Permissioned, connected big data
The availability of ever-bigger data sets has fuelled the appetite of brand owners and marketers to improve their ROI through better understanding of consumer behaviour, then tailoring their business decisions regarding marketing, new product development, customer acquisition strategy etc. towards the most responsive audiences. However, achieving this ideal has been frustrated by the sheer scale and complexity of the task, particularly as most of the data come from discrete and unrelated sources and most available solutions are based on imputing connections.
YouGov is unique in that it is gathering data from a large number of panellists and contributors from across the globe. Data are held in a data vault that the group calls the Cube. Crucially, all the panellists and contributors have proactively opted in to sharing their information with YouGov, either through submitting survey responses, taking part in the Opigram community or, for a smaller subset, giving the group specific permission to garner data from their web-connected devices through embedded software that runs in the background. Because all of the data, from whatever source, can be assigned to an individual, information can be gathered together and combined to give a sophisticated, in-depth portrait of the ‘typical’ supporter of a particular brand, for example. Unlike many other market researchers, YouGov is also able to revisit particular subsets of respondents to dive deeper into their responses.
With privacy concerns stymying many attempts to draw together big data and shape it into informative and actionable formats, this active permissioning is a crucial differentiator.
YouGov has also developed front-end data analytics tools that make it much simpler for clients to interrogate their own data, which it calls Crunch. The output is readily customised to integrate with the clients’ own systems and is already being used by some of the group’s largest customers in tracking studies.
The group has focused on running and developing products that are now standardised globally. It facilitates the rolling out of products such as BrandIndex and Omnibus to new countries and offers the ability to cater for larger, cross-territory projects and clients. For descriptions of these products, please see our recent Outlook report. The holding of the data in the Cube and the Crunch tools that have been developed for its use have built in an additional level of scalability to the group’s offer.
Custom research has been adapting from being simply a project-based offer to one that can be combined with data held in the Cube to drive additional value for the client (see Case Study, below).
YouGov caters to three constituencies: panellists (who can also be thought of as a community network or even as a fan club), the general public (which consumes the output of surveys through the media and whose regard for YouGov as an authoritative source is a core element of its value) and clients (who could be brand owners, brand agents/marketers or media organisations).
Without the panellists providing data, increasingly voluntarily rather than prompted by reward, there is no product or service to sell. Giving them the best possible user experience is therefore key.
YouGov’s position as an authoritative provider of content and context for media organisations is well established, with relationships with survey results attracting comment on a daily basis. Close partnerships exist with a broad range of key media organisations in all the markets in which YouGov operates.
The integration of YouGov tools into the marketer’s workflow gives the latter far richer contextual data, as well as an element of tracking either consumers’ or individuals’ views or behaviour over time. For example, the Profiles tool identifies characteristics of individuals associated with brands in how they differ from the norm, ie that they may be more likely to consume certain media sources, eat particular foods, travel, display particular behavioural traits, or subscribe to different political opinions than might otherwise be expected. This means that segmentation can be across multiple variables and adjusted in real time, giving actionable results so that campaigns can be iterated and honed or that, for example, sponsorship can be more effectively targeted.
BrandIndex, the group’s perception tracker, continues to build, with the historic data now covering over eight years in several geographies. As brand awareness and brand health data are collected both for clients and their peers, the contextual information is already in place for non-client peers, who can then be targeted from a sales perspective. With the product now available in 24 countries, BrandIndex data can be provided to clients on a global basis, with bridgeheads established into new territories with the support of existing clients. Both brands and their agencies are clients.
Taken in conjunction with the Profiles tool, brand owners can get good visibility as to who their customers are and how they react to campaigns. Further integration with custom research or via the Omnibus service can give a more granular view.
The CMD included a Case Study for a cruise liner operator, which was looking to broaden its customer base. The first stage of the study was to run a segmentation between ‘cruise fanatics’, ‘young fun-seekers’ and ‘tempted first timers’. This third category was then analysed using the Profiles tool to identify its demographic and lifestyle characteristics, together with brand preferences, media consumption, online/mobile behaviour, etc. Panellists in this target group were then asked for their views on four cruising options (destinations, concept, pricing, likelihood to buy, preference). From the answers received, a media campaign was planned across TV, print, web and programmatic. The campaign’s success was tracked over time against the general population on BrandIndex. Although the campaign was reaching the target audience and enquiry levels were up, conversion into sales was not on target. The client was able to come back to the original Cube data and identify people whose intention to purchase had gone up, but who had not yet bought a cruise. These people were then sent a short follow-up Collaborative Insights survey, which identified that the blocking issue related to the available dates being unsuitable, which was then fixed by the client. The ability to revisit and refine the analysis should retain the client’s level of interest longer.
The Collaborative Insights tool is a platform being developed for clients and researchers to interact with YouGov on more of a self-serve basis, with ease and speed-of-use key characteristics.
All the tools mentioned are about enabling brand owners, agencies and public-facing organisations to optimise their strategic decision-making, targeting it to where it is most likely to be effective. This might include decisions on content, strategies for spending on marketing campaigns, product development and customer acquisition etc. The tools are increasingly an embedded element of clients’ processes. With these standardised tools and processes, the roll-out to new territories is simplified. YouGov Profiles has now been launched in China (previously the UK, US, Germany). The ambition is to grow the group organically, through investment in building respondent panels in more of the important world markets, as well as leveraging existing client relationships. Taken together, these developments are positioning YouGov as a technology-led and data-driven partner to brand owners and marketers, with clear differentiation from traditional providers, and are increasing its capacity to earn good margins.
Exhibit 1: Financial summary
|
|
£000s |
2014 |
2015 |
2016e |
2017e |
Year end 31 July |
|
|
IFRS |
IFRS |
IFRS |
IFRS |
PROFIT & LOSS |
|
|
|
|
|
|
Revenue |
|
|
67,375 |
76,110 |
83,500 |
91,800 |
Cost of Sales |
|
|
(15,812) |
(17,472) |
(18,788) |
(20,196) |
Gross Profit |
|
|
51,564 |
58,638 |
64,713 |
71,604 |
EBITDA |
|
|
8,020 |
9,273 |
10,646 |
11,826 |
Operating Profit (before GW, except and share-based payments) |
7,936 |
9,239 |
10,843 |
12,223 |
Intangible Amortisation |
|
|
(3,965) |
(4,633) |
(4,633) |
(4,633) |
Share based payments |
|
|
(547) |
(669) |
(900) |
(1,100) |
Exceptionals |
|
|
(2,385) |
(1,072) |
0 |
0 |
Other |
|
|
(14) |
41 |
0 |
0 |
Operating Profit |
|
|
1,025 |
2,906 |
5,310 |
6,490 |
Net Interest |
|
|
(292) |
(220) |
(143) |
(123) |
Profit Before Tax (norm) |
|
|
7,630 |
9,060 |
10,700 |
12,100 |
Profit Before Tax (FRS 3) |
|
|
733 |
2,686 |
5,167 |
6,367 |
Tax |
|
|
(316) |
580 |
(2,461) |
(2,965) |
Profit After Tax (norm) |
|
|
7,314 |
9,640 |
8,239 |
9,136 |
Profit After Tax (FRS 3) |
|
|
417 |
3,266 |
2,504 |
3,403 |
|
|
|
|
|
|
|
Average Number of Shares Outstanding (m) |
|
|
98.0 |
101.0 |
103.9 |
104.2 |
EPS - normalised fully diluted (p) |
|
|
5.8 |
6.7 |
7.6 |
8.4 |
EPS - FRS 3 (p) |
|
|
0.4 |
3.2 |
2.4 |
3.2 |
Dividend per share (p) |
|
|
0.8 |
1.0 |
1.2 |
1.4 |
|
|
|
|
|
|
|
Gross Margin (%) |
|
|
76.5 |
77.0 |
77.5 |
78.0 |
EBITDA Margin (%) |
|
|
11.9 |
12.2 |
12.8 |
12.9 |
Operating Margin (before GW and except) (%) |
|
|
11.8 |
12.1 |
13.0 |
13.3 |
|
|
|
|
|
|
|
BALANCE SHEET |
|
|
|
|
|
|
Fixed Assets |
|
|
52,259 |
53,726 |
52,427 |
52,427 |
Intangible Assets |
|
|
46,650 |
46,145 |
46,000 |
46,000 |
Tangible Assets |
|
|
5,609 |
7,377 |
6,223 |
6,223 |
Investments |
|
|
0 |
204 |
204 |
204 |
Current Assets |
|
|
29,873 |
33,329 |
37,594 |
42,524 |
Stocks |
|
|
0 |
0 |
0 |
0 |
Debtors |
|
|
21,687 |
22,507 |
24,692 |
27,418 |
Cash |
|
|
7,429 |
10,017 |
12,145 |
14,552 |
Current Liabilities |
|
|
(21,480) |
(22,983) |
(25,215) |
(27,447) |
Creditors |
|
|
(21,296) |
(22,983) |
(25,215) |
(27,447) |
Short term borrowings |
|
|
(184) |
0 |
0 |
0 |
Long Term Liabilities |
|
|
(2,700) |
(2,449) |
(3,500) |
(3,250) |
Long term borrowings |
|
|
0 |
0 |
0 |
0 |
Other long term liabilities |
|
|
(2,700) |
(2,449) |
(3,500) |
(3,250) |
Net Assets |
|
|
57,952 |
61,623 |
61,307 |
64,254 |
|
|
|
|
|
|
|
CASH FLOW |
|
|
|
|
|
|
Operating Cash Flow |
|
|
8,600 |
10,091 |
11,000 |
12,000 |
Net Interest |
|
|
(295) |
(233) |
(143) |
(123) |
Tax |
|
|
(287) |
(730) |
(2,106) |
(2,587) |
Capex |
|
|
(5,759) |
(5,754) |
(5,500) |
(5,500) |
Acquisitions/disposals |
|
|
(1,003) |
(470) |
0 |
(36) |
Financing |
|
|
(101) |
454 |
0 |
0 |
Dividends |
|
|
(566) |
(773) |
(1,123) |
(1,346) |
Net Cash Flow |
|
|
589 |
2,585 |
2,128 |
2,407 |
Opening net debt/(cash) |
|
|
(6,656) |
(7,245) |
(10,017) |
(12,145) |
HP finance leases initiated |
|
|
0 |
0 |
0 |
0 |
Other |
|
|
0 |
187 |
0 |
0 |
Closing net debt/(cash) |
|
|
(7,245) |
(10,017) |
(12,145) |
(14,552) |
Source: Company accounts, Edison Investment Research
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