movvo case - com autores

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Movvo: Marketing Location-Based Big Data

Keywords: Market segmentation, Targeting, Customer lifetime value, new technology, start-ups Cláudia Costa Phd in Management, Teaching Assistant at NovaSBE, Nova School of Business and Economics, INOVA, Universidade Nova de Lisboa. Rua Marques da Fronteira, 20, 1099-038 LISBOA, Portugal. E-mail:[email protected]; Telephone: + 351 21 3822745 Nuno Camacho Assistant Professor of Marketing at Erasmus School of Economics, Erasmus University Rotterdam;Burgmeester Oudlaan, 50, H15-03, 3062 PA Rotterdam, The Netherlands Gonçalo Amorim Executive Director, BGI – IUL MIT Portugal Accelerator MIT Portugal – Innovation & Entrepreneurship Initiative, INDEG Non-executive director of Audax, ISCTE’s centre for entrepreneurship José Paulo Esperança Dean of ISCTE Business school. Full professor in Entrepreneurial Finance, BGI’s Chairman – IUL MIT Portugal Accelerator and founder and Chairman of Audax, ISCTE’s centre for entrepreneurship.

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Movvo: Marketing Location-Based Big Data

“In the beginning, I lost a lot of time perfecting the product instead of selling it. I’ve learned that you need to start listening to your customers as early as possible.”

Roberto Ugo, Co-founder Movvo

After four years of development and testing, Diana Almeida, Suzy Vasconcelos and

Roberto Ugo, the three founders of Movvo, were ready to launch a technology to detect

consumers’ movement patterns in enclosed spaces. However, investors urged the team

to make a clear decision about target markets. How would Movvo successfully

commercialize their technology? How would they position their offering vis-à-vis

competing technologies?

The company, based in Matosinhos, near Porto, was an academic spin-off which had

started in 2009, while the three founders were studying at the Faculty of Sciences of the

University of Porto. Now, in December 2013, the team felt Movvo was at a critical

juncture in its history. A winning go-to-market strategy could catapult Movvo to the $600

billion market for mobile location-based data servicesi.

Movvo had just secured €600,000 from three angel investors, one based in Boston and

two others in Lisbon. Now they had to decide which markets to target and how to enter

such markets. The three founders were divided about which customers to target and

how to position Movvo’s offering. Roberto Ugo, Movvo’s CTO, believed they should

target large retailers. Suzy Vasconcelos, who was responsible for sales, believed that

shopping malls would be the best beachhead market. Diana Almeida, in turn, believed

strongly on mass transportation, namely solutions to optimize the flow of passengers.

The debate was intense but a decision had to be made. Both investors and the team

were eager to finalize their go-to-market plan. Besides clarifying their value proposition

and choosing which customer or customers to target, the team had to define how to

demonstrate the value and overcome customers’ resistance to adoption (e.g. due to

privacy concerns), as well as define the best pricing and scaling model for Movvo.

Several emerging big data startups were struggling with exactly the same questions.

They expected 2014 to be a challenging, but also inspiring, year for the history of

Movvo.

Idea Generation and History

Diana, Suzy and Roberto gave their first steps in the field of indoor micro-location

technologies in 2009, while studying at the Faculty of Sciences of the University of

Porto. It all started as a game. In one of their courses, they decided to create an

algorithm to help one of their professors, who disliked shopping, spend less time in a

retail store. They were fascinated by the results. A simple mathematical algorithm

allowed them to propose shopping paths capable of saving countless shopping hours

per year, and countless boredom-related pain, to their professor!

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After numerous hours in their garage, programming and testing, Diana, Suzy and

Roberto developed their first prototype with which they won the 2009 edition of Sapo

Summerbits competitionii. After this victory, the team decided to start a company.

Originally named as “Around Knowledge”, the startup was created to commercialize

their technology, which they called BIPS (Bluetooth Indoors Positioning System). The

main goal was to improve intelligence about how shoppers “move around” in enclosed

spaces.

In May 2010, the team applied to a venture competition called Building Global

Innovatorsiii (BGI), a technology-focused accelerator sponsored and managed by the

ISCTE-IUL (a business school in Portugal with a strong entrepreneurship tradition) and

MIT Portugaliv, whose goal is to promote the emergence and growth of technology-

based startups. Joining the BGI accelerator was a turning point in Movvo’s growth

trajectory. BGI’s philosophy is to move startups quickly to execution mode and to rapidly

establish the international networks they need to achieve ambitious scaling goals:

through intensive mentoring, active networking, and frequent pitching to prospective

clients and investors - both in Lisbon and Cambridge, MA. BGI was able, in just two

years, to help shape Movvo into a high profile big data startup and one of the most

innovative newcomers in the Portuguese startup ecosystem. By the end of 2011 they

had perfected their technology and their prototype which allowed them to attract key

initial investors, such as Caixa Capital, Portugal’s public and largest institutional venture

capital firm

Raising Capital and Early Scaling

Movvo’s technology seemed to fit perfectly into an emerging trend towards big data and

evidence-based management, which attracted important early investors and allowed the

rapid early growth. Suzy, Diana and Roberto had to relinquish some equity to finance

their growth. Yet, together they still retained 70% of the company. The remainder was

split by the BGI accelerator (4.3%), by Caixa Capital Empreender fund (0.7%) and by

three angels, Boston-based Ted Selig (12.5%) and two Portuguese angels (who had,

together, 12.5% of the company). The new capital and BGI mentoring and coaching

spurred fast growth. In early 2013, the team had already doubled in size and by the

summer, Movvo had a team of 25+ FTEs, most of them based on its new headquarters

in the center of Porto. The company also opened a US office in Silicon Valley’s Plug and

Play Technology Center, with the goal of building a strong sales and marketing team,

and establishing important ties with other entrepreneurs and, above all, key investors.

Indoor Location Technologies that do not rely on Mobile Phones

Exhibit 1 shows competing approaches to gather location-based data without the need

to rely on mobile phone signals. Such technologies range from traditional self-reported

data (e.g. in-store surveys, or surveys conducted among travelers or citizens to

determine their usual paths and trajectories), manual systems like surveys or tally

counters (e.g. to count the number of visitors to a museum or to a store) to more

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sophisticated technologies using GPS monitoring, radio frequency identification tags,

infrared sensors or camera-based systems.

Manual technologies suffered from severe limitations. Surveys were expensive and time

consuming, prone self-report biases and considered intrusive by consumers. Tally

counters were very limited and also expensive, as they required constant human

intervention. GPS-based solutions, in turn, were not intrusive and, when used outdoors,

provided cost-effective solutions. Indoors, however, they were not useful due to poor

signal granularity meaning they were either inaccurate or simply could not provide

reliable data.

The limitations to location based data kept companies interested in technologies that

tracked movement reliably with minimal intrusive. Techniques such as security camera-

based surveillance and radio frequency identification (RFID) were still on demand.

Camera-based surveillance approaches often conflicted with data privacy laws,

especially regarding the employee protection laws (as companies could, in theory, also

use the system to constantly monitor employees). Moreover, tracking shoppers using

real-time video footage was very expensive and required very large amounts of storage

capacity. Hence, any analyses had to necessarily rely on small convenience samplesv.

The most promising “non-mobile” based technology was RFID-based cart tagging. One

of the pioneers in cart tracking was Herb Sorensenvi, who had a long career developing

systems and analytical techniques to monitor and understand consumers’ shopping

paths in supermarkets and malls. In the early 2000s, Herb Sorensen teamed with Prof.

Peter Fader from Wharton, and with industry partners such as Dr. Pepper/7 Up and later

Pepsivii to develop RFID-based solutions for real-time indoor location. The scientific

encouragement of Fader and financial support of Dr pepper/7up and Pepsi, allowed

Sorensen to launch one of the first large-scale studies of shopping paths using RFID

technologyviii. Herb Sorensen used these insights to establish his trademarked RFID-

tagging PathTracker®ix and to better understand patterns in shoppers’ paths, and offer

consulting services around such knowledge.

Despite all benefits, RFID-based solutions suffered from important limitations. First, these

solutions tagged carts, not individual shoppers. This meant that consumers who did not use

a cart were not tracked. In certain types of retailers (e.g. small urban supermarkets, fashion

retailers, etc), using a shopping cart was the exception rather than the rule. Second, it was

not possible to distinguish whether a consumer was shopping alone or in a group. Third,

each visit was recorded as a different occurrence, even for returning customers. Hence, it

was not possible to keep a longitudinal view of shoppers’ behavior, which would bring richer

insights. Fourth, with thousands of shopping carts to tag and maintain, keeping RFID-tags

working was expensive for retailers. Finally, RFID-based solutions seemed harder to us for

other types of prospective customers, such as transportation and mobility companies.

Movvo’s Real-Time Indoor Location: Taking Beacon Technology to the Next Level

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Given the limitations of earlier technologies and the increase in penetration of mobile and

smart phones, several players started offering mobile-based indoor location solutions. Five

major technologies operate in this space: (i) network-based, (ii) app-based, (iii) sim-based,

(iv) Wi-Fi based, and (v) hybrid indoor location systems, which used a combination of

network-based and app-based technologies. Movvo’s was a powerful new player in this new

wave of location-based data technologies that rely on people’s mobile phones to determine

and track their location in enclosed (or even outdoor) spaces.

The principle behind Movvo’s solution was simple. Powered mobile phones constantly

communicate wirelessly with the closest base stations through a wide variety of channels.

Beacons detect such radio frequency signals. By measuring power levels and antenna

patterns (using beacon technology) between a cell phone and two or three base stations,

one can triangulate such data and obtain a very accurate and fairly inexpensive, real-time,

estimate of a person’s location (Exhibit 2). Each beacon can cover up to 1 km radius of

movements (the location accuracy is inversely proportional do the distance to the nearest

beacon). Movvo combines such signals with proprietary, trade secret algorithms. For

instance, Movvo was able to use its patented technology to rely on Wi-Fi signals to further

enhance location accuracy.

In indoor applications, Movvo’s technology was clearly superior to competing technologies.

First, Movvo developed a full-stack solution based on hardware (beacons) and software

(algorithms and dashboards) capable of achieving a level of location precision previously

unseen with competing technologies. Roberto and his team had amassed a series of studies

and conducted pilot tests (see Fig. 1) that showed such superiority. Specifically, these tests

showed that Movvo’s solution was able to achieve a 10-fold improvement in location

precision, when compared with competing indoor location technologies. Such precision was

possible because modern buildings which are prepared to receive large crowds - such as

stadiums, airports, train and metro stations, supermarkets or malls – are equipped with a

large number of antennas and base stations, which improves the accuracy of Movvo’s

location-detection algorithm.

Figure 1 – Data from Real Case Pilot

Note: This pilot was conducted in

accuracy (meters) based on 3 different signals used by Movvo’s unique algorithm,

(3G). One can see that the accuracy of different signals vary depending on context (i.e. whether the mobile

device is fixed or moving along the X or Y axes). This and other similar pilot tests, supported Movvo’s hypothesis

that combining multiple signals leads

Second, Movvo’s technology offered important cost advantages. Movvo’s system built on

existing assets and infrastructure, which reduced substantially the

needed for clients to adopt thei

cost solution was possible because Movvo had engineer

shelf technologies and beacons

using trilateration algorithms (see

smartphones in most countries means that these beacons are ubiquitously available,

reducing the cost of using them for location purposes.

In fact, nowadays nearly every consumer carries a

continuously attempt to connect to existing networks using a variety of wireless protocols

such as GSM, Bluetooth or Wi

Movvo to determine, and anonymously captu

phone in an enclosed space. To do so, clients would simply need to install a few inexpensive

antennas (beacons) to increase signal granularity and a cheap desktop computer to receive,

process and store the signal.

competing alternatives that Movvo

Still, Movvo had to translate these benefits into a convincing unique selling proposition and

value demonstration strategy.

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ilot Conducted at a Major Fashion Brand’s Store.

Note: This pilot was conducted in Q2 of 2011 in Portugal. The table in Figure 1 shows Movvo’s positioning

) based on 3 different signals used by Movvo’s unique algorithm, Bluetooth,

One can see that the accuracy of different signals vary depending on context (i.e. whether the mobile


that combining multiple signals leads to a significant improvement in location accuracy.


existing assets and infrastructure, which reduced substantially the investment (

adopt their solution and start gathering location-based data. Such low

cost solution was possible because Movvo had engineered a way to use standard, off

shelf technologies and beacons (antennas) that inexpensively improve the signal information

ion algorithms (see Exhibit 2). The nearly universal penetration of


reducing the cost of using them for location purposes.

In fact, nowadays nearly every consumer carries a mobile device. Such mobile devices


such as GSM, Bluetooth or Wi-Fi, creating a constant and dense connectivity that allows

Movvo to determine, and anonymously capture, the precise real-time location of each mobile



signal. The system was so much cheaper and accurate

Movvo hoped it would not be too difficult to


stration strategy.

shows Movvo’s positioning

Bluetooth, Wi-Fi and GSM

One can see that the accuracy of different signals vary depending on context (i.e. whether the mobile



investment (CAPEX)

based data. Such low-

a way to use standard, off-the-

that inexpensively improve the signal information

). The nearly universal penetration of


mobile device. Such mobile devices


Fi, creating a constant and dense connectivity that allows

time location of each mobile



and accurate than

too difficult to sell its technology.


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In sum, anonymous real-time tracking of mobile phones seemed a novel and very promising

idea. However, could Movvo convince prospective customers that their technology would

outperform competing technologies?

Mobile Real-Time Location-Based Data: Competitive Landscape

Besides PathTracker (RFID-based cart tagging), there were four main competitors which

used mobile indoor location technologies or a combination of different mobile technologies.

Path Intelligence

The first key player in this space is Path Intelligence LLCx which combines mobile and non-

mobile technologies to determine people’s location in enclosed spaces. Path Intelligence’s

founders developed a technology based on smart sensors scattered throughout a store.

However, such solution was expensive and often impractical, which led the company to

develop a pedestrian path measurement technology that uses GSM signals for location

tracking. The company had its commercial launch in the UK in 2004 and had been growing

slowly but steadily since then by offering shopper location analytics to malls.

Path Intelligence helps malls understand shopper behavior, the relationship between retail

brands, and the performance of different departments or storesxi. Although several potential

customers saw value in this intelligence, Path Intelligence faced serious privacy-related

issues in the US, which led several malls to cancel their plans to launch Path Intelligence’s

services and forced the company change instead to retailers.

Movvo had also started to experience similar tensions. Several customers, especially in the

retail industry, expressed privacy-related concerns, which led to unwanted delays to contract

closure. In early 2014, a collaboration with Erasmus School of Economics, in Rotterdam, the

Netherlands, enabled a pilot at Albert Heijn, the largest brand of the Dutch retailer Ahold.

Sadly, the pilot had to be cancelled at the last minute due to privacy concerns. Even though

the legal department of Ahold authorized the pilot, the marketing department blocked it,

fearing damage to their brand among privacy-concerned consumersxii,.

Currently Path Intelligence operates in the UK where it targets mainly malls. Path

Intelligence sells its proprietary software and tailors dashboards to customers’ needs. In

addition, it assists shopping centers’ owners with consulting services where a member of

company is allocated to the client working as an outsourced research department.

Euclid

Another important player is Euclidxiii. Launched in 2011, in the city of San Francisco, it has

raised over $23 millionxiv. Euclid Analytics provides answers and insights to brick and mortar

retailers in the same way that web analytics services do for e-commerce. Using the existing

Wi-Fi infrastructure or through Euclid sensors, pings are recorded and send them to cloud-

based systems where the information is then translated into dashboards and regular alerts

to the clients.

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The company has a diversified approach to the market. Rather than specializing on a single

industry, Euclid targets all types of clients interested in location-based intelligence. This

includes retailers, quick-service restaurants, coffee shops, transportation, shopping centers,

transportation and evening events. Euclid also has a unique pricing model. Specifically, it

offers a baseline version (Euclid Express) of its offering for free but offers a series of add-

ons or extra services that drive the final price up (Exhibit 3). To use the baseline service

plan, clients need to have a Wi-Fi from Euclid Wi-Fi partners. Partners are technology

providers with access to a large base of consumers (retailers, shops, etc.) such as HP,

Cisco, Aruba networks among other. Upgrading to a higher plan allows customers to access

additional services and to pay fees, which, depending on the plan chosen, may require

clients to contact Euclid’s sales force for a custom proposal. Euclid’s upgraded plans

include, among other services, comparison among multiple locations, zoning (which reports

on the activity, duration and movements within zone) and phone support or client success

management tools (that includes implementation and ongoing support).

Nomi

New York-based Nomixv also targets retailers and helps them to better engage with their

customers. Nomi offers a platform that integrates marketing information from the on and

offline business. The technology requires retailers to install a large number of proprietary

beacons and then uses data from such beacons, together with Wi-Fi and video data to

generate in-store traffic analytics. When shoppers approach a certain section of the store,

nearby beacons capture their proximity and store such information in a database, which then

feeds an intelligence dashboard that clients can access (see Exhibit 4). Nomi positions its

solutions as “next generation door counting,” and promises to offer bricks-and-mortar stores

the same level, and depth, of shopper analytics that online stores have. When discussing

the value of Nomi’s offering, Marc Ferrentino, the company’s CEO and co-founder proudly

states that “we move beyond marketing attribution and interior analytics to offer merchants

the ability to transform their store into an interactive environment.”xvi

Experian Footfall

Finally, the largest competitor of Movvo is FootFall Ltd, a company founded in 1991 near

Birmingham, in the UK. The company initially sold hardware solutions designed to measure

pedestrian flows in UK shopping centers. Over time, the company evolved into a full-fledged

provider of intelligence services based on monitoring pedestrian flows.

In December 2005 the global information services group Experian purchased FootFall Ltd for

£36 million. Nowadays, Experian FootFall is a product of Experian PLC targeting retailers

and malls in 37 countries. Being part of Experian gave Footfall access to unique know-how,

scale and financial resources. Headquartered in Dublin, Ireland, Experian offers data and

analytical tools to clients in more than 80 countries. With revenues close to $5 billion in 2014

and approximately 15,000 employees in 41 countries, the company is listed on the London

Stock Exchange (EXPN) and in the FTSE 100 index.

An aspect which seemed to differentiate Experian Footfall from its competitors was value

demonstration. On the one hand, the company made serious efforts to educate prospective

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clients of the benefits of using their intelligence solutions. For instance, in their ‘total

consumer activity insight’ offering, the company shows that combining location-based data

with several other data streams - from sales and workforce activity to weather - offers a “360

degree overview of shopper behavior on a single platform” (see Exhibit 5). On the other

hand, the company frequently used its data to publish industry analyses and benchmarks

through offerings such as the “Global Retail Traffic Index”xvii and the “Euroshopper Trends”

reportsxviii. In addition, the company tried to produce a constant flow of white papers and

eBooks aimed at demonstrating how could their clients use location-based data to increase

their top-line and bottom-line growthxix.

Despite all their merits, none of these competitors could match Movvo’s solution in terms of

accuracy (up from up to 98% from real shoppers pinpointing location to less than 1m2 and

with near real time location). Moreover, Movvo’s solution offered a combination of features

that was unmatched by any other competitor (see Exhibit 6). The challenge, for Movvo, was

to demonstrate these benefits.

Go-To-Market Strategy

Both the founding team and investors were convinced that Movvo’s technology offered

important advantages over competing indoor location technologies. However, the first

challenge was to identify and select a target customer, streamline the positioning of Movvo

vis-à-vis existing competitors and define a go-to-market strategy. The team decided to

examine the demand of the different target customers it was considering, and to estimate the

size of each market opportunity in order to make an evidence-based decision.

Retailers

First, Movvo was considering two sub-segments within the retail industry: food retailers

(sometimes called fast-moving consumer goods or FMCG retailers; such as WalMart in the

US or Carrefour in Europe) or apparel retailers (such as Zara, Mango or H&M). The unique

selling proposition for these customers was clear: Movvo wanted to give bricks and mortar

retailers access to the same type of intelligence that helped online retailers, like Amazon,

grow spectacularly in recent decades.

Movvo had already conducted several pilots in different types of retailers to estimate the

potential of the retail market for the company. In a simulation for a mid-size (130 stores)

retailer, the company had estimated a potential yearly revenue of €300,000 for this retailer

alone which meant an average of €195 per month and per store.

In Europe, in 2013, there were exactly 1,143 retailers grossing over €200 Billionxx. Yet, given

its small sales force, Movvo was considering targeting first the world’s Top 250 retailers,

each of which had on average about 1,000 stores and an average turnover of $17 billion

(see Exhibit 7).

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In terms of business model Movvo was considering a subscription-based model. That is,

Movvo would bear the installation costs (no CAPEX for retailers) and generate revenues

based only on annual subscription. However, many retailers thought they were already

facing a problem of “data exhaustion” and, thus, it was not easy to convince them to invest in

yet a new big data solution. For this reason, Movvo had estimated that it would take at least

6 months of two highly paid sales reps to convince a single retailer to adopt and roll-out

Movvo’s solution in its stores. This would translate into a cost of about €100,000 per

prospective retailer. Even after such a strong sales effort, some of Movvo’s advisors

calculated that only about 5% of the prospective retailers would eventually sign a contract.

Malls

Second, the team looked at malls. Movvo’s solution could support mall managers by helping

them maximize their real estate value. For instance, by allowing retail property companies to

evaluate and monitor shoppers’ behavior in their malls, Movvo could help them maximize the

effectiveness of their in-mall marketing efforts, optimize the store mix, maximize rental

income and streamline the location of fixed and temporary stores for optimal shopper

experience.

In contrast to retailers, malls were more open to location-based data. In fact, malls had been

manually collecting this type of data for many years. Many malls employed staff solely for the

purpose of counting and monitoring mall traffic. They typically used counters, chronographs

and/or personal interviews for this purpose. Traditional market research companies, like

Nielsen and many others, would charge expensive fees for similar services, and offered in-

depth reports about customer’s behavior inside a building or around a specific product

purchase in respect to another.

While considering malls as its possible primary target, Movvo was especially focused on two

geographical markets: the U.S. and Europe as a whole. They had researched both markets

extensively.

In the US, the marketing research team had identified 6,106 malls with global leasing area

(GLA) greater than 30,000 m2. This represented a market opportunity of €732 million in

hardware sales (CAPEX for the mall) and yearly recurring revenues of €183 million (OPEX

for the mall, based on Movvo’s pricing), under a theoretical assumption of full market

penetration (i.e. installation in 100% of the malls). In addition, Movvo identified 1,582 outlets

which represented a market opportunity of €221 million (in CAPEX for the mall) and €55

million in yearly revenues. These centers are normally very large malls. Movvo could target

both types of malls, in the US, with a single sales force which would be attractive in terms of

market access.

The fragmented nature of the mall industry meant that Movvo had to approach sales

differently than in the case of retailers. Targeting malls would require only one sales rep per

mall who would spend, at most, 2 months in each prospect. This means, the sales cost per

mall would be, at most, €20,000. Such smaller time and financial investment, combined with

the fact that several competitors were already targeting U.S. malls, meant that only 1 out of

10 U.S. malls would eventually sign a contract. However, the larger number of malls (as

compared to retailers) could eventually compensate this lower sales effectiveness.

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In Europe, Movvo identified 5,721 malls with GLA greater than 30.000 m2. This would yield a

potential market size of €686 million in CAPEX and €172 million in yearly revenues. Hiring a

sales team would be cheaper in Europe, so Movvo had budgeted about a maximum of

€10,000 in sales expenditures per European mall. Moreover, Movvo expected stronger

competition in Europe, which meant that it expected a lower conversion rate (1 out of 20

malls).

Transport and Mobility

Transport and mobility companies had a strong interest in soft security systems capable of

helping them unobtrusively to monitor crowds and allow them to route people and manage

crowd flow. Movvo could offer actionable analytics regarding effective passenger use of

transportation companies’ services. This would make it easier for such companies to seek

fair compensation based on real usage. Moreover, transportation companies and consumers

were equally eager to abandon paper ticketing and visual validation processes and switch to

electronic and paperless validation methods. Thus, Movvo’s value proposition for

transportation companies could revolve around the simplicity and user-friendliness of its

services, as well as an error-proof payment system with low support requirements

(passengers would not even need to waive their cell phones, simply enter a bus or train and

the system would automatically detect the entry and exit of passengers and charge them

accordingly). Movvo had devised a solution to make the system work even if a cell phone

would be without network access.

Movvo’s customers in this segment would be transport companies. Movvo had thought about

a different pricing model that would involve an entrance fee of €10,000 to €20,000

(according to the size of the company) in order to set up the system within their operations,

and then retain a 5% commission on their ticket sales for the whole duration of the

collaboration. Such commission equals the average commission paid by transportation

companies to ticket sellers such as newspaper stands, tobacconists, etc. Considering an

average 3% commission to be paid to partner banks for the actual online payment services,

this would result in a net 2% operating margin per ticket sold for Movvo.

This seemed to be an attractive market as there were, in the U.S. alone, over 7,000 mass

transportation companies. Considering an average fare of $1.31 and over 13.5 Billion fares

collected, this market would represent a market opportunity for Movvo of over $60 million,

assuming a 15% penetration rate (see Exhibit 8). Movvo also estimated at least an

equivalent opportunity in Europe, if not greater as public transportation in Europe is more

popular than in the US (albeit more fragmented, in terms of company operators). This meant

that Movvo could be looking at a market opportunity in the public transportation space of,

easily, $120 million a year in U.S and Europe (excluding air traffic).

An interesting first market for Movvo could be the Massachusetts Bay Transportation

Authority (MBTA), given that part of the acceleration and network provided by BGI was

based in Boston/MIT. Assuming an average fare of $1.3, and similar penetration rate to

other markets, Movvo had estimated an annual opportunity of $2.2 million that seemed to be

low hanging fruit (Exhibit 8).

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Airports

Finally, Movvo was considering airports. Even though this seemed, at a first glance, a

smaller “niche” application, the fit between Movvo’s solution and airports’ “pain points” was

great. Airline traffic growth implied that increasingly large crowds gathered in airports. IATA,

for example, estimates that in 2015 the total number of scheduled passengers will be 3.5

Billion, an increase of 59% over a 10 year period with total revenues estimated to reach

$6.23 Billion by 2025xxi. IATA also projects that in 2034 passenger numbers will top 7.3

Billionxxii.

Airports seemed increasingly interested not only in crowd managing solutions but also on

solutions that would help them better monetize their passenger population. Hence, airports

were thus also open to solutions that would enable them to understand shopping behavior in

terminals. For these reasons, Movvo estimated airports' price sensitivity to be relatively low.

The Movvo team had segmented the market according to airports with 5 million or more

passengers (each of which had, on average, about 18 million annual passengers). It found

that 76 worldwide airports fulfilled this criterion. Of these, 45 had more than 10 million

passengers yearly (an average of 26 million per airport) and the largest 16 airports all had

more than 25 million passengers yearly, with an annual average of 42 million passengers.

Movvo expected to sell 25 antennas per each million passengers. The buying process for

airports, however, was long and uncertain. Preliminary market research analyses indicated

that each airport would require a sales investment of at least €25,000 in salary costs, travel

expenses and discounts. Movvo was confident to achieve a 75% penetration rate.

Movvo estimated the fixed investment per airport (CAPEX) to be about €1 million per airport

(irrespectively of size) plus €500 per antenna (numbers of antennas depended on the

terminal area, which was closely correlated with the volume of passengers). At the same

time, a market research analyst (from Dueto) prepared a spreadsheet for Roberto showing

that airports would earn an additional 50,000 per year in additional profits per million

passengers. Such profits came from a combination of better targeting of marketing efforts

given the new information regarding shopper behavior, and savings due reductions in delays

and increase operational efficiency. According to inside information from the airport industry,

unless an airport could have good prospects for a payback of less than 2.5 years, it was

unlikely that Movvo would succeed selling the solution. Movvo’s early estimations sized the

market opportunity for this segment at €95.6 million with a gross profit margin of 81% (5 year

plan, 75% penetration rate of worldwide airports of over 10 million passengers per year; see

Exhibit 9).

The Road Ahead

As Suzy, Diana and Roberto reviewed their go-to-market plan, it became clear that they had

to make several decisions to transform their idea into a high-value big data startup

First, Movvo would need to choose carefully which customer or customers to target. It would

be very important for Movvo to devise a convincing plan of its capacity to create superior

value for its chosen customer but, at the same time, demonstrate to investors that the team

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chooses an attractive market or markets to focus on.

Second, Movvo would need to clarify its plan to differentiate from competitors and avoid

being squeezed by several other players. How would Movvo position its offering against the

myriad of competing solutions available in the market? How would such positioning

constraint or reinforce Movvo’s value proposition and customer choice? Which

consequences would it have for branding and communication?

Third, what specific actions should Movvo take to educate its prospective customers about

the value of location-based data? One option, which Movvo was already pursuing, would be

to share their data for free with academic researchers in statistics, econometrics and

quantitative or behavioral marketing such that they could produce new knowledge based on

Movvo's data and demonstrate how it could be used to demonstrate the value of their

technology xxiii.

Fourth, Movvo had to convince investors of its capacity to monetize their technology. What

business model would Movvo pursue? What price points did Movvo have in mind and how

would it charge for it? Why?

Finally, the founders were divided regarding the decision to focus on a single customer or try

to target multiple customers simultaneously. Would targeting a single customer demonstrate

high focus and serve as the basis for a robust marketing strategy? Or would it signal low

ambition? If the team decided a focused approach, it would be important to demonstrate

ambition to investors. That could be done either by showing that the targeted market would

generate attractive enough revenues or that in a second stage the team had a plan to

expand to other customers. If they decided to target multiple customers at the onset, it would

be crucial to clarify how the company could combine its scaling ambition with focus.

Irrespectively of their decision to focus or grow rapidly across industry, investors would

enquire their growth plans outside their primary customers. Would they sell directly their

hardware to providers in other industries? Or would they prefer to license the technology to

other business analytics providers? And if so, would they license it to a single player

(exclusivity) or multiple players (non-exclusivity)?

Diana, Suzy and Roberto knew that the times ahead would be exciting. They felt they had

the fate of their company in hands. The decisions they were about to make regarding

customer targeting, brand positioning, business model choices and future growth would

determine their fate. If they succeeded, their startup could become a prime example of

Portugal’s fresh entrepreneurial blood, i.e. the type of ideas and teams that could take the

crisis-ridden economy to the next level of growth and prosperity. That was a dream worth

fighting for.

15

Exhibit 1 – Competing Tracking Technologies that do not use People’s Mobile Phones

In-store surveys

1 Manual Systems (e.g. Tally Counters)

GPS-based Personal Trackers (Personal Tracker GC007® by Level Systems

2)

RFID-based Cart Tracking (PathTracker® by TNS

3)

Infrared Sensors4

Camera-Based Systems5

(Thermal Imaging Solution by Nassli) Source: case writers.

1 http://uticaphoenix.net/customer-surveys-conducted-part-creating-healthy-places-vegetable-

promotion/ 2 http://www.levelna.com/level-personal-tracker-gc-007-en.htm

3 http://shopperscientist.com/2008-10-14.html

4 See e.g. http://www.sensourceinc.com/technology.htm and http://sensmax.eu/visitors-counting.html

5 http://www.nassli.com/en/services/details/10/People-Counting--Systems. See also:

http://www.smartgroup360.com/#!Smartech-provides-People-Counting-and-Security-for-Service-and-Hospitality/cu6k/B4F51FBC-2B2F-4794-9562-A6C69678D73D.

Exhibit 2 – Movvo’s Trilateration Technology

The Figure below depicts the key idea behind Movvo’s trilateration

systems distribution based on self GSM antennas to determine the location of a mobile

phone6. In the right side of the Figure, one can see a series of mobile devices (1a, 1b and

1c), which communicate with the closest based station

also with adjacent base stations. Movvo’s system them relies on a simple and inexpensive

desktop PC (3) to calculate the location of each mobile device based on the distance of the

device to the different base stations.

algorithm (see left side of the picture for a visual representation of this algorithm). The real

time location of the different devices can then be distributed through a series of desktop

computers, laptops and mobile devices, for instance through an Intranet, for rapid decision

making.

In outdoor applications, Movvo was able to achieve a precision of about 0.5 to 1 meter

in urban areas with sufficient mobile traffic and density of base stations.

clearly outperformed the standard precision offered by satellite

technologies, since such signals are detected with an error margin of 1% from the

beacon distance with a 3 to 4 seconds sampling process, which leads to a 5 met

on average, in the case of GPS

trilateration technology was even more impressive, offering an improved of at least 10

fold as compared with existing technologies.

Source: Movvo company information

6 Source: Movvo’s core patent.

16

Movvo’s Trilateration Technology

The Figure below depicts the key idea behind Movvo’s trilateration technology, which uses


. In the right side of the Figure, one can see a series of mobile devices (1a, 1b and

1c), which communicate with the closest based station (e.g. 2a for 1a and 2b for 1b), but



device to the different base stations. This calculation is made quickly using a trilateration

algorithm (see left side of the picture for a visual representation of this algorithm). The real


ptops and mobile devices, for instance through an Intranet, for rapid decision


in urban areas with sufficient mobile traffic and density of base stations. Such precision

clearly outperformed the standard precision offered by satellite-based location


beacon distance with a 3 to 4 seconds sampling process, which leads to a 5 met

on average, in the case of GPS. In indoor applications, the accuracy of Movvo’s

trilateration technology was even more impressive, offering an improved of at least 10

fold as compared with existing technologies.

Movvo company information (2012) & case writers research.

technology, which uses


. In the right side of the Figure, one can see a series of mobile devices (1a, 1b and

(e.g. 2a for 1a and 2b for 1b), but



This calculation is made quickly using a trilateration

algorithm (see left side of the picture for a visual representation of this algorithm). The real-


ptops and mobile devices, for instance through an Intranet, for rapid decision-


Such precision

based location


beacon distance with a 3 to 4 seconds sampling process, which leads to a 5 meter error,

In indoor applications, the accuracy of Movvo’s

trilateration technology was even more impressive, offering an improved of at least 10-

17

Exhibit 3 – Euclid’s Business Model

Source: www.euclidanalytics.com

Exhibit 4 – Nomi’s Dashboard

Source: http://thenextweb.com/insider/2014/01/09/nomi

market-customers

18

Nomi’s Dashboard

http://thenextweb.com/insider/2014/01/09/nomi-introduces-proximity-beacons-help

help-retailers-better-

19

Exhibit 5 – Experian Footfall’s Site Analytics (Total Consumer Activity Insight)

Source: Footfall website. http://www.footfall.com/wp-content/uploads/2015/01/Experian_FootFall_2015_Product_Brochure_FINAL_English.pdf

20

Exhibit 6 – Movvo vis-à-vis its main Competitors

Features

Path Tracker (RFID)

Path Intelligen

ce. Euclid Nomi

Experian Footfall

Movvo

GSM X √ X X X √

Wi-Fi X X √ √ X √

Bluetooth X X X X X √

Trilateration √ √ X X X √

Real Time Alerts X X X √ X √

No Hardware X X √ X X √

>95% sampling X √ X X X √

Mall solution √ √ X X √ √

Store solution √ X √ √ X √

Email reports √ X √ √ X √ Online dashboard

X X √ √ X √

API for App Integration

X X X X X √

API for loyalty app or card integration

X X X X X √

Customizable √ X √ X X √

RFID Tagging √ X X X X X

Source: Case Writers and Movvo’s own studies.

21

Exhibit 7 – Retail Segments

*Compound annual rate growth Source: National Retail Federation. https://nrf.com/news/2015-top-250-global-powers-of-retailing

Product sector profiles, 2013

No

Companies

Avg retail

revenue (U $m)

Share of top

250 companies

Share of top

250 revenues

Top 250250 $ 17,418 100.0% 100.0%

Apparel &

Accessories 44 $ 9,145 17.6% 9.2%

Fast-Moving

Consumer Goods 132 $ 22,269 52.8% 67.5%

Hardlines & Leisure

Goods 52 $ 12,620 20.8% 15.1%

Diversified22 $ 16,200 8.8% 8.2%

Source: Published company data and Planet Retail

0

1

2

3

4

5

6

7

8

9

Top 250 Apparel &

Accessories

Fast-Moving

Consumer

Goods

Hardlines &

Leisure Goods

Diversified

4,2

5,1

4,3

4,9

2

4,1

5,8

4

5,3

1

3,4

7,6

2,8

3,8

2,1

5,3

8,2

4,8

5,8

2,9

2008-2013 retail revenue CAGR*

2013 net profit margin

2013 retail revenue growth

2013 ROA

22

Exhibit 8 – US Market estimation for digital (app-enabled) payment system based on

Movvo’s offer

US Market, Public transportation, US Market, Public transportation, US Market, Public transportation, US Market, Public transportation, trips, 2011trips, 2011trips, 2011trips, 2011 USUSUSUS

Boston Boston Boston Boston

(MBTA)(MBTA)(MBTA)(MBTA)

Fares collected, passengers (Million) 13,557.6 471.9

Fare per Unlinked trip $1.3 $1.3

Total revenue (Million US$) $17,760.5 $618.2

Sizing Movvo's opportunity in the USSizing Movvo's opportunity in the USSizing Movvo's opportunity in the USSizing Movvo's opportunity in the US

Smart phone users (35% penetration rate), passengers / y (Million) 4,745.2 165.2

Assuming 1 in 2 Smartphone users convert to Movvo APP (Million) 2,372.6 82.6

Movvo's Movvo's Movvo's Movvo's annual annual annual annual opportunity (Assuming a fee of 2%, per sold ticket) / Millionopportunity (Assuming a fee of 2%, per sold ticket) / Millionopportunity (Assuming a fee of 2%, per sold ticket) / Millionopportunity (Assuming a fee of 2%, per sold ticket) / Million $62$62$62$62....2222 $2$2$2$2....2222

Source: BGI / Movvo’s estimates.

23

Exhibit Exhibit Exhibit Exhibit 9999 –––– Movvo’sMovvo’sMovvo’sMovvo’s Airport segment market sizing (World)Airport segment market sizing (World)Airport segment market sizing (World)Airport segment market sizing (World)

Movvo's Airport segment market sizingMovvo's Airport segment market sizingMovvo's Airport segment market sizingMovvo's Airport segment market sizing WorldWorldWorldWorld

Number of airports with 10 million or more passengers / y 45

Average passengers / y / Airport (Million) 12

Worlds´s market share (2013 figures), % 18.0%

Movvo'sMovvo'sMovvo'sMovvo's Airport revenue (early estimations)Airport revenue (early estimations)Airport revenue (early estimations)Airport revenue (early estimations)

Non-recurrent revenue (75% penetration rate /10M+) - EUR (Million) 45.0

Recurrent revenue - EUR (Million) 20.3

Movvo's opportunity (5 year plan)/ MillionMovvo's opportunity (5 year plan)/ MillionMovvo's opportunity (5 year plan)/ MillionMovvo's opportunity (5 year plan)/ Million €95€95€95€95....6666

COGS / Airport (10 million or more passengers / y)COGS / Airport (10 million or more passengers / y)COGS / Airport (10 million or more passengers / y)COGS / Airport (10 million or more passengers / y)

Cost per antenna €500

Average CAPEX per airport (antena only), EUR (Million) 0.200

Average installation cost, EUR (Million) 0.150

Average recurrent OPEX per airport (antenna only), EUR (Million) 0.063

Server, Communications and Software, EUR (Million) 0.016

Note: The expected penetration rate is for airports with 10 million or more passengers. If Movvo considered airports with 5

million or more passengers, the expected penetration rate would drop from 75% to 50%.

Source: BGI / Movvo’s estimates.

i A value that, according to McKinsey, would come especially from industries such as retail, mobility services, public sector administration and healthcare, see: http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation ii Coding competition sponsored by Portugal Telecom, Portugal’s largest telecom at the time.

iii In its first editions, BGI was called ISCTE-IUL MIT Portugal Venture Competition. ISCTE-IUL is the abbreviation

of the school’s original name, which was Instituto Superior de Ciências do Trabalho e da Empresa (Higher Institute of Business and Labour Sciences), with the addition of ‘Instituto Universitário de Lisboa’ (University Institute of Lisbon) added in 2009, to reflect the fact that ISCTE-IUL became one of the three Portuguese universities (along with the University of Porto and the University of Aveiro) which adopted a ‘Foundation Regime’, which prescribes management according to private law. See: http://www.iscte-iul.pt/en/home.aspx iv A formal partnership between the Portuguese government and the Massachusetts Institute of Technology with

the aim to promote innovation and university spinouts: http://www.mitportugal.org/ v http://shopperscientist.com/2008-10-14.html

vi http://shopperscientist.com/

vii http://www.amazon.com/Inside-Mind-Shopper-Science-Retailing/dp/0137126859

viii Sam Hui, Peter Fader, Eric Bradlow (2009), The Traveling Salesman Goes Shopping: The Systematic

Deviations of Grocery Paths from TSP-Optimality, Marketing Science, 28 (3), 566-57. Sam Hui, Eric Bradlow, Peter Fader (2009), Testing Behavioural Hypotheses using an Integrated Model of Grocery Store Shopping Paths, Journal of Consumer Research, 36 (3), 478-493 Sam Hui, Peter Fader, Eric Bradlow (2009), Path Data in Marketing: An Integrative Framework and Prospectus for Model-Building, Marketing Science, 28 (2), 320-335. ix http://www.tnscanada.ca/files/RSI-PathTracker.pdf

x http://www.pathintelligence.com/

xi https://www.crunchbase.com/organization/pathintelligence

xii http://www.dailymail.co.uk/sciencetech/article-2067187/Privacy-invasion-Shops-secretly-track-snooping-

mobile.html http://www.businessinsider.com/path-intelligence-tracks-your-every-move-2011-11?IR=T xiii

http://euclidanalytics.com/ xiv

https://www.crunchbase.com/organization/euclid xv

http://www.nomi.com/

24

xvi

http://thenextweb.com/insider/2014/01/09/nomi-introduces-proximity-beacons-help-retailers-better-market-customers/ xvii

http://www.retailtimes.co.uk/december-23-tipped-busiest-shopping-day-experian-footfall-data-shows/ xviii

http://www.slideshare.net/GaryC2/q4-2014-euroshopper-trends xix

http://www.footfall.com/resources/ebooks/ xx

http://ec.europa.eu/enterprise/policies/sme/facts-figures-analysis/performance-review/files/supporting-documents/2014/annual-report-smes-2014_en.pdf xxi

Source: Industry Financial Forecast Table (IATA Economics), Dec 2014 xxii

http://www.iata.org/pressroom/pr/pages/2014-10-16-01.aspx xxiii

http://thesis.eur.nl/pub/11769/Ti-Amataya,%20T.%20(340276tt).docx http://thesis.eur.nl/pub/11763/Nie,%20H.W.%20de%20(303470hn).pdf http://thesis.eur.nl/pub/11755/Hendriksen,%20K.R.%20(302118kh).docx

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