2.2m. At the end of June, appScatter was generating annualised recurring revenues (ARR) of £2.1m from c 150 licensees; we have used the platform’s pre-launch experience as a starting point in our forecasts although with limited historical financials, there is a high degree of risk to future forecasts.

We detail our main assumptions below, and given the level of uncertainty surrounding key inputs at this early stage in the company’s development, we also demonstrate what the impact would be on our forecasts should we flex certain key variables such as the conversion rate of registered users to paying licensees and price.

Basis of modelling

Revenue forecasting methodology; marketing driven

We have built up our forecasts using the following assumptions:

We take the most recently published figure for accounts registering an interest (ARI) in the service and factor in a modest growth assumption up to the public launch of the platform.

Post the commercial launch of the platform, we assume that direct marketing efforts will be increased with direct CPA monthly marketing spend rising to £50k in FY18 and £70k in FY19. The number of ARIs that this level of spend generates will be driven by the average CPA (cost of acquisition). We apply a conversion rate to the new registered users each month to estimate the number of new licensees each month.

The group’s experience to date implies a CPA per registered user of £12.35 and a conversion rate of 4.75% per month in FY17, which management believes may settle at approximately 3.00% per month from FY18 once some of the ‘pent up demand’ is converted. Assuming the largely fixed cost profile outlined above, this would convert to a c 33% EBITDA margin in the group’s second full year of trading post launch. As we discuss on page 16, over time, most SaaS companies spend a high share of revenues on marketing. Looking at the financial performance of established SaaS software companies, it is unusual to see revenue growth and strong operating margin growth so early in a company’s existence. Consequently, in our forecasts, while we believe the pent up demand may result in an initially higher conversion rate (we assume 4% for the first four months post launch) we have built in an element of caution and assume that the cost to acquire registered users will increase by 50% once the “low-hanging fruit” has been harvested and that the conversion rate drops to 2% after four months.

We assume a split by customer type of 53% small, 32% medium and 15% large – this is based on the company’s experience in H117. Small customers have five users and one app per licensee; medium and large customers have 10 users and 50 apps per licensee.

We forecast subscription revenues using a monthly charge of $12 per user and $60 per app (shifting from £10 and £50, respectively, in November), with a 30-day free trial period. We have factored in a 20% discount to this price to take account of potential volume discounts for larger customers.

We factor in a churn rate of 1.5% per month from the start of FY18 (customers are signed up for a minimum of 12 months).

Cost base fairly fixed in nature

Bar direct marketing and sales commissions, management believes that the large majority of costs should be fixed in nature. Cost of sales (£0.9k in H117) consist of engineering (eight employees), the cost of hosting the platform and mining the data, software for running and securing the platform, and engineering costs related to the ongoing use of the platform.

Operating expenses include staff costs (in addition to engineering headcount there are 16 in sales and marketing, four in admin, and five board members) and the cost of using external services providers related to the development of the platform. The services component within cost of sales is budgeted to drop in FY18, reflecting a lower level of development work being outsourced and the front-loading of work to strengthen the platform prior to public launch. Furthermore, until recently, the company has been incurring the cost of manually publishing apps to those app stores that had not been automated. Developer work to automate the interaction with all 50 supported app stores has now been completed so these costs should also start to come down. We assume that much of this saving will be re-invested in additional employed engineering staff. Sales and marketing costs reflect the £50k per month we assume for direct marketing (CPA) and a similar monthly amount for indirect marketing costs (conference attendance, brand building) and sales commissions (we assume 10%). Total headcount stands at 33; we forecast this to rise to 47 by the end of FY18. To support growth, we have assumed that the cost base will need to grow in FY19 and have factored in a 20% increase in non-staff costs, as well as an increase in headcount to 61.

Edison forecasts: Rapid expansion in margins

These assumptions result in very strong revenue growth with gross margins expanding to 65% in FY19 and EBITDA profitability achieved during H119.

Exhibit 11: Edison forecasts

£m

FY17e

FY18e

FY19e

Revenues

2.1

6.8

14.0

Revenue growth

227%

107%

Gross profit

(0.1)

3.0

9.1

Gross margin

-6%

44%

65%

EBITDA

(5.3)

(2.9)

1.8

EBITDA margin

-254%

-43%

13%

Operating profit (excluding SBP)

(5.6)

(3.2)

1.5

Operating margin

-269%

-48%

11%

Sales & marketing/revenues

48.6%

24.5%

14.9%

Cost of sales

2.20

3.81

4.87

Growth in cost of sales

72.9%

27.9%

Operating expenses (excl. D&A)

5.13

5.87

7.29

Growth in opex

14.6%

24.1%

Annualised MRR

2.03

9.81

17.51

Registered users

12,369

44,758

90,102

Licensees

210

900

1,586

Users

1,547

6,615

11,660

Apps

5,058

21,626

38,120

CPA

15.00

18.53

18.53

Conversion rate

N/A

2.5%

2.0%

Monthly churn

0.0%

1.5%

1.5%

Net cash/(debt) at year end

3.4

1.5

2.9

Source: Edison Investment Research. Note: Conversion rate is a simple average of monthly figures.

Balance sheet and cash flow: Comfortably funded

The company had no tangible fixed assets on the balance sheet at H117 and expects to spend a small amount on IT equipment for staff (all of which will be expensed) over the whole of FY17. The company does not capitalise development costs; however, it acquired the IP for the appScatter platform from appScatter LLC in 2016 for £0.8m, which is being amortised over three years.

At H117 there was a high receivables balance of £2.9m. This includes £1.1m owing for shares issued in FY16 for services to be provided in FY17 (down from £4.8m at the end of FY16) and a £0.6m prepayment related to IPO fund-raising costs. Trade receivables amounted to £0.6m and £0.1m reflects accrued income (together over two-thirds of H1 revenues). Management expects this to be substantially recovered by the year end and automated invoicing and payment will be introduced with the public launch of the platform, so we have assumed that receivables days normalise to 30 days.

At the end of H117, appScatter had £1.4m of payables and a £0.1m shareholder loan. Part of the £7.4m net IPO proceeds have been used to repay these debts. At 25 September the group reported £6.5m of cash. We forecast that by the year end, assuming the receivables are collected, appScatter will have £3.4m of net cash, sufficient to fund the business to break-even, which we forecast will be reached mid-2019.

Scenario analysis – many unknown quantities

The company is in the very early stages of its growth, and it will need to respond flexibly to the market to maximise its chances of building a sustainable business. This means that most of our assumptions are subject to change, in particular the price of the service and the amount it will cost appScatter to attract new licensees. We test these two sensitivities below; however, it is also worth noting that there are a couple of near-term initiatives that we are not yet reflecting in our forecasts:

Indirect sales: appScatter is in discussion with numerous potential strategic partners; however, our forecasts do not factor in any potential growth from partner referrals. We understand from management that it is at an advanced stage of negotiations with a potential strategic sales partner, which has relationships with over 200k app developers. Were the partner to convert even a small percentage of its client base, this would represent a significant step up in licensed users, with no associated CPA.

Marketplace: In our forecasts, we have not factored in any incremental revenues from marketplace add-ins as they have yet to be launched, and we expect a large number to be free (but increasing the stickiness of the platform). However, as explained earlier, commissions earned through referring users to partners could add upside to our revenue forecasts over time.

Data sales: Until pricing points and demand patterns become clearer, we have also excluded from forecasts the potential monetisation of the sale of the raw data being collected. Management expects to start to monetise this data from 2018, and again this may provide another source of upside to our estimates.

Service revenues: The company is generating c £25k per month in services for one customer, related to a range of non-platform related services. We assume this will continue for the remainder of 2017, but have not factored this into our forecasts in FY18. We understand from management that this contract may be extended and so would represent upside to our estimates. We exclude this revenue from ARR calculations.

Scenario: Higher conversion rate

The conversion rate to date has been higher than the 2% we forecast. It is possible (and management are hopeful) that post launch a higher rate is sustained for longer than we have forecast. All else being equal, were the conversion rate to stabilise at 3%, rather than the 2% forecast, this would imply an operating margin of 31% in FY19 – one of the highest in the sector (Exhibit 12).

Scenario: Lower pricing

appScatter’s pricing has been determined in reference to similar SaaS-based businesses, market testing and in consultation with its client base. We have already factored in an element of price discounting for larger customers in our forecasts. However, in the early years of development, companies often need to adapt their pricing policies as the market becomes established. Using the same CPA and churn assumptions as in our base case forecasts, we have modelled the impact of reducing the monthly rate charged for apps and users by 33% to reflect the possibility that the market is more price sensitive than forecast. We assume a higher conversion rate (3% as above) to reflect the attraction of this lower pricing.

The combination of a higher number of users at a lower price results in a slightly lower revenue profile compared to our base case and the operating margins are clearly affected; in this scenario the group would roughly break-even at the operating level in FY19.

Exhibit 12: Scenario analysis

Scenario – conversion rate

FY17e

FY18e

FY19e

Edison forecasts

FY17e

FY18e

FY19e

Scenario – pricing

FY17e

FY18e

FY19e

Revenues

2.1

7.7

18.7

Revenues

2.1

6.8

14.0

Revenues

1.8

5.1

12.5

Revenue growth

270%

143%

Revenue growth

227%

107%

Revenue growth

183%

143%

Gross profit

(0.1)

3.8

13.7

Gross profit

(0.1)

3.0

9.1

Gross profit

(0.4)

1.3

7.5

Gross margin

-6%

50%

73%

Gross margin

-6%

44%

65%

Gross margin

-21%

25%

60%

EBITDA

(5.3)

(2.1)

6.2

EBITDA

(5.3)

(2.9)

1.8

EBITDA

(5.5)

(4.5)

0.2

EBITDA margin

-254%

-27%

33%

EBITDA margin

-254%

-43%

13%

EBITDA margin

-303%

-88%

2%

Operating profit

(5.6)

(2.4)

5.9

Operating profit

(5.6)

(3.2)

1.5

Operating profit

(5.8)

(4.8)

(.1)

Operating margin

-269%

-31%

31%

Operating margin

-269%

-48%

11%

Operating margin

-320%

-94%

-1%

Annualised MRR

2.03

12.15

24.13

Annualised MRR

2.03

9.81

17.51

Annualised MRR

1.36

8.14

16.17

Registered interest

12,369

44,758

90,102

Registered interest

12,369

44,758

90,102

Registered interest

12,369

44,758

90,102

Licensees

210

1,129

2,195

Licensees

210

900

1,586

Licensees

210

1,129

2,195

Conversion rate

 

3.2%

3.0%

Conversion rate

2.5%

2.0%

Conversion rate

 

3.2%

3.0%

Monthly churn

0.0%

1.5%

1.5%

Monthly churn

0.0%

1.5%

1.5%

Monthly churn

0.0%

1.5%

1.5%

DCF 15% WACC/ p per share

107

DCF 15% WACC

67

DCF 15% WACC

51

DCF 12.5% WACC/ p per share

138

DCF 12.5% WACC

89

DCF 12.5% WACC

70

Source: Edison

Sales and marketing expenditure key to growth

In Exhibit 13 below, we present a selection of SaaS businesses in terms of revenue growth, gross margins, operating margins and sales and marketing expenditure. It is clear that these companies value growth over profitability, as demonstrated by the low operating margins and high levels of sales and marketing expenditure. We therefore think that appScatter may decide to boost its sales and marketing expenditure in order to more rapidly grow its licensee base and gain a dominant position in its niche. It is also with this in mind that we factor in a higher CPA to that experienced by the company to date in our forecasts.

Exhibit 13: Peer group financial performance

Revenue growth

FY14

FY15

FY16

FY17

Gross margins

FY14

FY15

FY16

FY17

Appian

47%

25%

20%

Appian

58%

57%

62%

Atlassian

45%

49%

43%

Atlassian

82%

83%

83%

Axway

10%

9%

6%

Axway

70%

70%

71%

Mobile Iron

25%

13%

10%

Mobile Iron

82%

81%

81%

Rosslyn Analytics

13%

37%

37%

Rosslyn Analytics

83%

84%

88%

Splunk

52%

49%

48%

42%

Splunk

88%

85%

83%

80%

Workday

71%

68%

47%

35%

Workday

N/A

N/A

N/A

N/A

Xero

83%

77%

67%

Xero

67%

71%

76%

Zendesk

76%

64%

49%

Zendesk

64%

68%

70%

2U

33%

36%

37%

2U

N/A

N/A

N/A

Average

 

46%

43%

36%

 

 

 Average

 

74%

75%

77%

Operating margins – non-GAAP

Sales & marketing/revenues

Appian*

(16%)

(5%)

(8%)

Appian

33%

35%

41%

Atlassian

19%

16%

17%

Atlassian

16%

21%

20%

Axway

15%

16%

17%

Axway

30%

29%

27%

Mobile Iron

(33%)

(35%)

(18%)

Mobile Iron

75%

71%

62%

Rosslyn Analytics

(151%)

(124%)

(62%)

Rosslyn Analytics

N/A

N/A

N/A

Splunk

(3%)

3%

4%

6%

Splunk

71%

76%

76%

69%

Workday

(19%)

(7%)

0%

2%

Workday

42%

40%

37%

37%

Xero*

(55%)

(61%)

(43%)

Xero

79%

75%

72%

Zendesk*

(52%)

(40%)

(33%)

Zendesk

61%

55%

54%

2U

(13%)

(4%)

2%

2U

59%

55%

52%

Average

 

52%

51%

49%

Source: Company accounts, Edison Investment Research. Note: Companies marked with an asterisk (*) are US GAAP only.

Key peer: Atlassian

We consider Atlassian to be of particular relevance to our analysis of appScatter, as there are many similarities between the two companies:

Atlassian is an enterprise software provider and its products are sold on a SaaS basis.

Its fees are comparable to those of appScatter (c $50 per user per month).

Atlassian’s main product, JIRA, is a workflow management system that is used by web and app developers (including appScatter), meaning that people who purchase JIRA will likely be target customers for appScatter itself. Hence, demand for their products is likely to be correlated.

Cost structure: the two companies are unusual relative to their SaaS peers in that they spend a relatively low proportion of their revenues on sales and marketing (c 20% vs c 50%), which contributes to their relatively high operating margins.

Valuation methodology

appScatter’s valuation hinges on the successful commercial launch of its platform. As this was late in FY17 (and the first month is free), FY17 figures (which we expect in March 2018) will not reflect revenues post this launch. More relevant to track performance at this early stage will be management’s communication regarding the total number of licensees (conversion rates), the exit ARR rate and any updates on the average customer size as well as channel partners.

The shares trade on an FY18 EV/sales rating of 4.1x, a discount to the average of its larger SaaS peer set (6.2x). In FY19, the discount is even more pronounced, trading on 2.0x EV/sales compared to the peer set on 5.1x. While considerably smaller scale than some of these peers, given that this would be appScatter’s first full year of trading we would expect the shares to re-rate upwards towards the peer set on evidence of the successful launch of the platform, and sustained growth during FY18. Our DCF indicates a valuation range of 67p (15% WACC) to 89p (12.5% WACC). At these levels, the shares would still trade at an FY19 EV/sales discount to many of its larger SaaS peers and consequently we do not believe our DCF assumptions to be overly aggressive.

The assumptions underlying our DCF and peer comparisons are outlined in more detail below.

Discounted cash flow valuation: 67p per share (at 15% WACC)

Although approximately 10,000 accounts have registered an interest in using the service, and the company is anticipating a high initial conversion rate after the platform launch, the business is still very early-stage and we consider execution risk to be relatively high. Forecasts are highly sensitive to fairly small changes in conversion rate, pricing and CPA. In our valuation of the group, we have tried to capture forecasting uncertainties by using a higher CPA than that experienced to date by management as well as by using a relatively high (15%) discount rate in our DCF, which returns a value of 67p. With the demonstrable success of the platform’s commercial launch and as the group moves towards break-even, we would expect the rate the market applies to come down fairly rapidly and so we also demonstrate the impact on the DCF valuation should we vary the discount rate. For example, using a 12.5% WACC (which we consider more typical for technology companies where the demand has been established but which are still loss making) we derive a valuation of 89p per share.

Underlying assumptions

Exhibit 14 shows a DCF matrix defined by discount rates and by terminal EBITDA margins. We model a 10-year period, with revenue growth fading steadily post the explicit three-year forecast period to 2% in perpetuity. Even though we use a fairly long 10-year forecast period, c 53% of the enterprise value for our base case valuation lies in the terminal period.

Key DCF assumptions:

Revenue growth falling steadily to 2% by 2028. CAGR of 21% over the period 2019 to 2027.

A terminal EBITDA margin of 25%, and an average EBITDA margin FY20-26 of 25%.

Cash tax at 19% for FY18-20, 18% in FY21 and 17% thereafter.

Capex/sales at 3% from FY20.

Exhibit 14: DCF base case (p/ share

Terminal EBITDA margin

15.00%

20.00%

25.00%

30.00%

35.00%

WACC

19.0%

31

39

46

53

61

18.0%

34

42

50

58

66

17.0%

36

45

55

64

73

16.0%

39

50

60

71

81

15.0%

43

55

67

78

90

14.0%

47

60

74

88

101

13.0%

52

68

83

99

115

12.5%

55

72

89

106

123

11.0%

65

87

108

130

152

10.0%

74

100

126

152

178

Source: Edison Investment Research

Scenarios: DCF sensitive to small changes in KPI assumptions

The DCF is sensitive to small changes in key variables. For instance, the same methodology applied to the ‘higher conversion rate’ scenario outlined earlier returns a value of 107p per share (15% WACC) or 138p share (12.5% WACC). The lower pricing scenario returns 51p (15%) and 70p (12.5%).

Peer multiples

In the absence of direct competition, we have selected a range of peers, which we group into three categories. Many companies could feature in multiple categories.

Similar business models: other SaaS businesses that also charge enterprise customers on a per user or per licence basis in return for the provision of their software. We note that some of these companies are significantly more mature than appScatter. However, should appScatter continue to grow successfully, similar margins and multiples may be applicable. If appScatter delivers to plan (or better) this will likely become the most relevant peer base.

Similar customer base: several companies from the digital marketing landscape provide app developers with services and tools to enable efficient monetisation and distribution of their products, which is a similar focus to appScatter.

UK small-cap software: we include this list as an illustration of the multiples attributed to UK listed software companies that are in earlier stages of development. This category includes a mix of companies that have performed very strongly and others that have disappointed on IPO or against recent consensus forecasts.

appScatter trades on an FY18 EV/sales multiple of 4.1x, a discount to the larger (more mature) SaaS peer set (6.2x). Assuming the group progresses in line with forecasts, this discount expands in FY19 when it trades on 2.0x sales (peers 5.1x) and 15.1x EV/EBITDA (peers 35.1x).

At our DCF valuation of 67p, the implied multiple of 5.7x FY18e EV/sales for appScatter is more in line with its peer average, although still at a discount to more established SaaS companies, notably Atlassian, Xero and Workday, which trade in the 6.0x to 10.4x range, which we feel is justified due to the early stage, unproven nature of the appScatter platform. However, if the company delivers in the short to mid-term, an upwards re-rating towards these multiples may be justified. At our 89p DCF valuation (12.5% WACC), this would imply an FY18 EV/sales multiple of 7.7x.

Exhibit 15: Summary of quoted peers – FY1 is first forecast year (2017 for most companies)

Name

Quoted currency

Year end

Market cap (m)

Sales growth (%)

Gross margin (%)

EBITDA margin (%)

EBIT margin (%)

EV/sales (x)

EV/EBITDA (x)

EV/EBIT (x)

P/E (x)

1FY

2FY

1FY

2FY

3FY

1FY

2FY

3FY

1FY

2FY

3FY

1FY

2FY

3FY

1FY

2FY

3FY

1FY

2FY

3FY

1FY

2FY

3FY

APPSCATTER

£

12/16

33

NA

227

(6.4)

43.8

65.2

N/A

N/A

13%

(280

(50)

9

13.3

4.1

2.0

N/A

N/A

15.1

N/A

N/A

18.3

N/A

N/A

24.6

SaaS companies

AXWAY SOFTWARE

12/16

475

3

4

69

70

71

14

16

17

13

14

15

1.6

1.6

1.5

11.4

10.1

8.8

12.9

11.4

9.8

15.7

13.8

12.3

ATLASSIAN CORP

US$

06/17

11,919

36

29

84

84

84

26

27

29

19

20

N/A

13.4

10.4

8.6

51.9

38.8

29.6

70.4

52.8

N/A

109.4

81.7

62.9

SALESFORCE.COM

US$

01/17

76,972

24

20

76

76

77

22

22

24

15

16

18

7.2

6.0

5.0

32.9

27.3

21.1

49.3

37.1

27.9

79.9

62.0

48.6

WORKDAY INC

US$

01/17

24,122

34

26

74

75

76

15

17

20

8

10

13

10.7

8.6

7.1

70.5

50.2

36.4

130.5

85.6

56.1

140.1

105.1

75.6

ZENDESK INC

US$

12/16

3,621

37

30

73

74

75

3

5

7

(10)

(7)

0

7.7

6.0

4.7

260.8

124

71.6

N/A

N/A

N/A

N/A

N/A

171

XERO LTD

NZ$

03/17

4,543

41

33

81

82

85

6

15

21

(7)

3

11

10.7

8.1

6.3

169.3

55.2

30.6

N/A

268.0

59.3

N/A

250.5

71.5

SPLUNK INC

US$

01/17

11,720

31

25

83

83

84

12

13

16

8

10

13

8.6

6.9

5.4

73.0

51.4

33.1

102.2

65.4

40.9

144.2

98.3

66.0

TABLEAU SOFTWARE INC-CL

US$

12/16

5,572

5

8

88

87

87

4

5

8

1

2

6

5.3

4.9

4.3

143.7

107

54.7

417.7

202.4

77.5

383.9

344.2

128.0

HUBSPOT INC

US$

12/16

3,051

37

27

81

82

82

6

8

10

2

3

5

6.9

5.4

4.4

118.2

72.0

45.3

N/A

209.8

90.0

N/A

194.4

108

HORTONWORKS

US$

12/16

1,445

38

27

71

73

74

(8)

9

17

(38)

(18)

(5)

5.4

4.3

3.5

N/A

48.9

20.0

(14.1)

(23.3)

(66.3)

N/A

N/A

N/A

Similar customer base

DIGITAL TURBINE

US$

03/17

129

24

20

29

32

N/A

1

6

N/A

(0)

3

N/A

1.2

1.0

N/A

81.9

17.6

N/A

N/A

34.0

N/A

(31.5)

25.6

N/A

XLMEDIA PLC

£

12/16

375

35

8

53

51

48

34

32

32

27

25

28

3.3

3.1

2.7

9.8

9.5

8.5

12.3

12.1

9.8

16.3

14.8

13.6

IMIMOBILE PLC

£

03/17

124

39

10

N/A

N/A

N/A

12

12

12

N/A

N/A

N/A

1.0

0.9

0.9

N/A

7.5

7.1

N/A

N/A

N/A

N/A

N/A

14.9

MOBILEIRON INC

US$

12/16

372

7

8

85

84

82

(9)

(0)

4

(13)

(5)

(0)

1.7

1.5

1.4

/A

N/A

32.2

N/A

N/A

N/A

N/A

N/A

N/A

UK software

ROSSLYN DATA TECHNOLOGIES

£

04/16

10

165

13

84

84

N/A

5

11

N/A

5

11

N/A

1.0

0.9

N/A

N/A

7.8

N/A

N/A

7.8

N/A

N/A

8.5

N/A

BANGO PLC

£

12/16

164

58

83

N/A

N/A

N/A

(31)

26

N/A

(60)

3

N/A

38.2

20.9

N/A

N/A

79.3

N/A

N/A

793.1

N/A

N/A

N/A

N/A

LOOPUP GROUP

£

12/16

118

15

35

N/A

N/A

N/A

19

25

31

N/A

N/A

N/A

6.8

5.1

3.7

36.3

20.3

12.2

N/A

N/A

N/A

76.5

40.7

19.6

D4T4 SOLUTIONS

£

03/17

60

31

10

51

51

N/A

21

22

N/A

20

21

N/A

2.4

2.1

N/A

11.1

9.9

N/A

11.5

10.2

N/A

N/A

13.6

N/A

FREEAGENT HOLDINGS PLC

£

03/17

32

29

28

N/A

N/A

N/A

2

16

N/A

(8)

6

N/A

2.7

2.1

N/A

143.7

13.1

N/A

N/A

32.5

N/A

N/A

38.8

N/A

BLUE PRISM GROUP PLC

£

10/16

1,034

143

69

99

99

99

(31)

(20)

(8)

(30)

(18)

1

43.7

25.8

20.1

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

N/A

Source: Edison Investment Research, Bloomberg. Note: Negative multiples are marked N/A. Price at 24 November 2017.

Sensitivities

appScatter is a development-stage business, with a scalable business model moving into commercialisation in a potentially very large market. Consequently, there are significant risks, uncertainties and potential. Our forecasts and the valuation of the company will be sensitive to the following factors:

Investing in the platform: We believe that the company’s focus in the short term will be to attract as many users to the platform as possible. There is likely to be pent-up demand for the paid-for service as a result of the company keeping registered users on a waiting list until the public launch of the platform, and we believe initial licensing sign-ups could be strong. With customers signing up for a minimum of 12 months, churn should not be an issue in the short term. However, as customers start to approach the year anniversary, the functionality of the platform and the level of customer service will be key considerations in the decision to stay with appScatter. We think it is likely that the company will want to invest more in product development and customer support to make the platform as compelling as possible. In this case, our assumptions of cost increases in FY19 could be too conservative and the gross and operating margins achievable might be more similar to other enterprise SaaS businesses.

Competition: Other companies already established in the mobile app development market may decide to offer competing services. While appScatter has a head start in terms of historic data collection, a well-funded competitor could attract users with an integrated service offering that appScatter is not currently in a position to provide.

App stores: As well as having access to customer data, appScatter uses public data provided by app stores. Its business model may be affected if stores stop releasing this data. It will also need to keep up-to-date with the changing requirements and processes of the app stores.

Reliance on third-party technology providers: The appScatter platform is hosted on AWS and is therefore reliant on AWS to maintain performance and security. Some development work is performed by third-party developers; their pricing, skills, experience and availability will influence the cost and quality of platform development.

Technology: appScatter will need to keep abreast of technological changes in app development, infrastructure, smartphone and other device technology.

Currency: appScatter is planning to switch its pricing from sterling to US dollars. The majority of costs are incurred in sterling and this will result in a mismatch between revenues and costs. The company expects to use hedging techniques to reduce this impact. If the company decides to offer more local pricing options, eg pounds, sterling or euros, then this is likely to add further complexity to hedging.

Data security: appScatter will need to ensure it meets all relevant data protection regulations. With the introduction of the General Data Protection Regulation (GDPR) in 2018, penalties for not following regulations could be severe.

Funding requirements: Depending on the pace of customer adoption, appScatter may need to raise more funds before it reaches cash flow break-even.

Stock overhang: After the IPO, management will continue to hold a material percentage of shares, although they will be locked in and will not be permitted to sell any shares for at least 12 months. Management has committed it will not sell any shares for at least two years.

We have also discussed in the financial section the sensitivity of our forecasts in relation to the pace of adoption, the cost to acquire customers, the rate of conversion to paying customers, renewal rates and the price of the service. While we have not explicitly modelled it, there is the risk that the pace of adoption is significantly below our base case, in which case the company would struggle to reach profitability.

Exhibit 16: Financial summary

£m

2015

2016

2017e

2018e

2019e

Year end 31 December

IFRS

IFRS

IFRS

IFRS

IFRS

INCOME STATEMENT

Revenue

 

 

0.0

0.0

2.1

6.8

14.0

Cost of Sales

0.0

0.0

(2.2)

(3.8)

(4.9)

Gross Profit

0.0

0.0

(0.1)

3.0

9.1

EBITDA

 

 

(2.1)

(3.5)

(5.3)

(2.9)

1.8

Normalised operating profit

 

 

(2.1)

(3.5)

(5.6)

(3.2)

1.5

Amortisation of acquired intangibles

0.0

0.0

0.0

0.0

0.0

Exceptionals

0.0

(5.2)

0.0

0.0

0.0

Share-based payments

0.0

0.0

(0.8)

(1.0)

(0.7)

Reported operating profit

(2.1)

(8.7)

(6.4)

(4.2)

0.8

Net Interest

(0.0)

(0.1)

0.0

0.0

0.0

Joint ventures & associates (post tax)

0.0

0.0

0.0

0.0

0.0

Exceptionals

0.0

0.0

0.0

0.0

0.0

Profit Before Tax (norm)

 

 

(2.1)

(3.6)

(5.6)

(3.2)

1.5

Profit Before Tax (reported)

 

 

(2.1)

(8.8)

(6.4)

(4.2)

0.8

Reported tax

0.0

0.0

0.0

0.0

0.0

Profit After Tax (norm)

(1.7)

(2.9)

(5.6)

(3.2)

1.5

Profit After Tax (reported)

(2.1)

(8.8)

(6.4)

(4.2)

0.8

Minority interests

0.0

0.0

0.0

0.0

0.0

Discontinued operations

0.0

0.0

0.0

0.0

0.0

Net income (normalised)

(1.7)

(2.9)

(5.6)

(3.2)

1.5

Net income (reported)

(2.1)

(8.8)

(6.3)

(4.2)

0.8

Basic average number of shares outstanding (m)

N/A

N/A

55

63

63

EPS – basic normalised (p)

 

 

N/A

N/A

(10.15)

(5.10)

2.40

EPS – diluted normalised (p)

 

 

N/A

N/A

(10.15)

(5.10)

2.19

EPS – basic reported (p)

 

 

N/A

N/A

(11.54)

(6.68)

1.29

Dividend (p)

0.00

0.00

0.00

0.00

0.00

BALANCE SHEET

Fixed Assets

 

 

0.8

1.0

0.7

0.4

0.1

Intangible Assets

0.8

1.0

0.6

0.3

0.0

Tangible Assets

0.0

0.0

0.1

0.1

0.1

Investments & other

0.0

0.0

0.0

0.0

0.0

Current Assets

 

 

0.0

5.1

5.4

2.2

4.1

Stocks

0.0

0.0

0.0

0.0

0.0

Debtors

0.0

0.3

1.3

0.6

1.1

Cash & cash equivalents

0.0

0.0

3.4

1.5

2.9

Other

0.0

4.8

0.7

0.2

0.0

Current Liabilities

 

 

(0.8)

(2.9)

(0.9)

(1.1)

(1.3)

Creditors

(0.5)

(2.0)

(0.6)

(0.8)

(1.0)

Tax and social security

(0.1)

(0.3)

(0.3)

(0.3)

(0.3)

Short term borrowings

(0.2)

(0.6)

0.0

0.0

0.0

Other

0.0

0.0

0.0

0.0

0.0

Long Term Liabilities

 

 

0.0

0.0

0.0

0.0

0.0

Long term borrowings

0.0

0.0

0.0

0.0

0.0

Other long term liabilities

0.0

0.0

0.0

0.0

0.0

Net Assets

 

 

0.1

3.2

5.2

1.5

2.8

Shareholders' equity

 

 

0.1

3.2

5.2

1.5

2.8

CASH FLOW

Op Cash Flow before WC and tax

(2.1)

(3.5)

(5.3)

(2.9)

1.8

Working capital

1.6

1.8

(2.4)

0.9

(0.4)

Exceptional & other

0.0

0.0

0.0

0.0

0.0

Tax

0.0

0.0

0.0

0.0

0.0

Net operating cash flow

 

 

(0.5)

(1.7)

(7.7)

(2.0)

1.4

Capex

0.0

0.0

(0.1)

0.0

0.0

Acquisitions/disposals

0.0

0.0

0.0

0.0

0.0

Net interest

(0.0)

(0.1)

0.0

0.0

0.0

Equity financing

0.4

1.5

11.8

0.0

0.0

Dividends

0.0

0.0

0.0

0.0

0.0

Other

0.0

0.0

0.0

0.0

0.0

Net Cash Flow

(0.1)

(0.2)

4.0

(2.0)

1.4

Opening net debt/(cash)

 

 

(0.0)

0.2

0.6

(3.4)

(1.5)

FX

0.0

0.0

0.0

0.0

0.0

Other non-cash movements

(0.2)

(0.2)

0.0

0.0

0.0

Closing net debt/(cash)

 

 

0.2

0.6

(3.4)

(1.5)

(2.9)

Source: appScatter accounts, Edison Investment Research

Contact details

Revenue by geography

Salisbury House
London Wall
London
EC2M 5PS
www.appscatter.com

N/A

Contact details

Salisbury House
London Wall
London
EC2M 5PS
www.appscatter.com

Revenue by geography

N/A

Management team

CEO: Philip Marcella

CFO: Manish Kotecha

Philip is a serial entrepreneur and seasoned British programmer. He started his first company in 1996, RMR Plc, and listed it on the London Stock Exchange in 2000 for £70m, raising £20m, with 300 employees and offices in both the UK and the US.

Manish has over 14 years of experience in the role of CFO/finance director for several publicly listed companies. During this time, he has been involved in numerous fund-raising initiatives for both equity and debt exceeding £200m. His key strengths include developing and implementing robust internal controls, reporting and compliance procedures suitable for growth companies.

Chairman: Clive Carver

Clive is a chartered accountant and worked with Coopers & Lybrand, Kleinwort Benson and Price Waterhouse Corporate Finance before becoming head of corporate finance at Seymour Pierce, Williams de Broe and finnCap successively. He is a qualified corporate treasurer and also a non-executive director of several AIM-listed companies.

Management team

CEO: Philip Marcella

Philip is a serial entrepreneur and seasoned British programmer. He started his first company in 1996, RMR Plc, and listed it on the London Stock Exchange in 2000 for £70m, raising £20m, with 300 employees and offices in both the UK and the US.

CFO: Manish Kotecha

Manish has over 14 years of experience in the role of CFO/finance director for several publicly listed companies. During this time, he has been involved in numerous fund-raising initiatives for both equity and debt exceeding £200m. His key strengths include developing and implementing robust internal controls, reporting and compliance procedures suitable for growth companies.

Chairman: Clive Carver

Clive is a chartered accountant and worked with Coopers & Lybrand, Kleinwort Benson and Price Waterhouse Corporate Finance before becoming head of corporate finance at Seymour Pierce, Williams de Broe and finnCap successively. He is a qualified corporate treasurer and also a non-executive director of several AIM-listed companies.

Principal shareholders

(%)

Philip Marcella

26.1

Octopus Investments

5.4

Legal & General

3.7

William Booth

3.3

Harwinder Singh

3.3

Edison is an investment research and advisory company, with offices in North America, Europe, the Middle East and AsiaPac. The heart of Edison is our world-renowned equity research platform and deep multi-sector expertise. At Edison Investment Research, our research is widely read by international investors, advisers and stakeholders. Edison Advisors leverages our core research platform to provide differentiated services including investor relations and strategic consulting. Edison is authorised and regulated by the Financial Conduct Authority. Edison Investment Research (NZ) Limited (Edison NZ) is the New Zealand subsidiary of Edison. Edison NZ is registered on the New Zealand Financial Service Providers Register (FSP number 247505) and is registered to provide wholesale and/or generic financial adviser services only. Edison Investment Research Inc (Edison US) is the US subsidiary of Edison and is regulated by the Securities and Exchange Commission. Edison Investment Research Limited (Edison Aus) [46085869] is the Australian subsidiary of Edison. Edison Germany is a branch entity of Edison Investment Research Limited [4794244]. www.edisongroup.com

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Research: Investment Companies

TR European Growth Trust — Small-cap value focus delivers outperformance

TR European Growth Trust (TRG) invests in a diversified portfolio of small and mid-cap European (ex-UK) companies, with the aim of achieving capital growth. Its manager at Janus Henderson Investors seeks companies that are undervalued as a result of being misunderstood by the market. The portfolio is weighted towards the smaller end of the market cap spectrum, where companies are less well-researched. As recovery has taken hold in Europe, TRG’s recent performance has been exceptionally strong. It has beaten its benchmark, the EMIX Smaller Europe ex-UK index, in share price and NAV total return terms over the last five discrete years to 31 October, and cumulatively over one, three and six months, and one, three, five and 10 years. It also ranks first in its peer group over one, three and five years. TRG actively manages gearing in a range of up to 15% of net assets (13% at end-October 2017), does not tend to hedge currency exposure, and currently offers a dividend yield of 1.2%.

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